GENOMIC AND BREEDING RESOURCES TO PRODUCE SEEDED AND HIGH BIOMASS INTERSPECIFIC HYBRIDS OF NAPIERGRASS AND PEARL MILLET By DEV RAJ PAUDEL A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2018
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GENOMIC AND BREEDING RESOURCES TO PRODUCE SEEDED AND HIGH BIOMASS INTERSPECIFIC HYBRIDS OF NAPIERGRASS AND PEARL MILLET
By
DEV RAJ PAUDEL
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
Botany .............................................................................................................. 21 Napiergrass Ancestry ....................................................................................... 22 Napiergrass in the United States ...................................................................... 23
Pearl Millet ........................................................................................................ 24 Interspecific Hybrids of Napiergrass and Pearl Millet (PMN Hybrids) ............... 25
Cytoplasmic Male Sterility (cms) ...................................................................... 25 Molecular Tools Applied in Plant Breeding ....................................................... 27
Quantitative Trait Loci Analysis ........................................................................ 30 Biomass yield ................................................................................................... 31 Flowering Time ................................................................................................. 31
Genes Related to Flowering ............................................................................. 33 Objectives ............................................................................................................... 34
2 SURVEYING THE GENOME AND CONSTRUCTING A HIGH-DENSITY GENETIC MAP OF NAPIERGRASS (CENCHRUS PURPUREUS SCHUMACH.) ......................................................................................................... 37
Methods .................................................................................................................. 41 Napiergrass Genome Survey ........................................................................... 41 SSR Identification and Marker Development .................................................... 42
Plant Materials and DNA Extraction ................................................................. 43 Genotyping-by-sequencing ............................................................................... 43 Comparative Genomics .................................................................................... 43 Sequence Analysis and SNP Calling ................................................................ 44 Linkage Map Construction ................................................................................ 45 Comparison Between Napiergrass and Pearl Millet Genome........................... 46
Genotyping-by-sequencing ............................................................................... 47 SNP Calling by Various SNP Callers ................................................................ 49 Genetic Linkage Map Construction .................................................................. 49 Comparison Between Genomes of Napiergrass and Pearl Millet ..................... 51
3 MAPPING QTLS CONTROLLING FLOWER NUMBER AND FLOWERING TIME IN NAPIERGRASS ........................................................................................ 84
Introduction ............................................................................................................. 84 Materials and Methods............................................................................................ 87
Development of a Mapping Population ............................................................. 87 Phenotyping the Mapping Population ............................................................... 88
Number of flowers ...................................................................................... 91 Flowering Time .......................................................................................... 91
Number of flowers ...................................................................................... 91 Flowering time ............................................................................................ 92
5 GENERATION OF INTERSPECIFIC HYBRIDS BETWEEN PEARL MILLET AND NAPIERGRASS AND EVALUATION OF THEIR PERFORMANCE. ............ 142
Introduction ........................................................................................................... 142 Materials and Methods.......................................................................................... 147
Description of Male Sterile Line of Pearl Millet ............................................... 147 Production of cms Lines of Pearl Millet .......................................................... 147 Production of PMN Hybrids ............................................................................ 148 Experimental Design ...................................................................................... 149
Traits Evaluated ............................................................................................. 149 Data Analysis ................................................................................................. 150
Table page 2-1 Parameters used for SNP calling for each software ........................................... 58
2-2 Repetitive elements present in the napiergrass genome .................................... 61
2-3 The sequence alignment of ten napiergrass sequence contigs to the pearl millet genome ..................................................................................................... 62
2-4 Frequency of classified repeat types (considering sequence complementary) in napiergrass ..................................................................................................... 63
2-5 Primer pairs developed for napiergrass SSR markers ........................................ 67
2-6 Summary of the alignment of non-redundant tags of napiergrass (Cenchrus purpureus) to the available genomes of different species .................................. 68
2-7 Alignment of individual napiergrass reads using Bowtie2 ................................... 69
2-8 Summary of the combined linkage map of napiergrass and the percentage of gaps less than 5 cM in male and female parent linkage maps ........................... 70
2-9 Summary of napiergrass single nucleotide polymorphism (SNP) markers mapped on the combined linkage map using 9 different software pipelines ....... 71
3-1 Descriptive statistics of flowering date and number of flowers for 185 F1
hybrids of a cross (N190 N122) at Citra, FL., in 2012, 2013 (Sinche 2013) and 2016 .......................................................................................................... 109
3-2 Detailed information about the QTLs for number of flowers. LG = Napiergrass linkage groups, Peak markers represent the marker at the highest peak of QTL, LSI represents the LOD-1 support interval in cM. .................................... 110
3-3 Detailed information about the QTLs for flowering time. LG = Napiergrass linkage groups, Peak markers represent the marker at the highest peak of QTL, LSI represents the LOD-1 support interval in cM. .................................... 111
3-4 List of putative flowering related genes from the genome of pearl millet (Varshney et al. 2017). ..................................................................................... 112
4-1 Genes and publications related to flowering used in this research ................... 130
5-1 Details of the cross types used in the experiment. ........................................... 175
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5-2 Descriptive statistics for different types of crosses. P-value represents Pr>F for Levene’s test for homogeneity of variance (center = median), CV = coefficient of variation. ...................................................................................... 176
5-3 Correlation coefficients and p-values for biomass weight and biomass-related traits for PMN hybrids evaluated in Citra, FL. ................................................... 177
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LIST OF FIGURES
Figure page 1-1 Distribution of napiergrass. Black dots represent local napiergrass and red
dots represent napiergrass listed as invasive species ........................................ 36
2-1 Sequence variation for SNPs called in various regions of the pearl millet genome. ............................................................................................................. 72
2-2 Micro-collinearity between contigs from napiergrass to the pearl millet genome. ............................................................................................................. 73
2-3 Inversion duplication between napiergrass and pearl millet (shown in bottom figure). 74
2-4 Estimated coverage of PstI restriction sites in the pearl millet genome. ............. 75
2-5 Histogram of uniquely mapped reads to the pearl millet genome. ...................... 76
2-6 Venn diagram showing concordant napiergrass SNPs called by five reference-based SNP callers, SAMtools, GBS-SNP-CROP, GATK, FreeBayes, and TASSEL. Numbers in parenthesis after the program name shows the total number of SNPs called by each program. ................................. 77
2-7 Genetic linkage map of the napiergrass female parent N190. ............................ 78
2-8 Genetic linkage map of the napiergrass male parent N122. ............................... 79
2-9 Genotyping by sequencing single nucleotide polymorphism (GBS-SNP) marker distribution for the 14 linkage groups of napiergrass. A black bar means a GBS-SNP marker. A blue bar represents segregation distortion region. The left scale plate represents genetic distance (centiMorgan as unit). . 80
2-10 Consensus genetic linkage map of napiergrass. ................................................ 81
2-11 Circos plot of the mapped TASSEL de-novo UNEAK napiergrass markers with pearl millet reference genome. Pearl millet pseudomolecules start with “PM” and are color coded for each pseudomolecule. Napiergrass linkage groups start with “LG” and are in green color. Each line corresponds to tags that showed significant BLAST hits to the pearl millet genome (> 80% identity and > 50 bp length)............................................................................................. 82
2-12 Syntenic regions between napiergrass linkage groups and the pearl millet genome. PM01 to PM07 are pearl millet pseudomolecules, LG01 to LG14 are napiergrass linkage groups. The small dots represent significant BLAST hits of mapped UNEAK tags to the pearl millet genome (>80% identity and >50 bp length). ................................................................................................... 83
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3-1 Histogram of number of flowers in the mapping population in 2012 in Citra, FL. X-axis represents the number of flowers and y-axis represents the count of plants. Average number of flowers of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013. ................................................. 98
3-2 Histogram of number of flowers in mapping population for 2013 in Citra, FL. X-axis represents the number of flowers and y-axis represents the count of plants. Average number of flowers of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013. ....................................................... 99
3-3 Scatterplot of number of flowers between 2012 and 2013 in Citra, FL. X-axis represents the number of flowers in 2012 and y-axis represents the number of flowers in 2013. Data is adapted from Sinche, 2013. ................................... 100
3-4 Histogram of number of days to first flowering in 2012 in Citra, FL. X-axis represents the number of days to flowering (FT) and y-axis represents the number of accessions. Days to first flowering of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013. ..................................... 101
3-5 Histogram of number of days to first flowering in 2013 in Citra, FL. X-axis represents the number of days to flowering (FT) and y-axis represents the number of accessions. Days to first flowering of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013. ..................................... 102
3-6 Histogram of first flowering date in the mapping population in 2016 in EREC, Belle Glade, FL. X-axis represents the number of days to flowering (FT) and y-axis represents the number of accessions. Days to first flowering of two parental lines, N122 and N190 are plotted. ...................................................... 103
3-7 Scatterplot of first date of flowering between different years and locations. X-axis and Y-axis represent the number of days to flowering (FT) in different years. Data for 2012 and 2013 are from Citra, FL and adapted from Sinche, 2013. Data for 2016 are from Belle Glade, FL. ................................................. 104
3-8 Scatterplot between number of flowers and days to flowering in 2012 in Citra, FL. X-axis represents the number of days to flowering (FT) in 2012 and y-axis represents the number of flowers per line in 2012. Data is adapted from Sinche, 2013..................................................................................................... 105
3-9 Scatterplot between days to flowering and number of flowers in 2013 in Citra, FL. X-axis represents the number of days to flowering (FT) in 2013 and y-axis represents the number of flowers per line in 2013. Data is adapted from Sinche, 2013..................................................................................................... 106
3-10 Linkage map of male parent N122 showing potential and stable QTLs. QTLs for number of flower (NF) are color coded in blue and that for flowering time (FT) are colored in green. A black bar means a GBS-SNP marker. Markers
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are labeled on the left and genetic distance (centimorgan as unit) are labeled on right. LG = Linkage group. ........................................................................... 107
3-11 Linkage map of female parent N190 showing potential QTLs. QTLs for number of flower (NF) are color coded in blue and that for flowering time (FT) are colored in green. A black bar means a GBS-SNP marker. Markers are labeled on the left and genetic distance (centimorgan as unit) are labeled on right. LG = Linkage group. ................................................................................ 108
4-1 Histogram of length of flowering related genes................................................. 131
4-2 Number of probes designed per gene. ............................................................. 132
4-3 Number of probes designed as a factor of the size of the gene. ...................... 133
4-4 Distribution of the targeted flowering genes in the genome of pearl millet. Each blue bar represents a flowering gene mapped on the pearl millet genome. ........................................................................................................... 134
4-5 Number of paired-end reads per sample. ......................................................... 135
4-6 Bayesian Information Criteria (BIC) vs. number of clusters in k-means clustering suggests K=3 in the germplasm collection. ...................................... 136
4-7 Projection of the napiergrass germplasm collection using first two linear discriminants (LDs) from discriminant analysis of principal components (DAPC). The shape of the points represent grouping by DAPC (circle = group 1; triangle = group 2; square = group 3) and the colors represent the origin continent: black = Americas; blue = Asia; and cyan = Africa. ........................... 137
4-8 Evolutionary relationships of taxa. G stands for group assigned by DAPC and are in different shapes (circle = group 1; triangle = group 2; square = group 3) and the fill colors represent the origin: black = Americas, blue = Asia, cyan = Africa, and white=unknown). ......................................................................... 138
4-9 Histogram for days to flowering trait in napiergrass germplasm collection. ...... 139
4-10 QQ plots for different models using GWASpoly using 78,129 SNP markers. DTF = Days to flowering. .................................................................................. 140
4-11 Manhattan plots for different models using GWASpoly. P values adjusted with FDR at 0.05. DTF = Days to flowering. ..................................................... 141
5-1 Seeds of pearl millet dwarf cms line (A), cms high biomass pearl millet line (B), PMN hybrid (C), and napiergrass (D), respectively from left to right. ......... 158
5-2 Boxplot of plant height (cm) for four different types of crosses studied. Small colored dots represent individual plant height of the cross. Different letters in
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blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ...................................................................... 159
5-3 Histogram of plant height (cm) for four different crosses. X-axis represent plant height in cm and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ........... 160
5-4 Boxplot of number of tillers for four different types of crosses studied. Small colored dots represent number of tillers for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ............................................ 161
5-5 Histogram of number of tillers for four different crosses. X-axis represent the number of tillers and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ........... 162
5-6 Boxplot of stem diameter (mm) for four different types of crosses studied. Small colored dots represent the stem diameter (mm) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17................................ 163
5-7 Histogram of stem diameter (mm) for four different crosses. X-axis represent the stem diameter (mm) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ... 164
5-8 Boxplot of leaf length (cm) for four different types of crosses studied. Small colored dots represent the leaf length (cm) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ............................................ 165
5-9 Histogram of leaf length (cm) for four different crosses. X-axis represent the leaf length (cm) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ........... 166
5-10 Boxplot of leaf width (mm) for four different types of crosses studied. Small colored dots represent the leaf width (mm) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ............................................ 167
5-11 Histogram of leaf width (mm) for four different crosses. X-axis represent the leaf width (mm) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ........... 168
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5-12 Boxplot of fresh biomass per plant (kg) for four different types of crosses studied. Small colored dots represent the fresh biomass per plant (kg) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ............................................................................................................. 169
5-13 Histogram of fresh biomass per plant (kg) for four different crosses. X-axis represent the fresh biomass per plant (kg) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ................................................................................................ 170
5-14 Boxplot of projected dry biomass (tons per ha) for four different types of crosses evaluated at seven months of growth. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ............................................................................................ 171
5-15 Histogram of dry biomass (tons per ha.) for four different crosses evaluated at seven months of growth. X-axis represent the dry biomass (tons per ha.) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ....................................... 172
5-16 Coefficient of variation (%) for the different traits evaluated. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17. ........... 173
5-17 Picture of four different crosses during harvest in Citra, FL. Cross A = Tift 85 × 25-17, Cross B = 787-S5 × 25-17, Cross C = MS 787 BC4 × 25-17, Cross D = P787 × 25-17. ............................................................................................ 174
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LIST OF ABBREVIATIONS
cms Cytoplasmic male sterility
GBS Genotyping by sequencing
LG Linkage Group
NGS Next-generation sequencing
PMN Pearl millet napiergrass
SNP Single nucleotide polymorphism
SSR Simple Sequence Repeat
TES Targeted exome sequencing
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
GENOMIC AND BREEDING RESOURCES TO PRODUCE SEEDED AND HIGH BIOMASS INTERSPECIFIC HYBRIDS OF NAPIERGRASS AND PEARL MILLET
By
Dev Raj Paudel
December 2018
Chair: Fredy Altpeter Cochair: Jianping Wang Major: Agronomy
Napiergrass (Cenchrus purpureus Schumach) is a promising candidate for
forage and lignocellulosic biofuel production due to its high biomass potential. However,
napiergrass is listed as an invasive species in Florida due to wind dispersed seeds.
Seed production in napiergrass is compromised by low temperatures. Therefore, late
flowering genotypes, which are not able to flower before low temperatures come in
Florida, could be utilized to improve biosafety of napiergrass. However, genetic and
genomic resources for napiergrass are limited that preclude exploiting marker assisted
selection (MAS) for crop improvement. Genetic linkage map is an important tool for
MAS. In this research, we constructed the first high-density genetic map of napiergrass
by genotyping-by-sequencing a bi-parental mapping population of 185 F1 hybrids. As a
result, we mapped 1,913 single nucleotide polymorphism (SNP) markers into 14 linkage
groups of napiergrass, spanning a length of 1,410 cM with a density of one marker per
0.73 cM.
This genetic map enabled us to identify three stable and three potential
quantitative trait loci (QTLs) controlling number of flowers and flowering time,
19
respectively in the mapping population. We also identified five candidate genes related
to flowering in close proximity to the QTLs detected.
Full characterization of germplasm collections is very critical to efficiently utilize
them in breeding programs. We used targeted enrichment sequencing to characterize
the napiergrass germplasm collection and identified 78k SNPs in the collection. We
inferred the structure of the germplasm collection and constructed its phylogeny.
Genome wide association studies revealed one significant SNP for flowering time.
Napiergrass is mostly vegetatively propagated, which makes the planting
process much complicated and labor intensive. To ameliorate this, we introgressed
cytoplasmic male sterility (cms) into elite lines of pearl millet and hybridized them with
napiergrass to produce seed derived, sterile pearl millet napiergrass (PMN) hybrids. We
evaluated the biomass yield and uniformity of PMN hybrids generated by using different
parental backgrounds. There was a tremendous variation in different biomass related
traits among the crosses. These seeded-yet-sterile PMN hybrids could have a major
impact in the forage and biofuel industry if large scale production of high-quality seeds
that give rise to high yielding progenies can be developed.
(DHL), near-isogenic lines (NILs) and full-sib F1 (pseudo-testcross) (Schneider 2005).
Quantitative traits, such as yield, height, flowering time, pest and disease resistance, in
plants have been mapped in genomes of several species. QTLs are valuable
information to develop markers linked to traits of interests for MAS in molecular
breeding programs. Genetic variability for flowering time exists in napiergrass (Sinche
2013). Therefore, identifying flowering time related QTLs will help in identifying markers
31
linked to late flowering trait, an important trait to reduce invasiveness. Sexual
hybridization of genetically distant parents and selection of late flowering, high yielding
accessions would increase the biofuel yield and enhance the biosafety of napiergrass.
Biomass yield
In a comparison of major energy crops for ethanol production, napiergrass
showed the highest dry biomass yield than sorghum, maize, sugarcane, switchgrass,
johnsongrass, and Erianthus (Ra et al. 2012). Napiergrass yields up to 84.8 Mg ha-1 yr-1
have been obtained in Puerto Rico (Vicente-Chandler, Silva, and Figarella 1959), while
in Florida, yields ranged between 35-45 Mg ha-1 yr-1 (Woodard and Sollenberger 2012;
Erickson et al. 2012). Biomass yield is a complex trait and studies have been conducted
to identify QTLs for various components affecting yield. In sorghum, QTLs for yield
related traits such as plant height, tiller number, leaf length, leaf width, stem diameter,
and flowering time have been identified (Hart et al. 2001; Murray et al. 2008; Xiao-ping
et al. 2011). Similarly, QTLs for leaf yield, stem yield, plant height and flowering time
have also been identified in Miscanthus sinensis (Gifford et al. 2015; Atienza et al.
2003). In Miscanthus, QTLs for yield co-segregated with other traits like number of
tillers, leaf area, leaf length, and leaf width (Gifford et al. 2015). In napiergrass, biomass
yield showed high correlations with number of tillers, plant height, and stem diameter
(Sinche 2013).
Flowering Time
Floral transition is the switch from vegetative growth to reproductive growth in
plants and it primarily determines flowering time. Flowering time is a key factor in plant
adaptation and is linked to various attributes like plant height, yield, number of leaves,
etc. (Durand et al. 2012). In many species, flowering is induced in response to the
32
length of periods of lightness and darkness associated with day and night length. These
species are categorized as short-day, long-day, intermediate-day, or day-neutral based
on their day length requirement (Schlegel 2009; Bastow and Dean 2002). Plants in
which flowering is favored by day lengths shorter than the critical and corresponding
long nights are called short-day plants (eg. Glycine max, Oryza sativa, and Zea mays).
The plants in which flowering is initiated when the day length is longer than the critical
are called long-day plants (eg. Hordeum vulgare, Triticum aestivum, and Solanum
tuberosum) (Garner 1933; Schlegel 2009; Bastow and Dean 2002). Napiergrass
belongs to the short-day plants (Osgood, Hanna, and Tew 1997; Singh, Singh, and
Obeng 2013).
Many studies have detected QTLs related to flowering time or earliness in
various crops. The genetic control of flowering time is in general quantitative in nature
(H. Lu et al. 2014). For example, in rice, 15 QTLs were associated with days to
flowering (Maheswaran et al. 2000), and in tomato, three QTLs related to earliness were
identified that were associated with flowering time, fruit set time, and ripening time
(Lindhout et al. 1994). However, a previous study in maize showed that flowering time
was a complex trait and no major QTL should be expected (Buckler et al. 2009). QTLs
for flowering related traits like 50% anthesis and heading date colocalized with other
QTLs for number of tillers, tiller diameter, leaf width, and leaf area in Miscanthus
(Gifford et al. 2015). In orchardgrass (Dactylis glomerata L)., 11 QTLs for heading date
and flowering time were found to be distributed on three linkage groups where
candidate genes such as hd1 and VRN1 were annotated (Zhao et al. 2016).
33
Flowering time in napiergrass is of prime importance as it is related to biosafety
and biomass quality. Late flowering napiergrass lines serve as potential bio-safe
biofuels as their flowering is compromised by low temperatures which usually occurs in
early December in Florida. Late flowering cultivars, therefore have less potential for
invasiveness than early flowering genotypes and may produce higher yields due to a
longer period of vegetative growth. Extensive phenotypic variation in flowering time is
an indication that flowering time is quantitative in nature and several genes might be
controlling the trait. Identifying QTLs for flowering time in napiergrass will help to
shorten the breeding cycle by the successful utilization of MAS. A biparental mapping
population from a cross between early-flowering and late-flowering lines will generate
progenies segregating for flowering time and this population can be genotyped in order
to identify QTLs for flowering time.
Genes Related to Flowering
Flowering time has been studied in some grass species and genomic studies
have identified a number of genes involved in flowering. Potential candidates can be
FRI, LEAFY, CO, DNF, MADS-box, and RID1 that have some roles in flowering
regulation in other species like Arabidopsis, wheat, maize, rice (Lee, Bleecker, and
Amasino 1993; Wuxing Li et al. 2013; Suárez-López et al. 2001; Morris et al. 2010; C.
Wu et al. 2008). For example, high levels of proteins encoded by FLOWERING LOCUS
T (FT) are correlated with early flowering and the lack of these causes late flowering
(Samach 2012). Major genes involved in photoperiod of flowering are highly conserved
between rice and Arabidopsis (C. Wu et al. 2008). For example, Hd1 QTL in rice that
promotes heading under short-day conditions corresponds to a gene homolog of
CONSTANS in Arabidopsis (Yano et al. 2000); Hd17 corresponds to a homolog of
34
Arabidopsis ELF3 (EARLY FLOWERING 3) (Matsubara et al. 2012; Matsubara et al.
2008); and Hd3a encodes a protein that is closely related to Arabidopsis FT (Kojima et
al. 2002). Mining of genome sequences that are available for several grass species for
flowering related genes and their characterization can identify candidate genes in
napiergrass. These candidate genes can be used for screening germplasm collections
for identification of haplotypes that confer late flowering. In addition to this, integrating
genomics with conventional breeding will help to shorten the breeding cycle for
selection.
Objectives
The overall objectives of this research are to identify genetic components (QTL
and candidate genes) underlying flowering time in napiergrass and to develop male and
female sterile PMN hybrids that are yielding high amounts of biomass and display
uniform seed progenies.
The substantial variation in flowering time in napiergrass can be utilized for
breeding late flowering varieties. This helps to make napiergrass a bio safe genotype for
biomass production in northern Florida where a freeze event typically occurs before
flowering of these late lines. The identification of QTLs controlling flowering time will
make the pre-selection for late flowering lines in breeding materials highly efficient.
Identifying polymorphisms within flowering genes in napiergrass germplasm collection
helps to reveal allelic variation in the germplasm. Development of a male sterile and
homozygous line of pearl millet will prevent its self-pollination, thus facilitating easy
crossing with napiergrass under field conditions. These high-biomass yielding male
sterile lines can then be used for hybridization with napiergrass.
35
The specific objectives of this research were to 1) construct genetic maps of
napiergrass, 2) identify QTLs for flowering time in napiergrass, 3) evaluate sequence
variations in a napiergrass germplasm collection, and 4) develop and evaluate male and
female sterile PMN hybrids.
36
Figure 1-1. Distribution of napiergrass. Black dots represent local napiergrass and red dots represent napiergrass listed as invasive species
-50
0
50
-100 0 100 200
Longitude
La
titu
de
37
CHAPTER 2 SURVEYING THE GENOME AND CONSTRUCTING A HIGH-DENSITY GENETIC
MAP OF NAPIERGRASS (CENCHRUS PURPUREUS SCHUMACH.)
Napiergrass (Cenchrus purpureus Schumach.) is a tropical forage grass and a
promising lignocellulosic biofuel feedstock due to its high biomass yield, persistence,
and nutritive value. However, its utilization for breeding has lagged behind other crops
due to limited genetic and genomic resources. In this study, next-generation sequencing
was first used to survey the genome of napiergrass. Napiergrass sequences displayed
high synteny to the pearl millet genome and showed expansions in the pearl millet
genome along with genomic rearrangements between the two genomes. An average
repeat content of 27.5% was observed in napiergrass including 5,339 simple sequence
repeats (SSRs). Furthermore, to construct a high-density genetic map of napiergrass,
genotyping-by-sequencing (GBS) was employed in a bi-parental population of 185 F1
hybrids. A total of 512 million high quality reads were generated and 287,093 SNPs
were called by using multiple de-novo and reference-based SNP callers. Single dose
SNPs were used to construct the first high-density linkage map that resulted in 1,913
SNPs mapped to 14 linkage groups, spanning a length of 1,410 cM and a density of 1
marker per 0.73 cM. This map can be used for many further genetic and genomic
studies in napiergrass and related species.
This chapter was published in Scientific Reports and is licensed under a Creative Commons Attribution 4.0 International License.
Paudel, D., Kannan, B., Yang, X., Harris-Shultz, K., Thudi, M., Varshney, R.K., Altpeter, F. and Wang, J., 2018. Surveying the genome and constructing a high-density genetic map of napiergrass (Cenchrus purpureus Schumach). Scientific reports, 8(1), p.14419.
(Taestivum_296_v2), pearl millet v1(Varshney et al. 2017) , and barley (ASM32608v1),
with Arabidopsis (Athaliana_167_TAIR9) as an outgroup control. The results showed
that the percentage of napiergrass sequence tags aligned to these grass species
ranged from 2.6% to 37.9% for barley and pearl millet genome, respectively (Table 2-6),
indicating a relatively close relationship between napiergrass and pearl millet.
49
SNP Calling by Various SNP Callers
Three de-novo SNP calling pipelines, TASSEL-UNEAK, Stacks, and GBS-SNP-
CROP identified 10,799, 6,871, and 4,521 SNPs, respectively. Reference based
pipelines were also applied by using pearl millet v1 (Varshney et al. 2017) as the
reference genome. However, the alignment rate was relatively low due to the
differences between the napiergrass and pearl millet genomes. The percentage of clean
reads aligned to the pearl millet genome using Bowtie2 ranged from 5.60% to 44.62%
with an average of 39.68%. Two samples had a small number of sequences (< 10% of
the average number of sequences per sample) and also the lowest percentage of
uniquely mapped reads (Table 2-7, Figure 2-5). Therefore, these samples were
removed from linkage map construction. Six different reference-based pipelines were
employed to call SNPs viz., TASSEL 4.3 (Glaubitz et al. 2014), Stacks 1.24 (Catchen et
al. 2011), GBS-SNP-CROP (Melo, Bartaula, and Hale 2016), SAMtools 1.2 mpileup (H.
Li et al. 2009), FreeBayes 0.9.21 (Garrison and Marth 2012), and GATK 3.3 (McKenna
et al. 2010). TASSEL 4.3, Stacks, and SAMtools identified 7,326, 4,920, 27,082 SNPs,
respectively in the mapping population, whereas FreeBayes, GBS-SNP-CROP, and
GATK that can handle ploidy identified 25,193, 2,906 and 197,475 SNPs, respectively.
The six reference-based SNP callers concordantly called only 11 SNPs (Figure 2-6,
only five programs are shown in figure due to Venn-diagram display limitations) and
207,391 non-redundant SNPs.
Genetic Linkage Map Construction
From a total of 549,944 SNPs called by both reference based and de-novo
pipelines, 287,093 SNPs were filtered for further analysis. Out of these, a total of 18,286
single-dose SNPs were genotyped in more than 180 progenies. Three individuals with
50
more than 10% missing sites were removed from further analysis. For linkage map
construction of each parental line, only the SNPs showing heterozygous in one parent
but homozygous in the other parent were selected. A total of 3,276 loci were
heterozygous in female parent but homozygous in male parent and segregated with an
expected ratio of 1:1 in the population, thus can be used for female parent linkage map
construction. Similarly, 3,417 loci were heterozygous in male parent but homozygous in
female parent and segregated with an expected ratio of 1:1 in the population, thus can
be used for male parent linkage map construction. For the female parental line, a total
of 1,606 SNPs were grouped and 899 loci were mapped on 14 linkage groups with a
total length of 1,555.17 cM averaging 1 marker every 1.72 cM (Figure 2-7). Inclusion of
segregation distorted (SD) markers increased the genetic distance of the female map by
28.13%. For the male parent, a total of 1,509 markers were grouped into 14 linkage
groups and 1,073 markers were mapped onto these 14 linkage groups with a total
length of 1,939.19 cM averaging 1 marker every 1.80 cM (Figure 2-8). Inclusion of SD
markers increased the total genetic distance of the male map by 38.41%.
A combined linkage map containing markers that segregated from both female
and male parents was constructed, which can facilitate future QTL mapping of the
population. To construct a combined linkage map, the markers showing heterozygous
on both parents in addition to male-parent heterozygous and female-parent
heterozygous markers were used. Therefore, a parent-averaged combined map was
constructed by using 378 heterozygous markers for both parents that segregated in a
1:2:1 ratio in the population, in combination with 3,417 male-parent heterozygous and
3,276 female-parent heterozygous markers. In total, 4,058 markers were grouped into
51
14 linkage groups out of which 1,913 markers were mapped. The final composite
linkage map spanned a length of 1,410.10 cM with an average of 0.73 cM between
markers. The largest linkage group was Linkage group 2 (LG 2), which spanned 142.40
cM and contained 170 markers (Table 2-8). Length of each linkage group ranged from
70.18 cM to 142.40 cM and density ranged from 0.88 to 1.77 markers per cM (Figure 2-
9, Table 2-8, Figure 2-10). Results of the χ2 test indicated that 114 (6.06%) of the 1,879
markers showed significant segregation distortion (0.001<P<0.05) on the combined
map. These distorted markers showed clustered distribution on three segregation
distortion regions (SDRs) in linkage groups LG07 and LG08 (Fig 2-9).
Among the different reference-based SNP callers, GATK called the highest
number of SNPs (197,475) followed by SAMtools and FreeBayes (Table 2-9). After
accounting for segregation ratio and missing data, SAMtools retained the largest
number of SNPs followed by TASSEL de-novo UNEAK. However, when considering the
total number of markers mapped on the combined linkage groups, TASSEL de-novo
UNEAK showed the highest percentage of SNPs mapped followed by Stacks (Table 2-
9).
Comparison Between Genomes of Napiergrass and Pearl Millet
Sequence tags of the markers that mapped on napiergrass linkage groups were
extracted and compared to the pearl millet genome. Among the 1,156 TASSEL de-novo
UNEAK tags positioned on the combined map, 663 were found to have significant
sequence similarities to the genome sequence of pearl millet. Considerable collinearity
was observed between the napiergrass and pearl millet genomes (Figure 2-11). For
each pearl millet pseudomolecule, two corresponding regions in the linkage groups
(LGs) of napiergrass genome were identified (Figure 2-11, Figure 2-12). However, some
52
pearl millet genomic regions had more than two corresponding regions on napiergrass
genome. For example, pseudomolecule 3 of pearl millet had regions corresponding to
three linkage groups LG03, LG12, and LG14 of napiergrass indicating possible
chromosomal rearrangement between the two species after speciation (Figure 2-11,
Figure 2-12).
Discussion
Despite its importance as a forage grass and its enormous potential as a biofuel
crop, molecular, genetic, and genomic studies have been severely limited in
napiergrass. Currently, there was no equivalent genome sequence in the public domain
to be used as a reference for napiergrass. In this study, an initial comparison between
the napiergrass survey sequences to 10 available grass genomes revealed that
napiergrass genomic sequences had the highest similarity with the pearl millet genome,
which could be explained by the presence of the A’A’ genome of napiergrass that is
homologous to the AA genome of pearl millet. Consequently, in this study we have
utilized pearl millet genome v1 (Varshney et al. 2017) as a reference for SNP calling
and also performed de-novo SNP calling without a reference genome. A total of 38.8%
of the napiergrass reads aligned to the pearl millet genome using Bowtie 2, which
performed better over BWA, another popular aligner (Langmead and Salzberg 2012;
Yang, Song, et al. 2017). The large portion of unaligned reads might be from the B
genome or the divergent chromosome regions of A genome between the two species.
From the genome survey comparison, the total length of all the alignments of
napiergrass reads was 25.1% longer in pearl millet indicating genic duplication or
expansion in pearl millet and genomic rearrangements between the two species during
evolution from their ancestral genome. This is consistent with a previously reported
53
genomic in situ hybridization, which verified that the pearl millet genome A was 24%
larger compared to the chromosomes of genome A’ of napiergrass (Reis et al. 2014).
For the 10 longest contigs in our assembly, average repeat content (27%) was lower
than reported from other grasses including sorghum (61%) (Paterson et al. 2009),
maize (85%) (Schnable et al. 2009), foxtail millet (46%) (G. Zhang et al. 2012), rice
(43.3%) (J. Yu et al. 2002), and pearl millet (77%) (Varshney et al. 2017). Low repeat
content in napiergrass could be attributed to the loss of genomic sequences after
hybridization. Rearrangements and loss of genomic sequences are common events
after hybridization (Kellis, Birren, and Lander 2004). Similar to other plant genomes,
long-terminal repeat (LTR) retrotransposons comprised the most abundant class (62.19
%) of repeats (Table 2-2). Significant relationships between napiergrass, pearl millet,
and P. squamulatum suggested their common origin and it was inferred that
napiergrass and pearl millet had concomitantly diverged from a common ancestor (Reis
et al. 2014; Martel et al. 2004; Martel et al. 1997) and the origin of napiergrass occurred
at the interspecific hybridization event, by combining genome A of the ancestor with
genome B of a still unknown second ancestor (Reis et al. 2014). Our study showed that
the napiergrass genome had considerable microcolinearity with the pearl millet genome
showing evidence of their relatedness and shared ancestry. Chromosome inverted
duplications on pseudomolecule 3 of pearl millet showed possible rearrangement after
speciation of napiergrass and pearl millet. Two corresponding regions on the
napiergrass linkage groups for each pearl millet chromosome corroborate the
hypothesis that these two genomes evolved from a common ancestor.
54
We developed a limited genomic assembly of napiergrass based on Illumina and
454 sequences. Nearly two thousand SSR markers were developed, which could be
immediately useful for applications in napiergrass breeding and genetics. With the
advancement of NGS, high throughput NGS-enabled genotyping technologies are
becoming readily accessible. Yet, SSR markers remain as a popular tool for genetic
studies, variety identification, monitoring of seed purity, and hybrid quality. They are
particularly important in laboratories which have limited resources and lack access to
NGS facilities or bioinformatic expertise. To our knowledge, this is the first study in
napiergrass where SSR markers were developed based on napiergrass genome
survey.
A genetic linkage map is an important tool to reveal the genome structure and to
identify marker-trait associations (Cai et al. 2015) which ultimately help in MAS (F. Lu et
al. 2013) to improve precision of selection. In this study, we used the GBS approach to
construct a combined high-density linkage map that spanned 1,473.9 cM with 1,917
markers on 14 linkage groups, which is a very critical tool for further genetic and
genomics studies of napiergrass. GBS has been extensively used for genotyping many
diploid organisms, however, SNP calling from the NGS data in allotetraploids like
napiergrass is particularly challenging due to existence of highly similar homeologous
copies, one corresponding to A genome and the other to B genome (Nagy et al. 2013).
Therefore, different strategies have been devised to construct linkage map in
allopolyploids. For example only uniquely aligned reads (single copy) were considered
for SNP calling and subsequent map construction (Trick et al. 2009; X. Zhou et al. 2014)
to avoid the collapsed alignment of homoeologous reads due to low divergence, recent
55
polyploidization event, and severe domestic bottlenecks (Pandey et al. 2012). SNP
calling in allotetraploid Brassica napus L. (rapeseed; 2n = 4x =38; AACC) was done by
utilizing only uniquely mapped reads (single copy) and a read depth minimum of three
to four reads at each potential SNP (Trick et al. 2009). Linkage map construction in
zoysiagrass (Zoysia matrella) was performed by utilizing single-dose markers after
calling SNPs using the maximum likelihood method in Stacks (Xiaoen Huang et al.
2016). Similarly, single dose markers from TASSEL de-novo UNEAK were used to
construct linkage maps in prairie cordgrass (Spartina pectinate)(Crawford et al. 2016).
In this study, we applied multiple SNP callers and strategies to maximize SNP
calling for linkage map construction for napiergrass. In the final combined genetic map,
the number of markers identified by different software varied dramatically. GATK called
the highest number of SNPs followed by SAMtools and FreeBayes initially. Both GATK
and SAMtools apply Bayesian method to compute the posterior probability for each
possible genotype and then choose the genotype with the highest probability as the
consensus genotype (X. Yu and Sun 2013). GBS-SNP-CROP and TASSEL showed a
low matching percentage, which is similar to results from previous research (Melo,
Bartaula, and Hale 2016). The number of useful markers for linkage group construction
was the highest in SAMtools (47.75%) followed by TASSEL de-novo UNEAK (35.68%).
However, the TASSEL de-novo UNEAK pipeline had the highest number of markers
mapped on the linkage groups (60.43%) followed by Stacks (13.43%). This indicated
that the network-based SNP discovery in TASSEL de-novo UNEAK and UStacks
pipeline (Kim et al. 2015) could be efficiently utilized for constructing linkage maps in
non-model species. Even though TASSEL was primarily designed for diploids, it is
56
powerful enough to give a large number of mapped markers compared to other
programs that handle polyploidy like FreeBayes, GATK, or GBS-SNP-CROP.
The SNP markers were relatively evenly distributed among the linkage groups
with more than 97.45% of marker interval being less than 5 cM. To our knowledge, this
linkage map with an average inter-marker distance of 0.7 cM is the first genetic linkage
map of napiergrass to date. A study based on an interspecific population of a cross
between pearl millet and napiergrass has been previously reported to link RAPD
markers with biomass related traits in Pennisetum (Smith et al. 1993). The large number
of markers and their even distribution in our study facilitate full-scale map coverage.
Few regions where the interval space was > 5 cM might be due to stretches of large
repeats or due to low coverage sequencing of GBS (Poland and Rife 2012; Mathew et
al. 2014). Segregation distortion is regarded as a potential evolutionary force and
including these markers for linkage map construction could increase genome coverage
as well as benefit QTL mapping (S. Xu 2008; D. R. Taylor and Ingvarsson 2003).
Including SDR markers and correcting for bias led to an increase in genetic distance
between distorted markers (S. Q. Xie, Feng, and Zhang 2014). The deviation from
expected Mendelian ratio shows disturbances in the transmission of genetic information
from one generation to the next and can be caused by chromosome loss or
rearrangements, genetic load, gametic selection, zygotic selection, or both (Faris,
Laddomada, and Gill 1998; Karkkainen, Koski, and Savolainen 1996; Bodénès et al.
2016). Napiergrass generally outcross through wind pollination that could result in high
levels of gene flow leading to genetic load. The assignment of napiergrass linkage
57
groups according to the pearl millet genome allows for future fine mapping and QTL
analysis.
In summary, this study reports for the first time a high-density genetic linkage
map using NGS-derived SNP markers, as well as the development of SSRs from
napiergrass genomic sequences. The napiergrass genome showed considerable
collinearity with the pearl millet genome and the genetic map contains 14 linkage groups
with low inter-marker interval. The results will be useful for future molecular breeding
programs such as identification of QTLs for important traits as well as MAS for the
genetic improvement of napiergrass and comparative genomics. These resources will
play a critical role in future whole genome sequencing projects and leveraging
molecular breeding of napiergrass.
58
Table 2-1. Parameters used for SNP calling for each software.
Reference based Parameters Remarks. [defaults]
TASSEL 4.3 -c 5 Min. number of times a tag must be present to be output <5> [1]
-mnMAF 0.01 Min. minor allele frequency <0.01>[0.01]
-mnMAC 100000 Min. minor allele count <100000>[10] (SNPs that pass either -mnMAF or -mnMAC will be output)
-misMat 2 Threshold genotypic mismatch rate above which the duplicate SNPs won’t be merged <2>[0.05]
-callHets When two genotypes at a replicate SNP disagree for a taxon, call it a heterozygote
Stacks -A CP CP type for genetic map
-m 3 Min. number of identical, raw reads required to create a stack <3>[3]
GBS-SNP-CROP -l 30
-sl 4:30
-tr 30
-m 32
Trimmomatic LEADING parameter
Trimmomatic SLIDINGWINDOW parameter
Trimmomatic TRAILING parameter
Trimmomatic MINLEN parameter
-rl 100
-pl 32
-p 0.01
-id 0.93
Raw GBS read length
Min. length required after merging to retain read
p-value for PEAR
Nucleotide identity value required for USEARCH read clustering
-Q 30
-q 0
-f 0
-F 2308
Phred score base call quality
Alignment quality
SAMtools flags
SAMtools flags
-mnHoDepth0 11 Min. depth required for calling a homozygote when the alternative allele depth = 0
-mnHoDepth1 48 Min. depth required for each allele when calling a heterozygote
-mnHetDepth 3 Min. depth required for each allele when calling a heterozygote
59
Table 2-1. Continued
Reference based Parameters Remarks. [defaults]
-altStrength 0.9 Across the pop. For a given putative bi-allelic SNP, this alternate allele strength is the minimum proportion of non-primary allele reads that are the secondary allele
-mnAlleleRatio 0.1 Min. required ratio of less frequent allele depth to more frequent allele depth
-mnCal 0.75 Min. acceptable proportion of genotyped individuals to retain a SNP
-mnAvgDepth 4 Min. avg. depth of an acceptable SNP
-mxAvgDepth 200 Max avg. depth of an acceptable SNP
SAMtools mpileup -uf Default
FreeBayes -C 2
--min-alternate-count Require at least <2> observations supporting an alternate allele within a single individual in order to evaluate the position [1]
-p 4 --ploidy <4> [2]
--use-best-n-alleles 4
Evaluate only the best N SNP alleles ranked by sum of supporting quality scores [all]
--min-coverage 5 Require at least <5> coverage to process a site [0]
GATK -T UnifiedGenotyper
Call SNPs and indels on a per-locus basis
-stand_call_conf 30
The min. phred-scaled confidence thresholds at which variants should be called <30> [30]
-stand_emit_conf 10
The minimum phred-scale confidence threshold at which variants should be emitted (and filtered with LowQual if less than the calling threshold) <10> [30]
-ploidy 4 Ploidy <4> [2]
-mbq 20 Minimum base quality required to consider a base for calling
-glm BOTH Genotype likelihoods calculation model <BOTH> includes SNPs and INDELs [SNP]
60
Table 2-1. Continued
de-novo based Parameters Remarks. Defaults []
UNEAK -e PstI Restriction enzyme used
-c 5 Min. count of a tag must be present to be output [5]
-e 0.03 Error tolerance rate in the network filter [0.03]
-mnMAF 0.05 Min. minor allele frequency [0.05]
-mxMAF 0.5 Max. minor allele frequency [0.5]
-mnC 0 Min. call rate (proportion that how many taxa are covered by at least one tag)
-mxC 1 Max. call rate [1]
Stacks -m 3 Min. number of identical, raw reads to create a stack
-M 2 No. of mismatches allowed between loci when processing a single individual [2]
-n 1 No. of mismatches allowed between loci when building the catalog [1]
-t Remove, or break up, highly repetitive RAD-Tags in the ustacks program
GBS-SNP-CROP Same as reference based except, script 3 settings not required
61
Table 2-2. Repetitive elements present in the napiergrass genome.
Transposable Element Count
DNA transposon
Tc1/Mariner 8
hAT 20
PIF/Harbinger 16
EnSpm 1
CACTA 4
Polinton 1
LTR Retrotransposon
LTR 37
LTR/Copia 5
LTR/Gypsy 5
Copia 4
Retrotransposon 3
Retroelement 1
Gypsy 2
Non-LTR Retrotransposons
L1 2
Pseudogene
tRNA 1
rRNA 8
rDNA-like 3
Others
Mutator 15
Telomeric 4
MobileElement 2
Micro-like sequence 1
Low-complexity 3
Simple Repeats 12
Unspecified 6
Total 164
62
Table 2-3. The sequence alignment of ten napiergrass sequence contigs to the pearl millet genome.
Total (average) 4,058 1,913 2,145 1,410.10 (1.37) (97.45) (91.65) (89.96)
71
Table 2-9. Summary of napiergrass single nucleotide polymorphism (SNP) markers mapped on the combined linkage map using 9 different software pipelines.
Software Number of
SNPs called
Total SNPs
used for map
construction
No. of SNPs on
map
Percentage of
SNPs on the
map (%)
FreeBayes 25,193 6 0 0.00
GATK 197,475 52 5 0.26
SAMtools 27,082 3,377 151 7.89
GBS-SNP-CROP 2,906 115 52 2.72
TASSEL 7,326 116 56 2.93
Stacks 4,920 447 257 13.43
GBS-SNP-CROP
de-novo
4,521 96 51 2.67
Stacks de-novo 6,871 339 185 9.67
TASSEL de-novo
UNEAK
10,799 2,523 1,156 60.43
Total 287,093 7,071 1,913
72
Figure 2-1. Sequence variation for SNPs called in various regions of the pearl millet genome.
0%
10%
20%
30%
40%
50%
60%
70%Intergenic
Up
5' UTR
Exon
Donor
Intron
Acceptor
Exon
3' UTR
Down
Intergenic
73
Figure 2-2. Micro-collinearity between contigs from napiergrass to the pearl millet genome.
74
Figure 2-3. Inversion duplication between napiergrass and pearl millet (shown in bottom figure).
75
Figure 2-4. Estimated coverage of PstI restriction sites in the pearl millet genome.
0
5
10
15
20
25
30
35
40
0 5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
10
0
Mo
re
Num
ber
of
sam
ple
s
Estimated coverage (X)
76
Figure 2-5. Histogram of uniquely mapped reads to the pearl millet genome.
0
10
20
30
40
50
60
70
80
90
100
<6 // 34 36 38 40 42 44 46 48
Num
ber
of
sam
ple
s
Percentage of uniquely mapped reads (%)
77
Figure 2-6. Venn diagram showing concordant napiergrass SNPs called by five reference-based SNP callers, SAMtools, GBS-SNP-CROP, GATK, FreeBayes, and TASSEL. Numbers in parenthesis after the program name shows the total number of SNPs called by each program.
78
Figure 2-7. Genetic linkage map of the napiergrass female parent N190.
Figure 2-9. Genotyping by sequencing single nucleotide polymorphism (GBS-SNP) marker distribution for the 14 linkage groups of napiergrass. A black bar means a GBS-SNP marker. A blue bar represents segregation distortion region. The left scale plate represents genetic distance (centiMorgan as unit).
81
Figure 2-10. Consensus genetic linkage map of napiergrass.
Figure 2-11. Circos plot of the mapped TASSEL de-novo UNEAK napiergrass markers with pearl millet reference genome. Pearl millet pseudomolecules start with “PM” and are color coded for each pseudomolecule. Napiergrass linkage groups start with “LG” and are in green color. Each line corresponds to tags that showed significant BLAST hits to the pearl millet genome (> 80% identity and > 50 bp length).
83
Figure 2-12. Syntenic regions between napiergrass linkage groups and the pearl millet genome. PM01 to PM07 are pearl millet pseudomolecules, LG01 to LG14 are napiergrass linkage groups. The small dots represent significant BLAST hits of mapped UNEAK tags to the pearl millet genome (>80% identity and >50 bp length).
84
CHAPTER 3 MAPPING QTLS CONTROLLING FLOWER NUMBER AND FLOWERING TIME IN
NAPIERGRASS
Introduction
Napiergrass (Cenchrus purpureus Schumach) is a tropical perennial grass that
originated from Africa (Singh, Singh, and Obeng 2013) and is an important fodder crop
widely used as feed for dairy cows (Farrell, Simons, and Hillocks 2002). In addition, due
to its high biomass potential, napiergrass is considered as a promising crop for
cellulosic biofuel with higher dry biomass yield compared to sorghum (Sorghum bicolor),
johnsongrass (Sorghum halepense), and Erianthus (Ra et al. 2012). Napiergrass was
introduced to the United States in 1913 (Burton 1990). Being non-edible, napiergrass
escapes the 'food versus fuel' debate as the biofuel feedstock and has competitive
advantage over tree species because it can be harvested for biomass in the first year
after planting. Furthermore, the lignin content, which is considered a hindrance to the
fermentation of biomass to ethanol, is much lower in napiergrass than woody biomass,
10% in napiergrass compared to 20-30% in woods (Tong, Smith, and Mccarty 1990;
Mckendry 2002). In addition, napiergrass tolerates multiple harvests after which it
shows better ratooning ability than energycane (Cuomo, Blouin, and Beatty 1996). This
supports a constant feedstock supply and minimizes transportation costs of biomass.
Napiergrass is a short-day plant and flowering in tropical climates occurs from
autumn through winter (Singh, Singh, and Obeng 2013). Early flowering cultivars
produce abundant wind dispersed seeds, which contribute to the high potential of
invasiveness or weediness (D’Antonio and Vitousek 1992; Loope, Hamann, and Stone
85
1988; Schofield 1989). Therefore, The Florida Exotic Plant Pest Council has listed
napiergrass as an invasive species (FLEPPC 2011). Controlling flowering or modifying
flowering time of napiergrass can minimize invasiveness and boost its potential as
biofuel feedstock. Late flowering will reduce the total seed production and low
temperatures during late season may even compromise development of viable seeds
(Grabowski et al. 2016), thus reducing invasiveness. Significant genotypic variation for
agronomic traits have been documented in napiergrass (Sinche et al. 2018). Flowering
time showed a considerable variation within an F1 population of napiergrass, thus there
is a good reason to believe that improvements of flowering time in napiergrass can be
achieved using genetic approaches. Genome editing tools could be utilized to regulate
flowering time (Jung et al. 2018; Jung and Müller 2009) and to avoid unintended
spreading of napiergrass. However, routine transformation and genome editing
protocols still need to be developed for napiergrass (S. Zhou et al. 2018; J. Wang et al.
2017). So far there are no quantitative trait loci (QTL) mapping reports for agronomic
traits in napiergrass. Even in this genomic era, there is no noticeable genomic data
publicly available for napiergrass. This presents a challenge to improve agronomic traits
in napiergrass breeding utilizing marker assisted selection (MAS). Therefore, more
genomic resources and a better understanding of the genetic basis of flowering time is
necessary in napiergrass.
Most studies on napiergrass were limited to assessing genetic diversity and
relatedness using random amplification of polymorphic DNA (RAPD), amplified
fragment length polymorphism (AFLP), isozymes, and simple sequence repeats (SSRs)
developed for other species like pearl millet (Cenchrus glaucum) and buffelgrass
86
(Pennisetum ciliare) (Lowe et al. 2003; Bhandari, Sukanya, and Ramesh 2006; Harris-
Shultz, Anderson, and Malik 2010; Kandel et al. 2016; Dowling et al. 2013; Dowling,
Burson, and Jessup 2014; López et al. 2014; Smith et al. 1993). Environmental
biosafety of napiergrass can be increased by utilizing molecular markers that are linked
to specific traits of interest such as late flowering. This MAS allows effective selection of
breeding materials. Identification of QTLs related to flowering will support MAS
programs for breeding napiergrass with delayed or less flowering characteristics,
limiting invasive potential of napiergrass. This may enhance the potential for utilizing
napiergrass as a forage and biofuel crop.
QTL mapping tries to identify stretches of DNA that are closely linked to genes
underlying the trait of interest by performing statistical analysis of genome-wide
molecular markers and traits measured in progeny of controlled crosses (Stinchcombe
and Hoekstra 2008). The advancement of next generation sequencing (NGS) has
hugely facilitated QTL identification and mapping. High density genetic maps developed
by genotyping-by-sequencing (GBS) have been used successfully to identify genes
related to flag leaf traits in wheat (Triticum aestivum) (Hussain et al. 2017), winter
hardiness and fall dormancy in Medicago sativa (Adhikari et al. 2018), bunch fruit weight
and height in palm (Elaeis guineensis) (Pootakham et al. 2015), and bloom date in
peach (Prunus persica) (Bielenberg et al. 2015). QTLs related to flowering time have
also been identified in other species as well. In pearl millet, QTLs for flowering time co-
mapped with QTLs for stover yield, grain yield, and biomass yield in LG4 and LG6
(Yadav et al. 2003). Similarly, three QTLs on LG2, LG3, and LG4 were identified for
grain yield across variable post-flowering moisture environments in pearl millet (Bidinger
87
et al. 2007). In wheat and barley, vernalization (Vrn) and photoperiod (Ppd) genes were
involved in flowering time variations (Cockram et al. 2007). Transcription factors such as
AP2 and agamous-like MADS-box were detected in Adzuki bean for QTLs related to
flowering time, maturity, and seed coat color (Y. Li et al. 2017). In rice, several different
QTLs for flowering time were identified and cloned in early and late flowering cultivars
that may have been involved in adaptation to cold regions (Izawa 2007; Xue et al.
2008). However, no QTL analyses in napiergrass have been reported so far. Whether
the orthologs in other species underlying flowering time also control the flowering time in
napiergrass is unknown.
The variation of flowering time in napiergrass accessions can be exploited to
understand the genetic basis of flowering in napiergrass. QTL analyses in napiergrass
is enabled by the generation of the first genetic map of napiergrass, which contains
1,913 SNP markers called from GBS of a pseudo-F2 mapping population, derived from
a cross between an early flowering line and a late flowering line of napiergrass (Paudel
et al. 2018). This map facilitates the identification of QTLs for various traits including
flowering related traits that can be utilized in the future for MAS. The main objective of
this research was to use this first genetic map of napiergrass to identify markers linked
to genes controlling flowering time and flower number through QTL analyses.
Materials and Methods
Development of a Mapping Population
A mapping population of 185 F1 hybrids was developed by crossing an early
flowering accession N122 and a late flowering accession N190 of napiergrass (Sinche
88
2013). Accession N190 produced a higher biomass and had a reduced number of thick
tillers compared to N122 (Sinche 2013).
The 185 F1 hybrids along with the two parents, N122 and N190, and an
established cultivar Merkeron were phenotyped as replicated single row plots at PSREU
in 2011 and 2012. Clones of the whole population were also planted at the Everglades
Research and Education Center (EREC) in Belle Glade, FL in 2015 and flowering date
was recorded in the following year after planting.
Phenotyping the Mapping Population
The field experimental design followed a randomized complete block design
(RCBD) with three replicates of 187 lines (185 F1 hybrids and 2 parents). One block
contained a single plant as a replicate of each line. Within each block the lines were
randomly assigned to estimate block effects. The first flowering date and flower
numbers of each line were recorded in October ~ December of 2012, 2013 on the plants
established at PSREU. The first flowering date was also recorded on the plants
established in EREC in October ~ December of 2016. The flowering date, defined as
the date when the first flower was visible, was documented weekly from the first week of
October to the first week of December. Flowering traits in 2012 and 2013 at PSREU
were obtained from a previous study (Sinche 2013). Flowering time (FT) was calculated
as the number of days between the first appearance of the flower and vernal equinox
(March 20) for the specific year (Lambert, Miller-Rushing, and Inouye 2010).
Genetic Map
The genetic map was generated based on the SNPs from GBS of the 185 lines
of the population (Paudel et al. 2018). Briefly, GBS was used to genotype the 185 F1
89
individuals and the two parents. SNPs were identified using various software tools to
construct a linkage map for maternal and paternal parent by employing a pseudo test-
cross strategy (Paudel et al. 2018). For the female parental line, a total of 899 SNP loci
mapped on 14 linkage groups with a total length of 1,555.17 cM were used. Similarly, for
the male parental line, a total of 1,073 markers that were grouped into 14 linkage
groups spanning a length of 1,939.19 cM were used (Paudel et al. 2018).
QTL Analysis
For QTL detection we chose the composite interval mapping method on
WinQTLcart 2.5 (S. Wang et al. 2005). Mean values of each trait across three replicates
from different years were used for the QTL analysis. The walking speed chosen for all
traits was 2 cM. A forward and backward stepwise regression method with a probability
of 0.1 and a window size of 10 cM were utilized to determine cofactors. LOD thresholds
used to determine the significance of identified QTLs was identified by using the
thousand-permutation test to each data set (p ≤ 0.05) (Churchill and Doerge 1994).
Adjacent QTLs on the same chromosome for the same trait were considered different
when the support intervals did not overlap (Haggard, Johnson, and St. Clair 2015). The
95% confidence interval was calculated for each QTL considering a 2-LOD support
interval (van Ooijen 1992). The QTL span was delimited using LOD-1 support interval
(LSI). The contribution rate (R2) was calculated as the percentage of phenotypic
variance explained by each QTL in proportion to the total phenotypic variance. QTLs
were named according to McCouch et al. (McCouch et al. 1997). Specifically, the QTLs
detected for number of flowers on the linkage map constructed for early flowering parent
N122 were designated “qNFE” (qtl Number of Flowers Early) followed by a linkage
90
group number and QTL number on the same linkage group for the same trait, separated
by a dash “-“. Similarly, the QTLs detected for flowering number based on the linkage
map constructed for late flowering parent N190 were named “qNFL”. The QTL for
flowering time on linkage map for early flowering parent N122 were named as “qFTE”
(qtl Flowering Time Early) and the QTLs for flowering time on map for late flowering
parent N190 were given “qFTL” (qtl Flowering Time Late). QTLs with a positive or
negative additive effect for a trait imply that the increased or decreased phenotypic
value is contributed by the QTL, respectively.
We categorized the identified QTLs as either stable and potential QTLs. QTLs
detected based on the phenotypic data from more than one year were considered as
stable, while those detected based on only one year’s phenotypic data were considered
as potential QTLs. The positions of the QTLs identified on each linkage map were
indicated by using MapChart 2.3 (Voorrips 2002).
Candidate Gene Identification
Sequences were extracted from the sequence tags, which generated the SNP
markers flanking QTL region. The extracted sequences were then BLASTed against the
pearl millet genome v1.1 (Varshney et al. 2017) to identify nucleotide matches of these
sequences to identify the QTL sequence intervals. Gene models and KEGG annotation
(Varshney et al. 2017) of pearl millet genes within the identified QTL sequence interval
were extracted. The function of each gene model was checked for its role in flowering
and a list of potential candidates was curated.
91
Results
Phenotypes
Number of flowers
The number of flowers of the 185 F1 individuals, and their parental lines, early
flowering line (N122) and a late flowering line (N190) ranged from 0 to 46 with an
average of 6 in 2012 (Figure 3-1, Table 3-1) and from 5 to 256 with an average of 61 in
2013 (Figure 3-2, Table 3-2). Many F1 individuals actually didn’t flower before the
harvest date (Dec. 6th, immediately before the first predicted frost) in 2012.The
correlation between number of flowers for 2012 and 2013 was 0.62 (Fig 3-3). The
heritability estimates for number of flowers ranged from 79% to 85% (Table 3-1).
Flowering Time
The days to flowering of the whole F1 population including the two parental lines,
ranged from 219 to 270 days with an average of 240 days in 2012 (Fig 3-4) and from
220 to 258 days with an average of 238 days in 2013 (Table 3-1, Fig 3-5). The average
number of days to flowering in 2016 was 233 days (Fig 3-6, Table 3-1).
The correlation between days to flowering for 2012 and 2013 was 0.34, while that
between 2012 and 2016 was 0.22. Correlation of days to flowering between 2013 and
2016 was 0.26 (Fig 3-7). The heritability for the days to flowering were estimated
ranging from 60% to 87% (Table 3-1). Days to flowering was negatively correlated with
number of flowers (r = - 0.42 for 2012 and r = -0.64 for 2013, Fig 3-8 and Fig 3-9).
QTL Analysis
Number of flowers
Three stable QTLs (qNFE-1-1, qNFE-1-2, and qNFE-1-3) were identified on LG1
of the genetic map for early parent N122 for number of flowers (Table 3-2, Figure 3-10).
92
Three potential QTLs (qNFE-2-1, qNFE-6-1, and qNFE-6-2) were identified on LGs, 2
and 6 (Table 3-2). The most important number of flowers QTL (qNFE-1-2) for N122
parent (R2=0.20, LOD=10.76) was found in LG1and was located at 123.7 ~ 126.8 cM.
The same linkage group harbored the two additional stable QTLs (qNFE-1-1 and qNFE-
1-3) that explained 15-17% of variance with LOD score ranging from 8.13 to 9.10 at
peak intervals from 133.5 cM - 134.6 cM and 116.9 cM - 118.2 cM. This suggests that
LG1 plays an important role for number of flowers in napiergrass.
Five potential QTLs for number of flowers were identified on LG 1, 4, and 5 of
the N190 map (Fig 3-11, Table 3-2). These potential QTLs had a PVE of 6-10% with a
LOD value ranging from 3.18 - 4.78. Four out of those five QTLs had additive effects in
favor of trait value. One potential QTL (qNFL-11-2) had a negative effect (Table 3-2).
Flowering time
A total of six potential QTLs were detected for FT. Two potential QTLs out of the
six were detected on the data of year 2013 on LG 1 of N190 and two potential QTLs
were detected on the same LG of N122 with R2 ranging from 0.11- 0.14 and LOD score
of 5.40-6.77. Two additional potential QTLs were identified on LG 7 of map N122. The
R2 for these potential QTLs ranged from 0.09 - 0.12 with LOD scores ranging from 3.91-
5.57. Both QTLs showed negative effect of small value (R2 = 0.07) on the trait.
Candidate Genes
Candidate genes were only identified in the genome regions corresponding to the
three stable flower number QTLs: qNFE-1-1, qNFE-1-2, and qNFE-1-3. Based on
BLAST analysis of sequences flanking the QTL marker against the pearl millet genome
(Varshney et al. 2017), three genome intervals spanning a genome size of 4 Mb, 12.37
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Mb and 1.4 Mb, respectively, were identified on pseudomolecule 1 of the pearl millet
genome, corresponding to the three QTLs. A total of 624 gene models were identified
within the three QTL intervals of the genome (160 gene models on qNFE-1-1, 397 on
qNFE-1-2, and 67 on qNFE-1-3). 295 of these models had KEGG annotation results (71
on qNFE-1-1, 191 on qNFE-1-2, and 33 on qNFE-1-3) (Varshney et al. 2017). This
region harbored potential candidate flowering genes such as AGAMOUS, DELLA, Floral
homeotic protein DEFICIENS, PPM1 (MIKCC MADS-domain protein), WRKY, and
SERK1 (Table 3-4).
Discussion
Napiergrass is an important forage crop with high potential as a cellulosic biofuel
feedstock. Breeding of non- or late flowering varieties that have an extended vegetative
growth is not only important for obtaining a high yield but also for reducing invasiveness.
So far, genes involved in flowering regulation have not been identified in napiergrass,
which has severely hindered the improvement of flowering time traits in napiergrass
breeding. Over the last few years, mapping of QTLs for economically important traits
and genome assembly has largely facilitated the breeding programs by the
development of MAS (Y. Wang et al. 2011). Recently, a high-density genetic map of
napiergrass has been developed (Paudel et al. 2018) that will ease identifying QTLs.
Exploring QTLs is important because many studies have identified candidate loci near
QTLs. For example in cereals, a QTL controlling heading date variation was close to loci
controlling photoperiod and vernalization (Laurie et al. 1994; Bezant et al. 1996). In rice,
four genes controlling flowering time (Hd1, Hd6, Hd3a, SE5) were identified from 14
QTLs (Yano et al. 2001). In maize, it was shown that flowering time variation was the
94
result of cumulative effect of several small QTLs and by using 5,000 RILs, a total of 36
QTLs were identified for days to anthesis (Dell’Acqua et al. 2015). Flowering time QTLs
in brassicas mapped to similar regions in homologous chromosomes within and
between species (Lagercrantz et al. 1996; Osborn et al. 1997). Flowering related genes
in Brassica showed a close sequence similarity to one end of chromosome 5 of
Arabidopsis, which contained many flowering related genes (Bohuon et al. 1998). This
type of syntenic results showed that the same QTLs may exist in other related species.
The availability of the reference genome of pearl millet (Varshney et al. 2017) largely
facilitates the comparative genomic approaches for napiergrass as these two species
share one genome (Jauhar and Hanna 1998).
In this study, we phenotyped a biparental F1 or pseudo-F2 population that
segregated for flowering time to identify QTLs related to flowering. The traits studied
showed a continuous variation indicating that the traits are quantitative in nature. We
observed a negative phenotypic correlation between the number of flowers and
flowering time in the F1 population. Days to flowering and number of flowers were
consistent in different years and locations for the population. The heritability for
flowering date ranged from 0.60-0.87 for napiergrass. It was higher than the heritability
reported for heading date in rice which ranged from 0.37 to 0.55 (L. Zhou et al. 2016)
and lower than that reported for maize (0.82 to 0.93) (C. Wang et al. 2010). For linkage
analysis, we employed a pseudo-testcross strategy. Linkage analysis of quantitative
traits in outcrossing polyploid species by using single dose markers (1:1) is a common
practice due to the limitation of the linkage analysis software (K. K. Wu et al. 1992). In
this approach, a pseudo-testcross strategy using heterozygous markers for one parent
95
and recessively homozygous markers in other parent is employed for linkage map
construction (S. Wu et al. 2010; W. Zhou et al. 2015). This strategy has been
successfully applied in tree plants such as Pinus elliottii and P. caribaea (Shepherd et
al. 2003), legumes such as alfalfa (Adhikari et al. 2018), as well as grasses such as
orchard grass (W. Xie et al. 2011), and sugarcane (Yang, Islam, et al. 2018).
In this study, three stable QTLs for number of flowers were identified on the
linkage map constructed for N122. An initial study done in pearl millet identified QTLs
related to grain number and panicle number in LG1 of pearl millet (Bidinger et al. 2007;
Yadav et al. 2003). However, the lack of shared markers did not allow a definitive
conclusion if these two QTLs represent the same locus.
Several QTL regions related to flowering time and number of flowers were
located at the end of the LG 1 of napiergrass. Across all years, the early flowering QTLs
with the greatest effect was found on LG 1 that correspond to pseudomolecule 1 of
pearl millet (Varshney et al. 2017). This highlighted the importance of this chromosome
for flowering and it might harbor genes that control flowering in napiergrass. QTL
identification has enabled us to link variations at the trait level to variations at the
sequence level. Since a QTL can harbor tens to hundreds of genes (Gelli et al. 2016),
the identification of genes responsible for phenotypic variation poses a major challenge.
Accurate identification of the underlying genes responsible for trait variation in QTL
regions is difficult in napiergrass due to the lack of genomic and transcriptomic
resources in napiergrass. Nevertheless, comparative genomic approaches utilizing
reference genome of pearl millet has helped us to identify putative candidate genes
related to flowering in napiergrass. Several candidate genes affecting flowering time in
96
model plants have been discussed previously (Kuittinen, Sillanpa, and Savolainen 1997;
Bouché et al. 2016). In rice, QTL analysis of flowering time identified genes such as
Hd1 (CO orthologue in Arabidopsis), Hd6 (CK2 orthologue in Arabidopsis), Hd3a (FT
orthologues in Arabidopsis), and SE5 (HY1 orthologue in Arabidopsis) (Yano et al.
2001).
In our study, we located important flowering time related genes including
AGAMOUS, DELLA, DEFICIENS, PPM1 (MIKCC MADS-domain protein), WRKY, and
SERK1 (Table 3-4) in the QTL area of napiergrass that showed sequence similarity to
the pearl millet genome. The AGAMOUS gene encodes a transcription factor that
regulates genes to determine stamen and carpel development (Yanofsky et al. 1990).
DELLA protein plays a key role in negative regulation of gibberellin biosynthesis that
regulates many cellular and developmental events include flowering, pollen maturation,
and the transition from vegetative growth to flowering (Yoshida et al. 2014). DEFICIENS
is an ortholog of APETALA3 in Arabidopsis that functions in petal and stamen organ
identity (Zahn et al. 2005). PPM1 is ubiquitously expressed throughout vegetative and
reproduction tissues and may have diverse functions (Singer, Krogan, and Ashton
2007). WRKY has been related to several abiotic responses and accelerates flowering
by regulating FLOWERING LOCUS T and LEAFY (Phukan, Jeena, and Shukla 2016).
SERK1 (SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASE1) acts as a
negative regulator of abscission metabolism and is required for anther development
(Lewis et al. 2010). Further investigations of these genes involved in flowering in wild
accessions of napiergrass are necessary to elucidate the basis for intraspecific variation
which is of great relevance to breeders. Future fine-mapping analysis by identifying
97
additional genetic variants in the QTL regions or by targeted resequencing of the QTL
intervals in a subset of individuals can lead towards accurate identification of targets for
improving flowering time in napiergrass and related crops. To verify the napiergrass
QTLs detected in our analysis, phenotyping should be repeated in multiple locations.
Breeders are more interested in QTLs that have large effect (Kearsey and Farquhar
1998) and the QTLs identified in this study can be used in the future for MAS in order to
breed late flowering napiergrass.
Conclusion
In summary, this study reports for the first time QTL analysis in napiergrass.
Three stable QTLs controlling number of flowers in napiergrass were identified, which
can explain 15%-20% of the phenotypic variation. We also identified three potential
QTLs controlling flowering time in napiergrass, which can explain 11%-14% of the
phenotypic variation. Gene models in pearl millet that mapped to two stable QTLs
harbored MADS-box transcription factors such as AGAMOUS and DEFICIENS, along
with other proteins such as DELLA, WRKY, and SERK1 that are involved in flowering
time regulation in plants. The QTLs detected in this study will be valuable information for
napiergrass breeding programs and help to understand the genetic basis of flowering.
This study confirms that flowering time and flower number are highly heritable traits in
napiergrass. Therefore, the late flowering napiergrass genotypes developed by Sinche
(2013) constitute a valuable germplasm resource to develop late flowering napiergrass
cultivars. In addition, validation of the putative candidate genes identified in this study
should lead to targets for genome editing to manipulate flowering time in napiergrass.
98
Figure 3-1. Histogram of number of flowers in the mapping population in 2012 in Citra, FL. X-axis represents the number of flowers and y-axis represents the count of plants. Average number of flowers of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013.
99
Figure 3-2. Histogram of number of flowers in mapping population for 2013 in Citra, FL. X-axis represents the number of flowers and y-axis represents the count of plants. Average number of flowers of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013.
100
Figure 3-3. Scatterplot of number of flowers between 2012 and 2013 in Citra, FL. X-axis represents the number of flowers in 2012 and y-axis represents the number of flowers in 2013. Data is adapted from Sinche, 2013.
0
100
200
0 10 20 30 40
Number of Flowers 2012
Nu
mb
er
of F
low
ers
20
13
101
Figure 3-4. Histogram of number of days to first flowering in 2012 in Citra, FL. X-axis represents the number of days to flowering (FT) and y-axis represents the number of accessions. Days to first flowering of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013.
102
Figure 3-5. Histogram of number of days to first flowering in 2013 in Citra, FL. X-axis represents the number of days to flowering (FT) and y-axis represents the number of accessions. Days to first flowering of two parental lines, N122 and N190 are plotted. Data is adapted from Sinche, 2013.
103
Figure 3-6. Histogram of first flowering date in the mapping population in 2016 in EREC, Belle Glade, FL. X-axis represents the number of days to flowering (FT) and y-axis represents the number of accessions. Days to first flowering of two parental lines, N122 and N190 are plotted.
104
Figure 3-7. Scatterplot of first date of flowering between different years and locations.
X-axis and Y-axis represent the number of days to flowering (FT) in different years. Data for 2012 and 2013 are from Citra, FL and adapted from Sinche, 2013. Data for 2016 are from Belle Glade, FL.
R2
0.11
220
230
240
250
220 230 240 250 260 270
Days to Flowering 2012
Da
ys to
Flo
we
rin
g 2
01
3
R2
0.04
220
230
240
250
220 230 240 250 260 270
Days to Flowering 2012
Da
ys to
Flo
we
rin
g 2
01
6
R2
0.06
220
230
240
250
220 230 240 250
Days to Flowering 2013
Da
ys to
Flo
we
rin
g 2
01
6
105
Figure 3-8. Scatterplot between number of flowers and days to flowering in 2012 in Citra, FL. X-axis represents the number of days to flowering (FT) in 2012 and y-axis represents the number of flowers per line in 2012. Data is adapted from Sinche, 2013.
0
10
20
30
40
220 230 240 250 260 270
Days to Flowering 2012
Nu
mb
er
of F
low
ers
20
12
106
Figure 3-9. Scatterplot between days to flowering and number of flowers in 2013 in Citra, FL. X-axis represents the number of days to flowering (FT) in 2013 and y-axis represents the number of flowers per line in 2013. Data is adapted from Sinche, 2013.
R2
0.41
0
100
200
220 230 240 250
Days to Flowering 2013
Nu
mb
er
of F
low
ers
20
13
107
Figure 3-10. Linkage map of male parent N122 showing potential and stable QTLs. QTLs for number of flower (NF) are color coded in blue and that for flowering time (FT) are colored in green. A black bar means a GBS-SNP marker. Markers are labeled on the left and genetic distance (centimorgan as unit) are labeled on right. LG = Linkage group.
Figure 3-11. Linkage map of female parent N190 showing potential QTLs. QTLs for number of flower (NF) are color coded in blue and that for flowering time (FT) are colored in green. A black bar means a GBS-SNP marker. Markers are labeled on the left and genetic distance (centimorgan as unit) are labeled on right. LG = Linkage group.
Table 3-1. Descriptive statistics of flowering date and number of flowers for 185 F1
hybrids of a cross (N190 N122) at Citra, FL., in 2012, 2013 (Sinche 2013) and 2016.
Statistic Flowering datea Number of flowers (plot-1) b
2012 2013 2016 2012 2013
Min 10/25 10/26 10/23 0 5
Max 12/06c 12/03 12/02 46 256
Mean 11/15 (240) d 11/13 (238) d 11/07 (232) d 6 61
SE 1 d 0.5 d 0.05 d 0.76 2.86
H2 0.60 0.87 0.64 0.85 0.79
a Flowering date: mm/dd
b 1.8 m long plots.
c The harvest concluded before the first predicted frost on 12/06/2012 and several genotypes did not flower until that day.
d Value in parenthesis indicates number of days to first flowering counted from March 20 of that year.
SE Standard error H2 Broad sense-heritability estimated on entry mean basis
110
Table 3-2. Detailed information about the QTLs for number of flowers. LG = Napiergrass linkage groups, Peak markers represent the marker at the highest peak of QTL, LSI represents the LOD-1 support interval in cM.
N122 qNFE-2-1 2 2013 TS6_10754586 3.48 0.08 + 3.1 cM - 4.8 cM dT38576, dT10631
N122 qNFE-6-1 6 2012 S4_17921468 3.64 0.06 + 84.3 cM - 86 cM dT31777, dT45232
N122 qNFE-6-2 6 2012 dT10193 3.45 0.06 + 91.1 cM - 91.9 cM dT52456, dT34107
N190 qNFL-1-1 1 2013 dT3328 4.04 0.10 + 5.6 cM - 6.7 cM dT38795, dT53891
N190 qNFL-4-1 4 2012 S1_6210761 3.66 0.07 + 7.6 cM - 8.8 cM
S5_128073888,
dT24515
N190 qNFL-4-2 4 2012 dT40995 4.78 0.10 + 16.2 cM - 16.7 cM dT16773, dT15817
N190 qNFL-11-1 11 2013 dT8910 4.22 0.08 + 47.5 cM - 52.9 cM dT29364, dC_223673
N190 qNFL-11-2 11 2013 dT43632 3.18 0.06 - 57.9 cM - 64.2 cM dT8910, dT3675
111
Table 3-3. Detailed information about the QTLs for flowering time. LG = Napiergrass linkage groups, Peak markers represent the marker at the highest peak of QTL, LSI represents the LOD-1 support interval in cM.
Parent QTL
code
Linkage
Group
Year Peak
markers
Peak
LOD
PVE (R2) Allele dir. LSI (cM) Flanking Markers
N122 qFTE-1-1 1 2013 dT53136 6.77 0.14 +
118.3 cM - 120.8
cM dT35128, dT39139
N122 qFTE-1-2 1 2013 dT48305 5.40 0.11 -
126.5 cM - 128.3
cM
S1_249594415,
dT32706
N122 qFTE-7-1 7 2012 dT44347 3.91 0.09 - 31 cM - 42.1 cM dT24498, dT6436
N122 qFTE-7-2 7 2016 dT39337 5.57 0.12 -
178.3 cM - 182.1
cM dT37830, dT44668
N190 qFTL-1-1 1 2013 dT41575 3.60 0.07 - 3.3 cM - 4.6 cM dT20387, dT3027
N190 qFTL-1-2 1 2013 dT1951 3.33 0.07 - 8.9 cM - 11.2 cM
dT2473,
C1_220011116
112
Table 3-4. List of putative flowering related genes from the genome of pearl millet (Varshney et al. 2017). Gene name Description Gene ID Chr Start position End position Exon
Figure 4-1. Histogram of length of flowering related genes.
0
10
20
30
40
50
60
70
80
100
-199
300
-399
500
-599
700
-799
900
-999
110
0-1
19
9
130
0-1
39
9
150
0-1
59
9
170
0-1
79
9
190
0-1
99
9
210
0-2
19
9
230
0-2
39
9
250
0-2
59
9
270
0-2
79
9
290
0-2
99
9
310
0-3
19
9
330
0-3
39
9
350
0-3
59
9
370
0-3
79
9
390
0-3
99
9
410
0-4
19
9
430
0-4
39
9
450
0-4
59
9
470
0-4
79
9
490
0-4
99
9
510
0-5
19
9
530
0-5
39
9
Num
be
r o
f ge
ne
s
Length of gene (bp), n=1213
132
Figure 4-2. Number of probes designed per gene.
0
10
20
30
40
50
60
0 20 40 60 80 100
Nu
mb
er
of
ge
ne
s
Number of probes designed per gene
133
Figure 4-3. Number of probes designed as a factor of the size of the gene.
134
Figure 4-4. Distribution of the targeted flowering genes in the genome of pearl millet. Each blue bar represents a flowering gene mapped on the pearl millet genome.
135
Figure 4-5. Number of paired-end reads per sample.
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
Num
be
r o
f re
ad
pa
irs
Sample
136
Figure 4-6. Bayesian Information Criteria (BIC) vs. number of clusters in k-means clustering suggests K=3 in the germplasm collection.
5 10 15 20
42
04
30
44
04
50
Value of BIC
versus number of clusters
Number of clusters
BIC
137
Figure 4-7. Projection of the napiergrass germplasm collection using first two linear discriminants (LDs) from discriminant analysis of principal components (DAPC). The shape of the points represent grouping by DAPC (circle = group 1; triangle = group 2; square = group 3) and the colors represent the origin continent: black = Americas; blue = Asia; and cyan = Africa.
-4
-2
0
2
-2.5 0.0 2.5 5.0
LD1
LD
2
138
Figure 4-8. Evolutionary relationships of taxa. G stands for group assigned by DAPC and are in different shapes (circle = group 1; triangle = group 2; square = group 3) and the fill colors represent the origin: black = Americas, blue = Asia, cyan = Africa, and white=unknown).
N43 G
2N
228 G
2
N51 G
2N
199 G
2
N210 G
2N
222 G
2N
203 G
2N
-Mer G
2N
212 G
2N
198
G2
N8
G2
N11
4 G
2N22
3 G
2
N21
4 G
2
N215 G2
N116 G2
N204 G2
N127 G2
N211 G2
N225 G2
N-6X G2
N205 G2
N128 G2
N129 G2
N12 G2
N132 G2
N74 G3N130 G3N9 G3
N14 G3N37 G3N239 G3N122 G3N168 G3N16 G3N20 G
3N172 G
3
NILR
I-16791 G1
N40 G
3
N35 G
3
N36 G
3
N34 G
3
N42 G
3
N39 G
3
N41 G
3
N32 G
3
N23 G
1
N24 G
1
N66 G
1
N56 G
1
N67 G
1
N68 G
1
N69 G
1N243 G
1
N244 G
1
N242 G
1
N240 G
1
N147 G
1
N17
9 G
1
N13
1 G
1
N15
2 G
1
N16
3 G
1
N17
1 G
1N164 G
1N181 G
1N183 G
1N170 G1N186 G1N182 G1N185 G1
N178 G1N137 G1N138 G1
N150 G1
N166 G1
N151 G1
N180 G1
N161 G1
N71 G1
N72 G1
N155 G1
N188 G1
N226 G1
N238 G1
N19 G1
N22 G1
N7 G
1
N70 G
1N
13 G1
N109 G
1N
ILRI-16786 G
1
N157 G
1N
190 G
1
NIL
RI-1
4984 G
1
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Figure 4-9. Histogram for days to flowering trait in napiergrass germplasm collection.
0
5
10
15
230 240 250 260 270 280
Days to flowering
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Figure 4-10. QQ plots for different models using GWASpoly using 78,129 SNP markers. DTF = Days to flowering.
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Figure 4-11. Manhattan plots for different models using GWASpoly. P values adjusted with FDR at 0.05. DTF = Days to flowering.
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CHAPTER 5 GENERATION OF INTERSPECIFIC HYBRIDS BETWEEN PEARL MILLET AND
NAPIERGRASS AND EVALUATION OF THEIR PERFORMANCE
Introduction
Napiergrass or elephantgrass (Cenchrus purpureus Schumach) is an important
forage crop that is widely used in Africa for dairy cows because of its high yields and
nutrient value (Singh, Singh, and Obeng 2013). Mature napiergrass reach plant heights
of 5-6 m and up to 20 nodes per stem (Boonman 1988) and out-yield other grasses by a
significant margin (Ra et al. 2012). Napiergrass is generally propagated via stem
cuttings. Clonal propagation increases propagation costs as cultivation is typically done
manually. Therefore, even cultivars of napiergrass with high biomass quality like Mott,
which produced average cattle gains of 0.97 kgd-1 compared to industry standard of
0.39 kgd-1 of ‘Pensacola’ bahiagrass (Paspalum notatum Flugge) (Sollenberger et al.
1989), have not been used commercially in the US because of the limitations
associated with the vegetative propagation (Diz and Schank 1993). Production of large
seeds that can be harvested and planted mechanically will propel the commercial
success of napiergrass.
Napiergrass is the fastest growing plant in the world (Karlsson and Vasil 1986)
with reported dry biomass yield of 45 dry t ha−1 year−1 in Florida (Woodard and
Sollenberger 2012) and as high as 80 dry t ha−1 in tropical countries (Vicente-Chandler,
Silva, and Figarella 1959). Therefore, napiergrass has a great potential as
lignocellulosic feedstock for biofuel production. Being non-edible and able to grow on
marginal land, napiergrass escapes the 'food versus fuel' debate and has competitive
advantage over tree species as it can be harvested for biomass in the first year after
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planting. Additionally, lignin content, which is considered a hindrance to the
fermentation of biomass to ethanol, is much lower in napiergrass than woody biomass,
10% in napiergrass compared to 20-30% in woods (Tong, Smith, and Mccarty 1990;
Mckendry 2002). Most perennial biomass crops like switchgrass (Panicum virgatum L.)
have establishment issues due to small seed size, slow growth rate, dormancy, and
negative response to high planting densities (Noble Research Institute 2007). Leading
bioenergy candidate grasses like switchgrass, Miscanthus, and energycane are not
capable of both direct seeding and high production of biomass in the establishment year
and in contrast to napiergrass do not tolerate multiple harvests per year (Singh, Singh,
and Obeng 2013).
Napiergrass is a short-day plant and flowering in tropical climates occurs from
autumn through winter (Singh, Singh, and Obeng 2013). Early flowering cultivars
produce an abundant amount of small and wind dispersed seeds making seed
collection difficult and increasing its invasive potential (D’Antonio and Vitousek 1992;
Loope, Hamann, and Stone 1988; Schofield 1989). Therefore, napiergrass is listed as
an invasive species by the Florida Exotic Plant Pest Council (FLEPPC 2011).
Invasiveness in napiergrass can be effectively controlled by developing interspecific
triploid hybrids between napiergrass and pearl millet (Cenchrus americanus, 2n=2x=14)
(Hanna 1981). The chromosomes in the A’ genome of napiergrass are homologous to
the A genome of pearl millet (Jauhar 1981) and these two species hybridize naturally to
produce pearl millet napiergrass (PMN) hybrids that are triploids (AA’ B genome) and
thus are sterile (Singh, Singh, and Obeng 2013). PMN hybrids do not set seed and so,
do not pose a threat of uncontrolled establishment through dissemination of seeds
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(Hanna and Monson 1980) and are not considered invasive (Jessup 2013). PMN
hybrids combine superior forage quality of pearl millet and high yielding ability of
napiergrass (Gupta and Mhere 1997; Osgood, Hanna, and Tew 1997). Some of these
hybrids produced higher biomass (18.9 Mg ha-1) than napiergrass (17.5 Mg ha-1) and
pearl millet (13.2 Mg ha-1) in Louisiana (Cuomo, Blouin, and Beatty 1996). Pearl millet
seeds are larger in comparison to napiergrass and no seed shattering occurs on the
pearl millet panicle (Fig 5-1). The shape and size of PMN seeds are similar to the
female pearl millet parent. These seeds can be planted using seed drills. Development
of seeded varieties that are sterile will represent a significant step in napiergrass
breeding because establishment of fields by seeds will allow automation of planting,
thus a significant cost reduction (Osgood, Hanna, and Tew 1997). Normally, the
resulting hybrid (2n=21) with AA’B genome has greater similarity to the napiergrass type
due to larger genetic contribution (66.7% chromosomes) and dominance of the
napiergrass B genome over the pearl millet A genome for genetic characters such as
earliness, inflorescence and leaf characteristics (Obok, Ova, and Iwo 2012; Gonzalez
and Hanna 1984). Most of the characteristics like resistance to pests, vigorous growth,
and high forage yield potential are on the B genome (Hanna 1987).
Both napiergrass and pearl millet are protogynous in nature, a phenomenon
where stigmas are exerted prior to anther exertion, therefore, they are predominately
cross-pollinated that results in high heterozygosity (Dowling, Burson, and Jessup 2014).
The heterozygous out-crossing nature of napiergrass and pearl millet leads to
significant segregation and lack of uniformity in progenies. Because of the
heterozygosity in napiergrass and pearl millet, PMN hybrids exhibit a high level of
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heterosis (Dowling, Burson, and Jessup 2014). For successful commercial application, it
is important to get a high level of heterosis in the hybrids while maintaining uniformity in
biomass and persistence of the progenies. Uniformity, the ability of a cultivar to
produce a specific phenotype instead of a varying phenotype, increases the amount of
predictability on the total biomass yield (Makumburage and Stapleton 2011). Using
PMN hybrids for commercial cultivation requires seeds that produce plants with a high
biomass yield and a certain level of uniformity. Selection for stand uniformity was found
to be associated with increased tolerance to environmental stress in maize (Tollenaar
and Wu 1999). PMN hybrid combinations can produce non-germinating seeds
(Aken’ova and Chheda 1973), or produce hybrids with varying yield potential (Hanna
and Monson 1980). Phenotypic variation can be due to genetic variation caused by
alleles segregating within a population, epistasis and mutations, or environment, caused
by fluctuating external condition (Fraser and Schadt 2010).
Commercial production of PMN hybrids can be facilitated by utilizing cytoplasmic
male sterile (cms) lines of pearl millet. ‘Tift 23A’, a cms line of pearl millet paved the way
to produce seed-propagated PMN hybrids that can facilitate seed harvest (J. B. Powell
and Burton 1966). However, the cms lines of pearl millet are dwarf type forages. It is
critical to choose the right parental combination in order to maximize yield of
interspecific PMN hybrids. For this, the cms trait in the dwarf forage type pearl millet
needs to be introgressed into high biomass pearl millet lines and homogenized so that
progenies from these will be uniform. The backcross method has been commonly used
to transfer entire sets of chromosome from foreign cytoplasm in order to create
cytoplasmic male-sterile genotypes (Acquaah 2012). These lines can then be used to
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cross with inbred napiergrass to produce progenies of male and female sterile PMN
hybrids.
Since PMN hybrids can produce forage until the season’s first frost, they have
the potential to address the fall forage deficit in the southeast USA (Cuomo, Blouin, and
Beatty 1996). PMN hybrids can be easily established using seeds and may produce
high yields in the first year itself. PMN hybrids are more resistant to most pests and
diseases than pearl millet (Hanna and Monson 1980). Expressed sequence tags –
simple short repeat (EST-SSR) markers can be used in order to confirm triploid hybrids
in a breeding program (Dowling et al. 2013). Therefore, developing seeded PMN
hybrids with high biomass yield and a certain level of uniformity should have a major
impact in the forage and biofuel industry. As such, the impact of different levels of
homozygosity/selfing of pearl millet and napiergrass parents on biomass yield and
uniformity will inform us about the best strategy to manage field breeding of PMN
hybrids. Therefore, it is necessary to identify the level of heterozygosity that is present
in the progenies from different crosses by evaluating biomass yield and uniformity of the
hybrids under field conditions. There have been no previous studies done on the impact
of different levels of selfing of pearl millet and napiergrass parents on the agronomic
performance of the PMN hybrids.
Introgression of cms into high biomass pearl millet should facilitate field
production of PMN seeds by open pollination of cms pearl millet with napiergrass. In this
study we report the biomass yield and uniformity of progenies produced from crosses
between four different parental types of female pearl millet (cms forage, high biomass,
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cms high biomass, and homozygous high biomass) and a napiergrass male parent (25-
17).
Materials and Methods
Description of Male Sterile Line of Pearl Millet
In this research we used a forage type pearl millet, Tift 85D2A4 (Tift 85) which is a
cytoplasmic male sterile line that flowers in about 75 days. The cms line of pearl millet
Tift 85 was received from Dr. Wayne Hanna, University of Georgia. The A4 cytoplasm
was transferred from a wild subspecies of pearl millet (Wayne Hanna, personal
communication). Tift 85D2B1 is used to maintain sterility of Tift 85D2A4 and it is self-
fertile with good seed set. It was developed by selecting a rust and leaf spot resistant
plant from a selfed population of a BC5 plant developed by backcrossing Tift23D2B1 to a
Cytoplasmic male sterility present in Tift 85 was introgressed into three high
biomass yielding pearl millet lines based on vigor: PI 288787 01 SD (787), PI 215603 01
SD (603), and DLSBF. PI 288787 01 SD is a late flowering accession collected in India
with an average of 19 nodes, 0.880 gm seed weight and 370 cm plant height (USDA,
ARS, and NGRP 1963). PI 215603 01 SD is a late flowering accession collected in India
with an average of 370 cm plant height and 0.720 gm seed weight (USDA, ARS, and
NGRP 1954). DLSBF is an African introduction from Burkina Faso (Wayne Hanna,
personal communication). Pollen from the pearl millet elite lines was dusted onto stigma
of dwarf type cms Tift 85. Thus produced F1 progeny was back crossed to a selfed high
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biomass line of pearl millet. Male sterility on individual plants were tested by visual
observation prior to crossing in each generation. Selfing of parental line and back
crossing with the most advanced line of the pearl millet continued. Finally, we were able
to introgress cms into one of the three elite pearl millet lines (line 787). We continued to
back cross the cms line 787 four times in order to get a cms version of the elite line of
pearl millet.
Production of PMN Hybrids
In order to obtain near inbred lines of napiergrass, two lines of napiergrass were
selfed, namely, Schank and 25-17. These two lines were selfed two times and flowering
was induced by controlling photoperiod in a growth chamber. However, selfed second
generation of napiergrass didn’t flower due to problems with photoperiod control in the
greenhouse. Therefore, crosses of parental napiergrass (25-17) with different types of
female pearl millet lines were performed (Table 5-1). The interspecific triploid (3x=21)
hybrids were produced in the growth chamber by pollinating pearl millet female plants
with napiergrass pollen.
These four crosses represent the cross of male napiergrass parent (25-17) with
four different types of female pearl-millet lines (cms parent – Tift85 [A], selfed 5th
generation -787S5 [B], BC4 generation – MS 787 BC4 [C], and original parent – P787
[D]).
Seedlings from these four crosses were grown and evaluated for triploidy based
on flowering. Nodes from the confirmed triploids were cut and replanted as replicated
clones into 6” pots. Five clones (nodes) for each plant were grown in the greenhouse in
UF/IFAS Plant Science Research and Education Unit (PSREU), Citra, FL.
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The pots were filled with sandy soil from the field in PSREU to mimic the soil
conditions of the field. The nodes were planted on Jan 30, 2018 and were irrigated twice
daily. Fertilizer at the rate of 20-20-20 was supplied through irrigation using Dosatron.
After the nodes germinated, the ‘N’ fertilizer rate was increased to 46-0-0 on Feb 21,
2018 in order to promote vegetative growth of the plants. The plants were transplanted
to the field on March 28, 2018. The plants were irrigated with 12mm weekly in the
absence of rainfall after planting. On April 20, 2018 the plants were fertilized with 33.62
kg ha-1 N; 11.21 kg ha-1 and 33.62 kg ha-1 K plus micronutrient package followed by
irrigation. The plants were further fertilized on May 10, 2018 with 84.06 kg ha-1 N; 20.17
kg ha-1 P and 84.06 kg ha-1 K plus micronutrient followed by irrigation. On June 18,
2018 the plants were re-fertilized with 67.25 kg ha-1 N and 89.66 kg ha-1 K using the 6-
0-8 liquid fertilizer plus micronutrient package followed by irrigation.
Experimental Design
Completely randomized block design (RCBD) with 5 replications was used for the
field experiment. Individuals of the same cross type were grouped together and
randomized. Row to row distance was 1.22 m (4 ft) and plant to plant distance was
maintained at 0.91 m (3ft).
Traits Evaluated
We measured yield related attributes of the PMN hybrids. Plant height, number of
tillers, stem diameter, leaf length, leaf width, fresh biomass, and dry biomass were
evaluated. For fresh biomass, all the tillers arising from individual plants were cut using
a brush cutter and fresh weight measured with a hanging scale after seven months of
planting. Samples for dry biomass were taken on August 20, 2018 by cutting 1-2 tillers
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per plant for 3 replications of each line and weighed. They were then dried in a plant
sample dryer at 48.8°C for 1 month after which the dry biomass weight of the samples
was measured. The dry biomass for replicates was averaged and its derived function
was used to extrapolate dry biomass from each line’s fresh biomass.
Data Analysis
Levene's test was done to compare the variances using 'car' package in R (Fox
et al. 2012). Analysis of variance (ANOVA) and Tukey's HSD test were carried out using
'agricolae' package in R (De Mendiburu 2014). Linear model was fit using the trait value
as dependent variable and cross type and replication as independent variable.
Results
Plant Height
Plant height of the four different crosses were significantly different to each other
(HSD, p<0.05). Variance due to block was significant which was accounted for in the
model. The interaction between plant height and block was not significant. Cross D had
significantly highest height followed by cross B, cross C, and cross A (Figure 5-2). The
coefficient of variation of height was highest in cross type A (23.98%) and the lowest CV
was on cross D (11.31%) (Table 5-2). Maximum height among all the groups was in
cross D, with a height of 426.72 cm. Lowest range of plant height was in cross B
(228.60 cm – 396.24 cm) and the highest in cross D (86.36 cm – 426.72 cm) (Figure 5-
3).
Tiller Number
Tiller number varied significantly between the four types of crosses (p<0.05).
Block effect was not significant for tiller number. Tiller number was significantly higher in
cross A followed by cross D (HSD, p<0.05) (Figure 5-4). Tiller number of cross B was at
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par with cross C. Lowest number of tillers (1) was found in cross C and the highest
number of tillers (43) was found in cross A. The CV of number of tillers was highest in
cross A and lowest in cross D (Table 5-2). Similarly, the range of number of tillers was
much greater in cross A (2 - 43) as compared to other crosses (3 – 22 in cross B, 1 – 27
in cross C, and 5 – 15 in cross D) (Figure 5-5).
Stem Diameter
Stem diameter between the groups were significantly different (p<0.05) and the
block effect was not significant. Significantly highest (17.25 mm) stem diameter was
found in cross B followed by cross C which was at par with cross D (Figure 5-6). Cross
A showed the highest CV (24.81%) for stem diameter while cross D showed the lowest
CV (14.16%) (Table 5-2). The range of stem diameter was wider in cross A (5.92 mm –
21.95 mm) compared to other crosses (10.04 – 25.71 mm for cross B, 9.63 mm – 22.65
mm for cross C, and 10.65 mm – 22.06 mm for cross D) (Figure 5-7).
Leaf Length
Leaf length was significantly different among the four crosses (p<0.05). Block
effect was not significant. Leaf length of cross D was at par with that of cross B, while it
was significantly higher than cross C or cross A (Figure 5-8). The minimum leaf length
was found in cross A (82.25 cm) and the maximum leaf length was found in cross D
(106.84 cm). The CV for leaf length was highest in cross A (28.32%) and the lowest CV
of leaf length was on cross D (10.15%) (Table 5-2). Range of leaf length was lower in
cross D (72.39 cm – 132.08 cm) as compared to other crosses (38.10 cm – 130.81 cm
for cross A, 26.67 cm – 129.54 cm for cross B, and 29.21 cm – 146.05 cm for cross D)
(Figure 5-9).
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Leaf Width
Leaf width was significantly different among the four crosses (p<0.05). Block
effect was not significant. Leaf width of cross D was at par with that of cross B, while it
was significantly higher than cross C or cross A (Figure 5-10). On the other hand, leaf
length of cross B was at par with cross D and cross C and significantly higher than
cross A. The minimum leaf width was found in cross C (25 mm) and the maximum leaf
width was found in cross B (69 mm). The CV for leaf width was highest in cross A
(18.80%) and the lowest CV of leaf width was on cross D (13.83%) (Table 5-2). Range
of leaf width was lower in cross D (27 mm – 62 mm) as compared to other crosses (23
mm – 59 mm for cross A, 30 mm – 69 mm for cross B, and 25 mm – 64 mm for cross C)
(Figure 5-11).
Plant Biomass
Fresh biomass per plant (kg) was significantly different (p < 0.05) among the four
crosses and the block effect was also significant. The mean response was different in
block 4 and block 5 as compared to the other blocks and the block effect was accounted
for in the model. The interaction between treatment and block was not significant.
Significantly highest biomass was found in cross D (8.54 kg/plant) followed by cross A
(6.88 kg/plant). Biomass for Cross C (4.97 kg/plant) and cross B (5.67 kg/plant) were at
par with each other (Fig 5-12). The lowest biomass was found in cross B and the
highest biomass was found in cross C (20.59 kg). The CV of biomass was lowest in
cross D (33.63%) and highest in cross A (67.40%). Similarly, the spread of biomass
values was wider in cross A (0.18 kg/plant – 19.16 kg/plant) as compared to other
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crosses (1.56 kg/plant – 14.28 kg/plant in cross B, 0.12 kg/plant – 20.59 kg/plant in
cross C and 3.06 kg/plant – 19.70 kg/plant in cross D) (Figure 5-13).
Dry Biomass
Dry biomass data from single plant was extrapolated to a larger land area in
order to get dry biomass in tons ha-1. Projected dry biomass (tons ha-1) was significantly
different (p < 0.05) among the four crosses. Significantly highest biomass was found in
cross D (24.83 tons ha-1). Dry biomass for the remaining three cross types were at par
with each other (18.39 tons ha-1, 18.34 tons ha-1, and 16.30 tons ha-1 for cross A, cross
B, and cross C, respectively) (Fig 5-14). Highest dry biomass was found in cross A
(54.87 tons ha-1) and the lowest was found in cross C (0.27 tons ha-1). The CV of dry
biomass was lowest in cross D (33.63%) and highest in cross A (67.40%). Similarly, the
range of biomass values among different progeny plants was much wider in cross A
(0.36 tons ha-1 - 54.51 tons ha-1) as compared to other crosses (3.84 tons ha-1 – 47.56
tons ha-1 in cross B, 0.27 tons ha-1 – 53.72 tons ha-1 in cross C, and 8.73 tons ha-1 –
48.59 tons ha-1 in cross D) (Figure 5-15).
Coefficient of Variation
The coefficient of variation (CV) for all measured traits is shown in Figure 5-16.
For all the traits evaluated, it was observed that the CV was least on cross D and
highest on cross A. CV was the highest for dry biomass (tons/ha.) (30.95% - 73.42%)
(Table 5-2) and least for leaf width (13.83-18.80%) among all types of crosses. A
relatively lower CV was observed for stem diameter (14.16-24.81%), plant height
(10.16-23.98%), and leaf length (10.15-28.32%). On the other hand, higher CV was
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observed for number of tillers (31.93-48.47%). A picture of four different crosses during
harvest is shown in Figure 5-17.
Correlation
All of the evaluated traits had significant, positive correlations with biomass
weight. Correlation coefficient was the highest for number of tillers (0.60) and lowest for
stem diameter (0.18). Correlation coefficients and p-values between biomass weight
and each trait are presented in Table 5-3.
Discussion
Both pearl millet and napiergrass are protogynous and are predominantly cross-
pollinated. This increases their heterozygosity which leads to a high level of heterosis in
PMN hybrids (Dowling, Burson, and Jessup 2014; J. B. Powell and Burton 1966).
Development of a cms pearl millet line optimized for biomass production will be the
most efficient system for commercial scale production of large seeded PMN hybrids
according to the method described by Powell and Burton (1966) (Dowling 2011). For
commercial success of PMN hybrids, phenotypic stability and robustness of the
genotype to consistently produce a specific phenotype is critical. To achieve this, we
introgressed cms into high biomass pearl millet lines and assessed the phenotypic
variation in biomass yield and related traits of PMN hybrids depending on different pearl
millet sources. It should be noted that our attempt to produce a male napiergrass parent
with higher level of homozygosity was not accomplished in time and therefore a
heterozygous napiergrass parent was used in all these crosses. Inherent heterozygosity
in this parent probably contributed to a high level of phenotypic variability in the
progenies.
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The biomass produced after seven months of growth was the highest in the cross
involving the original pearl millet parent (cross D) followed by the cms parent, Tift 85
(cross A). The CV for biomass was also lowest in cross D (30.95%). However, CV for
biomass of cross A was the highest (73.42%) as compared to cross B (39.66%) and
cross C (56.55%). For the other traits evaluated, lowest CV was mostly found in cross D
followed by cross B. As expected the high biomass pearl millet parent P787 used in
cross D had a higher combining ability than the forage-type pearl millet parent Tift 85
resulting in significant differences in biomass yield. The backcross of Tift 85 with P787
was not able to restore the superior combining ability of P787. Interestingly, progenies
from cross C using 787-S5 as parent showed a significant lower biomass yield than
progenies from crosses involving the heterozygous P787. Testing the biomass
accumulation of selfed progenies from P787 at every generation and selecting superior
individuals may help to generate male sterile 787 with superior combining ability. The
difference among CV of cross D and cross B was low. For plant height, the CV of cross
B was actually the lowest among the different crosses. CV for the various traits involving
cross C (BC4 generation of Tift85 and 25-17) was lower than cross A but higher than
cross B or cross D. We saw that the CV decreased in BC4 but hasn't reached the level
of P787 or 787-S5. In theory, BC4 genotype will be 93.75% identical to the recurrent
parent (Acquaah 2012). Therefore, in the line MS 787 BC4, we are in theory able to
introgress approximately 94% of the genome of P787 parent. Continuing the back cross
to more than BC7 generation will give 99% similarity to the P787 parent and may result
in cms progenies that are more uniform.
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Even though the average yield of PMN hybrids using cms parent Tift85 in cross A
was high, the high variability in yield would make these lines less acceptable for
commercial production because phenotypic uniformity contributes to a predictable yield.
Many factors such as variable genetic yield potential, uneven germination, variable
planting depth, soil clods, insect damage, and moisture might be responsible for non-
uniformity of plant stands etc. (Martin et al. 2005) which can be controlled by proper
agronomic practices, experimental setup, and statistical analysis. Also, the cultivar
resulting from back-crossing could differ from the initial cultivar beyond the transferred
gene(s) because of linkage drag from the association of undesirable traits with the
genes from the donor(Acquaah 2012).
Biomass yield of grasses is affected by different morphological traits. In sorghum,
biomass is correlated with plant height, number of tillers, leaf length, leaf width, stem
diameter, and flowering time (Hart et al. 2001; Murray et al. 2008; Xiao-ping et al. 2011).
Similarly, in Miscanthus, plant height, stem diameter, late flowering, and growth rate
showed the highest positive correlation with yield (Zub, Arnoult, and Brancourt-Hulmel
2011). In Trichloris crinite, foliage height and basal diameter were strongly correlated
with biomass yield (Cavagnaro et al. 2006). In napiergrass, plant height, number of
tillers, and stem diameter were significantly correlated with plant biomass (Sinche
2013). Our results show that in PMN hybrids, traits like plant height, leaf length, leaf
width, stem diameter, and number of tillers are significantly correlated with biomass
yield. This indicates that tall PMN hybrids with numerous thick tillers and wide and long
leaves produced more biomass than those with the opposite characteristics.
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In this study, we have developed a cms version of elite pearl millet line P787.
The development of this non-dwarf biomass-type cms line of pearl millet provides new
resources for pearl millet and napiergrass breeding that can be exploited for commercial
production of uniform PMN hybrids.
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Figure 5-1. Seeds of pearl millet dwarf cms line (A), cms high biomass pearl millet line (B), PMN hybrid (C), and napiergrass (D), respectively from left to right.
159
Figure 5-2. Boxplot of plant height (cm) for four different types of crosses studied. Small colored dots represent individual plant height of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
d b c a100
200
300
400
A B C D
Cross Type
Pla
nt h
eig
ht (c
m)
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Figure 5-3. Histogram of plant height (cm) for four different crosses. X-axis represent plant height in cm and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
C D
A B
100 200 300 400 100 200 300 400
0
10
20
30
0
10
20
30
Plant height (cm)
161
Figure 5-4. Boxplot of number of tillers for four different types of crosses studied. Small colored dots represent number of tillers for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
a c c b0
10
20
30
40
A B C D
Cross Type
Nu
mb
er
of tille
rs
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Figure 5-5. Histogram of number of tillers for four different crosses. X-axis represent the number of tillers and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
C D
A B
0 10 20 30 40 0 10 20 30 40
0
20
40
60
0
20
40
60
Number of tillers
163
Figure 5-6. Boxplot of stem diameter (mm) for four different types of crosses studied. Small colored dots represent the stem diameter (mm) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
c a b b5
10
15
20
25
A B C D
Cross Type
Ste
m d
iam
ete
r (m
m)
164
Figure 5-7. Histogram of stem diameter (mm) for four different crosses. X-axis represent the stem diameter (mm) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
C D
A B
5 10 15 20 25 5 10 15 20 25
0
10
20
30
40
0
10
20
30
40
Stem diameter (mm)
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Figure 5-8. Boxplot of leaf length (cm) for four different types of crosses studied. Small colored dots represent the leaf length (cm) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
c a b a
50
100
150
A B C D
Cross Type
Le
af le
ng
th (
cm
)
166
Figure 5-9. Histogram of leaf length (cm) for four different crosses. X-axis represent the leaf length (cm) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
C D
A B
50 100 150 50 100 150
0
10
20
30
40
0
10
20
30
40
Leaf length (cm)
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Figure 5-10. Boxplot of leaf width (mm) for four different types of crosses studied. Small colored dots represent the leaf width (mm) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
c ab b a
30
40
50
60
70
A B C D
Cross Type
Le
af w
idth
(m
m)
168
Figure 5-11. Histogram of leaf width (mm) for four different crosses. X-axis represent the leaf width (mm) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
C D
A B
30 40 50 60 70 30 40 50 60 70
0
10
20
30
0
10
20
30
Leaf width (mm)
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Figure 5-12. Boxplot of fresh biomass per plant (kg) for four different types of crosses studied. Small colored dots represent the fresh biomass per plant (kg) for individual plants of the cross. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
b c c a0
5
10
15
20
A B C D
Cross Type
Fre
sh
Bio
ma
ss (
kg
/pla
nt)
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Figure 5-13. Histogram of fresh biomass per plant (kg) for four different crosses. X-axis represent the fresh biomass per plant (kg) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
C D
A B
0 5 10 15 20 0 5 10 15 20
0
20
40
0
20
40
Fresh Biomass (kg/plant)
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Figure 5-14. Boxplot of projected dry biomass (tons per ha) for four different types of crosses evaluated at seven months of growth. Different letters in blue color indicate significant differences among crosses based on Tukey’s HSD test at p ≤ 0.05. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
b b b a0
20
40
A B C D
Cross Type
Dry
Bio
ma
ss (
ton
s/h
a.)
172
Figure 5-15. Histogram of dry biomass (tons per ha.) for four different crosses evaluated at seven months of growth. X-axis represent the dry biomass (tons per ha.) and y-axis represents the count of plants. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
C D
A B
0 20 40 0 20 40
0
5
10
15
20
0
5
10
15
20
Dry Biomass (tons/ha)
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Figure 5-16. Coefficient of variation (%) for the different traits evaluated. A = Tift 85 × 25-17, B = 787-S5 × 25-17, C = MS 787 BC4 × 25-17, D = P787 × 25-17.
LeafWidth StemDiam TillerNum
BioMass height LeafLength
A B C D A B C D A B C D
0
20
40
60
0
20
40
60
Cross type
Co
effic
ien
t o
f va
ria
tio
n (
%)
174
Figure 5-17. Picture of four different crosses during harvest in Citra, FL. Cross A = Tift 85 × 25-17, Cross B = 787-S5 × 25-17, Cross C = MS 787 BC4 × 25-17, Cross D = P787 × 25-17.
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Table 5-1. Details of the cross types used in the experiment.
Cross type Female parent ♀ (Pearl millet)
Male parent ♂ (Napiergrass)
Cross details Number of plants
A Tift85 × 25-17 cms parent 40 B 787-S5 × 25-17 Selfed 5th gen. 50 C MS 787 BC4 × 25-17 BC4 generation 27 D P787 × 25-17 Original parent 22
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Table 5-2. Descriptive statistics for different types of crosses. P-value represents Pr>F for Levene’s test for homogeneity of variance (center = median), CV = coefficient of variation.
Trait Cross type P-value Mean Standard Deviation CV (%) Min Max
Plant height 0.0000
Plant height A 0.0778 223.84 53.67 23.98 86.36 340.36
Plant height B 0.9584 313.41 31.84 10.16 228.60 396.24
Plant height C 0.8109 285.09 50.58 17.74 111.76 375.92
Plant height D 0.6924 328.32 37.13 11.31 86.36 426.72
Number of tillers 0.0000
Number of tillers A 0.0758 17.88 8.67 48.47 2.00 43.00
Number of tillers B 0.6613 9.46 3.69 39.03 3.00 22.00
Number of tillers C 0.1015 8.98 4.29 47.73 1.00 27.00
Number of tillers D 0.6265 12.82 4.09 31.93 5.00 15.00
Stem diameter 0.0000
Stem diameter A 0.3174 13.31 3.30 24.81 5.92 21.95
Stem diameter B 0.9829 17.26 2.73 15.80 10.04 25.71
Stem diameter C 0.4913 16.42 2.61 15.90 9.63 22.65
Stem diameter D 0.8523 16.41 2.32 14.16 10.65 22.06
Leaf length 0.0000
Leaf length A 0.2382 82.26 23.29 28.32 38.10 130.81
Leaf length B 0.5612 106.01 11.75 11.08 26.67 129.54
Leaf length C 0.6267 101.17 15.05 14.88 29.21 146.05
Leaf length D 0.1045 106.84 10.84 10.15 72.39 132.08
Leaf width 0.0529
Leaf width A 0.4596 40.75 7.66 18.80 23.00 59.00
Leaf width B 0.8497 47.61 6.88 14.46 30.00 69.00
Leaf width C 0.7394 46.04 7.92 17.20 25.00 64.00
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Table 5-2. Continued Trait Cross type P-
value
Mean Standard
Deviation
CV (%) Min Max
Leaf width D 0.1875 48.89 6.76 13.83 27.00 62.00
Fresh biomass 0.0000
Fresh biomass A 0.0214* 6.88 4.64 67.40 0.18 19.16
Fresh biomass B 0.6081 6.23 2.29 36.75 1.56 14.28
Fresh biomass C 0.0794 5.47 3.10 56.63 0.12 20.59
Fresh biomass D 0.7515 8.54 2.87 33.63 3.06 19.70
Dry biomass 0.0000
Dry biomass A 0.0207* 18.39 13.50 73.42 0.36 54.87
Dry biomass B 0.6462 18.34 7.27 39.66 3.84 47.56
Dry biomass C 0.1246 16.30 9.22 56.55 0.27 53.72
Dry biomass D 0.9255 24.83 7.68 30.95 8.73 48.59
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Table 5-3. Correlation coefficients and p-values for biomass weight and biomass-related traits for PMN hybrids evaluated in Citra, FL.
Plant height Leaf length Leaf width Stem diameter Number of tillers
R 0.43 0.36 0.25 0.18 0.6
p-value <0.001 <0.001 <0.001 <0.001 <0.001
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CHAPTER 6 CONCLUDING REMARKS
Summary
Napiergrass is an important forage and biofuel crop. However, its commercial
utilization has been lagging behind other crops due to limited genetic and genomic
resources. Before this study, there were no SSR markers derived from napiergrass
sequences publicly available, no genetic linkage map was available, and no QTL
studies for any traits were published. The lack of these genetic resources limits the
advances that can be achieved via breeding. In this project, we have developed genetic
and genomic resources that will be important in napiergrass breeding. In Chapter 2, we
constructed the first high-density genetic linkage map of napiergrass using NGS-derived
SNP markers. We also identified 5,339 SSRs using napiergrass genomic sequences
and successfully designed primers for 1,926 SSRs. These results will be useful for
future molecular breeding programs such as identification of QTLs for important traits
as well as MAS for the genetic improvement of napiergrass and comparative
genomics.
Early flowering cultivars of napiergrass produce abundant wind dispersed seeds,
which contribute to high potential of invasiveness. Controlling flowering or modifying
flowering time of napiergrass can help in reducing its invasiveness and boost its
potential as biofuel feedstock. Therefore, a better understanding of the genetic basis of
flowering time in napiergrass is necessary. To facilitate this, in Chapter 3, we
demonstrated that flowering time and number of flowers are highly heritable traits.
Therefore, we conducted the first QTL analysis in napiergrass and identified three
stable QTLs controlling number of flowers. We also identified three potential QTLs that
180
control flowering time in napiergrass. We were able to identify potential candidate genes
such as AGAMOUS, DEFICIENS, DELLA, WRKY, and SERK1 that were harbored by
the QTL regions. The QTLs detected in this study will be valuable tools for napiergrass
breeding and marker assisted selection. Similarly, the candidate genes identified could
be potential targets for genome editing to modify flowering time in napiergrass.
To improve our understanding of the genetic basis of flowering time in
napiergrass and to evaluate the genetic diversity of napiergrass, we used exome-
capture sequencing to study the germplasm collection of napiergrass, described in
Chapter 4. We identified 78,129 high quality SNPs in the germplasm collection that
serve as novel genomic resources for napiergrass breeding programs. We also
identified potential candidate genes like Calcium-binding protein CML, enoyl-CoA
hydratase, phytochrome B, and AP2-like factors in the germplasm collection. This study
showed the feasibility to apply targeted exome sequencing with probes designed from
CDS of pearl millet to target the genomic regions in napiergrass.
We also used traditional breeding approaches to increase biosafety of
napiergrass. For this, in Chapter 5, we introgressed cms available in forage type dwarf
pearl millet lines to high biomass yielding pearl millet lines. We then hybridized these
elite cms lines of pearl millet with napiergrass to generate PMN hybrids that are male
and female sterile and will not contribute to invasiveness with wind dispersed seeds. We
studied the uniformity of different types of parental combinations. Substantial variation
within each cross was found for biomass accumulation and yield related traits.
Uniformity of the progenies could be enhanced by using homozygous parents to make
the crosses. If efficient seed production can be accomplished under field conditions and
181
if uniformity can be improved without compromising biomass yield, PMN hybrids may
outperform alternative forage and biofuel crops in the near future.
Future work
The construction of the first genetic map of napiergrass has opened up new
avenues in napiergrass breeding. With this map, we were able to identify QTLs related
to flowering time and number of flowers in napiergrass. There are other traits like stem
diameter, tiller number, and yield, whose genetic basis needs to be further explored. It is
our hope that the genetic map developed in Chapter 2 can be utilized to explore other
traits of agronomic importance to identify QTLs and candidate genes for these traits.
Further, the sequence variations identified in the germplasm collection can be utilized in
the future to study the genetic architecture of other traits and to perform GWAS for
these traits. Moreover, introgression of cms into high biomass lines like P787 will
continue to obtain a homozygous cms line with superior combining ability with
napiergrass. Similarly, generation of homozygous or near-homozygous napiergrass
lines will have to be continued by repeated self-fertilization and selection of the best
performing lines. PMN hybrids from these homozygous or near homozygous parents
should be evaluated not only for increased uniformity but also for biomass yield which
may mainly depend on the different level of heterosis in the A and B genomes of the
hybrids and the combining ability of the parents. For commercial production of PMN
hybrid seeds under field condition different accessions need to be evaluated to
synchronize flowering time and identify parents that contribute to the highest seed yield.
Alternatively, highest yielding napiergrass lines can be evaluated in countries where
182
napiergrass is a native species, the preferred forage crop and invasiveness is not a
concern.
183
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BIOGRAPHICAL SKETCH
Dev Paudel received his Ph.D. in Agronomy from the University of Florida (UF) in
the fall of 2018. His research focuses on utilizing traditional plant breeding approaches
and modern bioinformatic and molecular techniques in the genetic improvement of
napiergrass (elephant grass) and its interspecific hybrids with pearl millet. This will
eventually help in sustainability of forage and biofuel feedstock.
Dev received his MS degree in Crop Science from Texas Tech University,
Lubbock, Texas, USA where his research focused on evaluating the potential of new
testing methods for cotton breeding. Plant breeders, ginners, farmers, and spinning
mills can use the information obtained from his research to make informed decisions for
increased profitability in the premium yarn market. After his MS degree, he worked at
the Texas A&M AgriLife Research, Pecos, Texas where he optimized nutrient media for
algae production. He has a BS degree in Agriculture from Tribhuvan University, Nepal.