Conifer Translational Genomics Network Coordinated Agricultural Project www.pinegenome.org/ctgn Genomics in Tree Breeding and Forest Ecosystem Management ----- Module 9 – Mapping Quantitative Trait Loci (QTL) Nicholas Wheeler & David Harry – Oregon State University
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Conifer Translational Genomics Network Coordinated Agricultural Project Genomics in Tree Breeding and Forest Ecosystem Management.
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Basic idea is to associate quantitative trait phenotypes with the presence of genetic markers
Use a population (full-sib family) segregating for markers and a quantitative trait– Marker genotypes are determined for progeny, as for linkage mapping– Same individuals must also be phenotyped– Offspring are grouped according to marker genotype– Phenotypes are averaged among offspring in different marker groups– If group means vary, then a QTL resides in the vicinity of the marker
Location and magnitude of QTL depend on– Marker density– Size of the mapping population– Likelihood of the offspring QTL genotype, given the marker genotype– Whether the trait is also affected by other QTL
Analytical approaches– Single marker– Interval mapping– Composite interval mapping
QTL profile: Single marker mapping significance testing Black diamonds depict the
likelihood of a statistical difference between phenotypic means among marker classes
Tests are repeated for each marker along the chromosome
A linkage map is not needed, but they provide additional support, particularly when tests of adjacent markers approach significance
The significance threshold shown here (red line) depicts a genome-wide threshold given the number of markers assayed Figure Credit: Doerge. 2002. Nat Rev Genet 3:43-52
Uses the mapped locations of other genetic markers to more accurately predict the location(s) of unseen QTL– Flanking markers predict probable haplotypes for nearby regions– Predictions are done in smaller intervals, essentially creating a sliding
window analysis moving along a chromosome from one end to the other
For each interval, individuals are grouped by their predicted genotypes and phenotypic variation is assessed
Interval mapping groups individuals differently from single-marker analyses – phenotypic analyses are similar once individuals are grouped
Interval vs. composite interval mapping (CIM) CIM is more robust to multiple QTL, particularly if they occur on the
same chromosome
IM and CIM differ in how the phenotypic data are evaluated– For interval mapping, phenotypic data are evaluated directly, without
adjusting for possible genetic influences from outside the interval– For CIM, intervals (genes) outside the interval are considered as well– Other intervals are used as covariates, not unlike in multiple regression
Basic strategy is to look for phenotypic differences among groups of individuals classified by genotype
Prerequisites for QTL mapping include– A mapping population – Individuals that have been genotyped and phenotyped– Statistical analyses of genotypes and phenotypes
Single-marker strategy– Simple to analyze using standard statistical tools – Analyses do not consider marker locations, but interpretation may be
Verification of QTL stability and interaction effects To be useful, QTL must be
stable across environments, years, and genetic backgrounds
Definition of verification: The repeated detection, at a similar position on the genetic map, of a QTL controlling a trait under more than one set of experimental conditions (Brown et al. 2003. Genetics)
Clonally replicated verification trial of Douglas-fir, age 4 years from cutting. Photo credit: Nicholas Wheeler, Oregon State University.
Spring cold hardiness (needles)EF- 1 (translation elongation factor-1) cold- inducedCABBP1 (chlorophyll a/b- binding protein type 1) downregulated under the water deficit
DER1- like (degradation of misfolded proteins) possibly cold- inducedCABBP2 (chlorophyll a/b- binding protein type 2) downregulated under the water deficit
F3H (flavanone-3- hydroxylase) upregulated under the water deficitLEA- II (late embryogenesis abundant type II) dehydrin- like protein cold- inducedMT- like ( metallothionein- like protein) stress- induced; downregulated under the water deficitSAHH (S- adenosyl-L- homocysteinas hydrolase) upregulated under the water deficit
EF- 1 (translation elongation factor-1) cold- inducedCABBP1 (chlorophyll a/b- binding protein type 1) downregulated under the water deficit
DER1- like (degradation of misfolded proteins) possibly cold- inducedCABBP2 (chlorophyll a/b- binding protein type 2) downregulated under the water deficit
F3H (flavanone-3- hydroxylase) upregulated under the water deficitLEA- II (late embryogenesis abundant type II) dehydrin- like protein cold- inducedMT- like ( metallothionein- like protein) stress- induced; downregulated under the water deficitSAHH (S- adenosyl-L- homocysteinas hydrolase) upregulated under the water deficit
Complex trait dissection and genetic architecture– Number of QTL influencing a trait– Size of the QTL effects (PVE)– Location of the QTL– Parental contribution of allelic effects– QTL by environment interactions
Population size affects both the number of QTL detected and the size of QTL effects (Beavis, 1995)– More individuals (~500) are better than fewer– Clonal studies increase reliability of phenotypic assessments, and
increase detection sensitivity for QTL of small effect
In trees, most QTL account for less than 5% of a trait’s phenotypic variation, although collectively, multiple QTL may account for a substantial amount of the total genetic variation for that trait
Beavis, W. D. 1994. The power and deceit of QTL experiments: Lessons from comparative QTL studies. p. 250-266. In Proceedings 49th Annual Corn and Sorghum Industry Research Conference. American Seed Trade Association, Washington, DC.
Brown, G. R., D. L. Bassoni, G. P. Gill, J. R. Fontana, N. C. Wheeler, R. A. Megraw, M. F. Davis, M. M. Sewell, G. A. Tuskan, and D. B. Neale. 2003. Identification of quantitative trait loci influencing wood property traits in loblolly pine (Pinus taeda L) III. QTL verification and candidate gene mapping. Genetics 164: 1537-1546.
Doerge, R. W. 2002. Mapping and analysis of quantitative trait loci in experimental populations. Nature Reviews Genetics 3: 43-52.
Flint J. and R. Mott. 2001. Finding the molecular basis of quantitative traits: Successes and pitfalls. Nature Reviews Genetics 2: 437-445.
Flint, J, W. Valdar, S. Shifman, and R. Mott. 2005. Strategies for mapping and cloning quantitative trait genes in rodents. Nature Reviews Genetics 6: 271-286. (Available online at: http://dx.doi.org/10.1038/nrg1576) (verified 16 March 2011).
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Grattapaglia, D. 2007. Marker–assisted selection in Eucalyptus. p. 251-281. In E. P. Guimaraes, J. Ruane, B. D. Scherf, A. Sonnino, and J. D. Dargie (ed.) Marker assisted selection: current status and future perspectives in crops, livestock, forestry and fish. Food and Agriculture Organization of the United Nations, Rome, Italy.
Jermstad, K. D., D. L. Bassoni, K. S. Jech, N. C. Wheeler, and D. B. Neale. 2001a. Mapping of QTL controlling adaptive traits in coastal Douglas-fir: I. Timing of vegetative bud flush. Theoretical and Applied Genetics 102:1142-1151.
Jermstad K.D, D.L. Bassoni, N. C. Wheeler, T. S. Anekonda, S. N. Aitken, W. T. Adams, and D. B. Neale. 2001b. Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. II. Spring and fall cold-hardiness. Theoretical and Applied Genetics 102: 1152-1158.
Jermstad, K. D., D. L. Bassoni, K. S. Jech, G. A. Ritchie, N. C. Wheeler, and D. B. Neale. 2003. Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. III. QTL by environment interactions and verification. Genetics 165: 1489-1506.
Price, A. H. 2006. Believe it or not QTLs are accurate! Trends in Plant Sciences 11: 213-216. (Available online at: http://dx.doi.org/10.1016/j.tplants.2006.03.006) (verified 16 March 2011).
Wheeler, N. C., K. D. Jermstad, K. Krutovsky, S. N. Aitken, G. T. Howe, J. Krakowski, and D. B. Neale. 2005. Mapping of quantitative trait loci controlling adaptive traits in coastal Douglas-fir. IV. Cold-hardiness QTL verification and candidate gene mapping. Molecular Breeding 15: 145-156. (Available online at: http://dx.doi.org/10.1007/s11032-004-3978-9) (verified 16 March 2011).
Basten, C., B. S. Weir, and Z. B. Zeng. QTL cartographer [Online]. Statistical Genetics & Bioinformatics, NC State University. Available at: http://statgen.ncsu.edu/qtlcart/manual/ (verified 16 March 2011).
GridQTL [Online]. Available at: http://www.gridqtl.org.uk/ (verified 16 March 2011).
R / QTL: (Online). Available at http://rqtl.org/ (Verified 19 March 2011). R/qtl: A QTL mapping environment Software for mapping quantitative trait loci in experimental crosses