Using Genetics, Genomics, and Breeding to Understand Diverse Maize Germplasm Sherry A. Flint-Garcia United States Department of Agriculture– Agriculture Research Service (USDA–ARS) Corn breeding and genetic analysis of agronomic traits relies on phenotypic variation and genetic variation in the genes controlling these traits. My research program focuses on understanding genetic diversity in maize so we can mine beneficial alleles from the appropriate germplasm sources for continued corn improvement. The process of domestication that began 9000 years ago has had profound consequences on maize, where modern corn has moderately reduced genetic diversity across nearly all genes in the genome relative to teosinte, and severely reduced levels of diversity for key genes targeted by domestication. The question that remains is whether these reductions in genetic diversity have impacted our ability to make progress in corn breeding today. As an outcrossing species, maize has tremendous genetic variation compared to most other crops. The complementary combination of genome-wide association mapping (GWAS) approaches, large HapMap datasets, and germplasm resources are leading to important discoveries of the relationship between genetic diversity and phenotypic variation in inbred lines. However, among the traits targeted during domestication and breeding are many yield component traits, including number of ears, kernel row number, seed size, and kernel composition. Therefore, we must reintroduce variation from landraces and/or teosinte if we hope to learn how domestication has impacted these yield component traits, and yield itself.
41
Embed
Using Genetics, Genomics, and Breeding to Understand ...imbgl.cropsci.illinois.edu/school/2014/4_SHERRY_FLINT-GARCIA.pdfUsing Genetics, Genomics, and Breeding to Understand Diverse
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Using Genetics, Genomics, and Breeding to Understand Diverse Maize
Germplasm
Sherry A. Flint-Garcia United States Department of Agriculture–
Agriculture Research Service (USDA–ARS)
Corn breeding and genetic analysis of agronomic traits relies on phenotypic variation and genetic variation in the genes controlling these traits. My research program focuses on understanding genetic diversity in maize so we can mine beneficial alleles from the appropriate germplasm sources for continued corn improvement. The process of domestication that began 9000 years ago has had profound consequences on maize, where modern corn has moderately reduced genetic diversity across nearly all genes in the genome relative to teosinte, and severely reduced levels of diversity for key genes targeted by domestication. The question that remains is whether these reductions in genetic diversity have impacted our ability to make progress in corn breeding today. As an outcrossing species, maize has tremendous genetic variation compared to most other crops. The complementary combination of genome-wide association mapping (GWAS) approaches, large HapMap datasets, and germplasm resources are leading to important discoveries of the relationship between genetic diversity and phenotypic variation in inbred lines. However, among the traits targeted during domestication and breeding are many yield component traits, including number of ears, kernel row number, seed size, and kernel composition. Therefore, we must reintroduce variation from landraces and/or teosinte if we hope to learn how domestication has impacted these yield component traits, and yield itself.
Using Genetics, Genomics, and Breeding to Understand Diverse Maize Germplasm
Sherry Flint-Garcia USDA-ARS Columbia, MO
Outline Introduction to Maize Domestication & Diversity Inbred Lines Teosinte, the wild ancestor Breeding with Zea
Maize Domestication
Domesticated from Zea mays ssp. parviglumis
Single domestication event ~9,000 years ago in Mexico
Intermediate form of landraces; populations adapted across the Americas to specific microclimates and/or human uses
Fine mapped by Pioneer-Dupont Zheng, et al. (2008) Nature Genetics High parent = 19% oil High allele = 0.29% additive effect High allele has Phenylalanine insertion in C-terminus
Phenylalanine insertion
Oil 4.4% 5.3% 3.6% 3.9%
Cook, et al. (2012) Plant Phys.
NAM Genome Wide Association (GWAS)
1.6 Million HapMap.v1 SNPs projected onto NAM Bootstrap (80%) sampling to test robustness of models
NAM Population: 24 HapMap.v1 SNPs in DGAT 281 Association Panel: 2 55K SNPs in DGAT (plus the 3 bp indel)
M1
M3 M5 M4
M2: Phe Insertion
Cook, et al. (2012) Plant Phys.
DGAT 1-2 (Chr6: 105,013,351-105,020,258)
Rare allele?
= B73 Genotype = Non-B73 Genotype
Cook, et al. (2012) Plant Phys.
M2 (indel)
M3 M5 M4 M1
Summary – NAM Kernel Composition Genetic Architecture of Kernel Composition Traits
Governed by many QTL (N = 21-26) with small to moderate effects
GWAS results confirm many QTL DGAT is our favorite gene, but we still don’t have the
complete story!
NAM In General We can identify the common alleles in maize, but still
have problems with rare alleles.
Cook, et al. (2012) Plant Phys.
Ames Plant Introduction Station Inbreds 2,815 inbred lines from the Ames PI Station Genotyping-by-sequencing (GBS) - 681,257 SNPs
Romay, et al. (2013) Genome Biology
Ames Plant Introduction Station Inbreds More than half of the SNPs in collection are rare!
Romay, et al. (2013) Genome Biology
Expired PVPs
77% 48%
42%
302 Association panel = 75%, NAM founders = 57%.
Teosinte - The Wild Ancestor
Development of Teo Introgression Libraries
B73 × teosinte (parviglumis)
10 accessions
F1 : B73 × teosinte
BC1 BC2 BC3 BC4 B73 F1 teosinte
Library Development 10 libraries of 887 BC4S2/DH Near Isogenic Lines (NILs) BC4S4 NILs: GBS & RAD sequencing = 33,000–600,000 SNPs Lines to be released in 2013
Z031E0035
Z035E0012
Illumina GoldenGate
768 SNP
assay
Library Coverage
c1 c2 c3 c4 c5 c6 c7 c8 c9 c10
10 maize-teosinte libraries 804 BC4S2 NILs and 83 BC4DH NILs Each line: 2.3 chromosomal segments 4.1% of the teosinte genome 3.3X genome coverage