Genetic Basis of Agronomic Traits Connecting Phenotype to Genotype Yu and Buckler (2006); Zhu et al....

Association Mapping and Re-sequencing Updates

Genetic Basis of Agronomic Traits

Connecting Phenotype to Genotype

Yu and Buckler (2006); Zhu et al. (2008)

Traditional F2 QTL Mapping Association Mapping

Use recombination events in F2 to narrow trait of interest to a genomic region

Rely on co-inheritance of functional polymorphism and DNA variant

Correlate molecular with phenotypic variation, rely on many generations of historical recombination, phenotype of interest may be associated with a smaller chromosomal segment

Association Mapping Workflow

1. Germplasm

2. Phenotyping

2. Genotyping

Genome-wide association mappingCandidate/targeted gene approach

3. Association Testing

Choose lines to include in mapping population to capture

as much diversity as possible

Grow and measure traits in replicated trials

Correlate phenotypic variation with genotypic variation

• Extent of linkage disequilibrium– Informs genotyping strategy– Amount of resolution

• Degree of population structure– Can lead to false associations

Association Mapping Considerations

2. Investigate the structure of LD within the association mapping population

4. Test for associations between molecular polymorphisms and variation in key traits

3. Grow and characterize the population for wide variety of traits + genotype

Sunflower Association Mapping (SAM) Objectives

1. Population genetics of the sunflower germplasm, select lines for inclusion

Core 12(~50% of allelic diversity)

Core 48(~60% of allelic diversity)

Core 96(~70% of allelic diversity)

Core 192(~80% of allelic diversity)

433 Cultivated Sunflower Lines

Core 288(~90% of allelic diversity)

SAM Population Line Selection

Mandel et al., TAG 2011

SAM Genetic Diversity

Mandel et al., PLoS Genetics 2013

SAM Genetic RelationshipsHA X RHA

Mandel et al., PLoS Genetics 2013

10k SNPs

Genome-Wide Patterns of FST

Mandel et al., PLoS Genetics 2013

RHA vs. HA

10k SNPs

Linkage Group 1

Genome-Wide Patterns of LD

Mandel et al., PLoS Genetics 2013

10k SNPs

Linkage Group 10

Genome-Wide Patterns of LD

Mandel et al., PLoS Genetics 2013

10k SNPs

Genome-Wide Patterns of LD

Mandel et al., PLoS Genetics 2013

10k SNPs

Background Genomic Diversity

• Substantial SNP genetic variation• Population structure RHA vs. HA

– Somewhat restricted to linkage groups• LD also varies extensively across the genome

• Phenotypic measurements

SAM Field Locations

Plant > 20K seeds288 inbred lines4 plants per line2 replicates3 locations7,200 plants15 people

Phenotyping:- Flowering time

- Plant architecture

- Pigmentation

- Leaf traits

- Dormancy/germination

- Wood-related traits

- Total biomass

- Oil-related traits

Genotyping strategies:- 10k SNP Infinium array

- GBS approach, ~ 40k SNPs

- Seed size/shape

SAM Phenotyping/Genotyping

- Leaf C and N

- Entire SAM re-sequencing

Flowering Time SNP associations10k SNP Array

Mandel et al., PLoS Genetics 201310k SNPs

Visualizing Associations – LG 10

Recessiveapical branching

No Branching Branching

Mandel et al., PLoS Genetics 2013 10k SNPs

Downy Mildew

Downy MildewBlack Stem

Sunflower Rust

Branching/Flowering

Elevated LD and Potential Targets of Selection

Mandel et al., PLoS Genetics 201310k SNPs

Co-Localization of QTL and SNP Associations

Days to FlowerTotal Branching

Mandel et al., PLoS Genetics 2013

10k SNPs

Cell-wall Chemistry SNP Associations GBS, Lignin at GA location

~40k SNPs

SAM Re-Sequencing Efforts

• Entire SAM population of 288 lines • South Africa ARC, Genome Canada/Quebec,

and INRA• Illumina Hi-Seq, 1 or 2 samples per lane

SAM Re-Sequencing Data Analysis WorkflowAdam Bewick and Ben Hsieh

• Sunflower genome– Version: Nov22k22.scf.split.fasta

• Read-trimming with prinseq-lite• Alignment with BWA• Produce VCF files with samtools

SAM Re-Sequencing CoverageAdam Bewick

191/288 lines have been run through the pipeline

LR, NO-I, O-I, OPV, NO, O

SAM Re-Sequencing Next Steps

• Next step is to assay genetic variation– Structural Variation: CNV – Adam’s talk– SNPs

• Use data for genome-wide investigations of genetic variation, association mapping, and evolutionary analyses

Association Genetics Summary

• Mapping panel is very diverse• LD varies across the genome• Association testing and SNPs and genomic

regions as candidates• Created permanent mapping resource• Sequenced genome and 288 re-sequenced

lines: GREAT resource!

Members of the:Burke LabLeebens-Mack LabRieseberg LabRaj Ayyampalayam

Undergrad TeamsAdam BewickBen HsiehJohn BowersMark ChapmanLaura Marek

Jenny DechaineSavithri NambeesanEd McAsseySteve KnappEleni Bachlava

Acknowledgments