"Implications of partitioned genetic diversity for linkage disequilibrium

mapping in elite UK cereal germplasm".

Donal O’Sullivan

SGC Meeting, JIC, 6-7th April 2006

Purpose

Why use ‘elite’ varieties?

• Most familiar and obvious material

• Reasonable levels of diversity present

• Relevant to current markets

• Obtainable in quantity

• Extensive ‘historic series’ of robust field data for most relevant phenotypes

To explore the prudent use of ‘populations’

of elite cereal varieties as LD mapping panels

Gediflux Barley SSR CB - PCO by Season and Row Type

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Include season='S' Include season='W' Exclude season='*' Exclude season='AS'

Partitioning in 500+ Gediflux Barley varieties

Association mapping in wheat:proof of principle

• Gediflux data set: 499 genotyped varieties.73 SSAPs, 42 SSRs, 72 NBS1B1R, pinb haplotypes

• Historic trial data: 193 varieties with 18 phenotypes (incomplete )

yield +/- treated, hardness (113 lines)Lodging, disease, etc.

• Use pinb as a candidate with known phenotypic effect

• Use SSRs for structured association• Analyse using “Structure” and “Strat”

• Use SSAPs for genomic control• Analyse trait by trait by logistic regression

Mining historic endosperm texture data

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Endosperm texture in Gedif lux

Historic NL trial data <2001

Structured association

Structure:

burn-in 1 million

iterations 1 million

No. populations (K) 8

No. replicate runs 2

K = 8 cluster 1 vs cluster 5

Proportion of each individual in cluster 1.

Proportion of each individual in cluster 5.

Gediflux 500+ winter wheat

Pedigree of lines with highest ancestry in clusters 1 and 5.

Cluster 1Cluster 5

Descendants of a cluster 5 founder

parents cluster 1 cluster 5

progeny cluster 1 13 1 cluster 5 3 19

odds ratio 82p-value 0.00001

Parentage of lines for clusters 1 and 5.

Cluster membership is genetic!

Association test using STRAT / structure

Pinb and hardness

test chi sq p-value

Assuming no structure in population 44.44 0

Corrected (run a) 21.89 0

Corrected (run b) 21.42 0

STRAT and structure - QC

Hardness and 55 SSAP markers, p-values <0.05

Test No. <0.05

Assuming no pop. structure 14

Adjusted, run a 6

Adjusted, run b 6

Expected 3

May be under correcting.

Pedigree relationships between SBCMV resistant varieties

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Red = Tested R, Blue = Tested S, Grey = Untested

Genomic Control

• Method: use multilocus genotype data to detect and correct for stratification

• Premise: admixture operates over the whole genome but LD operates locally at short scales

• 18 traits

• 58 SSAP

• 1044 logistic regression analyses

Genomic control: p-values, pinb

original GC Dry matter content treated 1.000 1.000Hagberg number treated 0.005 0.317Percent leaning treated 0.733 0.842Percent lodging treated 0.008 0.053Protein content treated 0.045 0.298Specific weight treated 0.132 0.526Straw length treated 0.053 0.403Yield treated 0.723 0.865Brown rust not treated 0.008 0.012Hagberg number not treated 0.583 0.828Percent leaning not treated 0.463 0.691Percent lodging not treated 0.264 0.276Mildew not treated 0.796 0.888Protein content not treated 0.001 0.184Septoria tritici not treated 0.091 0.426Specific weight not treated 0.080 0.288Straw length not treated 0.583 0.808Hardness 0.000 0.000

Test markers across all traits

No. of tests 1044

P-value <0.05

OBS original 313

OBS GC 34

EXP 52

May be overcorrecting

Genomic control: QC

Conclusions• Population structure may be evident e.g.

spring-winter/row number divide or less so– Carry out LD mapping within major sub-groups

• UK winter wheat shows cryptic population structure which groups varieties consistent with known pedigree

• Genomic control and/or structured association both effective in detecting known associations and reducing false +ves to realistic levels

• Roll on new phenotype and genotype data!

Acknowledgements

• NIAB– Fiona Leigh– John Law– Ian Mackay– Wayne Powell

• Gediflux Partners– Barley– Marion Roeder (IPK)– Wheat– Rob Koebner, Simon Orford (JIC)– Martin Ganal (Trait Genetics)