Nested association mapping

Graeme Hammer, Scott Chapman, Alan Cruickshank, Colleen Hunt, Adrian Hathorn, Yongfu Tao, Emma Mace

David Jordan

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ContextMedium size sorghum pre breeding program focused on improving the productivity of hybrid grain sorghum in highly variable water limited environments characterised by high levels of GxEDelivery of varieties via the private sector

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R linesB lines

Sample of elite MR SG UQ/DAF Germplasm (1990+)

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Grain Sorghum Females (B lines) Grain Sorghum Males R lines

A need to increase genetic diversity

Low diversityLow separation of male and female parents

Genetic diversity study ~1996

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Increasing genetic diversity while retaining 40 years of breeding

Jordan DR, et al (2011). Crop Sci 51:1444-1457

Pop size >5000 individualsNumber of families >100

Challenge of needing to increase genetic diversity in elite germplasm without destroying adaptive complexes built up over decades of breeding

Design parameters1. Needed to increase genetic diversity by crossing to our elite lines

with exotic material2. Needed to maintain existing adaptive complexes (height, maturity,

stay-green, midge resistance etc)3. Needed to be able to evaluate genes in a genetic context that was

relevant to our environments, management systems and populations

4. Need to develop material that could be used in breeding relatively quickly

5. We wanted to generate populations where molecular technologies could enhance our understanding of traits

Backcross nested association mapping BC NAM population

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Why backcross

Allowed the recovery of enough lines with appropriate height and maturity

elite alleles for important traits are at high frequency (genetic context and utility for breeding)

Simplifies the genetics and helps identify genes that contribute to yield in an elite background‒ contribution of exotic x exotic epistasis is

reduced‒ easy to generate isolines if required

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Box plot of the distribution of the percentage of non recurrent parent genome present in a sample of lines from 9 of the populations

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Selection for agronomic traits (eg height dtf etc)

We wanted to be able to rapidly evaluate the impact of QTL on yield in relevant genetic backgrounds and environments

Variation for major genes for height and flowering time in sorghum cause lack of adaptation

This lack of adaptation makes evaluation for complex traits like yield difficult and the results irrelevant for applied breeding

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ExampleF1 Backcross progeny

Sorghum bicolorsubsp. verticiliflorum

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Selection of Germplasm A range of strategies were used to choose

the exotic germplasm including:‒ visual phenotypic diversity and racial diversity‒ geographic diversity

‒ phenotypic extremes (published or unpublished)‒ elite lines from international breeding programs‒ fertile wild species.

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Context dependencies There is no absolute relative merit value for

a gene or trait or genotype The value of a gene always depends on the

context (genetic background, environment, management)

Context dependencies occur at all scalesand generate major challenges for crop improvement programs (eg epistasis, heterosis, GxExM)

Too much complexity to deal with so it is critical to get the context right when dissecting genetic architecture with a view to improving performance

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Phenotyping for yield as an MET Our BCNAM is part of an ongoing pre-

breeding activity Each year we evaluate 20 populations in

multi-environment trials (yield flowering time, height, head shape, stay-green…….)

15 new populations and 5 previous populations (allows analysis as a single experiment)

Environment characterisation to allow simulation modelling to be used as an analysis tool

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A resource for breeding and trait dissection

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Environment 4 > 5000 lines >100 exotic parents evaluated GBS marker data on ~1300 lines Data from more than >50 trials (>2M

phenotypic data points) The reference parent, the female

testers and 28 exotic donors have been re-sequenced >500 lines directly licensed to

commercial companies (many more derivatives licensed)

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Advantages of NAM for mapping Conventional bi-parental mapping populations

‒ Power to detect QTL (allele replication)‒ Limited sample of available alleles‒ Low resolution

Association mapping panels‒ Sample multiple alleles‒ High resolution due to historic recombination‒ Less power to detect QTL particularly rare alleles‒ Prone to false associations ‒ Genetic background effects are large and often

compromise phenotyping

Nested association mapping Yu et al. 2008‒ Combines the power of linkage analysis with the high

resolution of association mapping through the joint analysis of multiple interlinked populations with sequenced parents DO N

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A powerful population for mapping quantitative traitsstartsb1

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Structure of allelic variation The number of QTL

segregating in each population varied between 2 and 18 average >10

Most genotypes have a mixture of early and late alleles

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Comparative genomics: maize and sorghum•75 significant markers associated with flowering time in maize NAM

•Sequence mapped these significant maize markers onto the sorghum genome and compared them with our sorghum NAM flowering time QTL

•90% of the sorghum QTL were <10cM away from the comparable maize QTL

•37.5% were less than 1cM away

•Maize sorghum comparison provides mutual insights

McMullen et al 2009. Science 325, 737

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Mace, Hunt & Jordan, 2013. Theor Appl Genet. 126: 1377-1395DO NOT C

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Design and analysis

Genetic design factors BCNAM Reference parent Backcrossing derived populations with selection Large number of populations 50-100 individuals per population Total genotypes families 23 (~2500 individuals)

• Individual populations often lack power to detect quantitative traits on their own • Selection during population development makes joint linkage analysis difficult• Small populations allow greater numbers of populations to be tested for the

same amount of resource (> diversity, more QTL alleles)DO N

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Analysis methods AssociationGWAS (eg tassel)

Haplotype based and Joint linkage cpQTL (Mace et al 2013) 1 FarmCPU Joint inclusive composite interval mapping (JICIM)

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GWAS on NAM can lack power if population sizes are small Power of the GWAS

depends on LD and number of SNPsSNP allele frequencyNumber of QTL alleles Frequency of QTL alleles

Reference

NAM P1

NAM P2

NAM P3

NAM P4

NAM P5

SNP

QTL

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GWAS on NAM can lack power if population sizes are small Power of the GWAS

depends on LD and number of SNPsSNP allele frequencyNumber of QTL alleles Frequency of QTL alleles

Reference

NAM P1

NAM P2

NAM P3

NAM P4

NAM P5

1

1

1

1

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Reference

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NAM P2

NAM P3

NAM P4

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Haplotype based methods make use of positon information and multiple SNPs Allow access to multiple

alleles at a QTLGives greater power to detect

QTL in NAM type populations

Haplotype based methods

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Our experience GWAS for flowering time was

less effective than a haplotype based methodBoth methods found some unique

QTL but more were found by the haplotype based method

Methods QTL for flowering time

% of total QTL

Number of QTL unique to method

GWAS 25/40 62.5% 6

cpQTL 34/40 85% 15

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FarmCPU

PLOS Genetics | DOI:10.1371/journal.pgen.1005767 February 1, 2016

Our experience is that FarmCPU works well for QTL detection in our diversity panel and in our BCNAM population

SimulationSimulated 40qtl with equal effect and found >20 in our Association panel with a threshold based on the Bonferroni correction (adjusted for then number of independent tests)DO N

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Mapping Populations

X

NAM Population

Breeding Populations

Diversity Panel

n=213 n=563n=1385 n>900

NAM Breeding populations Diversity Panel

Mapping in complementary interlinked populations vary in LD and diversity

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Joint analysis of multiple populations

Different populations have different advantages and disadvantages Typically we no longer use a single type of population for genetic analysis of

traits of interest Multiple populations allows

‒ Use of reduced thresholds for QTL detection (detection in unique datasets)‒ Use of prior information ‒ Tracing alleles from one population to another using haplotypes there is the potential to

use prior information

Mapping Populations

X

NAM Population

Breeding Populations

Diversity Panel

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Diversity panel resource• >900 genotypes• Highly diverse genotypes• Excellent point of entry to explore native variation• High density SNP genotyping• Low LD high precision• High false positive rate

Mapping Populations

X

NAM Population

Breeding Populations

Diversity Panel

Resources for trait dissection: Diversity

Diversity panel

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Resources for trait dissection: BCNAM

Mapping Populations

X

NAM Population

Breeding Populations

Diversity Panel

Nested Association Mapping Resource• >100 exotic parental lines back-crossed to a single elite

genotype

• Less diversity than the diversity panel but provides opportunity to evaluate exotic alleles in an elite genetic background

• 23 populations, consisting of 1385 progeny, genotyped with high density SNP markers.

• Whole genome sequence data available for 17 of the 23 parental lines

• High LD high power low precision

Orange points NAM parents

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Elite sorghum breeding trials

• Advanced Yield Testing (multiple environments)

• 342 unique genotypes tested in hybrid combination with multiple females

• 563 hybrid combinations • High LD less diversity • Relevance to target

Resources for trait dissection: Breeding population

Mapping Populations

X

NAM Population

Breeding Populations

Diversity Panel Breeding diversity

Female lines: pinkMale line: dark blue

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Conclusions Use of complementary populations can improve the utility of genetic dissection

particularly when the aim is application in applied breeding Use of multiple populations can allow the use of lower thresholds Haplotype based methods are more powerful than standard GWAS methods in BCNAM

and in our diversity and breeding populations

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Acknowledgements

Adrian Hathorn

Emma Mace

Colleen Hunt

Alan Cruickshank

Bob Henzell

DAF technical team

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