Performance of Single Nucleotide Polymorphisms versus Haplotypes for Genome-Wide Association Analysis in Barley

Aaron J Lorenz,Jean-Luc Jannink,Martha T Hamblin,Ivan Baxter

doi:10.1371/journal.pone.0014079

Abstract

Genome-wide association studies (GWAS) may benefit from utilizing haplotype information for making marker-phenotype associations. Several rationales for grouping single nucleotide polymorphisms (SNPs) into haplotype blocks exist, but any advantage may depend on such factors as genetic architecture of traits, patterns of linkage disequilibrium in the study population, and marker density. The objective of this study was to explore the utility of haplotypes for GWAS in barley (Hordeum vulgare) to offer a first detailed look at this approach for identifying agronomically important genes in crops. To accomplish this, we used genotype and phenotype data from the Barley Coordinated Agricultural Project and constructed haplotypes using three different methods. Marker-trait associations were tested by the efficient mixed-model association algorithm (EMMA). When QTL were simulated using single SNPs dropped from the marker dataset, a simple sliding window performed as well or better than single SNPs or the more sophisticated methods of blocking SNPs into haplotypes. Moreover, the haplotype analyses performed better 1) when QTL were simulated as polymorphisms that arose subsequent to marker variants, and 2) in analysis of empirical heading date data. These results demonstrate that the information content of haplotypes is dependent on the particular mutational and recombinational history of the QTL and nearby markers. Analysis of the empirical data also confirmed our intuition that the distribution of QTL alleles in nature is often unlike the distribution of marker variants, and hence utilizing haplotype information could capture associations that would elude single SNPs. We recommend routine use of both single SNP and haplotype markers for GWAS to take advantage of the full information content of the genotype data.

Highlights

Recent advances in sequencing and genotyping technology have allowed the collection of large amounts of genome-wide single nucleotide polymorphism (SNP) data for many species, primarily with the goal of finding associations between alleles and phenotypes of interest
This quality was first observed in dense SNP data from The SNP Consortium Allele Frequency Project [5], and has led to a great interest in determining whether the power and accuracy of association mapping can be improved by grouping SNPs into haplotype blocks
Haplotype Blocks We identified haplotype blocks using three methods based on different properties of the data

Summary

Introduction

Recent advances in sequencing and genotyping technology have allowed the collection of large amounts of genome-wide single nucleotide polymorphism (SNP) data for many species, primarily with the goal of finding associations between alleles and phenotypes of interest. Patterns of variation can be described by the extent to which variation at linked SNPs is ‘‘block-like’’, i.e., most haplotypes fall into a few classes with little evidence of recombination. This quality was first observed in dense SNP data from The SNP Consortium Allele Frequency Project [5], and has led to a great interest in determining whether the power and accuracy of association mapping can be improved by grouping SNPs into haplotype blocks (see Zhao et al [6] for a review). Various rationales for testing for associations between phenotypes and haplotypes, rather than single SNPs, have been proposed, including that haplotypes: capture epistastic interactions between SNPs at a locus [7,8]; provide more information to estimate whether two alleles are IBD [9]; reduce the number of tests and the type I error rate [2]; allow informed testing between clades of haplotype alleles by capturing information from evolutionary history [10]; provide more power than single SNPs when an allelic series exists at a locus [11]

Objectives

Methods

Results

Discussion

Conclusion