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

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.

Efficient haplotype block recognition of very long and dense genetic sequences

“…Results might be different in a larger context, such as that of a GWAS based on the 1000 Genomes dataset, where the number of AV blocks is expected to be much smaller than the number of WP blocks, and the AV blocks are expected to be much larger than the WP ones. Our empirical analysis of the NARAC data also confirmed previous observations that SNP- and block-based analyses are complementary to each other [32,34]. In fact, in our analysis some loci were identified only by the single-SNP analysis, other loci were identified only by the haplotype-block analysis, and others by both methods.…”

Association mapping of resistance to rice blast in upland field conditions

“…Multiple alleles may exist at single resistance loci within populations with a broad genetic base, as we observed at the Pi33 locus in our indica panel. It may therefore be useful to complement GWAS based on single biallelic markers with GWAS based on haplotypes (Lorenz et al 2010). In the context of an autogamous plant species such as rice, this should be straightforward as it would not involve the complication of inferring the linkage phase of the markers.…”

Haplotype‐based genotyping‐by‐sequencing in oat genome research

“…The CL haplotype blocks were converted to marker scores that represented the probability of the minor haplotype and imported into TASSEL. The CL haplotype blocks of the full set and their respective individual markers (TL haplotype and GBS‐SNP) were used to populate a CL haplotype incidence matrix with the dimension i × (( b × k )− m ), where i is number of individuals, b is number of haplotype blocks, k is the number of alleles and m is the number of major alleles (Lorenz et al ., ). Each haplotype block has ( k −1) columns, and the haplotype incidence shows the probability that individual i carries a haplotype k (0,1).…”