Using haplotypes for the prediction of allelic identity to fine-map QTL: characterization and properties

Abstract

BackgroundNumerous methods have been developed over the last decade to predict allelic identity at unobserved loci between pairs of chromosome segments along the genome. These loci are often unobserved positions tested for the presence of quantitative trait loci (QTL). The main objective of this study was to understand from a theoretical standpoint the relation between linkage disequilibrium (LD) and allelic identity prediction when using haplotypes for fine mapping of QTL. In addition, six allelic identity predictors (AIP) were also compared in this study to determine which one performed best in theory and application.ResultsA criterion based on a simple measure of matrix distance was used to study the relation between LD and allelic identity prediction when using haplotypes. The consistency of this criterion with the accuracy of QTL localization, another criterion commonly used to compare AIP, was evaluated on a set of real chromosomes. For this set of chromosomes, the criterion was consistent with the mapping accuracy of a simulated QTL with either low or high effect. As measured by the matrix distance, the best AIP for QTL mapping were those that best captured LD between a tested position and a QTL. Moreover the matrix distance between a tested position and a QTL was shown to decrease for some AIP when LD increased. However, the matrix distance for AIP with continuous predictions in the [0,1] interval was algebraically proven to decrease less rapidly up to a lower bound with increasing LD in the simplest situations, than the discrete predictor based on identity by state between haplotypes (IBS hap), for which there was no lower bound. The expected LD between haplotypes at a tested position and alleles at a QTL is a quantity that increases naturally when the tested position gets closer to the QTL. This behavior was demonstrated with pig and unrelated human chromosomes.ConclusionsWhen the density of markers is high, and therefore LD between adjacent loci can be assumed to be high, the discrete predictor IBS hap is recommended since it predicts allele identity correctly when taking LD into account.

Genome-wide association analysis for heat tolerance at flowering detected a large set of genes involved in adaptation to thermal and other stresses

“…Haplotype based GWAS (Hap-GWAS) consists in testing the effect of haplotypes in sliding windows of SNPs (instead of individual SNP) across the genome for their association with phenotype. It is based on hypothesis that marker haplotypes are in greater LD with the QTL alleles than single markers [ 54 ]. If this is true, then the r 2 between the QTL and the haplotypes will increase, thereby increasing the power of the experiment [ 47 ].…”

“…The restricted maximum likelihood ratio test (RLRT) was used to decide for the existence of association for each tested position. Hap-GWAS was implemented using a script developed by Jacquin et al [ 54 ].…”

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice

“…R scripts were written to solve GBLUP and RKHS regression. The expectation-maximization (EM) algorithm (Dempster et al, 1977 ; Foulley, 2002 ; Jacquin et al, 2014 ) was used to maximize the restricted likelihoods (REML) of the mixed models, associated to GBLUP and RKHS regression respectively, in order to estimate the associated variance parameters. The Gaussian kernel was used for RKHS regression and SVR.…”

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“…For example, DHOEM could be useful in pedigree based gene-dropping simulations, where an insufficient amount of marker data prevents building a reliable genomic relationship matrix at the end of each gene-dropping procedure. Indeed, in [ 27 ] the additive relationship matrix was built using only pedigree information, as marker data were limited in their gene-dropping simulations. On the other hand, the benefits of using DHOEM might be complicated for QTL mapping simulation studies if not enough marker data are available to represent the real LD structure in a population.…”