Power and precision of alternate methods for linkage disequilibrium mapping of quantitative trait loci

Abstract

Linkage disequilibrium (LD) analysis in outbred populations uses historical recombinations to detect and fine map quantitative trait loci (QTL). Our objective was to evaluate the effect of various factors on power and precision of QTL detection and to compare LD mapping methods on the basis of regression and identity by descent (IBD) in populations of limited effective population size (N(e)). An 11-cM region with 6-38 segregating single-nucleotide polymorphisms (SNPs) and a central QTL was simulated. After 100 generations of random mating with N(e) of 50, 100, or 200, SNP genotypes and phenotypes were generated on 200, 500, or 1000 individuals with the QTL explaining 2 or 5% of phenotypic variance. To detect and map the QTL, phenotypes were regressed on genotypes or (assumed known) haplotypes, in comparison with the IBD method. Power and precision to detect QTL increased with sample size, marker density, and QTL effect. Power decreased with N(e), but precision was affected little by N(e). Single-marker regression had similar or greater power and precision than other regression models, and was comparable to the IBD method. Thus, for rapid initial screening of samples of adequate size in populations in which drift is the primary force that has created LD, QTL can be detected and mapped by regression on SNP genotypes without recovering haplotypes.

Figure 1.—

Effects of sample size, marker density (“# SNPs”), QTL effect, effective population size (N_e), number of generations since mutation (“# generations”), and model of analysis [regression on genotype (G) for 1, 2, or 4 SNPs or on haplotype (H) for 2 or 4 SNPs] on power to detect QTL. Based on 10,000 replicates.

Figure 2.—

Effects of sample size, marker density (“# SNPs”), QTL effect, effective population size (N_e), number of generations since mutation (“# generations”), and model of analysis [regression on genotype (G) for 1, 2, or 4 SNPs or on haplotype (H) for 2 or 4 SNPs] on precision of estimates of position for significant QTL. Based on 10,000 replicates.

Figure 3.—

Effects of sample size, marker density (“# SNPs”), QTL effect, effective population size (N_e), number of generations since mutation (“# generations”), and model of analysis [regression on genotype (G) for 1, 2, or 4 SNPs or on haplotype (H) for 2 or 4 SNPs] on precision of estimates of position for all QTL. Based on 10,000 replicates.