An a posteriori granddaughter design was applied to estimate quantitative trait loci genotypes of sires with many sons in the US Holstein population. The results of this analysis can be used to determine concordance between specific polymorphisms and segregating quantitative trait loci. Determination of the actual polymorphisms responsible for observed genetic variation should increase the accuracy of genomic evaluations and rates of genetic gain. A total of 52 grandsire families, each with ⩾100 genotyped sons with genetic evaluations based on progeny tests, were analyzed for 33 traits (milk, fat and protein yields; fat and protein percentages; somatic cell score (SCS); productive life; daughter pregnancy rate; heifer and cow conception rates; service-sire and daughter calving ease; service-sire and daughter stillbirth rates; 18 conformation traits; and net merit). Of 617 haplotype segments spanning the entire bovine genome and each including ~5×106 bp, 5 cM and 50 genes, 608 autosomal segments were analyzed. A total of 19 335 unique haplotypes were found among the 52 grandsires. There were a total of 133 chromosomal segment-by-trait combinations, for which the nominal probability of significance for the haplotype effect was <10−8, which corresponds to genome-wide significance of <10−4. The number of chromosomal regions that met this criterion by trait ranged from one for rear legs (rear view) to seven for net merit. For each of the putative quantitative trait loci, at least one grandsire family had a within-family contrast with a t-value of >3. Confidence intervals (CIs) were estimated by the nonparametric bootstrap for the largest effect for each of nine traits. The bootstrap distribution generated by 100 samples was bimodal only for net merit, which had the widest 90% CI (eight haplotype segments). This may be due to the fact that net merit is a composite trait. For all other chromosomes, the CI spanned less than a third of the chromosome. The narrowest CI (a single haplotype segment) was found for SCS. It is likely that analysis by more advanced methods could further reduce CIs at least by half. These results can be used as a first step to determine the actual polymorphisms responsible for observed quantitative variation in dairy cattle.
