Abstract
BackgroundThe sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used. This study compares the performance of three marker densities to refine six previously detected QTL regions for mastitis traits: 54 k markers of a medium-density SNP (single nucleotide polymorphism) chip (MD), imputed 777 k markers of a high-density SNP chip (HD), and imputed whole-genome sequencing data (SEQ). Each dataset contained data for 4496 Danish Holstein cattle. Comparisons were performed using a linear mixed model (LM) and a Bayesian variable selection model (BVS).ResultsAfter quality control, 587, 7825, and 78 856 SNPs in the six targeted regions remained for MD, HD, and SEQ data, respectively. In general, the association patterns between SNPs and traits were similar for the three marker densities when tested using the same statistical model. With the LM model, 120 (MD), 967 (HD), and 7209 (SEQ) SNPs were significantly associated with mastitis, whereas with the BVS model, 43 (MD), 131 (HD), and 1052 (SEQ) significant SNPs (Bayes factor > 3.2) were observed. A total of 26 (MD), 75 (HD), and 465 (SEQ) significant SNPs were identified by both models. In addition, one, 16, and 33 QTL peaks for MD, HD, and SEQ data were detected according to the QTL intensity profile of SNP bins by post-analysis of the BVS model.ConclusionsThe power to detect significant associations increased with increasing marker density. The BVS model resulted in clearer boundaries between linked QTL than the LM model. Using SEQ data, the six targeted regions were refined to 33 candidate QTL regions for udder health. The comparison between these candidate QTL regions and known genes suggested that NPFFR2, SLC4A4, DCK, LIFR, and EDN3 may be considered as candidate genes for mastitis susceptibility.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0129-1) contains supplementary material, which is available to authorized users.
Highlights
The sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used
With increasing marker densities, the peaks of putative QTL became sharper for the Bayesian variable selection model (BVS) model and the boundaries of adjacent QTL regions became more obvious for the linear mixed model (LM) model
The power of QTL detection can be increased by increasing marker densities and the BVS model outperforms the LM model in refining QTL locations with clear boundaries between linked QTL
Summary
The sensitivity of genome-wide association studies for the detection of quantitative trait loci (QTL) depends on the density of markers examined and the statistical models used. I.e. inflammation of the mammary gland, is a common and costly disease [1, 2] that is problematic in the dairy industry. It adversely affects both animal and human health, since milk from affected cattle can enter the food supply and pose a health risk [3]. A medium-density (MD) SNP chip with ~54 000 markers is widely used for GWAS in dairy cattle [17,18,19]. Two high-density (HD) SNP chips with 777 962 SNPs from Illumina Inc. [20]
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