Genome-wide association study (GWAS) is a powerful approach to identify genomic regions and genetic variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. We conducted a large-scale GWAS using 294,079 first-lactation Holstein cows and identified new additive and dominance effects on five production traits, three fertility traits, and somatic cell score. Four chromosomes had the most significant SNP effects on the five production traits, a Chr14 region containing DGAT1 mostly had positive effects on fat yield and negative effects on milk and protein yields, the 88.07–89.60 Mb region of Chr06 with SLC4A4, GC, NPFFR2 , and ADAMTS3 for milk and protein yields, the 30.03–36.67 Mb region of Chr20 with C6 and GHR for milk yield, and the 88.19–88.88 Mb region with ABCC9 as well as the 91.13–94.62 Mb region of Chr05 with PLEKHA5, MGST1, SLC15A5 , and EPS8 for fat yield. For fertility traits, the SNP in GC of Chr06, and the SNPs in the 65.02–69.43 Mb region of Chr01 with COX17, ILDR1 , and KALRN had the most significant effects for daughter pregnancy rate and cow conception rate, whereas SNPs in AFF1 of Chr06, the 47.54–52.79 Mb region of Chr07, TSPAN4 of Chr29, and NPAS1 of Chr18 had the most significant effects for heifer conception rate. For somatic cell score, GC of Chr06 and PRLR of Chr20 had the most significant effects. A small number of dominance effects were detected for the production traits with far lower statistical significance than the additive effects and for fertility traits with similar statistical significance as the additive effects. Analysis of allelic effects revealed the presence of uni-allelic, asymmetric, and symmetric SNP effects and found the previously reported DGAT1 antagonism was an extreme antagonistic pleiotropy between fat yield and milk and protein yields among all SNPs in this study.

BackgroundDominance effect may play an important role in genetic variation of complex traits. Full featured and easy-to-use computing tools for genomic prediction and variance component estimation of additive and dominance effects using genome-wide single nucleotide polymorphism (SNP) markers are necessary to understand dominance contribution to a complex trait and to utilize dominance for selecting individuals with favorable genetic potential.ResultsThe GVCBLUP package is a shared memory parallel computing tool for genomic prediction and variance component estimation of additive and dominance effects using genome-wide SNP markers. This package currently has three main programs (GREML_CE, GREML_QM, and GCORRMX) and a graphical user interface (GUI) that integrates the three main programs with an existing program for the graphical viewing of SNP additive and dominance effects (GVCeasy). The GREML_CE and GREML_QM programs offer complementary computing advantages with identical results for genomic prediction of breeding values, dominance deviations and genotypic values, and for genomic estimation of additive and dominance variances and heritabilities using a combination of expectation-maximization (EM) algorithm and average information restricted maximum likelihood (AI-REML) algorithm. GREML_CE is designed for large numbers of SNP markers and GREML_QM for large numbers of individuals. Test results showed that GREML_CE could analyze 50,000 individuals with 400 K SNP markers and GREML_QM could analyze 100,000 individuals with 50K SNP markers. GCORRMX calculates genomic additive and dominance relationship matrices using SNP markers. GVCeasy is the GUI for GVCBLUP integrated with an existing software tool for the graphical viewing of SNP effects and a function for editing the parameter files for the three main programs.ConclusionThe GVCBLUP package is a powerful and versatile computing tool for assessing the type and magnitude of genetic effects affecting a phenotype by estimating whole-genome additive and dominance heritabilities, for genomic prediction of breeding values, dominance deviations and genotypic values, for calculating genomic relationships, and for research and education in genomic prediction and estimation.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2105-15-270) contains supplementary material, which is available to authorized users.

BackgroundThe number of teats in pigs is related to a sow’s ability to rear piglets to weaning age. Several studies have identified genes and genomic regions that affect teat number in swine but few common results were reported. The objective of this study was to identify genetic factors that affect teat number in pigs, evaluate the accuracy of genomic prediction, and evaluate the contribution of significant genes and genomic regions to genomic broad-sense heritability and prediction accuracy using 41,108 autosomal single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing on 2936 Duroc boars.ResultsNarrow-sense heritability and dominance heritability of teat number estimated by genomic restricted maximum likelihood were 0.365 ± 0.030 and 0.035 ± 0.019, respectively. The accuracy of genomic predictions, calculated as the average correlation between the genomic best linear unbiased prediction and phenotype in a tenfold validation study, was 0.437 ± 0.064 for the model with additive and dominance effects and 0.435 ± 0.064 for the model with additive effects only. Genome-wide association studies (GWAS) using three methods of analysis identified 85 significant SNP effects for teat number on chromosomes 1, 6, 7, 10, 11, 12 and 14. The region between 102.9 and 106.0 Mb on chromosome 7, which was reported in several studies, had the most significant SNP effects in or near the PTGR2, FAM161B, LIN52, VRTN, FCF1, AREL1 and LRRC74A genes. This region accounted for 10.0% of the genomic additive heritability and 8.0% of the accuracy of prediction. The second most significant chromosome region not reported by previous GWAS was the region between 77.7 and 79.7 Mb on chromosome 11, where SNPs in the FGF14 gene had the most significant effect and accounted for 5.1% of the genomic additive heritability and 5.2% of the accuracy of prediction. The 85 significant SNPs accounted for 28.5 to 28.8% of the genomic additive heritability and 35.8 to 36.8% of the accuracy of prediction.ConclusionsThe three methods used for the GWAS identified 85 significant SNPs with additive effects on teat number, including SNPs in a previously reported chromosomal region and SNPs in novel chromosomal regions. Most significant SNPs with larger estimated effects also had larger contributions to the total genomic heritability and accuracy of prediction than other SNPs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-017-0311-8) contains supplementary material, which is available to authorized users.

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BackgroundThe availability of a unique unselected Holstein line since 1964 provided a direct comparison between selected and unselected Holstein genomes whereas large Holstein samples provided unprecedented statistical power for identifying high-confidence SNP effects. Utilizing these unique resources, we aimed to identify genome changes affected by selection since 1964.ResultsDirect comparison of genome-wide SNP markers between a Holstein line unselected since 1964 and contemporary Holsteins showed that the 40 years of artificial selection since 1964 resulted in genome landscape changes. Among the regions affected by selection, the regions containing 198 genes with fertility functions had a larger negative correlation than that of all SNPs between the SNP effects on milk yield and daughter pregnancy rate. These results supported the hypothesis that hitchhiking of genetic selection for milk production by negative effects of fertility genes contributed to the unintended declines in fertility since 1964. The genome regions subjected to selection also contained 67 immunity genes, the bovine MHC region of Chr23 with significantly decreased heterozygosity in contemporary Holsteins, and large gene clusters including T-cell receptor and immunoglobulin genes.ConclusionsThis study for the first time provided direct evidence that genetic selection for milk production affected fertility and immunity genes and that the hitchhiking of genetic selection for milk production by negative fertility effects contributed to the fertility declines since 1964, and identified a large number of candidate fertility and immunity genes affected by selection. The results provided novel understanding about genome changes due to artificial selection and their impact on fertility and immunity genes and could facilitate developing genetic methods to reverse the declines in fertility and immunity in Holstein cattle.Electronic supplementary materialThe online version of this article (10.1186/s12864-019-5459-x) contains supplementary material, which is available to authorized users.