BackgroundDense SNP genotypes are often combined with complex trait phenotypes to map causal variants, study genetic architecture and provide genomic predictions for individuals with genotypes but no phenotype. A single method of analysis that jointly fits all genotypes in a Bayesian mixture model (BayesR) has been shown to competitively address all 3 purposes simultaneously. However, BayesR and other similar methods ignore prior biological knowledge and assume all genotypes are equally likely to affect the trait. While this assumption is reasonable for SNP array genotypes, it is less sensible if genotypes are whole-genome sequence variants which should include causal variants.ResultsWe introduce a new method (BayesRC) based on BayesR that incorporates prior biological information in the analysis by defining classes of variants likely to be enriched for causal mutations. The information can be derived from a range of sources, including variant annotation, candidate gene lists and known causal variants. This information is then incorporated objectively in the analysis based on evidence of enrichment in the data. We demonstrate the increased power of BayesRC compared to BayesR using real dairy cattle genotypes with simulated phenotypes. The genotypes were imputed whole-genome sequence variants in coding regions combined with dense SNP markers. BayesRC increased the power to detect causal variants and increased the accuracy of genomic prediction. The relative improvement for genomic prediction was most apparent in validation populations that were not closely related to the reference population. We also applied BayesRC to real milk production phenotypes in dairy cattle using independent biological priors from gene expression analyses. Although current biological knowledge of which genes and variants affect milk production is still very incomplete, our results suggest that the new BayesRC method was equal to or more powerful than BayesR for detecting candidate causal variants and for genomic prediction of milk traits.ConclusionsBayesRC provides a novel and flexible approach to simultaneously improving the accuracy of QTL discovery and genomic prediction by taking advantage of prior biological knowledge. Approaches such as BayesRC will become increasing useful as biological knowledge accumulates regarding functional regions of the genome for a range of traits and species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2443-6) contains supplementary material, which is available to authorized users.

BackgroundInbreeding reduces the fitness of individuals by increasing the frequency of homozygous deleterious recessive alleles. Some insight into the genetic architecture of fitness, and other complex traits, can be gained by using single nucleotide polymorphism (SNP) data to identify regions of the genome which lead to reduction in performance when identical by descent (IBD). Here, we compared the effect of genome-wide and location-specific homozygosity on fertility and milk production traits in dairy cattle.MethodsGenotype data from more than 43 000 SNPs were available for 8853 Holstein and 4138 Jersey dairy cows that were part of a much larger dataset that had pedigree records (338 696 Holstein and 64 049 Jersey animals). Measures of inbreeding were based on: (1) pedigree data; (2) genotypes to determine the realised proportion of the genome that is IBD; (3) the proportion of the total genome that is homozygous and (4) runs of homozygosity (ROH) which are stretches of the genome that are homozygous.ResultsA 1% increase in inbreeding based either on pedigree or genomic data was associated with a decrease in milk, fat and protein yields of around 0.4 to 0.6% of the phenotypic mean, and an increase in calving interval (i.e. a deterioration in fertility) of 0.02 to 0.05% of the phenotypic mean. A genome-wide association study using ROH of more than 50 SNPs revealed genomic regions that resulted in depression of up to 12.5 d and 260 L for calving interval and milk yield, respectively, when completely homozygous.ConclusionsGenomic measures can be used instead of pedigree-based inbreeding to estimate inbreeding depression. Both the diagonal elements of the genomic relationship matrix and the proportion of homozygous SNPs can be used to measure inbreeding. Longer ROH (>3 Mb) were found to be associated with a reduction in milk yield and captured recent inbreeding independently and in addition to overall homozygosity. Inbreeding depression can be reduced by minimizing overall inbreeding but maybe also by avoiding the production of offspring that are homozygous for deleterious alleles at specific genomic regions that are associated with inbreeding depression.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-014-0071-7) contains supplementary material, which is available to authorized users.

Temperature and humidity levels above a certain threshold decrease milk production in dairy cattle, and genetic variation is associated with the amount of lost production. To enable selection for improved heat tolerance, the aim of this study was to develop genomic estimated breeding values (GEBV) for heat tolerance in dairy cattle. Heat tolerance was defined as the rate of decline in production under heat stress. We combined herd test-day recording data from 366,835 Holstein and 76,852 Jersey cows with daily temperature and humidity measurements from weather stations closest to the tested herds for test days between 2003 and 2013. We used daily mean values of temperature-humidity index averaged for the day of test and the 4 previous days as the measure of heat stress. Tolerance to heat stress was estimated for each cow using a random regression model with a common threshold of temperature-humidity index=60 for all cows. The slope solutions for cows from this model were used to define the daughter trait deviations of their sires. Genomic best linear unbiased prediction was used to calculate GEBV for heat tolerance for milk, fat, and protein yield. Two reference populations were used, the first consisted of genotyped sires only (2,300 Holstein and 575 Jersey sires), and the other included genotyped sires and cows (2,189 Holstein and 1,188 Jersey cows). The remainder of the genotyped sires were used as a validation set. All animals had genotypes for 632,003 single nucleotide polymorphisms. When using only genotyped sires in the reference set and only the first parity data, the accuracy of GEBV for heat tolerance in relation to changes in milk, fat, and protein yield were 0.48, 0.50, and 0.49 in the Holstein validation sires and 0.44, 0.61, and 0.53 in the Jersey validation sires, respectively. Some slight improvement in the accuracy of prediction was achieved when cows were included in the reference population for Holsteins. No clear improvements in the accuracy of genomic prediction were observed when data from the second and third parities were included. Correlations of GEBV for heat tolerance with Australian Breeding Values for other traits suggested heat tolerance had a favorable genetic correlation with fertility (0.29-0.39 in Holsteins and 0.15-0.27 in Jerseys), but unfavorable correlations for some production traits. Options to improve heat tolerance with genomic selection in Australian dairy cattle are discussed.

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Genetic parameters for daily somatic cell counts (SCC) of the first three parities were estimated for Australian Dairy Cattle. Most of the data analyses were carried out with a sire random regression model. The estimates were compared with those from conventional ten-trait analyses and animal models. In the first-parity estimates of heritabilities (h2) were low (0.04 to 0.05) at the beginning of the lactation and higher (0.11 to 0.13) at the end. The average h2 estimated from random regression sire model, random regression animal model and conventional multitrait sire model were 0.09, 0.09, and 0.08, respectively, in the first lactation. The average h2 were 0.09 and 0.11 in the second and third parities, respectively. Genetic correlations between daily log(e) SCC within parity were high for adjacent tests (nearly 1) and low (as low as 0.30) between the beginning and the end of the lactation. Generally, the genetic correlations between parities depend on how far apart they are and on whether they are on the same day in any two parities. Across parities, on average, genetic correlations between parities 1 and 3 were the lowest and those between 1 and 2 intermediate, while those between 2 and 3 were the highest. The estimated environmental correlations were lower than the genetic correlations, but the trends were generally similar. Differences in genetic parameter estimates due to model were small, except for some genetic correlations. The high residual error variances, the low h2, and the inconsistency in genetic correlations that were observed particularly at the beginning of the first lactation suggest that log(e) SCC early in the first lactation may be related to a spike in SCC as result of infection and (or) onset of lactation while SCC later in lactation represents a sustained response to infection. Accounting for the variation in heritabilities and correlations should improve the accuracy of genetic evaluations for SCC based on test day records.

The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single-nucleotide polymorphism (SNP) markers for 29 traits in Australian Holstein-Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The proportion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.

The existence of a genotype x environment interaction (G x E) for fertility traits, survival, and milk yield traits was examined by considering performance recorded in different calving systems (seasonal, split, and year round) or regions as different traits. For fertility traits and survival, G x E were also investigated by applying a random regression model using continuous environmental variables, such as level of herd milk production, temperature-humidity index, and herd size as environmental descriptors. The traits considered were calving interval, calving to first service interval (CFS), 25-d nonreturn rate at first service, pregnancy rate, survival, milk yield, fat yield, and protein yield and percentage. Data on Holstein-Friesian cows that calved between 1997 and 2005 were analyzed. The number of cows included in the analyses ranged from approximately 21,000 for pregnancy rate to approximately one-half million for survival. For all traits, heterogeneity in additive and phenotypic variances was observed. For example, for CFS the additive genetic and phenotypic variance in seasonal calving herds was only 9 and 15% of that in year-round calving herds, respectively. Genetic correlations among calving systems for milk yield traits were greater than 0.96. For calving interval, the lowest genetic correlation, of 0.83, was between split and year-round calving herds, but for CFS and pregnancy rate, genetic correlations as low as 0.37 were observed, although these estimates were associated with large standard errors. Genetic correlations between traits recorded in different Australian regions were greater than 0.89. Heritability and phenotypic variance for milk yield traits were the greatest in region 1, which consisted of Queensland, West Australia, South Australia, and New South Wales, and were least in region 3, which included Gippsland and Tasmania, in accordance with mean milk yield levels. Genetic correlations as low as 0.5 for some fertility traits between the 5th and 95th percentile of the distribution of the environmental descriptors, such as herd size and average herd milk production, were also observed. However, these estimates had large standard errors. Regardless of the environmental descriptor used, there was no evidence for the presence of a large G x E that resulted in economically significant reranking of bulls.

Excessive ambient temperature and humidity can impair milk production and fertility of dairy cows. Selection for heat-tolerant animals is one possible option to mitigate the effects of heat stress. To enable selection for this trait, we describe the development of a heat tolerance breeding value for Australian dairy cattle. We estimated the direct genomic values of decline in milk, fat, and protein yield per unit increase of temperature-humidity index (THI) using 46,726 single nucleotide polymorphisms and a reference population of 2,236 sires and 11,853 cows for Holsteins and 506 sires and 4,268 cows for Jerseys. This new direct genomic value is the Australian genomic breeding value for heat tolerance (HT ABVg). The components of the HT ABVg are the decline in milk, fat, and protein per unit increase in THI when THI increases above the threshold of 60. These components are weighted by their respective economic values, assumed to be equivalent to the weights applied to milk, fat, and protein yield in the Australian selection indices. Within each breed, the HT ABVg is then standardized to have a mean of 100 and standard deviation (SD) of 5, which is consistent with the presentation of breeding values for many other traits in Australia. The HT ABVg ranged from -4 to +3 SD in Holsteins and -3 to +4 SD in Jerseys. The mean reliabilities of HT ABVg among validation sires, calculated from the prediction error variance and additive genetic variance, were 38% in both breeds. The range in ABVg and their reliability suggests that HT can be improved using genomic selection. There has been a deterioration in the genetic trend of HT, and to moderate the decline it is suggested that the HT ABVg should be included in a multitrait economic index with other traits that contribute to farm profit.