Advances in genomics, molecular biology, and statistical genetics have created a paradigm shift in the way livestock producers pursue genetic improvement in their herds. The nexus of these technologies has resulted in combining genotypic and phenotypic information to compute genomically enhanced measures of genetic merit of individual animals. However, large numbers of genotyped and phenotyped animals are required to produce robust estimates of the effects of SNP that are summed together to generate direct genomic breeding values (DGV). Data on 11,756 Angus animals genotyped with the Illumina BovineSNP50 Beadchip were used to develop genomic predictions for 17 traits reported by the American Angus Association through Angus Genetics Inc. in their National Cattle Evaluation program. Marker effects were computed using a 5-fold cross-validation approach and a Bayesian model averaging algorithm. The accuracies were examined with EBV and deregressed EBV (DEBV) response variables and with K-means and identical by state (IBS)-based cross-validation methodologies. The cross-validation accuracies obtained using EBV response variables were consistently greater than those obtained using DEBV (average correlations were 0.64 vs. 0.57). The accuracies obtained using K-means cross-validation were consistently smaller than accuracies obtained with the IBS-based cross-validation approach (average correlations were 0.58 vs. 0.64 with EBV used as a response variable). Comparing the results from the current study with the results from a similar study consisting of only 2,253 records indicated that larger training population size resulted in higher accuracies in validation animals and explained on average 18% (69% improvement) additional genetic variance across all traits.

BackgroundReproductive traits such as number of stillborn piglets (SB) and number of teats (NT) have been evaluated in many genome-wide association studies (GWAS). Most of these GWAS were performed under the assumption that these traits were normally distributed. However, both SB and NT are discrete (e.g. count) variables. Therefore, it is necessary to test for better fit of other appropriate statistical models based on discrete distributions. In addition, although many GWAS have been performed, the biological meaning of the identified candidate genes, as well as their functional relationships still need to be better understood. Here, we performed and tested a Bayesian treatment of a GWAS model assuming a Poisson distribution for SB and NT in a commercial pig line. To explore the biological role of the genes that underlie SB and NT and identify the most likely candidate genes, we used the most significant single nucleotide polymorphisms (SNPs), to collect related genes and generated gene-transcription factor (TF) networks.ResultsComparisons of the Poisson and Gaussian distributions showed that the Poisson model was appropriate for SB, while the Gaussian was appropriate for NT. The fitted GWAS models indicated 18 and 65 significant SNPs with one and nine quantitative trait locus (QTL) regions within which 18 and 57 related genes were identified for SB and NT, respectively. Based on the related TF, we selected the most representative TF for each trait and constructed a gene-TF network of gene-gene interactions and identified new candidate genes.ConclusionsOur comparative analyses showed that the Poisson model presented the best fit for SB. Thus, to increase the accuracy of GWAS, counting models should be considered for this kind of trait. We identified multiple candidate genes (e.g. PTP4A2, NPHP1, and CYP24A1 for SB and YLPM1, SYNDIG1L, TGFB3, and VRTN for NT) and TF (e.g. NF-κB and KLF4 for SB and SOX9 and ELF5 for NT), which were consistent with known newborn survival traits (e.g. congenital heart disease in fetuses and kidney diseases and diabetes in the mother) and mammary gland biology (e.g. mammary gland development and body length).Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0189-x) contains supplementary material, which is available to authorized users.

High intramuscular fat (IMF) awards price premiums to beef producers and is associated with meat quality and flavor. Studying gene interactions and pathways that affect IMF might unveil causative physiological mechanisms and inform genomic selection, leading to increased accuracy of predictions of breeding value. To study gene interactions and pathways, a gene network was derived from genetic markers associated with direct measures of IMF, other fat phenotypes, feedlot performance, and a number of meat quality traits relating to body conformation, development, and metabolism that might be plausibly expected to interact with IMF biology. Marker associations were inferred from genomewide association studies (GWAS) based on high density genotypes and 29 traits measured on 10,181 beef cattle animals from 3 breed types. For the network inference, SNP pairs were assessed according to the strength of the correlation between their additive association effects across the 29 traits. The co-association inferred network was formed by 2,434 genes connected by 28,283 edges. Topological network parameters suggested a highly cohesive network, in which the genes are strongly functionally interconnected. Pathway and network analyses pointed towards a trio of transcription factors (TF) as key regulators of carcass IMF: PPARGC1A, HNF4G, and FOXP3. Importantly, none of these genes would have been deemed as significantly associated with IMF from the GWAS. Instead, a total of 313 network genes show significant co-association with the 3 TF. These genes belong to a wide variety of biological functions, canonical pathways, and genetic networks linked to IMF-related phenotypes. In summary, our GWAS and network predictions are supported by the current literature and suggest a cooperative role for the 3 TF and other interacting genes including CAPN6, STC2, MAP2K4, EYA1, COPS5, XKR4, NR2E1, TOX, ATF1, ASPH, TGS1, and TTPA as modulators of carcass and meat quality traits in beef cattle.