Advances in the molecular area of selection have expanded knowledge of the genetic architecture of complex traits through genome-wide association studies (GWAS). Several GWAS have been performed so far, but confirming these results is not always possible due to several factors, including environmental conditions. Thus, our objective was to identify genomic regions associated with traditional milk production traits, including milk yield, somatic cell score, fat, protein and lactose percentages, and fatty acid composition in a Holstein cattle population producing under tropical conditions. For this, 75,228 phenotypic records from 5,981 cows and genotypic data of 56,256 SNP from 1,067 cows were used in a weighted single-step GWAS. A total of 46 windows of 10 SNP explaining more than 1% of the genetic variance across 10 Bos taurus autosomes (BTA) harbored well-known and novel genes. The MGST1 (BTA5), ABCG2 (BTA6), DGAT1 (BTA14), and PAEP (BTA11) genes were confirmed within some of the regions identified in our study. Potential novel genes involved in tissue damage and repair of the mammary gland (COL18A1), immune response (LTTC19), glucose homeostasis (SLC37A1), synthesis of unsaturated fatty acids (LTBP1), and sugar transport (SLC37A1 and MFSD4A) were found for milk yield, somatic cell score, fat percentage, and fatty acid composition. Our findings may assist genomic selection by using these regions to design a customized SNP array to improve milk production traits on farms with similar environmental conditions.

Copy number variations (CNV) are an important source of genetic variation. CNV has been increasingly studied and frequently associated with diseases and productive traits in livestock animals. However, CNV‐based genome‐wide association studies (GWAS) in Santa Inês sheep, one of the principal sheep breeds in Brazil, have not yet been reported. Thus, the aim of this study was to investigate the association between CNV and growth, efficiency and carcass traits in sheep. The Illumina OvineSNP50 BeadChip array was used to detect CNV in 491 Santa Inês individuals. Then, CNV‐based GWAS was performed with a linear mixed model approach considering a genomic relationship matrix, for ten traits: (1) growth: body weight at three (W3) and six (W6) months of age; (2) efficiency: residual feed intake (RFI) and feed efficiency (FE) and (3) carcass: external carcass length (ECL), leg length (LL), carcass yield (CY), commercial cuts weight (CCW), loin eye area (LEA) and subcutaneous fat thickness (SFT). We identified 1,167 autosomal CNV in 438 sheep, with 294 non‐redundant CNV, ranging from 21.8 to 861.9 kb, merged into 216 distinct copy number variation regions (CNVRs). One significant CNV segment (pFDR‐value<0.05) in OAR3 was associated with CY, while another significant CNV in OAR6 was associated with RFI. Additionally, another 5 CNV segments were considered relevant for investigation in the future studies. The significant segments overlapped 4 QTLs and spanned 8 genes, including the SPAST,TGFA and ADGRL3 genes, involved in cell differentiation and energy metabolism. Therefore, the results of the present study increase knowledge about CNV in sheep, their possible impacts on productive traits, and provide information for future investigations, being especially useful for those interested in structural variations in the sheep genome.

The present study aimed to estimate covariance components of milk fatty acids (FA) and to compare the genomic estimated breeding values under general and heat-stress effects. Data consisted of 38,762 test-day records from 6,344 Holstein cows obtained from May 2012 through January 2018 on 4 dairy herds from Brazil. Single-trait repeatability test-day models with random regressions as a function of temperature-humidity index values were used for genetic analyses. The models included contemporary groups, parity order (1-6), and days in milk classes as fixed effects, and general and thermotolerance additive genetic and permanent environmental as random effects. Notably, differences in heritability estimates between environments (general and heat stress) increased (0.03 to 0.06) for unsaturated FA traits, such as unsaturated, monounsaturated, and polyunsaturated, at higher heat-stress levels. In contrast, heritability estimated between environments for saturated FA traits, including saturated FA, palmitic acid (C16:0), and stearic acid (C18:0) did not observe significant differences between environments. In addition, our study revealed negative genetic correlations between general and heat-stress additive genetic effects (antagonistic effect) for the saturated FA, C16:0, C18:0, and C18:1, which ranged from −0.007 to −0.32. Spearman's ranking correlation between genomic estimated breeding values ranged from −0.27 to 0.99. Results indicated a moderate to strong interaction of genotype by the environment for most FA traits comparing a heat-stress environment with thermoneutral conditions. Our findings point out novel opportunities to explore the use of FA milk profile and heat-stress models.

Context The economic efficiency of a dairy system is associated with the animal’s productive and reproductive abilities. Therefore, selection criteria should include milk production and quality traits as well as traits related to health and fertility. Since such phenotypes can present non-normal distributions, the use of threshold models is appropriate to study the genetic variation of such traits. Aim To estimate variance components for dairy production and functional traits in a Brazilian Holstein cattle population using linear and threshold models under a Bayesian approach. Methods Data comprised 64 657 test-day records for milk yield (MY, kg/day), casein percentage (CP, % of milk) and subclinical mastitis incidence (SCM), along with 4460 records for sexual precocity (PREC) from 5439 cows. Both SCM and PREC were defined as binary traits. Genetic analyses were based on linear (for MY and CP) and threshold (for SCM and PREC) models using Bayesian estimation. Non-informative and informative priors were considered for variance components, and these models were compared using the deviance information criterion (DIC) and the absolute difference between DIC (Δ). Key results Posterior means of heritability for MY, CP, SCM and PREC were 0.14, 0.39, 0.13 and 0.38 (based on non-informative priors) and 0.13, 0.27, 0.13 and 0.44 (considering informative priors), respectively. The model based on non-informative priors was better (lower DIC) for CP, whereas for PREC, the best model used informative priors. No differences between priors (Δ < 5) were observed for MY and SCM. Conclusions Threshold models were adequate for the analysis of non-normally distributed traits. The use of informative priors can be beneficial if specification is based on results from similar databases and models. Due to their high genetic variation, CP and PREC can be considered as selection criteria in animal breeding programs. In turn, accurate genetic evaluation for MY and SCM will depend on the pedigree and the information from genetically correlated traits. Implications Our study contributes to the understanding of the variation under important dairy production traits in a tropical Holstein population and provides information on the use of Bayesian threshold models as an appropriate method for the evaluation of non-normally distributed phenotypes.

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