Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle

Oliveira, Hinayah Rojas de; Lourenço, Daniela; Masuda, Yutaka; Misztal, I.; Tsuruta, S.; Jamrozik, J.; Brito, Luiz F.; Silva, F.F.; Cant, J.P.; Schenkel, F.S.

doi:10.3168/jds.2019-16821

Cited by 34 publications

(29 citation statements)

References 94 publications

(115 reference statements)

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“…Most of the significant genomic regions found in this study are located on BTA14 within or harboring the DGAT1 , FOXH1, and CYHR1 genes. The DGAT1 gene is well known due to its strong association with milk production traits, especially milk fat [27,28,29]. The CYP11B1 gene, which is a CYHR1 paralogous [30], has been reported to be associated with milk fat in buffalo [31].…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Association Study for Milk Fatty Acids in Holstein Cattle Accounting for the DGAT1 Gene Effect

Cruz

Oliveira

Brito

et al. 2019

Animals

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Simple SummaryMilk fat content and fatty acid composition are key traits for the dairy industry, as they directly influence consumer acceptance of dairy products and are associated with the chemical-physical characteristics of milk. In order to genetically improve milk fat composition, it is important to understand the biological mechanisms behind the phenotypic variability observed in these traits. In this study, we used a genomic dataset for 6692 animals and over 770,000 genetic markers distributed across the genome. We compared different statistical approaches to better identify the genes associated with milk fatty acid composition in Holstein cattle. Our results suggest that the DGAT1 gene accounts for most of the variability in milk fatty acid composition, and that the PLBD1 and MGST1 genes are important additional candidate genes in Holstein cattle.AbstractThe identification of genomic regions and candidate genes associated with milk fatty acids contributes to better understand the underlying biology of these traits and enables breeders to modify milk fat composition through genetic selection. The main objectives of this study were: (1) to perform genome-wide association analyses for five groups of milk fatty acids in Holstein cattle using a high-density (777K) SNP panel; and (2) to compare the results of GWAS accounting (or not) for the DGAT1 gene effect as a covariate in the statistical model. The five groups of milk fatty acids analyzed were: (1) saturated (SFA); (2) unsaturated (UFA); (3) short-chain (SCFA); (4) medium-chain (MCFA); and (5) long-chain (LCFA) fatty acids. When DGAT1 was not fitted as a covariate in the model, significant SNPs and candidate genes were identified on BTA5, BTA6, BTA14, BTA16, and BTA19. When fitting the DGAT1 gene in the model, only the MGST1 and PLBD1 genes were identified. Thus, this study suggests that the DGAT1 gene accounts for most of the variability in milk fatty acid composition and the PLBD1 and MGST1 genes are important additional candidate genes in Holstein cattle.

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Section: Discussionmentioning

confidence: 99%

Genome-Wide Association Study for Milk Fatty Acids in Holstein Cattle Accounting for the DGAT1 Gene Effect

Cruz

Oliveira

Brito

et al. 2019

Animals

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“…The optimal scaling factors (τ and ω) reported by Oliveira et al (2019d) for milk fat yield in the same Ayrshire (i.e., τ = 2 and ω = 0.6) and Jersey (i.e., τ = 1.5 and ω = 0.9) populations were used when calculating H. Thereafter, the postGSf90 software (Aguilar et al, 2014) was used to back-solve the genomic EBV predicted for the additive genomic random regression coefficients to SNP effects. The SNP effects calculated for the regression coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d), as fully described in Oliveira et al (2019c) and Freitas et al (2020).…”

Section: Short Communicationmentioning

confidence: 99%

Short communication: Time-dependent genetic parameters and single-step genome-wide association analyses for predicted milk fatty acid composition in Ayrshire and Jersey dairy cattle

Freitas

Oliveira

Silva

et al. 2020

Journal of Dairy Science

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Milk fat content and fatty acid (FA) composition have great economic value to the dairy industry as they are directly associated with taste and chemicalphysical characteristics of milk and dairy products. In addition, consumers' choices are not only based on the nutritional aspects of food, but also on products known to promote better health. Milk FA composition is also related to the metabolic status and physiological stages of cows and thus can also be used as indicator for other novel traits of interest (e.g., metabolic diseases and methane yield). Genetic selection is a promising alternative to manipulate milk FA composition. In this study, we aimed to (1) estimate time-dependent genetic parameters for 5 milk FA groups (i.e., short-chain, medium-chain, long-chain, saturated, and unsaturated) predicted based on milk mid-infrared spectroscopy, for Canadian Ayrshire and Jersey breeds, and (2) conduct a time-dependent, single-step genome-wide association study to identify genomic regions, candidate genes, and metabolic pathways associated with milk FA. We analyzed 31,709 test-day records of 9,648 Ayrshire cows from 268 herds, and 34,341 records of 11,479 Jersey cows from 883 herds. The genomic database contained a total of 2,330 Ayrshire and 1,019 Jersey animals. The average daily heritability ranged from 0.18 (long-chain FA) to 0.34 (medium-chain FA) in Ayrshire, and from 0.25 (long-chain and unsaturated FA) to 0.52 (mediumchain and saturated FA) in Jersey. Important genomic regions were identified in Bos taurus autosomes BTA3,

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“…In this context, Silva et al (2014) concluded that reaction norms should be used for proper genomic evaluation of total number of piglets born. Moreover, Oliveira et al (2019b) showed that random regression models can be used to estimate Single Nucleotide Polymorphism (SNP) effects over time in genome-wide association studies.…”

Section: Genetic and Genomic Selection To Enhance Animal Welfare And mentioning

confidence: 99%

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

et al. 2020

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Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and

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Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle

Cited by 34 publications

References 94 publications

Genome-Wide Association Study for Milk Fatty Acids in Holstein Cattle Accounting for the DGAT1 Gene Effect

Genome-Wide Association Study for Milk Fatty Acids in Holstein Cattle Accounting for the DGAT1 Gene Effect

Short communication: Time-dependent genetic parameters and single-step genome-wide association analyses for predicted milk fatty acid composition in Ayrshire and Jersey dairy cattle

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

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