Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle

Oliveira, Hinayah Rojas de; Cant, J.P.; Brito, Luiz F.; Feitosa, Fabieli Loise Braga; Chud, T.C.S.; Fonseca, Pablo Augusto de Souza; Jamrozik, J.; Silva, F.F.; Lourenço, Daniela; Schenkel, F.S.

doi:10.3168/jds.2019-16451

Cited by 55 publications

(52 citation statements)

References 73 publications

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“…A GWAS was done for GWRR in each breed/sex population, where the primary focus was identifying significant genomic regions by estimating variance explained by overlapping 10 SNP windows. A Manhattan plot of variance explained by window can be found in Figure 4, with significance considered at “low” (0.3%) and “high” (0.5%) variance explained thresholds, which have been previously used to establish significance (Medeiros de Oliveira Silva et al., 2017; Oliveira et al., 2019). Variance explained by the top five windows in each population can be found in Table 4, alongside genes mapped inside each window.…”

Section: Resultsmentioning

confidence: 99%

Evidence for recombination variability in purebred swine populations

Lozada-Soto

Maltecca

Wackel

et al. 2020

J Animal Breeding Genetics

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This study aimed to investigate interpopulation variation due to sex, breed and age, and the intrapopulation variation in the form of genetic variance for recombination in swine. Genome‐wide recombination rate and recombination occurrences (RO) were traits studied in Landrace (LR) and Large White (LW) male and female populations. Differences were found for sex, breed, sex‐breed interaction, and age effects for genome‐wide recombination rate and RO at one or more chromosomes. Dams were found to have a higher genome‐wide recombination rate and RO at all chromosomes than sires. LW animals had higher genome‐wide recombination rate and RO at seven chromosomes but lower at two chromosomes than LR individuals. The sex‐breed interaction effect did not show any pattern not already observable by sex. Recombination increased with increasing parity in females, while in males no effect of age was observed. We estimated heritabilities and repeatabilities for both investigated traits and obtained the genetic correlation between male and female genome‐wide recombination rate within each of the two breeds studied. Estimates of heritability and repeatability were low (h2 = 0.01–0.26; r = 0.18–0.42) for both traits in all populations. Genetic correlations were high and positive, with estimates of 0.98 and 0.94 for the LR and LW breeds, respectively. We performed a GWAS for genome‐wide recombination rate independently in the four sex/breed populations. The results of the GWAS were inconsistent across the four populations with different significant genomic regions identified. The results of this study provide evidence of variability for recombination in purebred swine populations.

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Section: Resultsmentioning

confidence: 99%

Evidence for recombination variability in purebred swine populations

Lozada-Soto

Maltecca

Wackel

et al. 2020

J Animal Breeding Genetics

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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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“…In addition, RR models based on LP are sensitive to few records per cow, mainly for estimations at the extremes of the lactation curve (Meyer, 2005;Misztal, 2006). This problem may be reduced using AR processes (Oliveira, Cant, et al, 2019). Another alternative to LP is the splines functions (Meyer, 2000).…”

Section: Discussionmentioning

confidence: 99%

“…The genetic correlation between TD may differ from unit, meaning that the expression at each DIM may have different additive genetic variance. Recently Oliveira, Cant, et al () reported that there are different genomic regions affecting milk‐related traits along and across lactations. In this context, RR model enables the variance modelling over a continuous scale, assuming that genetic correlations may change over time.…”

Section: Discussionmentioning

confidence: 99%

Autoregressive and random regression test‐day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Silva

Costa

Silva

et al. 2019

J Animal Breeding Genetics

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Autoregressive (AR) and random regression (RR) models were fitted to test‐day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.

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Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle

Cited by 55 publications

References 73 publications

Evidence for recombination variability in purebred swine populations

Evidence for recombination variability in purebred swine populations

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

Autoregressive and random regression test‐day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

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