Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle

Jiang, Jicai; Shen, Botong; O’Connell, Jeffrey R.; VanRaden, P.M.; Cole, J.B.; Ma, Li

doi:10.1186/s12864-017-3821-4

Cited by 51 publications

(53 citation statements)

References 36 publications

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“…where y is a phenotype vector of size n for a complex trait, b is a vector of covariate (other than genomic variants) effects and X is corresponding design matrix, a is a vector of variant effects and Z is corresponding genotype coding matrix (e.g., genotype coding for additive, dominance, or imprinting effects 34 ), g is a vector of polygenic effect for controlling population structure, G is a corresponding variance structure matrix (e.g., genomic relationship matrix), and e is the residual with variance structure R for modelling reliability or accuracy of phenotypic records as in model (1) We seek to identify independent association signals within a QTL region and to assign a posterior probability of causality (PPC) to each variant with fine-mapping. Following the method by Huang et al 7 , our fine-mapping approach includes three steps: forward selection 37 to add independent signals in the model, repositioning signals, and generating credible variant set for each signal.…”

Section: Bayesian Fine-mapping (Bfmap)mentioning

confidence: 99%

Fast Bayesian fine-mapping of 35 production, reproduction and body conformation traits in dairy cattle

Jiang

Cole

et al. 2018

Preprint

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Imputation has been routinely used to infer sequence variants in large genotyped populations based on reference populations of sequenced individuals. With increasing numbers of animals sequenced and the implementation of the 1000 Bull Genomes Project, fine-mapping of causal variants for complex traits is becoming possible in cattle. Using 404 ancestor bull sequences as reference, we imputed over 3 million selected sequence variants to 27,214 Holstein bulls with highly reliable phenotypes (breeding values) for 35 production, reproduction, and body conformation traits. We first performed whole-genome single-marker scans for each of the 35 traits using a mixed-model association test. The single-trait association statistics were then merged into multi-trait tests of 3 trait groups: production, reproduction, and body conformation.Both single-and multi-trait GWAS results were used to identify 282 candidate QTL regions for fine-mapping in the cattle genome. To facilitate fast and powerful fine-mapping analyses, we developed a Bayesian Fine-MAPping approach (BFMAP) to integrate fine-mapping with functional enrichment analysis. Our fine-mapping results identified 69 promising candidate genes for dairy traits, including ABCC9, VPS13B, MGST1, SCD, MKL1, and CSN1S1 for production traits; CHEK2, GC, and KALRN for reproduction traits; and TMTC2, ARRDC3, ZNF613, CCND2, and FGF6 for body conformation traits. Based on existing functional annotation data for cattle, we revealed biologically meaningful enrichment in our fine-mapped variants that can be readily tested in functional validation studies. In summary, these results demonstrated the utility of a fast Bayesian approach for fine-mapping and functional enrichment analysis, identified candidate causative genes and variants, and enhanced our understanding of the genetic basis of complex traits in dairy cattle.

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Section: Bayesian Fine-mapping (Bfmap)mentioning

confidence: 99%

Fast Bayesian fine-mapping of 35 production, reproduction and body conformation traits in dairy cattle

Jiang

Cole

et al. 2018

Preprint

Self Cite

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“…However, buffalo milk yield was known to be much lower than that of cow [2], which largely restricts the development of the dairy buffalo industry. The milk traits are complex quantitative traits with moderate heritability [3][4][5]. With the application of genome-wide association studies for mapping genes for complex traits in domestic animals, several necessary genes related to lactation phenotypes have been identified.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide identification of Diacylglycerol Acyltransferases (DGAT) family genes influencing Milk production in Buffalo

Liu

Wang

et al. 2020

BMC Genet

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Background: The diacylglycerol acyltransferases (DGAT) are a vital group of enzymes in catalyzing triacylglycerol biosynthesis. DGAT genes like DGAT1 and DGAT2, have been identified as two functional candidate genes affecting milk production traits, especially for fat content in milk. Buffalo milk is famous for its excellent quality, which is rich in fat and protein content. Therefore, this study aimed to characterize DGAT family genes in buffalo and to find candidate markers or DGAT genes influencing lactation performance. Results: We performed a genome-wide study and identified eight DGAT genes in buffalo. All the DGAT genes classified into two distinct clades (DGAT1 and DGAT2 subfamily) based on their phylogenetic relationships and structural features. Chromosome localization displayed eight buffalo DGAT genes distributed on five chromosomes. Collinearity analysis revealed that the DGAT family genes were extensive homologous between buffalo and cattle. Afterward, we discovered genetic variants loci within the genomic regions that DGAT genes located in buffalo. Seven haplotype blocks were constructed and were associated with buffalo milk production traits. Single marker association analyses revealed four most significant single nucleotide polymorphisms (SNPs) mainly affecting milk protein percentage or milk fat yield in buffalo. Genes functional analysis indicated that these DGAT family genes could influence lactation performance in the mammal through regulating lipid metabolism.Conclusion: In the present study, we performed a comprehensive analysis for the DGAT family genes in buffalo, which including identification, structural characterization, phylogenetic classification, chromosomal distribution, collinearity analysis, association analysis, and functional analysis. These findings provide useful information for an in-depth study to determine the role of DGAT family gens play in the regulation of milk production and milk quality improvement in buffalo.

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“…Interestingly, rs109376747 in LDLR also significantly associated with CHL_milk based on a dominance effect, although this effect (dominance effect) explained only a smaller proportion (0.41%) of the total phenotypic variance in CHL_milk compared with variance explained by the additive effect (2.47%) of this SNP (Table ). It has been reported that phenotypic variance explained by a dominance effect was much lower than that explained by an additive effect for milk traits in dairy cows (Ertl et al ; Aliloo et al ; Jiang et al ; Varona et al ). Moreover, SNPs in three other genes (rs137347384 in RBM19 , rs42016945 in PPARG and rs110862179 in SCAP) were also significantly associated with CHL_milk based on a dominance effect .…”

Section: Discussionmentioning

confidence: 98%

Targeted genotyping to identify potential functional variants associated with cholesterol content in bovine milk

et al. 2020

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Summary High blood cholesterol concentration, mainly caused by high dietary cholesterol, is a potential risk factor for human health. Dairy products are important sources of human dietary cholesterol intake. Therefore, monitoring bovine milk cholesterol concentration is important for human health benefit. Genetic selection for improvement of cow milk cholesterol content requires understanding of the genetics of milk cholesterol. For this purpose, we performed analyses of additive and dominance effects of 126 potentially functional SNPs within 43 candidate genes with milk cholesterol content [expressed as mg of cholesterol in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk)]. The additive and dominance effects of SNPs rs380643365 in AGPAT1 (P = 0.04) and rs134357240 in SOAT1 (P = 0.035) genes associated significantly with CHL_fat. Moreover, five (rs109326954 and rs523413537 in DGAT1, rs109376747 in LDLR, rs42781651 in FAM198B and rs109967779 in ACAT2) and four (rs137347384 in RBM19, rs109376747 in LDLR, rs42016945 in PPARG and rs110862179 in SCAP) SNPs were significantly associated with CHL_milk (P < 0.05) based on additive and dominance effect analyses respectively. Rs109326954 and rs523413537 in DGAT1 explained a considerable portion of the phenotypic variance of CHL_milk (7.54 and 6.84% respectively), and might be useful in selection programs for reduced milk cholesterol content. Several significantly associated SNPs were in genes (such as ACAT2 and LDLR) involved in cholesterol metabolism in the liver or cholesterol transport, suggesting multiple mechanisms regulating milk cholesterol content. Nine and seven SNPs identified by additive or dominance effect analyses associated significantly with milk yield and fat yield respectively. Further analyses are required to better understand the consequences of these variants and their potential use in genomic selection of the studied traits.

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Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle

Cited by 51 publications

References 36 publications

Fast Bayesian fine-mapping of 35 production, reproduction and body conformation traits in dairy cattle

Fast Bayesian fine-mapping of 35 production, reproduction and body conformation traits in dairy cattle

Genome-wide identification of Diacylglycerol Acyltransferases (DGAT) family genes influencing Milk production in Buffalo

Targeted genotyping to identify potential functional variants associated with cholesterol content in bovine milk

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