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

BMC Genomics

Research Article

Paul M. VanRaden¹  John B. Cole¹  Li Ma²  Botong Shen²  Jicai Jiang²  Jeffrey R. O’Connell³ 

[1] Animal Genomics and Improvement Laboratory, USDA, Building 5, 20705, Beltsville, MD, USA;Department of Animal and Avian Sciences, University of Maryland, 2123 Animal Science Building, 20742, College Park, MD, USA;University of Maryland Baltimore, 21201, Baltimore, MD, USA;

关键词: Variance decomposition;    Additive;    Dominance;    Imprinting;    Cattle;    Dairy traits;    Qtl;   

DOI  :  10.1186/s12864-017-3821-4

 received in 2017-04-07, accepted in 2017-05-25,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundAlthough genome-wide association and genomic selection studies have primarily focused on additive effects, dominance and imprinting effects play an important role in mammalian biology and development. The degree to which these non-additive genetic effects contribute to phenotypic variation and whether QTL acting in a non-additive manner can be detected in genetic association studies remain controversial.ResultsTo empirically answer these questions, we analyzed a large cattle dataset that consisted of 42,701 genotyped Holstein cows with genotyped parents and phenotypic records for eight production and reproduction traits. SNP genotypes were phased in pedigree to determine the parent-of-origin of alleles, and a three-component GREML was applied to obtain variance decomposition for additive, dominance, and imprinting effects. The results showed a significant non-zero contribution from dominance to production traits but not to reproduction traits. Imprinting effects significantly contributed to both production and reproduction traits. Interestingly, imprinting effects contributed more to reproduction traits than to production traits. Using GWAS and imputation-based fine-mapping analyses, we identified and validated a dominance association signal with milk yield near RUNX2, a candidate gene that has been associated with milk production in mice. When adding non-additive effects into the prediction models, however, we observed little or no increase in prediction accuracy for the eight traits analyzed.ConclusionsCollectively, our results suggested that non-additive effects contributed a non-negligible amount (more for reproduction traits) to the total genetic variance of complex traits in cattle, and detection of QTLs with non-additive effect is possible in GWAS using a large dataset.

【 授权许可】

CC BY   
© The Author(s). 2017

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