|JIANG, JICAI - University Of Maryland|
|SHEN, BOTONG - University Of Maryland|
|O'CONNELL, JEFFREY - University Of Maryland|
|MA, LI - University Of Maryland|
Submitted to: BMC Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/25/2017
Publication Date: 5/30/2017
Citation: Jiang, J., Shen, B., O'Connell, J.R., Van Raden, P.M., Cole, J.B., Ma, L. 2017. Dissection of additive, dominance, and imprinting effects for production and reproduction traits in Holstein cattle. Biomed Central (BMC) Genomics. 18:425.
Interpretive Summary: An animal’s genes can affect its phenotype, such as the amount of milk produced, in several ways. Most statistical models assume that each trait is affected by a large numbers of genes whose effects add-up to produce observed effects. Other effects can be difficult to detect because they are often small in size. For example, genes coming from one parent can have a larger or smaller effect than a gene coming from the other parent. In the past, those effects have typically been ignored because they are hard to calculate. This study used a very large dataset to study three different ways in which genes can affect phenotypes. The results show that the effects due to the parent from which a gene comes are larger than expected, particularly for traits related to fertility.
Technical Abstract: Although 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 remains controversial. To empirically answer these questions, we analyzed a large cattle dataset from USDA that consisted of 42,701 genotyped Holstein cows with pedigree information 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 reproductive 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. However, we observed little or no increase in prediction accuracy when incorporating non-additive effects for the eight traits included in this study. Collectively, our results suggested that non-additive effects contributed a non-negligible amount (more for reproductive 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.