Jiang Jicai, Shen Botong, O'Connell Jeffrey R, VanRaden Paul M, Cole John B, Ma Li
Department of Animal and Avian Sciences, University of Maryland, 2123 Animal Science Building, College Park, MD, 20742, USA.
University of Maryland Baltimore, Baltimore, MD, 21201, USA.
BMC Genomics. 2017 May 30;18(1):425. doi: 10.1186/s12864-017-3821-4.
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 remain controversial.
To 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.
Collectively, 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.
尽管全基因组关联研究和基因组选择研究主要关注加性效应,但显性效应和印记效应在哺乳动物生物学和发育中起着重要作用。这些非加性遗传效应在多大程度上导致表型变异,以及在遗传关联研究中是否能够检测到以非加性方式起作用的数量性状位点,仍然存在争议。
为了实证回答这些问题,我们分析了一个大型牛数据集,该数据集由42701头基因分型的荷斯坦奶牛组成,这些奶牛有基因分型的亲本以及八个生产和繁殖性状的表型记录。单核苷酸多态性(SNP)基因型在系谱中进行分型以确定等位基因的亲本来源,并应用三成分基因组最佳线性无偏预测(GREML)来获得加性、显性和印记效应的方差分解。结果表明,显性效应显著地对生产性状有非零贡献,但对繁殖性状没有贡献。印记效应显著地对生产和繁殖性状都有贡献。有趣的是,印记效应在繁殖性状上的贡献比在生产性状上的更大。通过全基因组关联研究(GWAS)和基于填充的精细定位分析,我们在RUNX2附近鉴定并验证了一个与产奶量相关的显性关联信号,RUNX2是一个已被证明与小鼠产奶相关的候选基因。然而,当将非加性效应纳入预测模型时,我们观察到所分析的八个性状的预测准确性几乎没有增加或没有增加。
总体而言,我们的结果表明,非加性效应在牛复杂性状的总遗传方差中贡献了不可忽略的量(对繁殖性状的贡献更大),并且在使用大型数据集的GWAS中检测具有非加性效应的数量性状位点是可能的。