Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
Dissecting closely linked association signals in combination with the mammalian phenotype database can identify candidate genes in dairy cattle
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DOI：
10.1186/s12863-019-0717-0
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发表时间：
2019-01-29
期刊：
影响因子：
2.9
通讯作者：
Sahana, Goutam
中科院分区：
文献类型：
--
作者：
Cai, Zexi;Guldbrandtsen, Bernt;Sahana, Goutam
全基因组关联研究（GWAS）已经成功地应用于牛的研究和育种。然而，从关联到确定因果变异并揭示潜在机制已被证明是复杂的。在奶牛种群中，由于被研究个体中密切的家族关系引起的远程连锁不平衡（LD），我们面临着挑战。远程LD使得区分一个或多个数量性状位点（QTL）在一个显示与表型相关的基因组区域中分离变得困难。在这项研究中，我们有两个目标：1)区分基因组区域中多个QTL的分离，以及2)利用外部信息对QTL的候选基因以及候选变体进行优先排序。结果我们观察到，将先导SNP固定为协变量有助于区分额外的密切关联信号。此后，利用哺乳动物表型数据库，我们成功地找到了候选基因，与先前的研究一致，证明了这种策略的力量。其次，我们利用变异注释信息在候选基因中搜索致病变异。变异信息成功地确定了已知的致病突变，并显示出定位于编码区域的致病突变的潜力。结论sour方法可以在一次分析中区分同一染色体上的多个QTL分离，无需人工输入。此外，利用来自哺乳动物表型数据库和变异效应预测因子的信息作为gwas后分析，有助于在牛中发现候选基因和致病突变。本研究不仅确定了奶牛产奶性状的候选基因，而且可以作为奶牛GWAS的常规方法。
BackgroundGenome-wide association studies (GWAS) have been successfully implemented in cattle research and breeding. However, moving from the associations to identify the causal variants and reveal underlying mechanisms have proven complicated. In dairy cattle populations, we face a challenge due to long-range linkage disequilibrium (LD) arising from close familial relationships in the studied individuals. Long range LD makes it difficult to distinguish if one or multiple quantitative trait loci (QTL) are segregating in a genomic region showing association with a phenotype. We had two objectives in this study: 1) to distinguish between multiple QTL segregating in a genomic region, and 2) use of external information to prioritize candidate genes for a QTL along with the candidate variants.ResultsWe observed fixing the lead SNP as a covariate can help to distinguish additional close association signal(s). Thereafter, using the mammalian phenotype database, we successfully found candidate genes, in concordance with previous studies, demonstrating the power of this strategy. Secondly, we used variant annotation information to search for causative variants in our candidate genes. The variant information successfully identified known causal mutations and showed the potential to pinpoint the causative mutation(s) which are located in coding regions.ConclusionsOur approach can distinguish multiple QTL segregating on the same chromosome in a single analysis without manual input. Moreover, utilizing information from the mammalian phenotype database and variant effect predictor as post-GWAS analysis could benefit in candidate genes and causative mutations finding in cattle. Our study not only identified additional candidate genes for milk traits, but also can serve as a routine method for GWAS in dairy cattle.