Keel Brittney N, Snelling Warren M, Lindholm-Perry Amanda K, Oliver William T, Kuehn Larry A, Rohrer Gary A
USDA, ARS, U.S. Meat Animal Research Center, Clay Center, NE, United States.
Front Genet. 2020 Jan 21;10:1339. doi: 10.3389/fgene.2019.01339. eCollection 2019.
The "large small " problem has posed a significant challenge in the analysis and interpretation of genome-wide association studies (GWAS). The use of prior information to rank genomic regions and perform SNP selection could increase the power of GWAS. In this study, we propose the use of gene expression data from RNA-Seq of multiple tissues as prior information to assign weights to SNP, select SNP based on a weight threshold, and utilize weighted hypothesis testing to conduct a GWAS. RNA-Seq libraries from hypothalamus, duodenum, ileum, and jejunum tissue of 30 pigs with divergent feed efficiency phenotypes were sequenced, and a three-way gene x individual x tissue clustering analysis was performed, using constrained tensor decomposition, to obtain a total of 10 gene expression modules. Loading values from each gene module were used to assign weights to 49,691 commercial SNP markers, and SNP were selected using a weight threshold, resulting in 10 SNP sets ranging in size from 101 to 955 markers. Weighted GWAS for feed intake in 4,200 pigs was performed separately for each of the 10 SNP sets. A total of 36 unique significant SNP associations were identified across the ten gene modules (SNP sets). For comparison, a standard unweighted GWAS using all 49,691 SNP was performed, and only 2 SNP were significant. None of the SNP from the unweighted analysis resided in known QTL related to swine feed efficiency (feed intake, average daily gain, and feed conversion ratio) compared to 29 (80.6%) in the weighted analyses, with 9 SNP residing in feed intake QTL. These results suggest that the heritability of feed intake is driven by many SNP that individually do not attain genome-wide significance in GWAS. Hence, the proposed procedure for prioritizing SNP based on gene expression data across multiple tissues provides a promising approach for improving the power of GWAS.
“大与小”问题在全基因组关联研究（GWAS）的分析与解读中构成了重大挑战。利用先验信息对基因组区域进行排序并进行单核苷酸多态性（SNP）选择，可提高GWAS的效能。在本研究中，我们提议使用来自多个组织的RNA测序（RNA-Seq）基因表达数据作为先验信息，为SNP赋予权重，基于权重阈值选择SNP，并利用加权假设检验进行GWAS。对30头具有不同饲料效率表型的猪的下丘脑、十二指肠、回肠和空肠组织的RNA-Seq文库进行测序，并使用约束张量分解进行三向基因×个体×组织聚类分析，共获得10个基因表达模块。每个基因模块的加载值用于为49,691个商业SNP标记赋予权重，并使用权重阈值选择SNP，得到10个SNP集，大小从101到955个标记不等。对4200头猪的采食量进行加权GWAS，对10个SNP集中的每一个分别进行。在十个基因模块（SNP集）中总共鉴定出36个独特的显著SNP关联。为作比较，使用所有49,691个SNP进行了标准的非加权GWAS，仅有2个SNP显著。与加权分析中的29个（80.6%）相比，非加权分析中的SNP均不在与猪饲料效率（采食量、平均日增重和饲料转化率）相关的已知数量性状位点（QTL）中，其中9个SNP位于采食量QTL中。这些结果表明，采食量的遗传力由许多在GWAS中个体未达到全基因组显著性的SNP驱动。因此，所提出的基于多个组织的基因表达数据对SNP进行优先级排序的程序为提高GWAS的效能提供了一种有前景的方法。
