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Multi‐tissue transcriptome‐wide association studies
Genetic Epidemiology ( IF 2.1 ) Pub Date : 2020-12-28 , DOI: 10.1002/gepi.22374
Nastasiya F Grinberg 1 , Chris Wallace 1, 2
Affiliation  

A transcriptome‐wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome‐wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross‐tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi‐tissue extensions of lasso regression and random forests (RF), joint lasso and RF‐MTL (multi‐task learning RF), respectively. We found that, on our chosen eQTL data set, multi‐tissue methods were generally more accurate than their single‐tissue counterparts, with RF‐MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi‐tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi‐tissue methods are competitive and, for some cell types, superior to single‐tissue approaches and hold much promise for TWAS studies.

中文翻译:

多组织转录组范围关联研究

全转录组关联研究 (TWAS) 尝试通过使用表达数量性状位点 (eQTL) 数据集将基因表达输入全基因组关联研究 (GWAS) 中,然后测试与感兴趣的性状的关联,从而识别疾病相关基因。监管过程可能在相关组织之间共享,TWAS 的一种自然延伸是利用基因表达中的跨组织相关性来提高预测准确性。在这里,我们分别研究了 lasso 回归和随机森林 (RF)、联合 lasso 和 RF-MTL(多任务学习 RF)的多组织扩展。我们发现,在我们选择的 eQTL 数据集上,多组织方法通常比单组织方法更准确,其中 RF-MTL 表现最好。模拟表明,这些好处通常会转化为识别出更多相关基因,但强调如果一个组织中存在该基因的 eQTL 信号,则关节套索可能会错误地识别另一个组织中的基因。将四种方法应用于 1 型糖尿病 GWAS,我们发现多组织方法为大多数考虑的组织找到了更多独特的相关基因。我们得出的结论是,多组织方法具有竞争力,并且对于某些细胞类型而言,优于单组织方法,并且为 TWAS 研究带来很大希望。
更新日期:2020-12-28
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