单核苷酸多态性
生命银行
全基因组关联研究
蛋白质组
生物
遗传关联
蛋白质组学
计算生物学
表型
数量性状位点
遗传学
基因
生物信息学
基因型
作者
B Chen,Chanhwa Lee,Amanda L. Tapia,Alexander P. Reiner,Hua Tang,Charles Kooperberg,JoAnn E. Manson,Yun Li,Laura M. Raffield
摘要
Abstract In most Proteome‐Wide Association Studies (PWAS), variants near the protein‐coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein‐level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r 2 between measured and predicted protein levels using this proposed approach, to the testing r 2 using only cis SNPs. The two methods usually resulted in similar testing r 2 , but some proteins showed a significant increase in testing r 2 with our method. For example, for cartilage acidic protein 1, the testing r 2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.
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