Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin.
Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska.
Genet Epidemiol. 2019 Oct;43(7):742-760. doi: 10.1002/gepi.22217. Epub 2019 Jul 22.
Genome-wide association studies (GWAS) have successfully identified thousands of genetic variants contributing to disease and other phenotypes. However, significant obstacles hamper our ability to elucidate causal variants, identify genes affected by causal variants, and characterize the mechanisms by which genotypes influence phenotypes. The increasing availability of genome-wide functional annotation data is providing unique opportunities to incorporate prior information into the analysis of GWAS to better understand the impact of variants on disease etiology. Although there have been many advances in incorporating prior information into prioritization of trait-associated variants in GWAS, functional annotation data have played a secondary role in the joint analysis of GWAS and molecular (i.e., expression) quantitative trait loci (eQTL) data in assessing evidence for association. To address this, we develop a novel mediation framework, iFunMed, to integrate GWAS and eQTL data with the utilization of publicly available functional annotation data. iFunMed extends the scope of standard mediation analysis by incorporating information from multiple genetic variants at a time and leveraging variant-level summary statistics. Data-driven computational experiments convey how informative annotations improve single-nucleotide polymorphism (SNP) selection performance while emphasizing robustness of iFunMed to noninformative annotations. Application to Framingham Heart Study data indicates that iFunMed is able to boost detection of SNPs with mediation effects that can be attributed to regulatory mechanisms.
全基因组关联研究 (GWAS) 已经成功鉴定了数千种与疾病和其他表型相关的遗传变异。然而，重大障碍阻碍了我们阐明因果变异、识别受因果变异影响的基因以及描述基因型影响表型的机制的能力。全基因组功能注释数据的日益普及为将先验信息纳入 GWAS 分析提供了独特的机会，以更好地理解变异对疾病病因的影响。尽管在将先验信息纳入 GWAS 中与性状相关变异的优先级排序方面已经取得了许多进展，但在联合分析 GWAS 和分子（即表达）数量性状基因座 (eQTL) 数据以评估关联证据方面，功能注释数据仅发挥了次要作用。为了解决这个问题，我们开发了一种新颖的中介框架 iFunMed，用于整合 GWAS 和 eQTL 数据，并利用公共可用的功能注释数据。iFunMed 通过一次整合多个遗传变异的信息并利用变异水平汇总统计数据扩展了标准中介分析的范围。数据驱动的计算实验传达了信息丰富的注释如何提高单核苷酸多态性 (SNP) 选择性能，同时强调了 iFunMed 对非信息注释的稳健性。应用于弗雷明汉心脏研究数据表明，iFunMed 能够增强对可以归因于调节机制的具有中介效应的 SNPs 的检测。
