背景:全基因组关联研究(GWAS)揭示了许多影响复杂性状和疾病发展风险的候选遗传变异。然而,突出显示的区域通常在非编码基因组中,发现功能性致病单核苷酸变体(SNV)是一项挑战。变体的优先级通常基于具有活性调控元件标记的基因组注释,但目前的方法仍然很难预测功能变异。为了解决这个问题,我们系统分析了6个活性调控元件标记物识别功能变异的能力.
结果:我们通过鉴定等位基因对DNA结合因子占用率的调控元件活性的测定,以分子数量性状基因座(molQTL)为基准,报告基因测定表达,和染色质可及性。我们确定了DNase足迹和发散增强子RNA(eRNA)的组合作为功能变体的标记。此签名提供了高精度,但是要权衡低召回,从而大幅减少候选变体集,以优先考虑用于功能验证的变体.我们将其作为使用DNase足迹和eRNA的称为FINDER-FunctionalSNVIdeNtification的框架提出。
结论:我们证明了使用白细胞计数性状对变异体进行优先排序的实用性,并分析变异体与前导变异体的连锁不平衡,以预测哮喘中的功能变异体。我们的发现对优先考虑GWAS的变体有影响,在预测评分算法的开发中,以及功能灵通的精细映射方法。
BACKGROUND: Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants.
RESULTS: We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER-Functional SNV IdeNtification using DNase footprints and eRNA.
CONCLUSIONS: We demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.