Levine Morgan E, Langfelder Peter, Horvath Steve
Department of Human Genetics, University of California, Box 708822, 695 Charles E. Young Drive South, Los Angeles, CA, 90095, USA.
Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, 90095, USA.
Methods Mol Biol. 2017;1613:277-290. doi: 10.1007/978-1-4939-7027-8_10.
Polygenic scores are useful for examining the joint associations of genetic markers. However, because traditional methods involve summing weighted allele counts, they may fail to capture the complex nature of biology. Here we describe a network-based method, which we call weighted SNP correlation network analysis (WSCNA), and demonstrate how it could be used to generate meaningful polygenic scores. Using data on human height in a US population of non-Hispanic whites, we illustrate how this method can be used to identify SNP networks from GWAS data, create network-specific polygenic scores, examine network topology to identify hub SNPs, and gain biological insights into complex traits. In our example, we show that this method explains a larger proportion of the variance in human height than traditional polygenic score methods. We also identify hub genes and pathways that have previously been identified as influencing human height. In moving forward, this method may be useful for generating genetic susceptibility measures for other health related traits, examining genetic pleiotropy, identifying at-risk individuals, examining gene score by environmental effects, and gaining a deeper understanding of the underlying biology of complex traits.
多基因评分对于检验遗传标记的联合关联很有用。然而，由于传统方法涉及对加权等位基因计数进行求和，它们可能无法捕捉生物学的复杂本质。在此，我们描述一种基于网络的方法，我们称之为加权单核苷酸多态性关联网络分析（WSCNA），并展示如何使用它来生成有意义的多基因评分。利用美国非西班牙裔白人人群中人类身高的数据，我们说明了该方法如何用于从全基因组关联研究（GWAS）数据中识别单核苷酸多态性（SNP）网络、创建特定于网络的多基因评分、检查网络拓扑结构以识别枢纽SNP，并获得对复杂性状的生物学见解。在我们的示例中，我们表明该方法比传统的多基因评分方法能解释人类身高变异中更大的比例。我们还识别出先前已被确定影响人类身高的枢纽基因和通路。展望未来，该方法可能有助于为其他与健康相关的性状生成遗传易感性测量指标、检验基因多效性、识别高危个体、按环境效应检验基因评分，以及更深入地理解复杂性状的潜在生物学机制。
