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.
Polygenic score Weighted network GWAS Height
This is a preview of subscription content, log in to check access.
Springer Nature is developing a new tool to find and evaluate Protocols. Learn more
McCarthy MI et al (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9(5):356–369CrossRefGoogle Scholar
Hindorff LA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106(23):9362–9367CrossRefPubMedPubMedCentralGoogle Scholar
Langfelder P et al (2012) A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models. Biochim Biophys Acta 1821(3):435–447CrossRefPubMedGoogle Scholar
Oldham MC, Horvath S, Geschwind DH (2006) Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A 103(47):17973–17978CrossRefPubMedPubMedCentralGoogle Scholar
Oldham MC, Langfelder P, Horvath S (2012) Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Syst Biol 6:63CrossRefPubMedPubMedCentralGoogle Scholar
Lui JC et al (2012) Synthesizing genome-wide association studies and expression microarray reveals novel genes that act in the human growth plate to modulate height. Hum Mol Genet 21(23):5193–5201CrossRefPubMedPubMedCentralGoogle Scholar