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A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores

  • Morgan E. LevineEmail author
  • Peter Langfelder
  • Steve Horvath
Part of the Methods in Molecular Biology book series (MIMB, volume 1613)

Abstract

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.

Key words

Polygenic score Weighted network GWAS Height 

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  • Morgan E. Levine
    • 1
    • 2
    Email author
  • Peter Langfelder
    • 2
  • Steve Horvath
    • 1
    • 3
  1. 1.Department of Human GeneticsUniversity of CaliforniaLos AngelesUSA
  2. 2.Semel Institute for Neuroscience and Human BehaviorUniversity of CaliforniaLos AngelesUSA
  3. 3.Department of BiostatisticsUniversity of CaliforniaLos AngelesUSA

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