ArticleMethods in molecular biology (Clifton, N.J.)2017
A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores.
Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Association of whole-person eigen-polygenic risk scores with Alzheimer's disease.Human molecular genetics · 2024Article
- Cancer Risk Score Prediction Based on a Single-Nucleotide Polymorphism Network.Healthcare informatics research · 2022Article
- Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization.Journal of molecular neuroscience : MN · 2022Article
- Article
- Data Integration in Poplar: 'Omics Layers and Integration Strategies.Frontiers in genetics · 2019Review
- Multi-Phenotype Association Decomposition: Unraveling Complex Gene-Phenotype Relationships.Frontiers in genetics · 2019Article
- On the Influence of Structural Connectivity on the Correlation Patterns and Network Synchronization.Frontiers in computational neuroscience · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
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