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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McCarthy MI et al (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9(5):356–369
Risch NJ (2000) Searching for genetic determinants in the new millennium. Nature 405(6788):847–856
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):9362–9367
Manolio TA et al (2009) Finding the missing heritability of complex diseases. Nature 461(7265):747–753
Hardy J, Singleton A (2009) Genomewide association studies and human disease. N Engl J Med 360(17):1759–1768
Dudbridge F (2013) Power and predictive accuracy of polygenic risk scores. PLoS Genet 9(3):e1003348
Wray NR, Goddard ME, Visscher PM (2007) Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res 17(10):1520–1528
Levine ME, Crimmins EM (2015) A genetic network associated with stress resistance, longevity, and cancer in humans. J Gerontol A Biol Sci Med Sci 71(6):703–712
Peterson RE et al (2011) Genetic risk sum score comprised of common polygenic variation is associated with body mass index. Hum Genet 129(2):221–230
Purcell SM et al (2014) A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506(7487):185–190
Wray NR, Goddard ME, Visscher PM (2008) Prediction of individual genetic risk of complex disease. Curr Opin Genet Dev 18(3):257–263
Eichler EE et al (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11(6):446–450
Cordell HJ (2009) Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10(6):392–404
Hemani G, Knott S, Haley C (2013) An evolutionary perspective on epistasis and the missing heritability. PLoS Genet. 9(2):e1003295
Ghazalpour A et al (2006) Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2(8):e130
Horvath S et al (2006) Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci U S A 103(46):17402–17407
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):435–447
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):17973–17978
Oldham MC, Langfelder P, Horvath S (2012) Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Syst Biol 6:63
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 4:Article 17
Patterson N, Price AL, Reich D (2006) Population structure and eigenanalysis. PLoS Genet. 2(12):e190
Langfelder P, Zhang B, Horvath S (2008) Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 24(5):719–720
Visscher PM (2008) Sizing up human height variation. Nat Genet 40(5):489–490
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):5193–5201
Lango Allen H et al (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467(7317):832–838
Liu JZ et al (2010) Genome-wide association study of height and body mass index in Australian twin families. Twin Res Hum Genet 13(2):179–193
Wood AR et al (2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46(11):1173–1186
Song L, Langfelder P, Horvath S (2012) Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 13:328
Horvath S, Dong J (2008) Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol. 4(8):e1000117
Langfelder P, Mischel PS, Horvath S (2013) When is hub gene selection better than standard meta-analysis? PLoS One 8(4):e61505
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Levine, M.E., Langfelder, P., Horvath, S. (2017). A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores. In: Tatarinova, T., Nikolsky, Y. (eds) Biological Networks and Pathway Analysis. Methods in Molecular Biology, vol 1613. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7027-8_10
Download citation
DOI: https://doi.org/10.1007/978-1-4939-7027-8_10
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7025-4
Online ISBN: 978-1-4939-7027-8
eBook Packages: Springer Protocols