Network-based model weighting to detect multiple loci influencing complex diseases.

Human Genetics
Wei Pan

Abstract

For genome-wide association studies, it has been increasingly recognized that the popular locus-by-locus search for DNA variants associated with disease susceptibility may not be effective, especially when there are interactions between or among multiple loci, for which a multi-loci search strategy may be more productive. However, even if computationally feasible, a genome-wide search over all possible multiple loci requires exploring a huge model space and making costly adjustment for multiple testing, leading to reduced statistical power. On the other hand, there are accumulating data suggesting that protein products of many disease-causing genes tend to interact with each other, or cluster in the same biological pathway. To incorporate this prior knowledge and existing data on gene networks, we propose a gene network-based method to improve statistical power over that of the exhaustive search by giving higher weights to models involving genes nearby in a network. We use simulated data under realistic scenarios, including a large-scale human protein-protein interaction network and 23 known ataxia-causing genes, to demonstrate potential gain by our proposed method when disease-genes are clustered in a network.

References

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Citations

Jan 8, 2010·Bioinformatics·Jason H MooreScott M Williams
Feb 27, 2010·Bioinformatics·Saket Navlakha, Carl Kingsford
Dec 10, 2013·Current Genetic Medicine Reports·Yu Liu, Mark R Chance
Dec 17, 2008·Medical Hypotheses·Kristen F Gorman, Felix Breden
Jan 17, 2012·Wiley Interdisciplinary Reviews. Systems Biology and Medicine·Celine LefebvreAndrea Califano
Nov 19, 2010·Nature Reviews. Genetics·Kai WangHakon Hakonarson
Aug 3, 2016·Medical Sciences : Open Access Journal·Alireza TorabiChun Xu
Aug 9, 2017·Computational and Mathematical Methods in Medicine·Stefanie FriedrichsBenjamin Hofner

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