2016
DOI: 10.1534/genetics.116.188391
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Gene and Network Analysis of Common Variants Reveals Novel Associations in Multiple Complex Diseases

Abstract: Genome-wide association (GWA) studies typically lack power to detect genotypes significantly associated with complex diseases, where different causal mutations of small effect may be present across cases. A common, tractable approach for identifying genomic elements associated with complex traits is to evaluate combinations of variants in known pathways or gene sets with shared biological function. Such gene-set analyses require the computation of gene-level P-values or gene scores; these gene scores are also … Show more

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Cited by 61 publications
(115 citation statements)
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(236 reference statements)
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“…To identify genes associated with ALL in each of the two case-control datasets and the trio analysis dataset, we performed gene-level tests of association using PEGASUS (20). Briefly, individual SNP statistics are drawn from a chi-square distribution correlated by empirical LD, and the distribution of the sum of correlated chi-square statistics within a gene is the null distribution for gene-level statistics; this distribution is then numerically integrated to calculate a gene-level p -value with machine precision (20).…”
Section: Methodsmentioning
confidence: 99%
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“…To identify genes associated with ALL in each of the two case-control datasets and the trio analysis dataset, we performed gene-level tests of association using PEGASUS (20). Briefly, individual SNP statistics are drawn from a chi-square distribution correlated by empirical LD, and the distribution of the sum of correlated chi-square statistics within a gene is the null distribution for gene-level statistics; this distribution is then numerically integrated to calculate a gene-level p -value with machine precision (20).…”
Section: Methodsmentioning
confidence: 99%
“…Briefly, individual SNP statistics are drawn from a chi-square distribution correlated by empirical LD, and the distribution of the sum of correlated chi-square statistics within a gene is the null distribution for gene-level statistics; this distribution is then numerically integrated to calculate a gene-level p -value with machine precision (20). We calculate gene-level p -values or “scores” for 19,000 genes using gene boundaries of 50,000 bp upstream and downstream of the genes to account for regulatory regions; gene start and end positions are downloaded from the UCSC Genome Browser.…”
Section: Methodsmentioning
confidence: 99%
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