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A Novel Statistic for Genome-Wide Interaction Analysis

Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in ge...

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Autores principales: Wu, Xuesen, Dong, Hua, Luo, Li, Zhu, Yun, Peng, Gang, Reveille, John D., Xiong, Momiao
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944798/
https://www.ncbi.nlm.nih.gov/pubmed/20885795
http://dx.doi.org/10.1371/journal.pgen.1001131
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author Wu, Xuesen
Dong, Hua
Luo, Li
Zhu, Yun
Peng, Gang
Reveille, John D.
Xiong, Momiao
author_facet Wu, Xuesen
Dong, Hua
Luo, Li
Zhu, Yun
Peng, Gang
Reveille, John D.
Xiong, Momiao
author_sort Wu, Xuesen
collection PubMed
description Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were seen in two independent studies of psoriasis. These included five interacting pairs of SNPs in genes LST1/NCR3, CXCR5/BCL9L, and GLS2, some of which were located in the target sites of miR-324-3p, miR-433, and miR-382, as well as 15 pairs of interacting SNPs that had nonsynonymous substitutions. Our results demonstrated that genome-wide interaction analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies.
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spelling pubmed-29447982010-09-30 A Novel Statistic for Genome-Wide Interaction Analysis Wu, Xuesen Dong, Hua Luo, Li Zhu, Yun Peng, Gang Reveille, John D. Xiong, Momiao PLoS Genet Research Article Although great progress in genome-wide association studies (GWAS) has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked). The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001<FDR<0.003, respectively, which were seen in two independent studies of psoriasis. These included five interacting pairs of SNPs in genes LST1/NCR3, CXCR5/BCL9L, and GLS2, some of which were located in the target sites of miR-324-3p, miR-433, and miR-382, as well as 15 pairs of interacting SNPs that had nonsynonymous substitutions. Our results demonstrated that genome-wide interaction analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies. Public Library of Science 2010-09-23 /pmc/articles/PMC2944798/ /pubmed/20885795 http://dx.doi.org/10.1371/journal.pgen.1001131 Text en Wu et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Wu, Xuesen
Dong, Hua
Luo, Li
Zhu, Yun
Peng, Gang
Reveille, John D.
Xiong, Momiao
A Novel Statistic for Genome-Wide Interaction Analysis
title A Novel Statistic for Genome-Wide Interaction Analysis
title_full A Novel Statistic for Genome-Wide Interaction Analysis
title_fullStr A Novel Statistic for Genome-Wide Interaction Analysis
title_full_unstemmed A Novel Statistic for Genome-Wide Interaction Analysis
title_short A Novel Statistic for Genome-Wide Interaction Analysis
title_sort novel statistic for genome-wide interaction analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2944798/
https://www.ncbi.nlm.nih.gov/pubmed/20885795
http://dx.doi.org/10.1371/journal.pgen.1001131
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