A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia

The International Schizophrenia Consortium (ISC)

Research output: Contribution to journalArticle

56 Citations (Scopus)

Abstract

Background: After the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases. Methods: A novel approach was proposed, namely generalised additive model (GAM) for GWAS (gamGWAS), for gene set enrichment analysis of GWAS data, specifically adjusting the gene length bias or the number of single-nucleotide polymorphisms per gene. GAM is applied to estimate the probability of a gene to be selected as significant given its gene length, followed by weighted resampling and computation of empirical p values for the rank of pathways. We demonstrated gamGWAS in two schizophrenia GWAS datasets from the International Schizophrenia Consortium and the Genetic Association Information Network. Results: The gamGWAS results not only confirmed previous findings, but also highlighted several immune related pathways. Comparison with other methods indicated that gamGWAS could effectively reduce the correlation between pathway p values and its median gene length. Conclusion gamGWAS can effectively relieve the long gene bias and generate reliable results for GWAS data analysis. It does not require genotype data or permutation of sample labels in the original GWAS data; thus, it is computationally efficient.

Original languageEnglish
Pages (from-to)96-103
Number of pages8
JournalJournal of Medical Genetics
Volume49
Issue number2
Early online date20 Dec 2011
DOIs
Publication statusPublished - 1 Feb 2012

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Genome-Wide Association Study
Schizophrenia
Genes
Information Services
Single Nucleotide Polymorphism
Psychiatry
Genotype

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Cite this

A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia. / The International Schizophrenia Consortium (ISC).

In: Journal of Medical Genetics, Vol. 49, No. 2, 01.02.2012, p. 96-103.

Research output: Contribution to journalArticle

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title = "A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia",
abstract = "Background: After the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases. Methods: A novel approach was proposed, namely generalised additive model (GAM) for GWAS (gamGWAS), for gene set enrichment analysis of GWAS data, specifically adjusting the gene length bias or the number of single-nucleotide polymorphisms per gene. GAM is applied to estimate the probability of a gene to be selected as significant given its gene length, followed by weighted resampling and computation of empirical p values for the rank of pathways. We demonstrated gamGWAS in two schizophrenia GWAS datasets from the International Schizophrenia Consortium and the Genetic Association Information Network. Results: The gamGWAS results not only confirmed previous findings, but also highlighted several immune related pathways. Comparison with other methods indicated that gamGWAS could effectively reduce the correlation between pathway p values and its median gene length. Conclusion gamGWAS can effectively relieve the long gene bias and generate reliable results for GWAS data analysis. It does not require genotype data or permutation of sample labels in the original GWAS data; thus, it is computationally efficient.",
author = "{The International Schizophrenia Consortium (ISC)} and Peilin Jia and Lily Wang and Fanous, {Ayman H.} and Xiangning Chen and Kendler, {Kenneth S.} and Zhongming Zhao and Morris, {Derek W.} and O'Dushlaine, {Colm T.} and Elaine Kenny and Quinn, {Emma M.} and Michael Gill and Aiden Corvin and O'Donovan, {Michael C.} and Kirov, {George K.} and Craddock, {Nick J.} and Holmans, {Peter A.} and Williams, {Nigel M.} and Lucy Georgieva and Ivan Nikolov and N. Norton and H. Williams and Draga Toncheva and Vihra Milanova and Owen, {Michael J.} and Hultman, {Christina M.} and Paul Lichtenstein and Thelander, {Emma F.} and Patrick Sullivan and Andrew McQuillin and Khalid Choudhury and Susmita Datta and Jonathan Pimm and Srinivasa Thirumalai and Vinay Puri and Robert Krasucki and Jacob Lawrence and Digby Quested and Nicholas Bass and Hugh Gurling and Caroline Crombie and Gillian Fraser and Kuan, {Soh Leh} and Nicholas Walker and {St Clair}, David and Blackwood, {Douglas H.R.} and Muir, {Walter J.} and McGhee, {Kevin A.} and Ben Pickard and Pat Malloy and Maclean, {Alan W.}",
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AU - The International Schizophrenia Consortium (ISC)

AU - Jia, Peilin

AU - Wang, Lily

AU - Fanous, Ayman H.

AU - Chen, Xiangning

AU - Kendler, Kenneth S.

AU - Zhao, Zhongming

AU - Morris, Derek W.

AU - O'Dushlaine, Colm T.

AU - Kenny, Elaine

AU - Quinn, Emma M.

AU - Gill, Michael

AU - Corvin, Aiden

AU - O'Donovan, Michael C.

AU - Kirov, George K.

AU - Craddock, Nick J.

AU - Holmans, Peter A.

AU - Williams, Nigel M.

AU - Georgieva, Lucy

AU - Nikolov, Ivan

AU - Norton, N.

AU - Williams, H.

AU - Toncheva, Draga

AU - Milanova, Vihra

AU - Owen, Michael J.

AU - Hultman, Christina M.

AU - Lichtenstein, Paul

AU - Thelander, Emma F.

AU - Sullivan, Patrick

AU - McQuillin, Andrew

AU - Choudhury, Khalid

AU - Datta, Susmita

AU - Pimm, Jonathan

AU - Thirumalai, Srinivasa

AU - Puri, Vinay

AU - Krasucki, Robert

AU - Lawrence, Jacob

AU - Quested, Digby

AU - Bass, Nicholas

AU - Gurling, Hugh

AU - Crombie, Caroline

AU - Fraser, Gillian

AU - Kuan, Soh Leh

AU - Walker, Nicholas

AU - St Clair, David

AU - Blackwood, Douglas H.R.

AU - Muir, Walter J.

AU - McGhee, Kevin A.

AU - Pickard, Ben

AU - Malloy, Pat

AU - Maclean, Alan W.

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N2 - Background: After the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases. Methods: A novel approach was proposed, namely generalised additive model (GAM) for GWAS (gamGWAS), for gene set enrichment analysis of GWAS data, specifically adjusting the gene length bias or the number of single-nucleotide polymorphisms per gene. GAM is applied to estimate the probability of a gene to be selected as significant given its gene length, followed by weighted resampling and computation of empirical p values for the rank of pathways. We demonstrated gamGWAS in two schizophrenia GWAS datasets from the International Schizophrenia Consortium and the Genetic Association Information Network. Results: The gamGWAS results not only confirmed previous findings, but also highlighted several immune related pathways. Comparison with other methods indicated that gamGWAS could effectively reduce the correlation between pathway p values and its median gene length. Conclusion gamGWAS can effectively relieve the long gene bias and generate reliable results for GWAS data analysis. It does not require genotype data or permutation of sample labels in the original GWAS data; thus, it is computationally efficient.

AB - Background: After the recent successes of genome-wide association studies (GWAS), one key challenge is to identify genetic variants that might have a significant joint effect on complex diseases but have failed to be identified individually due to weak to moderate marginal effect. One popular and effective approach is gene set based analysis, which investigates the joint effect of multiple functionally related genes (eg, pathways). However, a typical gene set analysis method is biased towards long genes, a problem that is especially severe in psychiatric diseases. Methods: A novel approach was proposed, namely generalised additive model (GAM) for GWAS (gamGWAS), for gene set enrichment analysis of GWAS data, specifically adjusting the gene length bias or the number of single-nucleotide polymorphisms per gene. GAM is applied to estimate the probability of a gene to be selected as significant given its gene length, followed by weighted resampling and computation of empirical p values for the rank of pathways. We demonstrated gamGWAS in two schizophrenia GWAS datasets from the International Schizophrenia Consortium and the Genetic Association Information Network. Results: The gamGWAS results not only confirmed previous findings, but also highlighted several immune related pathways. Comparison with other methods indicated that gamGWAS could effectively reduce the correlation between pathway p values and its median gene length. Conclusion gamGWAS can effectively relieve the long gene bias and generate reliable results for GWAS data analysis. It does not require genotype data or permutation of sample labels in the original GWAS data; thus, it is computationally efficient.

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