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

Jia, Peilin, Wang, Lily, Fanous, Ayman H., Chen, Xiangning, Kendler, Kenneth S., Zhao, Zhongming, Morris, Derek W., O'Dushlaine, Colm T., Kenny, Elaine, Quinn, Emma M., Gill, Michael, Corvin, Aiden, O'Donovan, Michael C., Kirov, George K., Craddock, Nick J., Holmans, Peter A., Williams, Nigel M., Georgieva, Lucy, Nikolov, Ivan, Norton, N., Williams, H., Toncheva, Draga, Milanova, Vihra, Owen, Michael J., Hultman, Christina M., Lichtenstein, Paul, Thelander, Emma F., Sullivan, Patrick, McQuillin, Andrew, Choudhury, Khalid, Datta, Susmita, Pimm, Jonathan, Thirumalai, Srinivasa, Puri, Vinay, Krasucki, Robert, Lawrence, Jacob, Quested, Digby, Bass, Nicholas, Gurling, Hugh, Crombie, Caroline, Fraser, Gillian, Kuan, Soh Leh, Walker, Nicholas, St Clair, David, Blackwood, Douglas H. R., Muir, Walter J., McGhee, Kevin A., Pickard, Ben, Malloy, Pat, Maclean, Alan W., Van Beck, Margaret, Wray, Naomi R., Visscher, Peter M., Macgregor, Stuart, Pato, Michele T., Medeiros, Helena, Middleton, Frank, Carvalho, Celia, Morley, Christopher, Fanous, Ayman, Conti, David, Knowles, James A., Paz Ferreira, Carlos, Macedo, Antonio, Helena Azevedo, M., Pato, Carlos N., Stone, Jennifer L., Ruderfer, Douglas M., Ferreira, Manuel A. R., Purcell, Shaun M., Stone, Jennifer L., Chambert, Kimberly, Ruderfer, Douglas M., Kuruvilla, Finny, Gabriel, Stacey B., Ardlie, Kristin, Daly, Mark J., Scolnick, Edward M. and Sklar, Pamela (2012) A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia. Journal of Medical Genetics, 49 2: 96-103. doi:10.1136/jmedgenet-2011-100397


Author Jia, Peilin
Wang, Lily
Fanous, Ayman H.
Chen, Xiangning
Kendler, Kenneth S.
Zhao, Zhongming
Morris, Derek W.
O'Dushlaine, Colm T.
Kenny, Elaine
Quinn, Emma M.
Gill, Michael
Corvin, Aiden
O'Donovan, Michael C.
Kirov, George K.
Craddock, Nick J.
Holmans, Peter A.
Williams, Nigel M.
Georgieva, Lucy
Nikolov, Ivan
Norton, N.
Williams, H.
Toncheva, Draga
Milanova, Vihra
Owen, Michael J.
Hultman, Christina M.
Lichtenstein, Paul
Thelander, Emma F.
Sullivan, Patrick
McQuillin, Andrew
Choudhury, Khalid
Datta, Susmita
Pimm, Jonathan
Thirumalai, Srinivasa
Puri, Vinay
Krasucki, Robert
Lawrence, Jacob
Quested, Digby
Bass, Nicholas
Gurling, Hugh
Crombie, Caroline
Fraser, Gillian
Kuan, Soh Leh
Walker, Nicholas
St Clair, David
Blackwood, Douglas H. R.
Muir, Walter J.
McGhee, Kevin A.
Pickard, Ben
Malloy, Pat
Maclean, Alan W.
Van Beck, Margaret
Wray, Naomi R.
Visscher, Peter M.
Macgregor, Stuart
Pato, Michele T.
Medeiros, Helena
Middleton, Frank
Carvalho, Celia
Morley, Christopher
Fanous, Ayman
Conti, David
Knowles, James A.
Paz Ferreira, Carlos
Macedo, Antonio
Helena Azevedo, M.
Pato, Carlos N.
Stone, Jennifer L.
Ruderfer, Douglas M.
Ferreira, Manuel A. R.
Purcell, Shaun M.
Stone, Jennifer L.
Chambert, Kimberly
Ruderfer, Douglas M.
Kuruvilla, Finny
Gabriel, Stacey B.
Ardlie, Kristin
Daly, Mark J.
Scolnick, Edward M.
Sklar, Pamela
Title A bias-reducing pathway enrichment analysis of genome-wide association data confirmed association of the MHC region with schizophrenia
Journal name Journal of Medical Genetics   Check publisher's open access policy
ISSN 1468-6244
0022-2593
Publication date 2012-02-01
Sub-type Article (original research)
DOI 10.1136/jmedgenet-2011-100397
Open Access Status Not yet assessed
Volume 49
Issue 2
Start page 96
End page 103
Total pages 8
Place of publication London, United Kingdom
Publisher BMJ Publishing Group
Language eng
Formatted 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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