Analysis of genome-wide association study data using the protein knowledge base

BMC Genetics

Methodology Article

Melanie Bahlo¹  Martin Oti²  Bruno Gaeta³  Merridee A Wouters⁴  Diane Fatkin⁵  Jason Y Liu⁶  Sara Ballouz⁷ 

[1] Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 3052, Parkville, VIC, Australia;Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands;School of Computer Science and Engineering, University of New South Wales, 2052, Kensington, NSW, Australia;School of Life and Environmental Sciences, Deakin University, 3217, Geelong, VIC, Australia;School of Medical Sciences, University of New South Wales, 2052, Kensington, NSW, Australia;Molecular Cardiology and Biophysics Division, Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia;Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia;Structural and Computational Biology Division, Victor Chang Cardiac Research Institute, 2010, Darlinghurst, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, 2052, Kensington, NSW, Australia;

关键词: Disease Gene;    Near Neighbour;    Significant SNPs;    Enrichment Ratio;    Gene Selection Method;   

DOI  :  10.1186/1471-2156-12-98

 received in 2011-03-30, accepted in 2011-11-13,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundGenome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.ResultsHere we developed and benchmarked several protocols for GWAS data analysis using different in-silico gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system Gentrepid to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without a priori knowledge, by exhaustive comparison of genes in distinct loci. Because Gentrepid uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.ConclusionsResults suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.

【 授权许可】

Unknown   
© Ballouz et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.