TY - JOUR
T1 - A multi-dimensional evidence-based candidate gene prioritization approach for complex diseases-schizophrenia as a case
AU - Sun, Jingchun
AU - Jia, Peilin
AU - Fanous, Ayman H.
AU - Webb, Bradley T.
AU - van den Oord, Edwin J.C.G.
AU - Chen, Xiangning
AU - Bukszar, Jozsef
AU - Kendler, Kenneth S.
AU - Zhao, Zhongming
N1 - Funding Information:
Funding: NIH grants (AA017437 and LM009598), NARSAD Young Investigator Award, and Thomas F. and Kate Miller Jeffress Memorial Trust grant (to Z.Z.); a grant from the Department of Veterans Affairs Merit Review program (to A.F.).
PY - 2009
Y1 - 2009
N2 - Motivation: During the past decade, we have seen an exponential growth of vast amounts of genetic data generated for complex disease studies. Currently, across a variety of complex biological problems, there is a strong trend towards the integration of data from multiple sources. So far, candidate gene prioritization approaches have been designed for specific purposes, by utilizing only some of the available sources of genetic studies, or by using a simple weight scheme. Specifically to psychiatric disorders, there has been no prioritization approach that fully utilizes all major sources of experimental data. Results: Here we present a multi-dimensional evidence-based candidate gene prioritization approach for complex diseases and demonstrate it in schizophrenia. In this approach, we first collect and curate genetic studies for schizophrenia from four major categories: association studies, linkage analyses, gene expression and literature search. Genes in these data sets are initially scored by category-specific scoring methods. Then, an optimal weight matrix is searched by a two-step procedure (core genes and unbiased P-values in independent genome-wide association studies). Finally, genes are prioritized by their combined scores using the optimal weight matrix. Our evaluation suggests this approach generates prioritized candidate genes that are promising for further analysis or replication. The approach can be applied to other complex diseases.
AB - Motivation: During the past decade, we have seen an exponential growth of vast amounts of genetic data generated for complex disease studies. Currently, across a variety of complex biological problems, there is a strong trend towards the integration of data from multiple sources. So far, candidate gene prioritization approaches have been designed for specific purposes, by utilizing only some of the available sources of genetic studies, or by using a simple weight scheme. Specifically to psychiatric disorders, there has been no prioritization approach that fully utilizes all major sources of experimental data. Results: Here we present a multi-dimensional evidence-based candidate gene prioritization approach for complex diseases and demonstrate it in schizophrenia. In this approach, we first collect and curate genetic studies for schizophrenia from four major categories: association studies, linkage analyses, gene expression and literature search. Genes in these data sets are initially scored by category-specific scoring methods. Then, an optimal weight matrix is searched by a two-step procedure (core genes and unbiased P-values in independent genome-wide association studies). Finally, genes are prioritized by their combined scores using the optimal weight matrix. Our evaluation suggests this approach generates prioritized candidate genes that are promising for further analysis or replication. The approach can be applied to other complex diseases.
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U2 - 10.1093/bioinformatics/btp428
DO - 10.1093/bioinformatics/btp428
M3 - Article
C2 - 19602527
AN - SCOPUS:70349884348
SN - 1367-4803
VL - 25
SP - 2595
EP - 2602
JO - Bioinformatics
JF - Bioinformatics
IS - 19
ER -