title: The uniform-score gene set analysis for identifying common pathways associated with different diabetes traits.
abstract: BACKGROUND
  Genetic heritability and expression study have shown that different diabetes traits have common genetic components and pathways. A computationally efficient pathway analysis of GWAS results will benefit post-GWAS study of SNP associations and identification of common genetic pathways from diabetes GWAS can help to improve understanding of the disease pathogenesis.
  
  
  RESULTS
  We proposed a uniform-score gene-set analysis (USGSA) with implemented package to unify different gene measures by a uniform score for identifying pathways from GWAS data, and use a pre-generated permutation distribution table to quickly obtain multiple-testing adjusted p-value. Simulation studies of uniform score for four gene measures (minP, 2ndP, simP and fishP) have shown that USGSA has strictly controlled family-wise error rate. The power depends on types of gene measure. USGSA with a two-stage study strategy was applied to identify common pathways associated with diabetes traits based on public dbGaP GWAS results. The study identified 7 gene sets that contain binding motifs at promoter region of component genes for 5 transcription factors (TFs) of FOXO4, TCF3, NFAT, VSX1 and POU2F1, and 1 microRNA of mir-218. These gene sets include 25 common genes that are among top 5% of the gene associations over genome for all GWAS. Previous evidences showed that nearly all of these genes are mainly expressed in the brain.
  
  
  CONCLUSIONS
  USGSA is a computationally efficient approach for pathway analysis of GWAS data with promoted interpretability and comparability. The pathway analysis suggested that different diabetes traits share common pathways and component genes are potentially regulated by common TFs and microRNA. The result also indicated that the central nervous system has a critical role in diabetes pathogenesis. The findings will be important in formulating novel hypotheses for guiding follow-up studies.
authors.affiliation: Center of Biostatistics & Bioinformatics, University of Mississippi Medical Center, Jackson, MS, USA. hmei@umc.edu.|Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. hmei@umc.edu.
authors.forename: Hao
authors.initials: H
authors.lastname: Mei
authors.affiliation: Department of Biology, Tougaloo College, Jackson, MS, USA. lli@tougaloo.edu.
authors.forename: Lianna
authors.initials: L
authors.lastname: Li
authors.affiliation: Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. arrow64@163.com.
authors.forename: Shijian
authors.initials: S
authors.lastname: Liu
authors.affiliation: Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. riversailer@hotmail.com.
authors.forename: Fan
authors.initials: F
authors.lastname: Jiang
authors.affiliation: Center of Biostatistics & Bioinformatics, University of Mississippi Medical Center, Jackson, MS, USA. mgriswold@umc.edu.
authors.forename: Michael
authors.initials: M
authors.lastname: Griswold
authors.affiliation: Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA. tmosley@umc.edu.
authors.forename: Thomas
authors.initials: T
authors.lastname: Mosley
mesh_terms: D001483:Base Sequence; D030541:Databases, Genetic; D003920:Diabetes Mellitus / Q000235:genetics* / Q000473:pathology; D015894:Genome, Human; D055106:Genome-Wide Association Study; D006801:Humans; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism; D020641:Polymorphism, Single Nucleotide; D011401:Promoter Regions, Genetic; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism
chemical_list: C547471:MIRN218 microRNA, human; D035683:MicroRNAs; D014157:Transcription Factors