BACKGROUND: Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts. METHODS: Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (µStat) are not confounded by unrealistic assumptions (e.g., linearity, independence). RESULTS: By incorporating knowledge about relationships between SNPs, µGWAS (GWAS based on µStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes. CONCLUSION: With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development.
BACKGROUND: Genome-wide association studies (GWAS) have had limited success when applied to complex diseases. Analyzing SNPs individually requires several large studies to integrate the often divergent results. In the presence of epistasis, multivariate approaches based on the linear model (including stepwise logistic regression) often have low sensitivity and generate an abundance of artifacts. METHODS: Recent advances in distributed and parallel processing spurred methodological advances in nonparametric statistics. U-statistics for structured multivariate data (µStat) are not confounded by unrealistic assumptions (e.g., linearity, independence). RESULTS: By incorporating knowledge about relationships between SNPs, µGWAS (GWAS based on µStat) can identify clusters of genes around biologically relevant pathways and pinpoint functionally relevant regions within these genes. CONCLUSION: With this computational biostatistics approach increasing power and guarding against artifacts, personalized medicine and comparative effectiveness will advance while subgroup analyses of Phase III trials can now suggest risk factors for adverse events and novel directions for drug development.
Authors: P Billuart; T Bienvenu; N Ronce; V des Portes; M C Vinet; R Zemni; H Roest Crollius; A Carrié; F Fauchereau; M Cherry; S Briault; B Hamel; J P Fryns; C Beldjord; A Kahn; C Moraine; J Chelly Journal: Nature Date: 1998-04-30 Impact factor: 49.962
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Janghoo Lim; Tong Hao; Chad Shaw; Akash J Patel; Gábor Szabó; Jean-François Rual; C Joseph Fisk; Ning Li; Alex Smolyar; David E Hill; Albert-László Barabási; Marc Vidal; Huda Y Zoghbi Journal: Cell Date: 2006-05-19 Impact factor: 41.582
Authors: Sven Cichon; Nick Craddock; Mark Daly; Stephen V Faraone; Pablo V Gejman; John Kelsoe; Thomas Lehner; Douglas F Levinson; Audra Moran; Pamela Sklar; Patrick F Sullivan Journal: Am J Psychiatry Date: 2009-04-01 Impact factor: 18.112
Authors: K M Wittkowski; V Sonakya; B Bigio; M K Tonn; F Shic; M Ascano; C Nasca; G Gold-Von Simson Journal: Transl Psychiatry Date: 2014-01-28 Impact factor: 6.222
Authors: Knut M Wittkowski; Christina Dadurian; Martin P Seybold; Han Sang Kim; Ayuko Hoshino; David Lyden Journal: PLoS One Date: 2018-07-02 Impact factor: 3.240