ArticlePharmacogenomics2013
From single-SNP to wide-locus: genome-wide association studies identifying functionally related genes and intragenic regions in small sample studies.
Article in Pharmacogenomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The pseudogenes of eukaryotic translation elongation factors (EEFs): Role in cancer and other human diseases.Genes & diseases · 2022Review
- Genetic interactions between Ror2 and Wnt9a, Ror1 and Wnt9a and Ror2 and Ror1: Phenotypic analysis of the limb skeleton and palate in compound mutants.Genes to cells : devoted to molecular & cellular mechanisms · 2019Article
- Complex polymorphisms in endocytosis genes suggest alpha-cyclodextrin as a treatment for breast cancer.PloS one · 2018Article
- A novel computational biostatistics approach implies impaired dephosphorylation of growth factor receptors as associated with severity of autism.Translational psychiatry · 2014Article
- Feature selection methods for identifying genetic determinants of host species in RNA viruses.PLoS computational biology · 2013Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundGenome-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.
methodsRecent 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).
resultsBy 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.
conclusionWith 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.
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