Literature DB >> 29727703

Statistical methods for genome-wide association studies.

Maggie Haitian Wang1, Heather J Cordell2, Kristel Van Steen3.   

Abstract

Genome-wide association studies (GWAS) detect common genetic variants associated with complex disorders. With their comprehensive coverage of common single nucleotide polymorphisms and comparatively low cost, GWAS are an attractive tool in the clinical and commercial genetic testing. This review introduces the pipeline of statistical methods used in GWAS analysis, from data quality control, association tests, population structure control, interaction effects and results visualization, through to post-GWAS validation methods and related issues.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  Association tests; GWAS; Quality control; Review; Statistical methods

Mesh：

Year:  2018        PMID： 29727703     DOI： 10.1016/j.semcancer.2018.04.008

Source DB:  PubMed          Journal:  Semin Cancer Biol        ISSN： 1044-579X            Impact factor:   15.707


  8 in total

1.  Performing post-genome-wide association study analysis: overview, challenges and recommendations.

Authors:  Yagoub Adam; Chaimae Samtal; Jean-Tristan Brandenburg; Oluwadamilare Falola; Ezekiel Adebiyi
Journal:  F1000Res       Date:  2021-10-04

2.  Including diverse and admixed populations in genetic epidemiology research.

Authors:  Amke Caliebe; Fasil Tekola-Ayele; Burcu F Darst; Xuexia Wang; Yeunjoo E Song; Jiang Gui; Ronnie A Sebro; David J Balding; Mohamad Saad; Marie-Pierre Dubé
Journal:  Genet Epidemiol       Date:  2022-07-16       Impact factor: 2.344

3.  Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies.

Authors:  Jiabo Wang; Jianming Yu; Alexander E Lipka; Zhiwu Zhang
Journal:  Methods Mol Biol       Date:  2022

4.  GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction.

Authors:  Jiabo Wang; Zhiwu Zhang
Journal:  Genomics Proteomics Bioinformatics       Date:  2021-09-04       Impact factor: 6.409

Review 5.  Statistical Methods and Software for Substance Use and Dependence Genetic Research.

Authors:  Tongtong Lan; Bo Yang; Xuefen Zhang; Tong Wang; Qing Lu
Journal:  Curr Genomics       Date:  2019-04       Impact factor: 2.236

6.  Can statistical adjustment guided by causal inference improve the accuracy of effect estimation? A simulation and empirical research based on meta-analyses of case-control studies.

Authors:  Ruohua Yan; Tianyi Liu; Yaguang Peng; Xiaoxia Peng
Journal:  BMC Med Inform Decis Mak       Date:  2020-12-11       Impact factor: 2.796

7.  Exploring the Pleiotropic Genes and Therapeutic Targets Associated with Heart Failure and Chronic Kidney Disease by Integrating metaCCA and SGLT2 Inhibitors' Target Prediction.

Authors:  Huanqiang Li; Ziling Mai; Sijia Yu; Bo Wang; Wenguang Lai; Guanzhong Chen; Chunyun Zhou; Jin Liu; Yongquan Yang; Shiqun Chen; Yong Liu; Jiyan Chen
Journal:  Biomed Res Int       Date:  2021-09-08       Impact factor: 3.411

8.  Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies.

Authors:  Anna Niehues; Daniele Bizzarri; Marcel J T Reinders; P Eline Slagboom; Alain J van Gool; Erik B van den Akker; Peter A C 't Hoen
Journal:  BMC Genomics       Date:  2022-07-31       Impact factor: 4.547
  8 in total

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