Prioritizing GWAS results: A review of statistical methods and recommendations for their application.

[1]  D. Clayton,et al.  Genome-wide association study and meta-analysis finds over 40 loci affect risk of type 1 diabetes , 2009, Nature Genetics.

[2]  J. Hirschhorn.  Genomewide association studies--illuminating biologic pathways. , 2009, The New England journal of medicine.

[3]  Peter Kraft,et al.  Genetic risk prediction--are we there yet? , 2009, The New England journal of medicine.

[4]  D. Goldstein.  Common genetic variation and human traits. , 2009, The New England journal of medicine.

[5]  A. Singleton,et al.  Genomewide association studies and human disease. , 2009, The New England journal of medicine.

[6]  P. Rosenberg,et al.  Pathway analysis by adaptive combination of P‐values , 2009, Genetic epidemiology.

[7]  Dirkje S Postma,et al.  Common and different genetic background for rheumatoid arthritis and coeliac disease. , 2009, Human molecular genetics.

[8]  M. Rietschel,et al.  Dissecting the phenotype in genome-wide association studies of psychiatric illness , 2009, British Journal of Psychiatry.

[9]  Ming-Huei Chen,et al.  Genome-wide association meta-analysis for total serum bilirubin levels. , 2009, Human molecular genetics.

[10]  Manuel A. R. Ferreira,et al.  Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. , 2009, American journal of human genetics.

[11]  C. Wijmenga,et al.  Using genome‐wide pathway analysis to unravel the etiology of complex diseases , 2009, Genetic epidemiology.

[12]  Ludwig Kappos,et al.  Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci , 2009, Nature Genetics.

[13]  Andrew D. Johnson,et al.  Genome-wide association study of blood pressure and hypertension , 2009, Nature Genetics.

[14]  Christian Gieger,et al.  Meta-Analysis of 28,141 Individuals Identifies Common Variants within Five New Loci That Influence Uric Acid Concentrations , 2009, PLoS genetics.

[15]  H. Cordell.  Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[16]  Laura J. Scott,et al.  Edinburgh Research Explorer Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution , 2022 .

[17]  Claudio J. Verzilli,et al.  Multilocus Bayesian meta-analysis of gene-disease associations. , 2009, American journal of human genetics.

[18]  Toshiko Tanaka,et al.  Genome-wide association study of vitamin B6, vitamin B12, folate, and homocysteine blood concentrations. , 2009, American journal of human genetics.

[19]  Inês Barroso,et al.  Meta-Analysis of Genome-Wide Scans for Human Adult Stature Identifies Novel Loci and Associations with Measures of Skeletal Frame Size , 2009, PLoS genetics.

[20]  Thomas Lumley,et al.  Common variants at ten loci influence QT interval duration in the QTGEN Study , 2009, Nature Genetics.

[21]  David C. Wilson,et al.  Diverse genome-wide association studies associate the IL12/IL23 pathway with Crohn Disease. , 2009, American journal of human genetics.

[22]  P. Matthews,et al.  Pathway and network-based analysis of genome-wide association studies in multiple sclerosis , 2009, Human molecular genetics.

[23]  J. Hein,et al.  Using biological networks to search for interacting loci in genome-wide association studies , 2009, European Journal of Human Genetics.

[24]  Julian Little,et al.  Systematic Reviews of Genetic Association Studies , 2009, PLoS medicine.

[25]  N. Craddock,et al.  Gene-wide analyses of genome-wide association data sets: evidence for multiple common risk alleles for schizophrenia and bipolar disorder and for overlap in genetic risk , 2009, Molecular Psychiatry.

[26]  Trevor J. Hastie,et al.  Genome-wide association analysis by lasso penalized logistic regression , 2009, Bioinform..

[27]  M. McCarthy,et al.  Interrogating Type 2 Diabetes Genome-Wide Association Data Using a Biological Pathway-Based Approach , 2009, Diabetes.

[28]  Luigi Ferrucci,et al.  Common variation in the beta-carotene 15,15'-monooxygenase 1 gene affects circulating levels of carotenoids: a genome-wide association study. , 2009, American journal of human genetics.

[29]  John P A Ioannidis,et al.  Meta-analysis in genome-wide association studies. , 2009, Pharmacogenomics.

[30]  David M. Evans,et al.  Common variants in the region around Osterix are associated with bone mineral density and growth in childhood , 2009, Human molecular genetics.

[31]  Quan Long,et al.  Detecting disease-associated genotype patterns , 2009, BMC Bioinformatics.

[32]  Hong Wang,et al.  Prioritizing risk pathways: a novel association approach to searching for disease pathways fusing SNPs and pathways , 2009, Bioinform..

[33]  R. Collins,et al.  Common variants at 30 loci contribute to polygenic dyslipidemia , 2009, Nature Genetics.

[34]  Jason H. Moore,et al.  Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission , 2009, Human Genetics.

[35]  Cisca Wijmenga,et al.  Shared and distinct genetic variants in type 1 diabetes and celiac disease. , 2008, The New England journal of medicine.

[36]  Vincent Plagnol,et al.  Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci , 2008, Nature Genetics.

[37]  Steven Gallinger,et al.  Meta-analysis of genome-wide association data identifies four new susceptibility loci for colorectal cancer , 2008, Nature Genetics.

[38]  N. Schork,et al.  Pathway analysis of seven common diseases assessed by genome-wide association. , 2008, Genomics.

[39]  Manuel A. R. Ferreira,et al.  Practical aspects of imputation-driven meta-analysis of genome-wide association studies. , 2008, Human molecular genetics.

[40]  J. Rioux,et al.  Autoimmune diseases: insights from genome-wide association studies. , 2008, Human molecular genetics.

[41]  Jun Zhu,et al.  A combinatorial approach to detecting gene-gene and gene-environment interactions in family studies. , 2008, American journal of human genetics.

[42]  Gary E. Swan,et al.  Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence , 2008, Bioinform..

[43]  Muin J Khoury,et al.  The continued need to synthesize the results of genetic associations across multiple studies , 2008, Genetics in Medicine.

[44]  Li Ma,et al.  Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies , 2008, BMC Bioinformatics.

[45]  J. Lau,et al.  Synthesis of genetic association studies for pertinent gene-disease associations requires appropriate methodological and statistical approaches. , 2008, Journal of clinical epidemiology.

[46]  C. Hoggart,et al.  Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies , 2008, PLoS genetics.

[47]  Jason H. Moore,et al.  Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases , 2008, Human Genetics.

[48]  Alastair Forbes,et al.  Genetic determinants of ulcerative colitis include the ECM1 locus and five loci implicated in Crohn's disease , 2008, Nature Genetics.

[49]  K. Lange,et al.  Coordinate descent algorithms for lasso penalized regression , 2008, 0803.3876.

[50]  Yi Wang,et al.  Exploration of gene–gene interaction effects using entropy-based methods , 2008, European Journal of Human Genetics.

[51]  Yijun Zuo,et al.  An entropy-based approach for testing genetic epistasis underlying complex diseases. , 2008, Journal of theoretical biology.

[52]  Claudio J. Verzilli,et al.  Bayesian meta-analysis of genetic association studies with different sets of markers. , 2007, American journal of human genetics.

[53]  K. Mossman.  The Wellcome Trust Case Control Consortium, U.K. , 2008 .

[54]  Stephen J. Chanock,et al.  Polymorphism Interaction Analysis (PIA): a method for investigating complex gene-gene interactions , 2008, BMC Bioinformatics.

[55]  Aris Floratos,et al.  Pattern-based mining strategy to detect multi-locus association and gene × environment interaction , 2007, BMC proceedings.

[56]  James W Baurley,et al.  Hierarchical Bayes prioritization of marker associations from a genome‐wide association scan for further investigation , 2007, Genetic epidemiology.

[57]  K. Taylor,et al.  Genome-Wide Association , 2007, Diabetes.

[58]  Ingo Wegener,et al.  Detecting high-order interactions of single nucleotide polymorphisms using genetic programming , 2007, Bioinform..

[59]  Kai Wang,et al.  Pathway-based approaches for analysis of genomewide association studies. , 2007, American journal of human genetics.

[60]  Evangelos Evangelou,et al.  Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations , 2007, PloS one.

[61]  Jun S. Liu,et al.  Bayesian inference of epistatic interactions in case-control studies , 2007, Nature Genetics.

[62]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[63]  R. Tibshirani,et al.  PATHWISE COORDINATE OPTIMIZATION , 2007, 0708.1485.

[64]  Jason H. Moore,et al.  Evaporative cooling feature selection for genotypic data involving interactions , 2007, Bioinform..

[65]  Gary K. Chen,et al.  Enriching the analysis of genomewide association studies with hierarchical modeling. , 2007, American journal of human genetics.

[66]  P. Deloukas,et al.  A genome-wide association study for celiac disease identifies risk variants in the region harboring IL2 and IL21 , 2007, Nature Genetics.

[67]  John P.A. Ioannidis,et al.  Non-Replication and Inconsistency in the Genome-Wide Association Setting , 2007, Human Heredity.

[68]  D. Maraganore,et al.  A Genomic Pathway Approach to a Complex Disease: Axon Guidance and Parkinson Disease , 2007, PLoS genetics.

[69]  Jason H. Moore,et al.  Tuning ReliefF for Genome-Wide Genetic Analysis , 2007, EvoBIO.

[70]  Hongyu Zhao,et al.  Evidence for association between multiple complement pathway genes and AMD , 2007, Genetic epidemiology.

[71]  J. Ioannidis,et al.  Meta-Analysis in Genome-Wide Association Datasets: Strategies and Application in Parkinson Disease , 2007, PLoS ONE.

[72]  D. Allison,et al.  Detection of gene x gene interactions in genome-wide association studies of human population data. , 2007, Human heredity.

[73]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[74]  Todd Holden,et al.  A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. , 2006, Journal of theoretical biology.

[75]  Paul Scheet,et al.  A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. , 2006, American journal of human genetics.

[76]  Paolo Vineis,et al.  A road map for efficient and reliable human genome epidemiology , 2006, Nature Genetics.

[77]  Marylyn D. Ritchie,et al.  GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease , 2006 .

[78]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[79]  Kenneth Lange,et al.  Association testing with Mendel , 2005, Genetic epidemiology.

[80]  P. Donnelly,et al.  Genome-wide strategies for detecting multiple loci that influence complex diseases , 2005, Nature Genetics.

[81]  Ingo Ruczinski,et al.  Identifying interacting SNPs using Monte Carlo logic regression , 2005, Genetic epidemiology.

[82]  William Shannon,et al.  Detecting epistatic interactions contributing to quantitative traits , 2004, Genetic epidemiology.

[83]  Eran Halperin,et al.  Haplotype reconstruction from genotype data using Imperfect Phylogeny , 2004, Bioinform..

[84]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[85]  S Loesgen,et al.  Meta‐Analysis of Linkage Studies for Complex Diseases: An Overview of Methods and a Simulation Study , 2004, Annals of human genetics.

[86]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[87]  D. Altman,et al.  Measuring inconsistency in meta-analyses , 2003, BMJ : British Medical Journal.

[88]  I. Daubechies,et al.  An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.

[89]  Jason H. Moore,et al.  Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions , 2003, Bioinform..

[90]  S. Kardia,et al.  Synergistic Polymorphisms of β1- and α2C-Adrenergic Receptors and the Risk of Congestive Heart Failure , 2002 .

[91]  Minerva M. Carrasquillo,et al.  Genome-wide association study and mouse model identify interaction between RET and EDNRB pathways in Hirschsprung disease , 2002, Nature Genetics.

[92]  S. Kardia,et al.  Synergistic polymorphisms of beta1- and alpha2C-adrenergic receptors and the risk of congestive heart failure. , 2002, The New England journal of medicine.

[93]  D. Postma,et al.  Gene-gene interaction in asthma: IL4RA and IL13 in a Dutch population with asthma. , 2002, American journal of human genetics.

[94]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[95]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[96]  N. Laird,et al.  The family based association test method: strategies for studying general genotype–phenotype associations , 2001, European Journal of Human Genetics.

[97]  C. Sing,et al.  A combinatorial partitioning method to identify multilocus genotypic partitions that predict quantitative trait variation. , 2001, Genome research.

[98]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[99]  Daniel Rabinowitz,et al.  A Unified Approach to Adjusting Association Tests for Population Admixture with Arbitrary Pedigree Structure and Arbitrary Missing Marker Information , 2000, Human Heredity.

[100]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[101]  Wenjiang J. Fu.  Penalized Regressions: The Bridge versus the Lasso , 1998 .

[102]  George Davey Smith,et al.  Meta-analysis: Principles and procedures , 1997, BMJ.

[103]  J. Ioannidis,et al.  Quantitative Synthesis in Systematic Reviews , 1997, Annals of Internal Medicine.

[104]  N. Schork,et al.  Selected Methodological Issues in Meiotic Mapping of Obesity Genes in Humans: Issues of Power and Efficiency , 1997, Behavior genetics.

[105]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[106]  Russell V. Lenth,et al.  MeWAnalysis by the Confidence Profile Method: The Statistical Synthesis of Evidence , 1993 .

[107]  J. Fleiss,et al.  The statistical basis of meta-analysis. , 1993, Statistical methods in medical research.

[108]  E. Boerwinkle,et al.  The use of measured genotype information in the analysis of quantitative phenotypes in man. , 1987, Annals of human genetics.

[109]  F. Santosa,et al.  Linear inversion of ban limit reflection seismograms , 1986 .

[110]  N. Laird,et al.  Meta-analysis in clinical trials. , 1986, Controlled clinical trials.

[111]  H. L. Taylor,et al.  Deconvolution with the l 1 norm , 1979 .

[112]  J. Claerbout,et al.  Robust Modeling With Erratic Data , 1973 .

[113]  H. Grüneberg,et al.  Introduction to quantitative genetics , 1960 .

[114]  Allan Birnbaum,et al.  Combining Independent Tests of Significance , 1954 .