Bayesian multiple logistic regression for case-control GWAS
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Johannes Söding | Heribert Schunkert | Saikat Banerjee | J. Söding | H. Schunkert | Saikat Banerjee | Lingyao Zeng | Lingyao Zeng
[1] M. Stephens,et al. Imputation-Based Analysis of Association Studies: Candidate Regions and Quantitative Traits , 2007, PLoS genetics.
[2] C. Gieger,et al. Genomewide association analysis of coronary artery disease. , 2007, The New England journal of medicine.
[3] Tanya M. Teslovich,et al. Biological, Clinical, and Population Relevance of 95 Loci for Blood Lipids , 2010, Nature.
[4] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.
[5] Gregory A. Poland,et al. Fine Mapping Causal Variants with an Approximate Bayesian Method Using Marginal Test Statistics , 2015, Genetics.
[6] D. Altshuler,et al. Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies , 2012, PLoS genetics.
[7] M. Pirinen,et al. Prospects of Fine-Mapping Trait-Associated Genomic Regions by Using Summary Statistics from Genome-wide Association Studies. , 2017, American journal of human genetics.
[8] C. Gieger,et al. Genome-wide association study identifies a new locus for coronary artery disease on chromosome 10 p 11 . 23 , 2010 .
[9] M. Stephens,et al. Bayesian variable selection regression for genome-wide association studies and other large-scale problems , 2011, 1110.6019.
[10] J. Marchini,et al. Genotype imputation for genome-wide association studies , 2010, Nature Reviews Genetics.
[11] Pim van der Harst,et al. Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease , 2017, Circulation research.
[12] E. Eskin,et al. Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies , 2014, PLoS genetics.
[13] P. Visscher,et al. Estimating missing heritability for disease from genome-wide association studies. , 2011, American journal of human genetics.
[14] Eugene Baulin,et al. An updated version of NPIDB includes new classifications of DNA–protein complexes and their families , 2015, Nucleic Acids Res..
[15] Karen L. Mohlke,et al. Novel Loci for Adiponectin Levels and Their Influence on Type 2 Diabetes and Metabolic Traits: A Multi-Ethnic Meta-Analysis of 45,891 Individuals , 2012, PLoS genetics.
[16] Xiang Zhu,et al. Bayesian large-scale multiple regression with summary statistics from genome-wide association studies , 2016, bioRxiv.
[17] Mary K. Wojczynski,et al. Genome-Wide Association of Body Fat Distribution in African Ancestry Populations Suggests New Loci , 2013, PLoS genetics.
[18] C. Morrison,et al. Hormonal Contraception and the Risk of HIV Acquisition: An Individual Participant Data Meta-analysis , 2015, PLoS medicine.
[19] Thomas W. Mühleisen,et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease , 2011, Nature Genetics.
[20] Nicholette D. Palmer,et al. Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes , 2014, PLoS genetics.
[21] Daniel J Schaid,et al. Incorporating Functional Annotations for Fine-Mapping Causal Variants in a Bayesian Framework Using Summary Statistics , 2016, Genetics.
[22] P. Visscher,et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. , 2017, American journal of human genetics.
[23] Eun Yong Kang,et al. Identifying Causal Variants at Loci with Multiple Signals of Association , 2014, Genetics.
[24] M. Stephens,et al. Genome-wide Efficient Mixed Model Analysis for Association Studies , 2012, Nature Genetics.
[25] Matti Pirinen,et al. Efficient computation with a linear mixed model on large-scale data sets with applications to genetic studies , 2012, 1207.4886.
[26] Matti Pirinen,et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies , 2015, bioRxiv.
[27] Andrew D. Johnson,et al. Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms , 2017, Nature Genetics.
[28] Kristin G Ardlie,et al. Genetic Analysis in UK Biobank Links Insulin Resistance and Transendothelial Migration Pathways to Coronary Artery Disease , 2017, Nature Genetics.
[29] P. Elliott,et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.
[30] J. Danesh,et al. Large-scale association analysis identifies new risk loci for coronary artery disease , 2013 .
[31] Manolis Kellis,et al. Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases , 2016, Nucleic acids research.
[32] Mark C. Field,et al. RAB-Like 2 Has an Essential Role in Male Fertility, Sperm Intra-Flagellar Transport, and Tail Assembly , 2012, PLoS genetics.
[33] Simon C. Potter,et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.
[34] Claudio J. Verzilli,et al. Multilocus Bayesian meta-analysis of gene-disease associations. , 2009, American journal of human genetics.
[35] C. Gieger,et al. Genome-wide association study identifies a new locus for coronary artery disease on chromosome 10p11.23. , 2011, European heart journal.
[36] S. Yusuf,et al. Global burden of cardiovascular diseases: Part II: variations in cardiovascular disease by specific ethnic groups and geographic regions and prevention strategies. , 2001, Circulation.
[37] P. Visscher,et al. GCTA: a tool for genome-wide complex trait analysis. , 2011, American journal of human genetics.
[38] J. Danesh,et al. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease , 2016 .
[39] Sylvia Richardson,et al. JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects , 2016, Genetic epidemiology.
[40] Helen E. Parkinson,et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog) , 2016, Nucleic Acids Res..
[41] Eleazar Eskin,et al. Improved methods for multi-trait fine mapping of pleiotropic risk loci , 2016, bioRxiv.
[42] S. Yusuf,et al. Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. , 2001, Circulation.
[43] D. Schaid,et al. From genome-wide associations to candidate causal variants by statistical fine-mapping , 2018, Nature Reviews Genetics.
[44] Benjamin J. Wright,et al. New susceptibility locus for coronary artery disease on chromosome 3q22.3 , 2009, Nature Genetics.
[45] N. Mehta. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. , 2011, Circulation. Cardiovascular genetics.
[46] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[47] Xiang Zhou,et al. Polygenic Modeling with Bayesian Sparse Linear Mixed Models , 2012, PLoS genetics.
[48] Andrew P Morris,et al. Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes , 2016, European Journal of Human Genetics.
[49] Fabian J. Theis,et al. DeepWAS : Directly integrating regulatory information into GWAS using 1 deep learning supports master regulator MEF 2 C as risk factor for major 2 depressive disorder 3 4 , 2016 .
[50] Tamara S. Roman,et al. New genetic loci link adipose and insulin biology to body fat distribution , 2014, Nature.
[51] P. Donnelly,et al. A new multipoint method for genome-wide association studies by imputation of genotypes , 2007, Nature Genetics.
[52] D. Levy,et al. Prediction of coronary heart disease using risk factor categories. , 1998, Circulation.