Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

[1]  M. Rienstra,et al.  Letter to editor: Reply on question of Marques JR et al. regarding the paper entitled: "The LifeLines cohort study: Prevalence and treatment of cardiovascular disease and risk factors". , 2019, International journal of cardiology.

[2]  A. Price,et al.  Explicit modeling of ancestry improves polygenic risk scores and BLUP prediction , 2014, bioRxiv.

[3]  P. Visscher,et al.  Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model , 2015, PLoS genetics.

[4]  Laura J. Scott,et al.  Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder , 2015, American journal of human genetics.

[5]  Han Xu,et al.  Partitioning heritability by functional category using GWAS summary statistics , 2015, bioRxiv.

[6]  B. Berger,et al.  Efficient Bayesian mixed model analysis increases association power in large cohorts , 2014, Nature Genetics.

[7]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[8]  Han Xu,et al.  Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. , 2014, American journal of human genetics.

[9]  Ross M. Fraser,et al.  Defining the role of common variation in the genomic and biological architecture of adult human height , 2014, Nature Genetics.

[10]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[11]  Doug Speed,et al.  MultiBLUP: improved SNP-based prediction for complex traits , 2014, Genome research.

[12]  Saharon Rosset,et al.  Effective genetic-risk prediction using mixed models. , 2014, American journal of human genetics.

[13]  Dajiang J. Liu,et al.  Meta-Analysis of Gene Level Tests for Rare Variant Association , 2013, Nature Genetics.

[14]  S. Djurovic,et al.  Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia , 2013, Molecular Psychiatry.

[15]  Simon C. Potter,et al.  Genome-wide Association Analysis Identifies 14 New Risk Loci for Schizophrenia , 2013, Nature Genetics.

[16]  D. Altshuler,et al.  Validating therapeutic targets through human genetics , 2013, Nature Reviews Drug Discovery.

[17]  P. Visscher,et al.  Pitfalls of predicting complex traits from SNPs , 2013, Nature Reviews Genetics.

[18]  S. Purcell,et al.  Pleiotropy in complex traits: challenges and strategies , 2013, Nature Reviews Genetics.

[19]  Jonathan P. Beauchamp,et al.  GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment , 2013, Science.

[20]  Jaana M. Hartikainen,et al.  Large-scale genotyping identifies 41 new loci associated with breast cancer risk , 2013, Nature Genetics.

[21]  Patrick Neven,et al.  Genome-wide association studies identify four ER negative–specific breast cancer risk loci , 2013, Nature Genetics.

[22]  Nilanjan Chatterjee,et al.  Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies , 2013, Nature Genetics.

[23]  F. Dudbridge.  Power and Predictive Accuracy of Polygenic Risk Scores , 2013, PLoS genetics.

[24]  Xiang Zhou,et al.  Polygenic Modeling with Bayesian Sparse Linear Mixed Models , 2012, PLoS 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]  J. Danesh,et al.  Large-scale association analysis identifies new risk loci for coronary artery disease , 2013 .

[27]  Jane E. Carpenter,et al.  A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. , 2012, Human molecular genetics.

[28]  Tanya M. Teslovich,et al.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes , 2012, Nature Genetics.

[29]  M Erbe,et al.  Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. , 2012, Journal of dairy science.

[30]  Benjamin A. Logsdon,et al.  A novel variational Bayes multiple locus Z-statistic for genome-wide association studies with Bayesian model averaging , 2012, Bioinform..

[31]  Justin Zobel,et al.  SparSNP: Fast and memory-efficient analysis of all SNPs for phenotype prediction , 2012, BMC Bioinformatics.

[32]  Peter Kraft,et al.  Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis , 2012, Nature Genetics.

[33]  P. Visscher,et al.  A Better Coefficient of Determination for Genetic Profile Analysis , 2012, Genetic epidemiology.

[34]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[35]  M. Stephens,et al.  Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies , 2012 .

[36]  Michael Jones,et al.  Genome-wide association analysis identifies three new breast cancer susceptibility loci , 2012, Nature Genetics.

[37]  Jane E. Carpenter,et al.  A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11. , 2012, Human molecular genetics.

[38]  P. D. de Bakker,et al.  Genome‐wide meta‐analysis identifies novel multiple sclerosis susceptibility loci , 2011, Annals of neurology.

[39]  Anders D. Børglum,et al.  Genome-wide association study identifies five new schizophrenia loci , 2011, Nature Genetics.

[40]  Peter Kraft,et al.  Evaluation of polygenic risk scores for predicting breast and prostate cancer risk , 2011, Genetic epidemiology.

[41]  Simon C. Potter,et al.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis , 2011, Nature.

[42]  Mark I McCarthy,et al.  Genomic inflation factors under polygenic inheritance , 2011, European Journal of Human Genetics.

[43]  N. Mehta.  Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. , 2011, Circulation. Cardiovascular genetics.

[44]  Thomas W. Mühleisen,et al.  Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease , 2011, Nature Genetics.

[45]  P. Visscher,et al.  Estimating missing heritability for disease from genome-wide association studies. , 2011, American journal of human genetics.

[46]  Jane E. Carpenter,et al.  Australian Breast Cancer Tissue Bank (ABCTB). , 2011, Biopreservation and biobanking.

[47]  Daniel Gianola,et al.  Predicting genetic predisposition in humans: the promise of whole-genome markers , 2010, Nature Reviews Genetics.

[48]  Naomi R. Wray,et al.  Estimating Effects and Making Predictions from Genome-Wide Marker Data , 2010, 1010.4710.

[49]  Henrik,et al.  Association analyses of 249,796 individuals reveal eighteen new loci associated with body mass index , 2012 .

[50]  Ayellet V. Segrè,et al.  Hundreds of variants clustered in genomic loci and biological pathways affect human height , 2010, Nature.

[51]  Nilanjan Chatterjee,et al.  Estimation of effect size distribution from genome-wide association studies and implications for future discoveries , 2010, Nature Genetics.

[52]  P. Visscher,et al.  Common SNPs explain a large proportion of heritability for human height , 2011 .

[53]  William S Bush,et al.  Evidence for polygenic susceptibility to multiple sclerosis--the shape of things to come. , 2010, American journal of human genetics.

[54]  P. Visscher,et al.  The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling , 2010, PLoS genetics.

[55]  Tanya M. Teslovich,et al.  Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index , 2010 .

[56]  W. G. Hill,et al.  The Limits of Individual Identification from Sample Allele Frequencies: Theory and Statistical Analysis , 2009, PLoS genetics.

[57]  Joseph T. Glessner,et al.  From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes , 2009, PLoS genetics.

[58]  Peter M Visscher,et al.  Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. , 2009, Human molecular genetics.

[59]  P. Visscher,et al.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder , 2009, Nature.

[60]  M. Goddard.  Genomic selection: prediction of accuracy and maximisation of long term response , 2009, Genetica.

[61]  John A Woolliams,et al.  A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value , 2009, Genetics Selection Evolution.

[62]  Hans D. Daetwyler,et al.  Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach , 2008, PloS one.

[63]  R. Wilkins.  Polygenes, risk prediction, and targeted prevention of breast cancer. , 2008, The New England journal of medicine.

[64]  N. Cook,et al.  Rationale, design, and methodology of the Women's Genome Health Study: a genome-wide association study of more than 25,000 initially healthy american women. , 2008, Clinical chemistry.

[65]  I Misztal,et al.  Technical note: Computing strategies in genome-wide selection. , 2008, Journal of dairy science.

[66]  Simon C. Potter,et al.  Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.

[67]  W. Willett,et al.  A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer , 2007, Nature Genetics.

[68]  David M. Young,et al.  Applied Iterative Methods , 2004 .

[69]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

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

[71]  G. Grimmett,et al.  Probability and random processes , 2002 .

[72]  D. Falconer.  The inheritance of liability to diseases with variable age of onset, with particular reference to diabetes mellitus , 1967, Annals of human genetics.

[73]  R. Fisher.  XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. , 1919, Transactions of the Royal Society of Edinburgh.

[74]  L. Penrose,et al.  THE CORRELATION BETWEEN RELATIVES ON THE SUPPOSITION OF MENDELIAN INHERITANCE , 2022 .