Multivariate extension of penalized regression on summary statistics to construct polygenic risk scores for correlated traits

[1]  Michael F. Green,et al.  Mapping genomic loci implicates genes and synaptic biology in schizophrenia , 2022, Nature.

[2]  V. Reus Faculty Opinions recommendation of Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. , 2021, Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature.

[3]  N. Wray,et al.  Could Polygenic Risk Scores Be Useful in Psychiatry?: A Review. , 2020, JAMA psychiatry.

[4]  John P. Rice,et al.  A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied Across Multiple Cohorts , 2020, Biological Psychiatry.

[5]  B. Vilhjálmsson,et al.  Improved genetic prediction of complex traits from individual-level data or summary statistics , 2020, Nature Communications.

[6]  Jianxin Shi,et al.  A Penalized Regression Framework for Building Polygenic Risk Models Based on Summary Statistics From Genome-Wide Association Studies and Incorporating External Information , 2020, Journal of the American Statistical Association.

[7]  Bjarni J. Vilhjálmsson,et al.  LDpred2: better, faster, stronger , 2020, bioRxiv.

[8]  Katherine M. Siewert,et al.  Population-specific causal disease effect sizes in functionally important regions impacted by selection , 2019, Nature Communications.

[9]  Doug Speed,et al.  Evaluating and improving heritability models using summary statistics , 2019, Nature Genetics.

[10]  Doug Speed,et al.  SumHer better estimates the SNP heritability of complex traits from summary statistics , 2018, Nature Genetics.

[11]  Mary E. Haas,et al.  Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations , 2018, Nature Genetics.

[12]  A. Bureau,et al.  Polygenic risk scores distinguish patients from non‐affected adult relatives and from normal controls in schizophrenia and bipolar disorder multi‐affected kindreds , 2018, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[13]  Stephan Ripke,et al.  Improving genetic prediction by leveraging genetic correlations among human diseases and traits , 2018, Nature Communications.

[14]  S. Djurovic,et al.  Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation , 2017, Nature Communications.

[15]  P. Visscher,et al.  Multi-trait analysis of genome-wide association summary statistics using MTAG , 2017, Nature Genetics.

[16]  Wei Liu,et al.  Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction , 2017, PLoS genetics.

[17]  Pak Chung Sham,et al.  Polygenic scores via penalized regression on summary statistics , 2016, bioRxiv.

[18]  M. Maziade At Risk for Serious Mental Illness - Screening Children of Patients with Mood Disorders or Schizophrenia. , 2017, The New England journal of medicine.

[19]  B. Neale,et al.  Linkage disequilibrium dependent architecture of human complex traits reveals action of negative selection , 2016, bioRxiv.

[20]  G. Cardoso,et al.  Social determinants of mental health: A review of the evidence , 2016 .

[21]  Pak Chung Sham,et al.  Local True Discovery Rate Weighted Polygenic Scores Using GWAS Summary Data , 2016, Behavior genetics.

[22]  M. Daly,et al.  An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.

[23]  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.

[24]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[25]  秀俊 松井,et al.  Statistics for High-Dimensional Data: Methods, Theory and Applications , 2014 .

[26]  Ross M. Fraser,et al.  A General Approach for Haplotype Phasing across the Full Spectrum of Relatedness , 2014, PLoS genetics.

[27]  Catherine Boileau,et al.  Cohort profile of the CARTaGENE study: Quebec's population-based biobank for public health and personalized genomics. , 2013, International journal of epidemiology.

[28]  V. Carli,et al.  Childhood trauma and psychosis in a prospective cohort study: cause, effect, and directionality. , 2013, The American journal of psychiatry.

[29]  Sara van de Geer,et al.  Statistics for High-Dimensional Data: Methods, Theory and Applications , 2011 .

[30]  J. Sareen,et al.  Relationship between household income and mental disorders: findings from a population-based longitudinal study. , 2011, Archives of general psychiatry.

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

[32]  Donald E. Myers,et al.  Linear and Generalized Linear Mixed Models and Their Applications , 2008, Technometrics.

[33]  H. Zou The Adaptive Lasso and Its Oracle Properties , 2006 .

[34]  Y. Chagnon,et al.  Shared and specific susceptibility loci for schizophrenia and bipolar disorder: a dense genome scan in Eastern Quebec families , 2005, Molecular Psychiatry.

[35]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[36]  P. Szatmari,et al.  Clinical and methodological factors related to reliability of the best-estimate diagnostic procedure. , 1997, The American journal of psychiatry.

[37]  C. Dion,et al.  Reliability of best-estimate diagnosis in genetic linkage studies of major psychoses: results from the Quebec pedigree studies. , 1992, The American journal of psychiatry.

[38]  Jared X. Van Snellenberg,et al.  Meta-analytic evidence for familial coaggregation of schizophrenia and bipolar disorder. , 2009, Archives of general psychiatry.

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