Accounting for age of onset and family history improves power in genome-wide association studies

[1]  B. Neale,et al.  Incorporating family history of disease improves polygenic risk scores in diverse populations , 2021, bioRxiv.

[2]  N. Wray,et al.  Risk of Early-Onset Depression Associated With Polygenic Liability, Parental Psychiatric History, and Socioeconomic Status. , 2021, JAMA psychiatry.

[3]  Judy H. Cho,et al.  Utility of polygenic embryo screening for disease depends on the selection strategy , 2020, bioRxiv.

[4]  M. Daly,et al.  An efficient and accurate frailty model approach for genome-wide survival association analysis controlling for population structure and relatedness in large-scale biobanks , 2020, bioRxiv.

[5]  Blaine R. Roberts,et al.  Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture , 2020, Nature Communications.

[6]  R. Mägi,et al.  Genomic architecture and prediction of censored time-to-event phenotypes with a Bayesian genome-wide analysis , 2020, Nature Communications.

[7]  T. Werge,et al.  Polygenic Risk and Progression to Bipolar or Psychotic Disorders Among Individuals Diagnosed With Unipolar Depression in Early Life. , 2020, The American journal of psychiatry.

[8]  P. Kraft,et al.  Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms , 2020 .

[9]  Seunggeun Lee,et al.  A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank. , 2020, American journal of human genetics.

[10]  Manuel A. R. Ferreira,et al.  Age-of-onset information helps identify 76 genetic variants associated with allergic disease , 2020, PLoS genetics.

[11]  Terry M Therneau,et al.  Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models , 2020, Statistics in medicine.

[12]  N. Wray,et al.  Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits , 2020, Biological Psychiatry.

[13]  Ryan D. Chow,et al.  The aging transcriptome and cellular landscape of the human lung in relation to SARS-CoV-2 , 2020, Nature Communications.

[14]  Audrey Y. Chu,et al.  Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers , 2020, Nature Medicine.

[15]  N. Patterson,et al.  Liability threshold modeling of case-control status and family history of disease increases association power , 2020, Nature Genetics.

[16]  Liang He,et al.  Fast Algorithms for Conducting Large-Scale GWAS of Age-at-Onset Traits Using Cox Mixed-Effects Models , 2020, Genetics.

[17]  Trevor Hastie,et al.  Fast Lasso method for Large-scale and Ultrahigh-dimensional Cox Model with applications to UK Biobank , 2020, bioRxiv.

[18]  William J. Astle,et al.  Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations , 2020, Cell.

[19]  John J. McGrath,et al.  Efficient toolkit implementing best practices for principal component analysis of population genetic data , 2019, bioRxiv.

[20]  Karsten B. Sieber,et al.  A catalog of genetic loci associated with kidney function from analyses of a million individuals , 2019, Nature Genetics.

[21]  Y. Bossé,et al.  Benefits and limitations of genome-wide association studies , 2019, Nature Reviews Genetics.

[22]  P. Visscher,et al.  A resource-efficient tool for mixed model association analysis of large-scale data , 2019, Nature Genetics.

[23]  Robert Karlsson,et al.  RICOPILI: Rapid Imputation for COnsortias PIpeLIne , 2019, bioRxiv.

[24]  J. Denny,et al.  Cox regression increases power to detect genotype-phenotype associations in genomic studies using the electronic health record , 2019, BMC Genomics.

[25]  John P. Rice,et al.  Identification of common genetic risk variants for autism spectrum disorder , 2019, Nature Genetics.

[26]  Samuel E. Jones,et al.  Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms , 2019, Nature Communications.

[27]  Timothy J. Hohman,et al.  Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk , 2019, Nature Genetics.

[28]  Dajiang J. Liu,et al.  Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use , 2018, Nature Genetics.

[29]  R. Marioni,et al.  Genotype effects contribute to variation in longitudinal methylome patterns in older people , 2018, Genome Medicine.

[30]  R. Marioni,et al.  Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions , 2018, Nature Neuroscience.

[31]  Anthony J. Payne,et al.  Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps , 2018, Nature Genetics.

[32]  Alicia R. Martin,et al.  Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder , 2018, Nature Genetics.

[33]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[34]  Sonja W. Scholz,et al.  Expanding Parkinson’s disease genetics: novel risk loci, genomic context, causal insights and heritable risk , 2018 .

[35]  Tanya M. Teslovich,et al.  Biobank-driven genomic discovery yields new insight into atrial fibrillation biology , 2018, Nature Genetics.

[36]  Jonathan P. Beauchamp,et al.  Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals , 2018, Nature Genetics.

[37]  Tyrone D. Cannon,et al.  Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence , 2018, Nature Genetics.

[38]  A. Price,et al.  Mixed-model association for biobank-scale datasets , 2018, Nature Genetics.

[39]  Andrey Ziyatdinov,et al.  Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr , 2018, Bioinform..

[40]  Warren W. Kretzschmar,et al.  Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression , 2017, Nature Genetics.

[41]  R. Marioni,et al.  GWAS on family history of Alzheimer’s disease , 2018, bioRxiv.

[42]  D. Melzer,et al.  Human longevity: 25 genetic loci associated in 389,166 UK biobank participants , 2017, Aging.

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

[44]  Chun Jimmie Ye,et al.  Covariate Selection for Association Screening in Multi-Phenotype Genetic studies , 2017, Nature Genetics.

[45]  Sven Sandin,et al.  The Heritability of Autism Spectrum Disorder , 2017, JAMA.

[46]  Kaare Christensen,et al.  Heritability of Schizophrenia and Schizophrenia Spectrum Based on the Nationwide Danish Twin Register , 2017, Biological Psychiatry.

[47]  Melissa J. Green,et al.  Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder , 2017, bioRxiv.

[48]  P. Visscher,et al.  10 Years of GWAS Discovery: Biology, Function, and Translation. , 2017, American journal of human genetics.

[49]  M. Daly,et al.  The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders , 2017, Molecular Psychiatry.

[50]  Yaniv Erlich,et al.  Case–control association mapping by proxy using family history of disease , 2017, Nature Genetics.

[51]  Stefan N Hansen,et al.  Estimating a population cumulative incidence under calendar time trends , 2017, BMC Medical Research Methodology.

[52]  N. Patterson,et al.  Mixed Model Association with Family-Biased Case-Control Ascertainment , 2016, bioRxiv.

[53]  J. Mefford,et al.  Genetic Mechanisms Leading to Sex Differences Across Common Diseases and Anthropometric Traits , 2016, Genetics.

[54]  Klaus P. Ebmeier,et al.  Epidemiology and Heritability of Major Depressive Disorder, Stratified by Age of Onset, Sex, and Illness Course in Generation Scotland: Scottish Family Health Study (GS:SFHS) , 2015, PloS one.

[55]  R. Kuja‐Halkola,et al.  Heritability of attention‐deficit hyperactivity disorder in adults , 2015, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[56]  Esben Agerbo,et al.  Polygenic Risk Score, Parental Socioeconomic Status, Family History of Psychiatric Disorders, and the Risk for Schizophrenia: A Danish Population-Based Study and Meta-analysis. , 2015, JAMA psychiatry.

[57]  P. Visscher,et al.  Mixed model with correction for case-control ascertainment increases association power. , 2015, American journal of human genetics.

[58]  N. Wray,et al.  Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis , 2015, Nature Genetics.

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

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

[61]  Bjarni J. Vilhjálmsson,et al.  A mixed-model approach for genome-wide association studies of correlated traits in structured populations , 2012, Nature Genetics.

[62]  R. Kay.  The Analysis of Survival Data , 2012 .

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

[64]  O. Mors,et al.  The Danish Psychiatric Central Research Register , 2011, Scandinavian journal of public health.

[65]  L. Thygesen,et al.  Introduction to Danish (nationwide) registers on health and social issues: Structure, access, legislation, and archiving , 2011, Scandinavian journal of public health.

[66]  C. Pedersen,et al.  The Danish Civil Registration System , 2011, Scandinavian journal of public health.

[67]  Dirk Eddelbuettel,et al.  Rcpp: Seamless R and C++ Integration , 2011 .

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

[69]  Hon-Cheong So,et al.  A Unifying Framework for Evaluating the Predictive Power of Genetic Variants Based on the Level of Heritability Explained , 2010, PLoS genetics.

[70]  D. Falconer.  The inheritance of liability to certain diseases, estimated from the incidence among relatives , 1965 .

[71]  Matti Pirinen,et al.  Identification of 15 new psoriasis susceptibility loci highlights the role of innate immunity , 2012 .

[72]  P. Froguel,et al.  Genetic associations with human longevity at the APOE and ACE loci , 1994, Nature Genetics.

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