A statistical boosting framework for polygenic risk scores based on large-scale genotype data
暂无分享,去创建一个
[1] A. Mayr,et al. Statistical learning for sparser fine-mapped polygenic models: the prediction of LDL-cholesterol , 2022, bioRxiv.
[2] R. Tibshirani,et al. Significant sparse polygenic risk scores across 813 traits in UK Biobank , 2022, PLoS genetics.
[3] N. Klein,et al. Deselection of base-learners for statistical boosting—with an application to distributional regression , 2021, Statistical methods in medical research.
[4] M. Nöthen,et al. Breast and prostate cancer risk: the interplay of polygenic risk, rare pathogenic germline variants, and family history , 2021, Genetics in Medicine.
[5] A. Auton,et al. Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets , 2021, Nature Communications.
[6] A. Mayr,et al. Randomized boosting with multivariable base-learners for high-dimensional variable selection and prediction , 2021, BMC Bioinform..
[7] J. Cuzick,et al. The importance of ethnicity: Are breast cancer polygenic risk scores ready for women who are not of White European origin? , 2021, International Journal of Cancer.
[8] B. Vilhjálmsson,et al. Improved genetic prediction of complex traits from individual-level data or summary statistics , 2020, Nature Communications.
[9] S. A. Lambert,et al. The Polygenic Score Catalog: an open database for reproducibility and systematic evaluation , 2020, medRxiv.
[10] Trevor Hastie,et al. Genetics of 35 blood and urine biomarkers in the UK Biobank , 2020, Nature Genetics.
[11] Ben Lehner,et al. Biophysical ambiguities prevent accurate genetic prediction , 2020, Nature Communications.
[12] Trevor Hastie,et al. Fast Lasso method for Large-scale and Ultrahigh-dimensional Cox Model with applications to UK Biobank , 2020, bioRxiv.
[13] G. Koppelman,et al. The genetics of asthma and the promise of genomics-guided drug target discovery. , 2020, The Lancet. Respiratory medicine.
[14] Bjarni J. Vilhjálmsson,et al. LDpred2: better, faster, stronger , 2020, bioRxiv.
[15] Lars G Fritsche,et al. The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities , 2019, Statistics in medicine.
[16] Trevor Hastie,et al. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank , 2019, bioRxiv.
[17] Ioannis Ntzoufras,et al. High-dimensional variable selection via low-dimensional adaptive learning , 2019, Electronic Journal of Statistics.
[18] Naomi R. Wray,et al. Improved polygenic prediction by Bayesian multiple regression on summary statistics , 2019, Nature Communications.
[19] M. Sabatine. PCSK9 inhibitors: clinical evidence and implementation , 2018, Nature Reviews Cardiology.
[20] P. Donnelly,et al. The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.
[21] Timothy Shin Heng Mak,et al. Tutorial: a guide to performing polygenic risk score analyses , 2018, bioRxiv.
[22] T. Ge,et al. Polygenic prediction via Bayesian regression and continuous shrinkage priors , 2018, bioRxiv.
[23] David R Williams,et al. Lack Of Diversity In Genomic Databases Is A Barrier To Translating Precision Medicine Research Into Practice. , 2018, Health affairs.
[24] Andrey Ziyatdinov,et al. Efficient analysis of large-scale genome-wide data with two R packages: bigstatsr and bigsnpr , 2018, Bioinform..
[25] Benjamin Hofner,et al. Boosting for statistical modelling-A non-technical introduction , 2018 .
[26] Ivana V. Yang,et al. The environment, epigenome, and asthma. , 2017, The Journal of allergy and clinical immunology.
[27] W. Chung,et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers , 2017, JAMA.
[28] Pak Chung Sham,et al. Polygenic scores via penalized regression on summary statistics , 2016, bioRxiv.
[29] Bernd Bischl,et al. Probing for Sparse and Fast Variable Selection with Model-Based Boosting , 2017, Comput. Math. Methods Medicine.
[30] Matthias Schmid,et al. Approaches to Regularized Regression – A Comparison between Gradient Boosting and the Lasso , 2016, Methods of Information in Medicine.
[31] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[32] P. Visscher,et al. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores , 2015, bioRxiv.
[33] P. Visscher,et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index , 2015, Nature Genetics.
[34] P. Visscher,et al. Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model , 2015, PLoS genetics.
[35] Jack Euesden,et al. PRSice: Polygenic Risk Score software , 2014, Bioinform..
[36] Carson C Chow,et al. Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.
[37] B. Berger,et al. Efficient Bayesian mixed model analysis increases association power in large cohorts , 2014, Nature Genetics.
[38] Ross M. Fraser,et al. Defining the role of common variation in the genomic and biological architecture of adult human height , 2014, Nature Genetics.
[39] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[40] P. Visscher,et al. Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs. , 2013, American journal of human genetics.
[41] Jian Huang,et al. Incorporating group correlations in genome-wide association studies using smoothed group Lasso. , 2013, Biostatistics.
[42] F. Kronenberg,et al. Lipoprotein(a): resurrected by genetics , 2013, Journal of internal medicine.
[43] Doug Speed,et al. Improved heritability estimation from genome-wide SNPs. , 2012, American journal of human genetics.
[44] Torsten Hothorn,et al. Prediction intervals for future BMI values of individual children - a non-parametric approach by quantile boosting , 2012, BMC Medical Research Methodology.
[45] Gonçalo R. Abecasis,et al. Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability , 2011, PLoS genetics.
[46] C. Wijmenga,et al. A genetic perspective on coeliac disease. , 2010, Trends in molecular medicine.
[47] G. Gibson. Hints of hidden heritability in GWAS , 2010, Nature Genetics.
[48] P. Visscher,et al. Common SNPs explain a large proportion of heritability for human height , 2011 .
[49] C. Newton-Cheh,et al. Blood pressure and human genetic variation in the general population , 2010, Current opinion in cardiology.
[50] Torsten Hothorn,et al. Model-based Boosting 2.0 , 2010, J. Mach. Learn. Res..
[51] B. Maher. Personal genomes: The case of the missing heritability , 2008, Nature.
[52] S. Geer. HIGH-DIMENSIONAL GENERALIZED LINEAR MODELS AND THE LASSO , 2008, 0804.0703.
[53] Hongzhe Li,et al. Group additive regression models for genomic data analysis. , 2008, Biostatistics.
[54] Peter Buhlmann,et al. BOOSTING ALGORITHMS: REGULARIZATION, PREDICTION AND MODEL FITTING , 2007, 0804.2752.
[55] A. Tsybakov,et al. Sparsity oracle inequalities for the Lasso , 2007, 0705.3308.
[56] Hongzhe Li,et al. Nonparametric pathway-based regression models for analysis of genomic data. , 2007, Biostatistics.
[57] Nicolai Meinshausen,et al. Relaxed Lasso , 2007, Comput. Stat. Data Anal..
[58] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[59] Y. Ritov,et al. Persistence in high-dimensional linear predictor selection and the virtue of overparametrization , 2004 .
[60] P. Bühlmann,et al. Boosting With the L2 Loss , 2003 .
[61] A. Rao,et al. Estimation of Genetic Parameters: principles , 2003 .
[62] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[63] Wenjiang J. Fu,et al. Asymptotics for lasso-type estimators , 2000 .
[64] Arthur E. Hoerl,et al. Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.
[65] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .