A COVARIANCE-ENHANCED APPROACH TO MULTI-TISSUE JOINT EQTL MAPPING WITH APPLICATION TO TRANSCRIPTOME-WIDE ASSOCIATION STUDIES.

[1]  Helen E. Parkinson,et al.  The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 , 2018, Nucleic Acids Res..

[2]  R. Spielman,et al.  Natural variation in human gene expression assessed in lymphoblastoid cells , 2003, Nature Genetics.

[3]  C. Wallace,et al.  Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics , 2013, PLoS genetics.

[4]  Jun S. Liu,et al.  The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans , 2015, Science.

[5]  Michael Wainberg,et al.  Opportunities and challenges for transcriptome-wide association studies , 2019, Nature Genetics.

[6]  Nicola J. Rinaldi,et al.  Genetic effects on gene expression across human tissues , 2017, Nature.

[7]  P. Visscher,et al.  Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets , 2016, Nature Genetics.

[8]  Pradeep Ravikumar,et al.  QUIC: quadratic approximation for sparse inverse covariance estimation , 2014, J. Mach. Learn. Res..

[9]  James G. Scott,et al.  Proximal Algorithms in Statistics and Machine Learning , 2015, ArXiv.

[10]  Samuel E. Jones,et al.  Red blood cell distribution width: Genetic evidence for aging pathways in 116,666 volunteers , 2017, bioRxiv.

[11]  Noah Simon,et al.  A Sparse-Group Lasso , 2013 .

[12]  M. Pourahmadi,et al.  Regularized multivariate regression models with skew-t error distributions , 2014 .

[13]  Alkes L. Price,et al.  Integrative approaches for large-scale transcriptome-wide association studies , 2015 .

[14]  Shane J. Neph,et al.  Systematic Localization of Common Disease-Associated Variation in Regulatory DNA , 2012, Science.

[15]  R. Stoughton,et al.  Genetics of gene expression surveyed in maize, mouse and man , 2003, Nature.

[16]  Junhui Wang,et al.  Joint estimation of sparse multivariate regression and conditional graphical models , 2013, ArXiv.

[17]  Stephen P. Boyd,et al.  Proximal Algorithms , 2013, Found. Trends Optim..

[18]  M. Yuan,et al.  Model selection and estimation in the Gaussian graphical model , 2007 .

[19]  R. Guigó,et al.  Transcriptome genetics using second generation sequencing in a Caucasian population , 2010, Nature.

[20]  Hongyu Zhao,et al.  A statistical framework for cross-tissue transcriptome-wide association analysis , 2018, Nature Genetics.

[21]  Hongzhe Li,et al.  A SPARSE CONDITIONAL GAUSSIAN GRAPHICAL MODEL FOR ANALYSIS OF GENETICAL GENOMICS DATA. , 2011, The annals of applied statistics.

[22]  G. Lettre,et al.  Integrative analysis of vascular endothelial cell genomic features identifies AIDA as a coronary artery disease candidate gene , 2019, Genome Biology.

[23]  S. Hunt,et al.  Genome-Wide Associations of Gene Expression Variation in Humans , 2005, PLoS genetics.

[24]  J. Terrovitis,et al.  Red blood cell distribution width is a significant prognostic marker in advanced heart failure, independent of hemoglobin levels. , 2014, Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese.

[25]  Xiang Zhou,et al.  Prediction of gene expression with cis-SNPs using mixed models and regularization methods , 2017, BMC Genomics.

[26]  Eran Segal,et al.  GenoExp: a web tool for predicting gene expression levels from single nucleotide polymorphisms , 2015, Bioinform..

[27]  Kathryn S. Burch,et al.  Leveraging Polygenic Functional Enrichment to Improve GWAS Power. , 2019, American journal of human genetics.

[28]  Eleazar Eskin,et al.  Colocalization of GWAS and eQTL Signals Detects Target Genes , 2016 .

[29]  S. Kaul,et al.  CARF is a multi-module regulator of cell proliferation and a molecular bridge between cellular senescence and carcinogenesis , 2017, Mechanisms of Ageing and Development.

[30]  Ivan Rusyn,et al.  An empirical Bayes approach for multiple tissue eQTL analysis , 2013, Biostatistics.

[31]  Kaanan P. Shah,et al.  A gene-based association method for mapping traits using reference transcriptome data , 2015, Nature Genetics.

[32]  Adam J. Rothman,et al.  Shrinking characteristics of precision matrix estimators , 2017, 1704.04820.

[33]  N. Cox,et al.  Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS , 2010, PLoS genetics.

[34]  F. Triposkiadis,et al.  Red blood cell distribution width as a prognostic marker in patients with heart failure and diabetes mellitus , 2017, Cardiovascular Diabetology.

[35]  M. Stephens,et al.  A Statistical Framework for Joint eQTL Analysis in Multiple Tissues , 2012, PLoS genetics.

[36]  Joseph K. Pickrell,et al.  Understanding mechanisms underlying human gene expression variation with RNA sequencing , 2010, Nature.

[37]  Xiao-Li Meng,et al.  Maximum likelihood estimation via the ECM algorithm: A general framework , 1993 .

[38]  Mary Goldman,et al.  Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics , 2016, Nature Communications.

[39]  Adam J Rothman,et al.  Sparse Multivariate Regression With Covariance Estimation , 2010, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[40]  Adam J. Rothman,et al.  Sparse permutation invariant covariance estimation , 2008, 0801.4837.

[41]  Ji Zhu,et al.  Regularized Multivariate Regression for Identifying Master Predictors with Application to Integrative Genomics Study of Breast Cancer. , 2008, The annals of applied statistics.

[42]  J. Friedman,et al.  New Insights and Faster Computations for the Graphical Lasso , 2011 .

[43]  Kenneth Lange,et al.  MM optimization algorithms , 2016 .

[44]  Alexander Gusev,et al.  Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits. , 2017, American journal of human genetics.

[45]  X. Wen,et al.  Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization , 2016, bioRxiv.

[46]  Lin S. Chen,et al.  Imputing Gene Expression in Uncollected Tissues Within and Beyond GTEx. , 2015, American journal of human genetics.

[47]  A. Zellner.  An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias , 1962 .

[48]  W. G. Hill,et al.  Heritability in the genomics era — concepts and misconceptions , 2008, Nature Reviews Genetics.

[49]  Milton Pividori,et al.  Integrating predicted transcriptome from multiple tissues improves association detection , 2018, bioRxiv.

[50]  Yufeng Liu,et al.  Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood , 2012, J. Multivar. Anal..

[51]  N. Samani,et al.  Influence of a Coronary Artery Disease–Associated Genetic Variant on FURIN Expression and Effect of Furin on Macrophage Behavior , 2018, Arteriosclerosis, thrombosis, and vascular biology.

[52]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[53]  J. Fish,et al.  Hypoxia-inducible Expression of a Natural cis-Antisense Transcript Inhibits Endothelial Nitric-oxide Synthase* , 2007, Journal of Biological Chemistry.

[54]  Jian Yang,et al.  Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits , 2016, Genome Medicine.