Genetics of 35 blood and urine biomarkers in the UK Biobank
暂无分享,去创建一个
Trevor Hastie | Robert Tibshirani | Anna Shcherbina | Michael Wainberg | Junyang Qian | Yosuke Tanigawa | Jonathan K. Pritchard | Manuel A. Rivas | Samuli Ripatti | Themistocles L. Assimes | David Amar | Mark J. Daly | Guhan Ram Venkataraman | Matthew Aguirre | Nasa Sinnott-Armstrong | R. Tibshirani | T. Hastie | T. Assimes | M. Rivas | M. Daly | J. Pritchard | Junyang Qian | Yosuke Tanigawa | Matthew Aguirre | S. Ripatti | A. Shcherbina | N. Sinnott-Armstrong | M. Wainberg | Vineeta Agarwala | D. Amar | J. Pirruccello | H. Ollila | Vineeta Agarwala | Hanna M. Ollila | Nina J. Mars | James P. Pirruccello | Fatima Rodriguez | N. Mars | G. Venkataraman | F. Rodriguez | Nasa Sinnott-Armstrong | Michael Wainberg
[1] P. Shannon,et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.
[2] Dermot F. Reilly,et al. Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease , 2018 .
[3] Matthew Aguirre,et al. Phenome-wide burden of copy number variation in UK Biobank , 2019, bioRxiv.
[4] G. Abecasis,et al. Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis , 2018, bioRxiv.
[5] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[6] T. Hankemeier,et al. Genome-wide association study of plasma lipids , 2019, bioRxiv.
[7] M. Pirinen,et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA , 2016, Nature Communications.
[8] Evan Z. Macosko,et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types , 2017, Nature Genetics.
[9] S. Hillier,et al. Oestradiol Assays: Applications and Guidelines for the Provision of a Clinical Biochemistry Service , 1988, Annals of clinical biochemistry.
[10] Alexander Pertsemlidis,et al. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9 , 2005, Nature Genetics.
[11] M. Rivas,et al. Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study , 2018, Nature Communications.
[12] N. Lundbom,et al. Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors. , 2009, Gastroenterology.
[13] Alicia R. Martin,et al. Clinical use of current polygenic risk scores may exacerbate health disparities , 2019, Nature Genetics.
[14] J. Stockman,et al. A New Equation to Estimate Glomerular Filtration Rate , 2011 .
[15] P. O’Reilly,et al. Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels , 2018, Nature Communications.
[16] P. Visscher,et al. Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans , 2019, Genetics.
[17] Cathie Sudlow,et al. Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank , 2016, PloS one.
[18] C. Smith,et al. γδ T cells promote inflammation and insulin resistance during high fat diet‐induced obesity in mice , 2015, Journal of leukocyte biology.
[19] Beata Franczyk,et al. Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome , 2017, International journal of molecular sciences.
[20] Sylvia Stracke,et al. Genome-wide Association Studies Identify Genetic Loci Associated With Albuminuria in Diabetes , 2015, Diabetes.
[21] R. D'Agostino,et al. A genome-wide association study for blood lipid phenotypes in the Framingham Heart Study , 2007, BMC Medical Genetics.
[22] G. Bell,et al. Mutation in hepatocyte nuclear factor-1 beta gene (TCF2) associated with MODY. , 1997, Nature genetics.
[23] David A. Knowles,et al. Inferring relevant cell types for complex traits using single-cell gene expression , 2017, bioRxiv.
[24] Tanya M. Teslovich,et al. Genetic and Pharmacologic Inactivation of ANGPTL3 and Cardiovascular Disease , 2017, The New England journal of medicine.
[25] H. Yki-Järvinen. Fat in the liver and insulin resistance , 2005, Annals of medicine.
[26] Christian Gieger,et al. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis , 2017, PLoS Medicine.
[27] Raquel S. Sevilla,et al. Exome-wide association study of plasma lipids in >300,000 individuals , 2017, Nature Genetics.
[28] B. Neale,et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.
[29] Amita Sharma,et al. Mutations in SLC34A3/NPT2c are associated with kidney stones and nephrocalcinosis. , 2014, Journal of the American Society of Nephrology : JASN.
[30] B. Pasaniuc,et al. Contrasting the genetic architecture of 30 complex traits from summary association data , 2016, bioRxiv.
[31] Jonathan C. Cohen,et al. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. , 2006, The New England journal of medicine.
[32] J. Takeda,et al. Frameshift Mutation, A263fsinsGG, in the Hepatocyte Nuclear Factor-1β Gene Associated With Diabetes and Renal Dysfunction , 1998, Diabetes.
[33] M. Kanai,et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases , 2018, Nature Genetics.
[34] A. Price,et al. Distinguishing genetic correlation from causation across 52 diseases and complex traits , 2017, Nature Genetics.
[35] Y. J. Kim,et al. Meta-analysis identifies multiple loci associated with kidney function–related traits in east Asian populations , 2012, Nature Genetics.
[36] Trevor Hastie,et al. A Fast and Flexible Algorithm for Solving the Lasso in Large-scale and Ultrahigh-dimensional Problems , 2019 .
[37] Cardiovascular endocrinology: Is ANGPTL3 the next PCSK9? , 2017, Nature Reviews Endocrinology.
[38] Bogdan Pasaniuc,et al. Local genetic correlation gives insights into the shared genetic architecture of complex traits , 2016, bioRxiv.
[39] Jonathan C. Cohen,et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. , 2010, The New England journal of medicine.
[40] Y. Iwamoto,et al. Liver and Kidney Function in Japanese Patients With Maturity-Onset Diabetes of the Young , 1998, Diabetes Care.
[41] Cassandra N. Spracklen,et al. Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey , 2018, PLoS genetics.
[42] G. Breen,et al. Multi-polygenic score approach to trait prediction , 2017, Molecular Psychiatry.
[43] Hiroyuki Kuroki,et al. Mutation in hepatocyte nuclear factor–1β gene (TCF2) associated with MODY , 1997, Nature Genetics.
[44] Tanya M. Teslovich,et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways , 2012, Nature Genetics.
[45] E. Rimm,et al. A prospective study of dietary calcium and other nutrients and the risk of symptomatic kidney stones. , 1993, The New England journal of medicine.
[46] V. M. V. Program. A catalog of genetic loci associated with kidney function from analyses of a million individuals , 2019 .
[47] Audrey Y. Chu,et al. 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function , 2017, Scientific Reports.
[48] M. Daly,et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.
[49] P. Donnelly,et al. The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.
[50] Eric Boerwinkle,et al. Rare loss-of-function mutations in ANGPTL family members contribute to plasma triglyceride levels in humans. , 2008, The Journal of clinical investigation.
[51] Christopher M. DeBoever,et al. Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight novel adipocyte biology , 2018, bioRxiv.
[52] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[53] M. Pontoglio,et al. AKT2 is essential to maintain podocyte viability and function during chronic kidney disease , 2013, Nature Medicine.
[54] Fabian J Theis,et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations , 2012, Nature Genetics.
[55] Dajiang J. Liu,et al. Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants contributing to lipid levels and coronary artery disease , 2017, Nature Genetics.
[56] Tanya M. Teslovich,et al. Genetics of Blood Lipids Among ~300,000 Multi-Ethnic Participants of the Million Veteran Program , 2018, Nature Genetics.
[57] M. Daly,et al. An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.
[58] Christian Gieger,et al. Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation , 2015, PLoS genetics.
[59] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[60] Alexander Gusev,et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights , 2016, Nature Genetics.
[61] Jin-an Zhang,et al. Testosterone level and risk of type 2 diabetes in men: a systematic review and meta-analysis , 2017, Endocrine connections.
[62] C. Schmid,et al. A new equation to estimate glomerular filtration rate. , 2009, Annals of internal medicine.
[63] Stefan Neubauer,et al. Characterisation of liver fat in the UK Biobank cohort , 2017, PloS one.
[64] Tanya M. Teslovich,et al. Discovery and refinement of loci associated with lipid levels , 2013, Nature Genetics.
[65] Karsten B. Sieber,et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals , 2019, Nature Genetics.
[66] Mingyao Li,et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease , 2018, Science.
[67] Janan T. Eppig,et al. Expanding the mammalian phenotype ontology to support automated exchange of high throughput mouse phenotyping data generated by large-scale mouse knockout screens , 2015, Journal of Biomedical Semantics.
[68] K. Roeder,et al. Genomic Control for Association Studies , 1999, Biometrics.
[69] J. Hokanson,et al. Evaluation of urinary biomarkers for coronary artery disease, diabetes, and diabetic kidney disease. , 2009, Diabetes technology & therapeutics.
[70] Valeriia Haberland,et al. The MR-Base platform supports systematic causal inference across the human phenome , 2018, eLife.
[71] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[72] V. Bird,et al. A Prospective Study of Dietary Calcium and Other Nutrients and the Risk of Symptomatic Kidney Stones , 2021, 50 Studies Every Urologist Should Know.