Export 68 results:
Author Title [ Year(Asc)]
Liu, Y. et al. ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies. Am J Hum Genet 104, 410-421 (2019).
Dahl, A., Guillemot, V., Mefford, J., Aschard, H. & Zaitlen, N. Adjusting for Principal Components of Molecular Phenotypes Induces Replicating False Positives. Genetics 211, 1179-1189 (2019).
Weghorn, D. et al. Applicability of the Mutation-Selection Balance Model to Population Genetics of Heterozygous Protein-Truncating Variants in Humans. Mol Biol Evol 36, 1701-1710 (2019).
Wang, Q. et al. A Bayesian framework that integrates multi-omics data and gene networks predicts risk genes from schizophrenia GWAS data. Nat Neurosci 22, 691-699 (2019).
Chen, S. & Onnela, J. - P. A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network. Sci Rep 9, 16674 (2019).
Tanigawa, Y. et al. Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology. Nat Commun 10, 4064 (2019).
Yang, H. et al. De novo pattern discovery enables robust assessment of functional consequences of non-coding variants. Bioinformatics 35, 1453-1460 (2019).
Dennis, J. et al. Diagnostic Algorithms to Study Post-Concussion Syndrome Using Electronic Health Records: Validating a Method to Capture an Important Patient Population. J Neurotrauma 36, 2167-2177 (2019).
Saito, Y. et al. Differential NOVA2-Mediated Splicing in Excitatory and Inhibitory Neurons Regulates Cortical Development and Cerebellar Function. Neuron 101, 707-720.e5 (2019).
Martin, S. et al. Drug-Resistant Juvenile Myoclonic Epilepsy: Misdiagnosis of Progressive Myoclonus Epilepsy. Front Neurol 10, 946 (2019).
Chen, H. et al. Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies. Am J Hum Genet 104, 260-274 (2019).
Seplyarskiy, V. B. et al. Error-prone bypass of DNA lesions during lagging-strand replication is a common source of germline and cancer mutations. Nat Genet 51, 36-41 (2019).
Uricchio, L. H., Kitano, H. C., Gusev, A. & Zaitlen, N. A. An evolutionary compass for detecting signals of polygenic selection and mutational bias. Evol Lett 3, 69-79 (2019).
Abul-Husn, N. S. et al. Exome sequencing reveals a high prevalence of BRCA1 and BRCA2 founder variants in a diverse population-based biobank. Genome Med 12, 2 (2019).
O'Connor, L. J. et al. Extreme Polygenicity of Complex Traits Is Explained by Negative Selection. Am J Hum Genet 105, 456-476 (2019).
Xu, M. et al. Genome sequencing analysis identifies Epstein-Barr virus subtypes associated with high risk of nasopharyngeal carcinoma. Nat Genet 51, 1131-1136 (2019).
McInnes, G. et al. Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. Bioinformatics 35, 2495-2497 (2019).
Unlu, G. et al. GRIK5 Genetically Regulated Expression Associated with Eye and Vascular Phenomes: Discovery through Iteration among Biobanks, Electronic Health Records, and Zebrafish. Am J Hum Genet 104, 503-519 (2019).
Frésard, L. et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med 25, 911-919 (2019).
J Weissenkampen, D. et al. Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits. Curr Protoc Hum Genet 101, e83 (2019).
Luo, R., Sedlazeck, F. J., Lam, T. - W. & Schatz, M. C. A multi-task convolutional deep neural network for variant calling in single molecule sequencing. Nat Commun 10, 998 (2019).
Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat Genet 51, 592-599 (2019).
Abul-Husn, N. S. & Kenny, E. E. Personalized Medicine and the Power of Electronic Health Records. Cell 177, 58-69 (2019).
Aguirre, M., Rivas, M. A. & Priest, J. Phenome-wide Burden of Copy-Number Variation in the UK Biobank. Am J Hum Genet 105, 373-383 (2019).
Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. Elife 8, (2019).
Schoech, A. P. et al. Quantification of frequency-dependent genetic architectures in 25 UK Biobank traits reveals action of negative selection. Nat Commun 10, 790 (2019).
Ultra-Rare Genetic Variation in the Epilepsies: A Whole-Exome Sequencing Study of 17,606 Individuals. Am J Hum Genet 105, 267-282 (2019).
Park, D. S. et al. An ancestry-based approach for detecting interactions. Genet Epidemiol 42, 49-63 (2018).
Hu, Y. et al. A common loss-of-function variant is associated with lower vitamin B concentration in African Americans. Blood 131, 2859-2863 (2018).
Verbanck, M., Chen, C. - Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50, 693-698 (2018).
Westra, H. - J. et al. Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes. Nat Genet 50, 1366-1374 (2018).
Gazal, S. et al. Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations. Nat Genet 50, 1600-1607 (2018).
Regier, A. A. et al. Functional equivalence of genome sequencing analysis pipelines enables harmonized variant calling across human genetics projects. Nat Commun 9, 4038 (2018).
Slowikowski, K., Wei, K., Brenner, M. B. & Raychaudhuri, S. Functional genomics of stromal cells in chronic inflammatory diseases. Curr Opin Rheumatol 30, 65-71 (2018).
Belbin, G. M., Nieves-Colón, M. A., Kenny, E. E., Moreno-Estrada, A. & Gignoux, C. R. Genetic diversity in populations across Latin America: implications for population and medical genetic studies. Curr Opin Genet Dev 53, 98-104 (2018).
Wojcik, G. L. et al. Imputation-Aware Tag SNP Selection To Improve Power for Large-Scale, Multi-ethnic Association Studies. G3 (Bethesda) 8, 3255-3267 (2018).
DeBoever, C. et al. Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. Nat Commun 9, 1612 (2018).
Liu, Z. & Lin, X. Multiple phenotype association tests using summary statistics in genome-wide association studies. Biometrics 74, 165-175 (2018).
Bastarache, L. et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science 359, 1233-1239 (2018).
Sordillo, J. E., Kraft, P., Wu, A. Chen & Asgari, M. M. Quantifying the Polygenic Contribution to Cutaneous Squamous Cell Carcinoma Risk. J Invest Dermatol 138, 1507-1510 (2018).
Li, H. et al. A synthetic-diploid benchmark for accurate variant-calling evaluation. Nat Methods 15, 595-597 (2018).
Sun, R., Carroll, R. J., Christiani, D. C. & Lin, X. Testing for gene-environment interaction under exposure misspecification. Biometrics 74, 653-662 (2018).