Publications

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A
Raffield, L. M. et al. Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts. Am J Hum Genet 106, 112-120 (2020).
Raffield, L. M. et al. Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts. Am J Hum Genet 106, 112-120 (2020).
DeBoever, C. et al. Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases. Am J Hum Genet 106, 611-622 (2020).
B
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).
C
Tanigawa, Y. et al. Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology. Nat Commun 10, 4064 (2019).
Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53, 1415-1424 (2021).
Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53, 1415-1424 (2021).
Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53, 1415-1424 (2021).
Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53, 1415-1424 (2021).
Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet 53, 1415-1424 (2021).
Keys, K. L. et al. On the cross-population generalizability of gene expression prediction models. PLoS Genet 16, e1008927 (2020).
McAllister, K. et al. Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. Am J Epidemiol 186, 753-761 (2017).
D
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).
Li, X. et al. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat Genet 52, 969-983 (2020).
Li, X. et al. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat Genet 52, 969-983 (2020).
Li, X. et al. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat Genet 52, 969-983 (2020).
E
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).
Zhong, X. et al. Electronic health record phenotypes associated with genetically regulated expression of CFTR and application to cystic fibrosis. Genet Med 22, 1191-1200 (2020).
Goleva, S. B., Lake, A. M., Torstenson, E. S., Haas, K. F. & Davis, L. K. Epidemiology of Functional Seizures Among Adults Treated at a University Hospital. JAMA Netw Open 3, e2027920 (2020).
Kousi, M. et al. Evidence for secondary-variant genetic burden and non-random distribution across biological modules in a recessive ciliopathy. Nat Genet 52, 1145-1150 (2020).
F
Qian, J. et al. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet 16, e1009141 (2020).
Qian, J. et al. A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank. PLoS Genet 16, e1009141 (2020).
Li, R. et al. Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. Biostatistics (2020). doi:10.1093/biostatistics/kxaa038
Li, R. et al. Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. Biostatistics (2020). doi:10.1093/biostatistics/kxaa038
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).
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).
G
Rochtus, A. et al. Genetic diagnoses in epilepsy: The impact of dynamic exome analysis in a pediatric cohort. Epilepsia 61, 249-258 (2020).
Sinnott-Armstrong, N. et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat Genet 53, 185-194 (2021).
Sinnott-Armstrong, N. et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat Genet 53, 185-194 (2021).
McInnes, G. et al. Global Biobank Engine: enabling genotype-phenotype browsing for biobank summary statistics. Bioinformatics 35, 2495-2497 (2019).
I
Dietlein, F. et al. Identification of cancer driver genes based on nucleotide context. Nat Genet 52, 208-218 (2020).
Frésard, L. et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med 25, 911-919 (2019).
Li, X. et al. The impact of rare variation on gene expression across tissues. Nature 550, 239-243 (2017).
Amariuta, T. et al. Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements. Nat Genet 52, 1346-1354 (2020).
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).
Ji, Y. et al. Incorporating European GWAS findings improve polygenic risk prediction accuracy of breast cancer among East Asians. Genet Epidemiol (2021). doi:10.1002/gepi.22382
Ritchie, M. D. et al. Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186, 771-777 (2017).
Sun, R. et al. Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer. Genet Epidemiol 45, 99-114 (2021).
Sun, R. et al. Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer. Genet Epidemiol 45, 99-114 (2021).
Sun, R. et al. Integration of multiomic annotation data to prioritize and characterize inflammation and immune-related risk variants in squamous cell lung cancer. Genet Epidemiol 45, 99-114 (2021).
L
Ritz, B. R. et al. Lessons Learned From Past Gene-Environment Interaction Successes. Am J Epidemiol 186, 778-786 (2017).
M
DeBoever, C. et al. Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. Nat Commun 9, 1612 (2018).
Baldassari, A. R. et al. Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits. Circ Genom Precis Med 13, e002680 (2020).
Baldassari, A. R. et al. Multi-Ethnic Genome-Wide Association Study of Decomposed Cardioelectric Phenotypes Illustrates Strategies to Identify and Characterize Evidence of Shared Genetic Effects for Complex Traits. Circ Genom Precis Med 13, e002680 (2020).
N
Contreras, M. G. et al. Native American Ancestry and Air Pollution Interact to Impact Bronchodilator Response in Puerto Rican Children with Asthma. Ethn Dis 31, 77-88 (2021).
P
Tcheandjieu, C. et al. A phenome-wide association study of 26 mendelian genes reveals phenotypic expressivity of common and rare variants within the general population. PLoS Genet 16, e1008802 (2020).
Bastarache, L. et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science 359, 1233-1239 (2018).
Sohail, M. et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. Elife 8, (2019).
Aguirre, M. et al. Polygenic risk modeling with latent trait-related genetic components. Eur J Hum Genet (2021). doi:10.1038/s41431-021-00813-0
Aguirre, M. et al. Polygenic risk modeling with latent trait-related genetic components. Eur J Hum Genet (2021). doi:10.1038/s41431-021-00813-0

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