Publications

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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).
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).
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).
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).
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).
Frésard, L. et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med 25, 911-919 (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).
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).
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).
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).
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).
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).
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).
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).
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).
Chen, S. & Lin, X. Analysis in case-control sequencing association studies with different sequencing depths. Biostatistics 21, 577-593 (2020).
DeBoever, C. et al. Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases. Am J Hum Genet 106, 611-622 (2020).
Claussnitzer, M. et al. A brief history of human disease genetics. Nature 577, 179-189 (2020).
Walker, R. W. et al. A common variant in PNPLA3 is associated with age at diagnosis of NAFLD in patients from a multi-ethnic biobank. J Hepatol 72, 1070-1081 (2020).
Keys, K. L. et al. On the cross-population generalizability of gene expression prediction models. PLoS Genet 16, e1008927 (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).
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).
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).
Mefford, J. et al. Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models. J Comput Biol 27, 599-612 (2020).
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).
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
Dietlein, F. et al. Identification of cancer driver genes based on nucleotide context. Nat Genet 52, 208-218 (2020).
Dietlein, F. et al. Identification of cancer driver genes based on nucleotide context. Nat Genet 52, 208-218 (2020).
Liu, B. & Montgomery, S. B. Identifying causal variants and genes using functional genomics in specialized cell types and contexts. Hum Genet 139, 95-102 (2020).
Gay, N. R. et al. Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx. Genome Biol 21, 233 (2020).
C Y Mak, A. et al. Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand and Gene-By-Air-Pollution Interaction. Genetics 215, 869-886 (2020).
C Y Mak, A. et al. Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand and Gene-By-Air-Pollution Interaction. Genetics 215, 869-886 (2020).
Rau, C. D. et al. Modeling epistasis in mice and yeast using the proportion of two or more distinct genetic backgrounds: Evidence for "polygenic epistasis". PLoS Genet 16, e1009165 (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).
McCaw, Z. R., Lane, J. M., Saxena, R., Redline, S. & Lin, X. Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies. Biometrics 76, 1262-1272 (2020).
McCaw, Z. R., Lane, J. M., Saxena, R., Redline, S. & Lin, X. Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies. Biometrics 76, 1262-1272 (2020).

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