Export 13 results:
Author [ Title(Asc)] Year
Filters: First Letter Of Title is I  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
Goddard, P. C. et al. Integrative genomic analysis in African American children with asthma finds three novel loci associated with lung function. Genet Epidemiol 45, 190-208 (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).
Ritchie, M. D. et al. Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol 186, 771-777 (2017).
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
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).
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).
Kichaev, G. et al. Improved methods for multi-trait fine mapping of pleiotropic risk loci. Bioinformatics 33, 248-255 (2017).
Li, X. et al. The impact of rare variation on gene expression across tissues. Nature 550, 239-243 (2017).
Gay, N. R. et al. Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx. Genome Biol 21, 233 (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).
Frésard, L. et al. Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts. Nat Med 25, 911-919 (2019).
Bonder, M. Jan et al. Identification of rare and common regulatory variants in pluripotent cells using population-scale transcriptomics. Nat Genet 53, 313-321 (2021).
Dietlein, F. et al. Identification of cancer driver genes based on nucleotide context. Nat Genet 52, 208-218 (2020).