Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses
Zhang et al.
Nature Genetics volume 52, pages572–581(2020)
Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration
Rinki Ratnapriya, Olukayode A. Sosina, Margaret R. Starostik, Madeline Kwicklis, Rebecca J. Kapphahn, Lars G. Fritsche, Ashley Walton, Marios Arvanitis, Linn Gieser, Alexandra Pietraszkiewicz, Sandra R. Montezuma, Emily Y. Chew, Alexis Battle, Gonçalo R. Abecasis, Deborah A. Ferrington, Nilanjan Chatterjee & Anand Swaroop
Nature Genetics volume 51, pages606–610(2019)
Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects
Guanghao Qi & Nilanjan Chatterjee
Nature Communications volume 10, Article number: 1941 (2019
Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits.
Zhang Y, Qi G, Park JH, Chatterjee N.
Nat Genet. 2018 Sep;50(9):1318-1326. doi: 10.1038/s41588-018-0193-x. Epub 2018 Aug 13.
Power Analysis for Genetic Association Test (PAGEANT) provides insights to challenges for rare variant association studies.
Derkach A, Zhang H, Chatterjee N.
Bioinformatics. 2018 May 1;34(9):1506-1513. doi: 10.1093/bioinformatics/btx770.
PMID: 29194474 [PubMed - in process]
Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.
Song M, Wheeler W, Caporaso NE, Landi MT, Chatterjee N.
Genet Epidemiol. 2018 Mar;42(2):146-155. doi: 10.1002/gepi.22093. Epub 2017 Nov 26.
PMID: 29178451 [PubMed - indexed for MEDLINE]
Winner's Curse Correction and Variable Thresholding Improve Performance of Polygenic Risk Modeling Based on Genome-Wide Association Study Summary-Level Data.
Shi J, Park JH, Duan J, Berndt ST, Moy W, Yu K, Song L, Wheeler W, Hua X, Silverman D, Garcia-Closas M, Hsiung CA, Figueroa JD, Cortessis VK, Malats N, Karagas MR, Vineis P, Chang IS, Lin D, Zhou B, Seow A, Matsuo K, Hong YC, Caporaso NE, Wolpin B, Jacobs E, Petersen GM, Klein AP, Li D, Risch H, Sanders AR, Hsu L, Schoen RE, Brenner H; MGS (Molecular Genetics of Schizophrenia) GWAS Consortium; GECCO (The Genetics and Epidemiology of Colorectal Cancer Consortium); GAME-ON/TRICL (Transdisciplinary Research in Cancer of the Lung) GWAS Consortium; PRACTICAL (PRostate cancer AssoCiation group To Investigate Cancer Associated aLterations) Consortium; PanScan Consortium; GAME-ON/ELLIPSE Consortium, Stolzenberg-Solomon R, Gejman P, Lan Q, Rothman N, Amundadottir LT, Landi MT, Levinson DF, Chanock SJ, Chatterjee N.
PLoS Genet. 2016 Dec 30;12(12):e1006493. doi: 10.1371/journal.pgen.1006493. eCollection 2016 Dec.
PMID: 28036406 [PubMed - indexed for MEDLINE] Free PMC Article
Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits.
Qi G, Chatterjee N.
PLoS Genet. 2018 Oct 5;14(10):e1007549. doi: 10.1371/journal.pgen.1007549. [Epub ahead of print]
PMID: 30289880 [PubMed - as supplied by publisher] Free Article
A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations
Zhang H, Wheeler W, Hyland PL, Yang Y, Shi J, Chatterjee N, Yu K.
PLoS Genet. 2016; Jun 30;12(6):e1006122. doi: 10.1371/journal.pgen.1006122
Projecting the performance of risk prediction from polygenic analyses of genome-wide association studies
Chatterjee N, Wheeler B, Sampson S, Hartge P, Chanock S and Park J.
Nature Genetics 2013; 45:400-5 (Editorial covering this article appeared in the same issue of NG, NCI Press Release: link)
Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants
Park JH, Gail MH, Weinberg CR, Carroll RJ, Chung CC, Wang Z, Chanock SJ, Fraumeni JF Jr, Chatterjee N.
Proc Natl Acad Sci 2011; 108:18026-31
A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits
Bhattacharjee S, Rajaraman P, Jacobs KB, Wheeler WA, Melin BS, Hartge P; GliomaScan Consortium, Yeager M, Chung CC, Chanock SJ, Chatterjee N.
Am J Hum Genet 2012; 90:821-35
Estimating effect size distribution from genome-wide association studies and implications for future discoveries
Park J, Wacholder S, Gail M, Peters U, Jacobs K, Chanock S, Chatterjee N.
Nature Genetics 2010; 42:570-5. (Editorial and New/Views Appeared in the same issue of NG. Featured as “leading edge” article in Cell (2010, Volume 142, Page 179))
Using principal components of genetic variation for robust and powerful detections of gene-gene interactions in case-control and case-only studies
Bhattacharjee S, Zhaoming W, Ciampa J, Kraft P, Chanock S, Yu K, Chatterjee N.
Am J Hum Genet 2010; 86:331-42. (The first author won a Young Investigator Award for 2010 from the Epidemiology Section of American Statistical Association)
A new statistic and its power to infer membership in a genome-wide association study using genotype frequencies
Jacobs KB, Yeager M, Wacholder S, Craig D, Kraft P, Hunter DJ, Paschal J, Manolio TA, Tucker M, Hoover RN, Thomas GD, Chanock SJ, Chatterjee N.
Nat Genet 2009; 41:1253-7. (Commentary on this article appeared in the same issue of NG).
Case-Only Analysis of Gene-Environment Interactions Using Polygenic Risk Scores
Allison Meisner, Prosenjit Kundu, Nilanjan Chatterjee
American Journal of Epidemiology, Volume 188, Issue 11, November 2019, Pages 2013–2020
Shrinkage estimators for robust and efficient inference in haplotype-based case-control studies
Chen YH, Chatterjee N, Carroll RJ.
J Am Stat Assoc 2009; 104: 220-233 (cited in 2011 COPSS Snedecor Award)
Exploiting gene-environment independence for analysis of case-control studies: an empirical Bayes-type shrinkage estimator to trade-off between bias and efficiency
Mukherjee B, Chatterjee N.
Biometrics 2008; 64: 685-94
Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions
Chatterjee N, Kalaylioglu Z, Moslehi R, Peters U, Wacholder S.
Am J Hum Genet 2006;79:1002-1016
Semiparametric maximum-likelihood estimation in case-control studies of gene-environment interactions
Chatterjee N, Carroll RJ.
Biometrika 2005;92:399-418
Exploiting gene-environment independence in family-based case-control studies: Increased power for detecting associations, interactions and joint-effects
Chatterjee N, Zeynep K, Carroll R.
Genet Epidemiol 2005; 28:138-156
Common genetic variation and risk of gallbladder cancer in India: a case-control genome-wide association study.
Mhatre S, Wang Z, Nagrani R, Badwe R, Chiplunkar S, Mittal B, Yadav S, Zhang H, Chung CC, Patil P, Chanock S, Dikshit R, Chatterjee N, Rajaraman P.
Lancet Oncol. 2017 Apr;18(4):535-544. doi: 10.1016/S1470-2045(17)30167-5. Epub 2017 Mar 5. PMID: 28274756 [PubMed - indexed for MEDLINE]
Analysis of heritability and share heritability based on genomewide association studies for thirteen cancer types
Sampson J…………………Chanock S, Chatterjee N.
J Natl Cancer Inst 2015: 107(12):div279
Hundreds of variants clustered in genomic loci and biological pathways affect human height
GIANT consortium.
Nature 2010; 14:239-44
Association of analyses of 249,796 individuals reveal 18 new loci associated with body mass index
GIANT consortium (400 authors).
Nat Genet 2010; 42:937-948
A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci
Rothman N#, Garcia-Closas M, Chatterjee N, Maltas N, Wu Xifeng et al.
Nature Genetics 2010; 42:978-84
A genome-wide association study of lung cancer identifies a region of chromosome 5p15 associated with risk for adenocarcinoma
Landi MT, Chatterjee N, Yu K, Goldin LR, Goldstein AM, Rotunno M, Mirabello L et al.
Am J Hum Genet 2009; 85: 679-691
Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification
Parichoy Pal Choudhury, Amber N Wilcox, Mark N Brook, Yan Zhang, Thomas Ahearn, Nick Orr, Penny Coulson, Minouk J Schoemaker, Michael E Jones, Mitchell H Gail, Anthony J Swerdlow, Nilanjan Chatterjee, Montserrat Garcia-Closas
JNCI: Journal of the National Cancer Institute, Volume 112, Issue 3, 1 March 2020, Pages 278–285/em>
Development and application of polygenic risk prediction models for stratified disease prevention
Chatterjee N, Shi J and Garcia-Closas M.
Nature Review Genetics, 2016, 17:392-406
Breast cancer risk from modifiable and non-modifiable risk factors among Caucasian women in the United States
Maas P,…………..Garcia-Closas M, Chatterjee N.
Journal of the American Medical Association (JAMA)-Oncology 2016 May 26. doi: 10.1001/jamaoncol.2016.1025. (Press release link)
Combined association of genetic and environmental risk factors: Implications for prevention of breast cancer
Garcia-Closas M, Burak N, Chatterjee N.
Journal of the National Cancer Institute 2014; 106. dju305 doi:10.1093/jnci/dju305
Common genetic polymorphisms modify the effect of smoking on absolute risk of bladder cancer
Garcia-Closas M, Rothman N, Figueroa JD, Prokunina-Olsson L, Han S, Baris,…, Silverman D, Chatterjee N.
Cancer Research 2013, 73:2211-20. (commentary in Nature Review Urology 2013, 10:374-5)
Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancer
Park JH, Gail MH, Greene MH, Chatterjee N.
J Clin Oncol 2012; 20:2157-62
Generalized meta-analysis for multiple regression models across studies with disparate covariate information
Prosenjit Kundu, Runlong Tang, Nilanjan Chatterjee
Biometrika, Volume 106, Issue 3, September 2019, Pages 567–585
Post-selection inference following aggregate level hypothesis testing in large scale genomic data.
Heller R, Chatterjee N, Krieger A and Shi J.
Journal of the Amerixan statistical Association, Theory&Methods, Advanced Online Publication.
Constrained maximum likelihood estimation for model calibration using summary-level information from external big data sources
Chatterjee N, Chen Y.H., Maas P and Carroll R.J.
J of Am Stat Assoc 2016, 111:107-117 (followed with Discussion)
Analysis of cohort studies with multivariate, partially observed, disease classification data
Chatterjee N, Sinha S, Diver R, Feigelson, H.
Biometrika 2010; 97: 683-698
