Publications

Zhang Z, Kim A, Suboc N, Mancuso N, Gazal S. Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping. https://www.medrxiv.org/content/10.1101/2025.01.18.25320755v1

Zhao et al. LUCIDus: An R Package For Estimating Latent Unknown Clusters By Integrating Multi-omics Data (LUCID) With Phenotypic Traits. R Journal. (in press)
Arai H, et al. Predictive value of CDC37 gene expression for targeted therapy in metastatic colorectal cancer. Eur J Cancer, 2024. Apr:201:113914. (Read here)

Battaglin F, et al. CCR5 and CCL5 gene expression in colorectal cancer: comprehensive profiling and clinical value. J Immunother Cancer, 2024. 12(1). (Read here)

Bradfield JP, et al. Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes. Genome Biol, 2024. 25(1):22 (Read here)

Chen Z, et al. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes. Nat Commun, 2024. 15(1):3557. (Read here)

Drew DA, et al. Two genome-wide interaction loci modify the association of nonsteroidal anti-inflammatory drugs with colorectal cancer. Sci Adv, 2024. 10(22): eadk3121. (Read here)

Goodrich JA, et al. Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease. Environ Int. 2024 Aug;190:108930. doi: 10.1016/j.envint.2024.108930. Epub 2024 Aug 3. (Read here)

Goodrich J.A., et al. Postprandial Metabolite Profiles and Risk of Prediabetes in Young People: A Longitudinal Multicohort Study. Diabetes Care, 2024. 47(1):151-159. (Read here)

Guirette M, et al.; CHARGE Gene-Lifestyle Interactions Working Group. Genome-Wide Interaction Analysis With DASH Diet Score Identified Novel Loci for Systolic Blood Pressure. Hypertension. 2024 Mar;81(3):552-560. doi: 10.1161/HYPERTENSIONAHA.123.22334. Epub 2024 Jan 16. PMID: 38226488; PMCID: PMC10922535. (Read here)

Jiang L, Shen J, Darst BF, Haiman CA, Mancuso N, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data. Genet Epidemiol. 2024 Oct;48(7):291-309. doi: 10.1002/gepi.22577. Epub 2024 Jun 17. PMID: 38887957. (Read here)

Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk, medRxiv, 2024 (Read here)

Nagarajan P, et al. A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations. medRxiv, 2024. (Read here)

Papadimitriou N, et al. Genome-wide interaction study of dietary intake of fibre, fruits, and vegetables with risk of colorectal cancer. EBioMedicine, 2024. 104:105146. (Read here)

Shen J, Jiang L, Wang K, Wang A, Chen F, Newcombe PJ, Haiman CA, Conti DV. Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction. Genet Epidemiol. 2024 Sep;48(6):241-257. doi: 10.1002/gepi.22562. Epub 2024 Apr 12. (Read here)

Tian Y, et al. Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk. Br J Cancer, 2024. 130(10): 1687-1696. (Read here)

Zhao Y, Jia Q, Goodrich J, Darst B, Conti DV. An extension of latent unknown clustering integrating multi-omics data (LUCID) incorporating incomplete omics data. Bioinform Adv. 2024 Aug 24;4(1):vbae123. doi: 10.1093/bioadv/vbae123 (Read here)

Zhu X, et al.; CHARGE Gene-Lifestyle Interactions Working Group. An approach to identify gene-environment interactions and reveal new biological insight in complex traits. Nat Commun, 2024. Apr 22;15(1):3385. doi: 10.1038/s41467-024-47806-3. (Read here)
Aglago EK, et al. (2023). A genetic locus within the FMN1/GREM1 gene region interacts with body mass index in colorectal cancer risk. Cancer Res. (Read here)

Carreras-Torres R, et al. (2023). Genome-wide Interaction Study with Smoking for Colorectal Cancer Risk Identifies Novel Genetic Loci Related to Tumor Suppression, Inflammation, and Immune Response. Cancer Epidemiol Biomarkers Prev 32, 315-328. (Read here)

Chen F, et al. (2023). Evidence of Novel Susceptibility Variants for Prostate Cancer and a Multiancestry Polygenic Risk Score Associated with Aggressive Disease in Men of African Ancestry. Eur Urol 84, 13-21. (Read here)

Darst BF et al. (2023). Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry. Am J Hum Genet. 7:1200-1206. (Read here)

Dimou N, et al. (2023). Probing the diabetes and colorectal cancer relationship using gene – environment interaction analyses. Br J Cancer. 129:511-520. (Read here)

Fernandez-Rozadilla C, et al. (2023). Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries. Nat Genet 55, 89-99. (Read here)

Goodrich JA, et al. (2023). Metabolic Signatures of Youth Exposure to Mixtures of Per- and Polyfluoroalkyl Substances: A Multi-Cohort Study. Environ Health Perspect 131, 27005. (Read here)

Hou K, et al. (2023). Causal effects on complex traits are similar for common variants across segments of different continental ancestries within admixed individuals. Nat Genet 55, 549-558. (Read here)

Kawaguchi ES, Kim AE, Lewinger JP, and Gauderman WJ. (2023). Improved two-step testing of genome-wide gene-environment interactions. Genet Epidemiol 47, 152-166. (Read here)

Mills C, Marconett CN, Lewinger JP, and Mi H. (2023). PEACOCK: a machine learning approach to assess the validity of cell type-specific enhancer-gene regulatory relationships. NPJ Syst Biol Appl 9, 9. (Read here)

Queen K, Nguyen MN, Gilliland FD, Chun S, Raby BA, and Millstein J. (2023). ACDC: a general approach for detecting phenotype or exposure associated co-expression. Front Med (Lausanne) 10, 1118824. (Read here)

Sharma N and Millstein J. (2023). CausNet: generational orderings based search for optimal Bayesian networks via dynamic programming with parent set constraints. BMC Bioinformatics 24, 46. (Read here)

Thomas M, et al. (2023). Combining Asian-European Genome-Wide Association Studies of Colorectal Cancer Improves Risk Prediction Across Race and Ethnicity. medRxiv. (Read here)
Devall MAM, Dampier CH, Eaton S, Ali MW, Plummer SJ, Bryant J, Gauderman WJ, Peters U, Powell SM, and Casey G. (2022). Transcriptomic Response to Calcium in Normal Colon Organoids is Impacted by Colon Location and Sex. Cancer Prev Res (Phila) 15, 679-688. (Read here)

Goodrich JA, Walker D, Lin X, Wang H, Lim T, McConnell R, Conti DV, Chatzi L, and Setiawan VW (2022). Exposure to perfluoroalkyl substances and risk of hepatocellular carcinoma in a multiethnic cohort. JHEP Rep 4, 100550. (Read here)

Haas CB, et al. (2022). Interactions between folate intake and genetic predictors of gene expression levels associated with colorectal cancer risk. Sci Rep 12, 18852. (Read here)

Jordahl KM, et al. (2022). Beyond GWAS of Colorectal Cancer: Evidence of Interaction with Alcohol Consumption and Putative Causal Variant for the 10q24.2 Region. Cancer Epidemiol Biomarkers Prev 31, 1077-1089. (Read here)

Kawaguchi ES, Li G, Lewinger JP, and Gauderman WJ. (2022). Two-step hypothesis testing to detect gene-environment interactions in a genome-wide scan with a survival endpoint. Stat Med 41, 1644-1657. (Read here)

Laville V, et al. (2022). Gene-lifestyle interactions in the genomics of human complex traits. Eur J Hum Genet 30, 730-739. (Read here)

Liu Z, Mushayahama T, Queme B, Ebert D, Muruganujan A, Mills C, Thomas PD, and Mi H. (2022). Annotation Query (AnnoQ): an integrated and interactive platform for large-scale genetic variant annotation. Nucleic Acids Res 50, W57-W65. (Read here)

Lu Z, Gopalan S, Yuan D, Conti DV, Pasaniuc B, Gusev A, and Mancuso, N. (2022). Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies. Am J Hum Genet 109, 1388-1404. (Read here)

Midya V, et al. (2022). Association of Prenatal Exposure to Endocrine-Disrupting Chemicals With Liver Injury in Children. JAMA Netw Open 5, e2220176. (Read here)

Millstein J, Battaglin F, Arai H, Zhang W, Jayachandran P, Soni S, Parikh AR, Mancao C, and Lenz HJ (2022). fdrci: FDR confidence interval selection and adjustment for large-scale hypothesis testing. Bioinform Adv 2, vbac047. (Read here)

Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou LP, Mi H. (2022). PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci. 31(1):8-22. (Read here)

Tian Y, et al. (2022). Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk. J Natl Cancer Inst 114, 1135-1148. (Read here)
Conti DV, Darst BF, Moss LC, Saunders EJ, Sheng X,et al. (2021). Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat Genet. 2021 Jan;53(1):65-75. Erratum in: Nat Genet. 2021 Jan 20;: PMID: 33398198; PMCID: PMC8148035. (Read here)

Jiang L, Xu S, Mancuso N, Newcombe PJ, Conti DV (2021). A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis. Am J Epidemiol. 190(6):1148-1158. (Read here)

Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS. (2021). A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics. 6(24):5640-5648. (Read here)

Rud D, Marjoram P, Siegmund K, Shibata D. (2021). Functional human genes typically exhibit epigenetic conservation. PLoS One. 202116(9):e0253250. (Read here)

Vega Yon GG, Thomas DC, Morrison J, Mi H, Thomas PD, Marjoram P. Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees. PLOS Computational Biology 17(2): e1007948 (2021). (Read here)

Weinstock, J.S., Laurie, C.A., Broome, J.G., Taylor, K.D., Guo, X., Shuldiner, A.R., O’Connell, J.R., Lewis, J.P., Boerwinkle, E., Barnes, K.C., et al. (2023). The genetic determinants of recurrent somatic mutations in 43,693 blood genomes. Sci Adv 9, eabm4945. (Read here)

Zeng C, Thomas DC, Lewinger JP. Incorporating prior knowledge into regularized regression. Bioinformatics. 37(4):514-521 (2021). (Read here)
Hvitfeldt E, Xia C, Siegmund K, Shibata D, Marjoram P. Epigenetic conservation is a beacon of function: an analysis using Methcon5 software for studying gene methylation. JCO Clin Cancer Inform. 2020; 4: CCI.19.00109. (Read here)

Xu T, et al. Epigenetic plasticity potentiates a rapid cyclical shift to and from an aggressive cancer phenotype. Int J Cancer. 2020 Jun 1;146(11):3065-3076. (Read here)

Stratakis N, et al. Prenatal Exposure to Perfluoroalkyl Substances Associated with Increased Susceptibility to Liver Injury in Children. Hepatology. 2020 Aug 1. Read here

Stratakis N, et al. Association of fish consumption and mercury exposure during pregnancy with metabolic health and inflammatory biomarkers in children. JAMA Network Open. 2020;3(3):e201007. (Read here)

Jin R, et al. Perfluoroalkyl substances and severity of nonalcoholic fatty liver in Children: An untargeted metabolomics approach. Environment International 2020; 134: 105220. (Read here)

Khankari NK, et al. Mendelian Randomization of Circulating Polyunsaturated Fatty Acids and Colorectal Cancer Risk. Cancer Epidemiology Biomarkers and Prevention 2020 Apr;29(4):860-870. (Read here)

Xia Z, et al. Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk. Cancer Med 2020 May;9(10):3563-3573. (Read here)

Thomas PD, Hill DP, Mi H, Osumi-Sutherland D, Van Auken K, Carbon S, Balhoff JP, Albou LP, Good B, Gaudet P, Lewis SE, Mungall CJ. Gene Ontology Causal Activity Modeling (GO-CAM) moves beyond GO annotations to structured descriptions of biological functions and systems. Nature Genetics 2019 51:1429-1433. (Read here)

Vega Yon G, & Marjoram P. “slurmR: A lightweight wrapper for HPC with Slurm”. Journal of Open Source Software 2019; 4(39), 1493 (Read here)

Yang Z, Pandey P, Marjoram P, Siegmund KD. iMutSig: a web application to identify the most similar mutational signature using shiny. F1000 Research 2020; 9:586. (Read here)

Barrett M, Millstein J. partition: A fast and flexible framework for data reduction in R. Journal of Open Source Software 2020; 5: 1991. (Read here)

Zeng C, Thomas DC, Lewinger JP. Incorporating prior knowledge into regularized regression. Bioinformatics 2020 Sep 11:btaa776. biorxiv/2020/03/05/2020 (Read here)

Millstein J, Battaglin F, Barrett M, Cao S, Zhang W, Stintzing S, Heinemann V, Lenz HJ. Partition: a surjective mapping approach for dimensionality reduction. Bioinformatics, 2020; 36: 676-681. (Read here)

Peng C, Thomas DC, Conti DV. A latent unknown clustering integrating multi-omics data (LUCID) with phenotypic traits. Bioinformatics. 2020; 36: 842-850. (Read here)

Pandey P, Yang Z, Shibata D, Marjoram P, Siegmund KD. Molecular signatures in colon cancer. BMC Res Notes 12, 788 (2019) (Read here)

Alderete TL, et al. “Perfluoroalkyl substances, metabolomic profiling, and alterations in glucose homeostasis among overweight and obese Hispanic children: A proof-of-concept analysis.” Environment International 2019; 126: 445-453. (Read here)

Watt GP, et al. A Pathway-Specific Genetic Risk Score is Associated with Increased Risk of Radiation-Associated Contralateral Breast Cancer, JAMA Netw Open. 2019 Sep; 2(9): e1912259. (Read here)

Schmit SL, et al. Novel Common Genetic Susceptibility Loci for Colorectal Cancer. J Natl Cancer Inst 2019; 111:146-157. (Read here)

Bentley AR, et al. Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nature Genetics 2019; 51:636-648. (Read here)

Jiang X, et al. Shared heritability and functional enrichment across six solid cancers. Nature Communications 2019; 10:431. (Read here)

Huyghe JR, et al. Discovery of common and rare genetic risk variants for colorectal cancer. Nature Genetics 2019; 51:76-87. (Read here)

Wang J, Asante I, Baron JA, Figueiredo JC, Haile R, Joan Levine A, Newcomb PA, Templeton AS, Schumacher FR, Louie SG, Casey G, Conti DV. Genome-wide association study of circulating folate one-carbon metabolites. Genetic Epidemiology 2019; 43: 1030-1045. (Read here)

Xu S, Gilliland FD, Conti DV. Elucidation of causal direction between asthma and obesity: a bi-directional Mendelian randomization study. International Journal of Epidemiology 2019; 48: 899-907. (Read here)

Weaver GM, Lewinger JP. Xrnet: Hierarchical regularized regression to incorporate external data. Journal of Open Source Software 2019; 4: 1761 (Read here)

Vega Yon G, Marjoram P. fmcmc: A friendly MCMC framework. Journal of Open Source Software 2019; 4, 1427. (Read here)

Moss LC, Gauderman WJ, Lewinger JP, Conti DV. Using Bayes model averaging to leverage both gene main effects and GxE interactions to identify genomic regions in genome-wide association studies. Genetic Epidemiology 2019; 43: 150-165. (Read here)

Gauderman WJ, Kim A, Conti DV, Morrison J, Thomas DC, Vora H1, Lewinger JP. A unified model for the analysis of gene environment interaction. Am J Epidemiol. 2019; 188: 760-767 (AJE “Article of the Year”, 2019) (Read here)

Croteau-Chonka DC, et al. Gene coexpression networks in whole blood implicate multiple interrelated molecular pathways in obesity in people with asthma. Obesity (Silver Spring). 2018 Dec;26(12):1938-1948. (Read here)

Du Z, et al. Genetic risk of prostate cancer in Ugandan men. Prostate 78, 2018; 370-376. (Read here)

Park SL, et al. Association of internal smoking dose with blood DNA methylation in three populations with different lung cancer risks. Clin Epigenetics 2018 Aug 23; 10(1):110. PMC 6108111. (Read here)

Dadaev T, et al. Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants. Nature Communications 2018; 9: 2256. (Read here)

Schumacher FR, et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nature Genetics 2018; 50: 928-936. (Read here)

Matejcic M, et al. Germline variation at 8q24 and prostate cancer risk in men of European ancestry. Nature Communications 2018; 9: 4616. (Read here)

Laville V,.et al. VarExp: estimating variance explained by genome-wide GxE summary statistics. Bioinformatics 2018 ;34: 3412-3414. (Read here)

Ryser MD, Yu M, Grady W, Siegmund K, Shibata D. Epigenetic heterogeneity in human colorectal tumors reveals preferential conservation and evidence of immune surveillance. Scientific Reports, 2018, Nov 23;8(1):17292. (Read here)

Kogan V, et al. Genetic-Epigenetic Interactions in Asthma Revealed by a Genome-Wide Gene-Centric Search, Hum Hered 2017/2018;83:130–152 (Read here)

Moss L, Gauderman WJ, Lewinger, JP, Conti DV. Using Bayes Model Averaging to Leverage Both Gene Main Effects and GxE Interactions to Identify Genomic Regions in Genome-Wide Association Studies. Gen. Epi. 43(2):150-165 (2019). (Read here)

Jiang X, et al. Shared heritability and functional enrichment among breast, colorectal, head and neck, lung, ovary and prostate cancer, Nature Communications, 10:431 (2019). (Read here)

Rao DC, et al. Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale. Circ Cardiovasc Genet. 2017 Jun;10(3). (Read here)

Schmit SL, et al. Novel genetic susceptibility loci for colorectal cancer. JNCI: Journal of the National Cancer Institute, Volume 111, Issue 2, February 2019, Pages 146–157, (Read here)

Jeon J, et al. Determining risk of colorectal cancer and starting age of screening based on lifestyle, environmental and genetic risk factors. Gastroenterology 2018,154(8):2152-2164.e19. (Read here)

Assi N, et al. Metabolic signature of healthy lifestyle and its relationship with risk of hepatocellular carcinoma in a large European cohort. American Journal of Clinical Nutrition 2018; 108: 117-126. (Read here)

Gauderman WJ, Kim A, Conti D, Morrison J, Thomas DC, Lewinger JP. A unified model for the analysis of gene environment interaction. American Journal of Epidemiology, Volume 188, Issue 4, April 2019, Pages 760–767. (Read here)

Liu J, Liang G, Siegmund KD, Lewinger JP. Data integration by multi-tuning parameter elastic net regression. BMC Bioinformatics 2018; 19:369. (Read here)

Ryser MD, Min BH, Siegmund KD, Shibata D. Spatial mutation patterns as markers of early colorectal tumor cell mobility. Proc Natl Acad Sci U S A. 2018 May 29;115(22):5774-5779. (Read here)

Thomas DC. Estimating the effect of targeted screening strategies: an application to colonoscopy and colorectal cancer. Epidemiology 2017, 28: 470-476. (Read here)

Thomas DC. What Does “Precision Medicine” Have to Say About Prevention? Epidemiology 2017;28(4): 479-483. (Read here)

Pereira M, Thompson JR, Weichenberger CX, Thomas DC, Minelli C. Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects. Genetic Epidemiology 2017, 41:320-31. (Read here)

McAllister K, et al. Current challenges and new opportunities for gene-environment interaction studies of complex diseases. American Journal of Epidemiology 2017:186:753-761. (Read here)

Ritchie MD, et al. Incorporation of biological knowledge into the study of GxE: State of the science. American Journal of Epidemiology 2017: 186: 771-777. (Read here)

Patel C, et al. Opportunities and challenges for environmental exposure assessment in population-based studies. American Journal of Epidemiology 2017; 186: 753-61. (Read here)

Robson M, et al. Association of common genetic variants with contralateral breast cancer risk in the WECARE study. JNCI 2017; 109 (10) (Read here)

Assi N, et al. Are metabolomics mediating the relationship between lifestyle factors and hepatocellular carcinoma risk? Results from a nested case-control study in EPIC. Cancer Epidemiology, Biomarkers and Prevention. vol 27(5) 2018. (Read here)

Ritz BR, et al. Lessons Learned from Past Gene-Environment (GxE) Interaction Successes. American Journal of Epidemiology 2017; 186(7):778-786. (Read here)

Jeon J, et al. Determining Risk of Colorectal Cancer and Starting Age of Screening Based on Lifestyle, Environmental, and Genetic Factors. Gastroenterology. Volume 154, Issue 8, June 2018, Pages 2152-2164.e19. (Read here)

Manrai AK, et al. (2017) Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health. Annu Rev Public Health. 38(1), 279-294. (Read here)

Raskin L, Guo Y, Du L, Clendenning M, Rosty C; Colon Cancer Family Registry (CCFR), Lindor NM, Gruber SB, Buchanan DD. (2017) Targeted sequencing of established and candidate colorectal cancer genes in the Colon CancerFamily Registry Cohort. Oncotarget. 2017; 8(55):93450-93463. (Read here)

Stram, DO. (2017). Multi-SNP Haplotype Analysis Methods for Association Analysis. In R. C. Elston (Ed.), Statistical Human Genetics: Methods and Protocols (pp. 485-504). New York, NY: Springer New York. (Read here)

Sun R, Hu Z, Sottoriva A, Graham TA, Harpak A, Ma Z, Fischer JM, Shibata D, Curtis C. (2017) Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nature Genetics, 49(7), 1015-1024. (Read here)

Gauderman WJ, et al. (2017). Update on the State of the Science for Analytical Methods for Gene-Environment Interactions (GxE). American Journal of Epidemiology 2017; 186:762-770. (Read here)

Zhang Z, et al. (2017) Correction of confidence intervals in excess relative risk models using Monte Carlo dosimetry systems with shared errors. PLoS ONE, 12(4), e0174641. (Read here)

Zhao, J., Salomon, M. P., Shibata, D., Curtis, C., Siegmund, K., & Marjoram, P. (2017). Early mutation bursts in colorectal tumors. PLoS ONE, 12(3), e0172516. (Read here)

Reiner A, et al. Breast cancer family history and contralateral breast cancer risk in young women: An update from the Women’s Environmental Cancer and Radiation Epidemiology Study. Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 36(15), 1513–1520. (Read here)

Assi N, et al. Metabolic signature of healthy lifestyle and its relationship with risk of hepatocellular carcinoma in a large European cohort. The American Journal of Clinical Nutrition, Volume 108, Issue 1, July 2018, Pages 117–126, (Read here)

Dadaev, T, et al. Fine-mapping of Prostate Cancer Susceptibility Loci in a Large Meta-Analysis Identifies Candidate Causal Variants. Nature Communications volume 9, Article number: 2256 (2018). (Read here)

Gauderman, WJ, Lewinger JP, Conti, DV, Morrison, J, Kim, A, & Thomas, DC (2017). A unified model for the analysis of gene environment interaction. American Journal of Epidemiology, Volume 188, Issue 4, April 2019, Pages 760–767. (Read here)

Du M, et al. Genetic predisposition modifies the effect of multiple environmental factors on risk of colorectal tumors. JNCI.

Schmit SL, et al. Novel genetic susceptibility loci for colorectal cancer. JNCI: Journal of the National Cancer Institute, Volume 111, Issue 2, February 2019, Pages 146–157. (Read here)

Huyghe JR, et al.. Discovery of common and rare risk loci for colorectal cancer. Nature Genetics volume 51, pages76–87(2019). (Read here)

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