Grant Number:	1R01CA195789-01A1 Interpret this number
Primary Investigator:	Hsu, Li
Organization:	Fred Hutchinson Cancer Research Center
Project Title:	Statistical Methods for Genetic Epidemiology Studies
Fiscal Year:	2016

¿ DESCRIPTION (provided by applicant): Personalized medicine or individualized lifestyle recommendations based on both genetic and environmental factors are being promoted as the future of public health. Recent developments in The Human Genome Project and high throughput technologies have offered many opportunities in improving risk prediction and elucidating the underlying biological mechanism by integrating both genetic and environmental data. The objective of this application is to develop statistical methods for estimating absolute risk using both gene and environment data, assessing gene-environment interaction and translating findings into public health and personalized recommendations for intervention. Accurate age-specific absolute risk prediction is critical in patient management and disease prevention. Key to such translation is development of statistical tools for risk estimation. There are two urgent and unmet needs: (a) lack of statistical tools to develop robust risk prediction models, which take advantage of multiple sources including both cohort and case control studies and population-wide reports on age-specific disease rates and exposure distributions; (b) lack of guidance on how a developed prediction model should be used in the clinical setting to aid decision making with statistical rigor. Aim 1 is to develop statistical methods for estimatig robust age-specific absolute risk under complex study designs and individualized recommended age to start intervention. To better develop individually tailored risk prediction and provide guidance on potential lifestyle and screening intervention, it is important to understand how gene and environment work in synergy, as differences in genetic makeup can cause people to respond differently to the same environmental exposure (GxE). As whole genome sequencing studies are being conducted, much progress has been made for rare variant association, but little has been done toward GxE for rare variants, in part because there is a lack of adequate data to detect and estimate the effect of GxE for individual rare variants. Toward this end the functional information generated from the recent large collaborative initiatives such as ENCODE and TCGA can provide guidance on how to aggregate variants with shared functional characteristics and therefore leveraging data across variants. To our knowledge, there is no method yet to incorporate such information for GxE. Aim 2 is to develop methods for assessing GxE risks for rare variants by integrating the functional information. The proposed work will be applied to the Genetics and Epidemiology of Colorectal Cancer Consortium (PI: Ulrike Peters; Lead Biostatistician: Li Hsu). The growing consortium has currently over 40,000 participants from population-based case-control and cohort studies with detailed data on both environmental risk factors and genome-wide association and whole genome sequencing data. Since the methods are also applicable to other complex diseases, we will develop open source software based in R and make it publicly available.




Unveiling challenges in Mendelian randomization for gene-environment interaction.
Authors: Gorfine M. , Qu C. , Peters U. , Hsu L. .
Source: Genetic Epidemiology, 2024 Jun; 48(4), p. 164-189.
EPub date: 2024-02-29 00:00:00.0.
PMID: 38420714
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Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population.
Authors: Zheng J. , Hsu L. .
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Validation of a genetic-enhanced risk prediction model for colorectal cancer in a large community-based cohort.
Authors: Su Y.R. , Sakoda L.C. , Jeon J. , Thomas M. , Lin Y. , Schneider J.L. , Udaltsova N. , Lee J.K. , Lansdorp-Vogelaar I. , Peterse E.F.P. , et al. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2023-01-09 00:00:00.0; , .
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Genetic regulation of DNA methylation yields novel discoveries in GWAS of colorectal cancer.
Authors: Barfield R. , Huyghe J.R. , Lemire M. , Dong X. , Su Y.R. , Brezina S. , Buchanan D.D. , Figueiredo J.C. , Gallinger S. , Giannakis M. , et al. .
Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2022-03-03 00:00:00.0; , .
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Risk Projection for Time-to-event Outcome Leveraging Summary Statistics With Source Individual-level Data.
Authors: Zheng J. , Zheng Y. , Hsu L. .
Source: Journal Of The American Statistical Association, 2022; 117(540), p. 2043-2055.
EPub date: 2021-04-22 00:00:00.0.
PMID: 36687294
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Re-calibrating pure risk integrating individual data from two-phase studies with external summary statistics.
Authors: Zheng J. , Zheng Y. , Hsu L. .
Source: Biometrics, 2021-08-13 00:00:00.0; , .
EPub date: 2021-08-13 00:00:00.0.
PMID: 34390251
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A COVARIANCE-ENHANCED APPROACH TO MULTI-TISSUE JOINT EQTL MAPPING WITH APPLICATION TO TRANSCRIPTOME-WIDE ASSOCIATION STUDIES.
Authors: Molstad A.J. , Sun W. , Hsu L. .
Source: The Annals Of Applied Statistics, 2021 Jun; 15(2), p. 998-1016.
EPub date: 2021-07-12 00:00:00.0.
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Response to Li and Hopper.
Authors: Thomas M. , Sakoda L.C. , Hoffmeister M. , Rosenthal E.A. , Lee J.K. , van Duijnhoven F.J.B. , Platz E.A. , Wu A.H. , Dampier C.H. , de la Chapelle A. , et al. .
Source: American Journal Of Human Genetics, 2021-03-04 00:00:00.0; 108(3), p. 527-529.
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Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.
Authors: Thomas M. , Sakoda L.C. , Hoffmeister M. , Rosenthal E.A. , Lee J.K. , van Duijnhoven F.J.B. , Platz E.A. , Wu A.H. , Dampier C.H. , de la Chapelle A. , et al. .
Source: American Journal Of Human Genetics, 2020-09-03 00:00:00.0; 107(3), p. 432-444.
EPub date: 2020-08-05 00:00:00.0.
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A general framework for functionally informed set-based analysis: Application to a large-scale colorectal cancer study.
Authors: Dong X. , Su Y.R. , Barfield R. , Bien S.A. , He Q. , Harrison T.A. , Huyghe J.R. , Keku T.O. , Lindor N.M. , Schafmayer C. , et al. .
Source: Plos Genetics, 2020 08; 16(8), p. e1008947.
EPub date: 2020-08-24 00:00:00.0.
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Estimation of Absolute Risk of Colorectal Cancer Based on Healthy Lifestyle, Genetic Risk, and Colonoscopy Status in a Population-Based Study.
Authors: Carr P.R. , Weigl K. , Edelmann D. , Jansen L. , Chang-Claude J. , Brenner H. , Hoffmeister M. .
Source: Gastroenterology, 2020 Jul; 159(1), p. 129-138.e9.
EPub date: 2020-03-14 00:00:00.0.
PMID: 32179093
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Practical implementation of frailty models in Mendelian risk prediction.
Authors: Huang T. , Gorfine M. , Hsu L. , Parmigiani G. , Braun D. .
Source: Genetic Epidemiology, 2020-06-07 00:00:00.0; , .
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Adjusted time-varying population attributable hazard in case-control studies.
Authors: Zhao W. , Zheng J. , Chen Y.Q. , Hsu L. .
Source: Statistical Methods In Medical Research, 2020 01; 29(1), p. 243-257.
EPub date: 2019-02-25 00:00:00.0.
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Learning-based biomarker-assisted rules for optimized clinical benefit under a risk constraint.
Authors: Wang Y. , Zhao Y.Q. , Zheng Y. .
Source: Biometrics, 2019-12-13 00:00:00.0; , .
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Head-to-Head Comparison of Family History of Colorectal Cancer and a Genetic Risk Score for Colorectal Cancer Risk Stratification.
Authors: Weigl K. , Hsu L. , Knebel P. , Hoffmeister M. , Timofeeva M. , Farrington S. , Dunlop M. , Brenner H. .
Source: Clinical And Translational Gastroenterology, 2019 12; 10(12), p. e00106.
PMID: 31800541
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Healthy Lifestyle Factors Associated With Lower Risk of Colorectal Cancer Irrespective of Genetic Risk.
Authors: Carr P.R. , Weigl K. , Jansen L. , Walter V. , Erben V. , Chang-Claude J. , Brenner H. , Hoffmeister M. .
Source: Gastroenterology, 2018-09-07 00:00:00.0; , .
EPub date: 2018-09-07 00:00:00.0.
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Novel Common Genetic Susceptibility Loci for Colorectal Cancer.
Authors: Schmit S.L. , Edlund C.K. , Schumacher F.R. , Gong J. , Harrison T.A. , Huyghe J.R. , Qu C. , Melas M. , Van Den Berg D.J. , Wang H. , et al. .
Source: Journal Of The National Cancer Institute, 2018-06-16 00:00:00.0; , .
EPub date: 2018-06-16 00:00:00.0.
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A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics.
Authors: Su Y.R. , Di C. , Bien S. , Huang L. , Dong X. , Abecasis G. , Berndt S. , Bezieau S. , Brenner H. , Caan B. , et al. .
Source: American Journal Of Human Genetics, 2018-05-03 00:00:00.0; 102(5), p. 904-919.
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Multivariate association analysis with somatic mutation data.
Authors: He Q. , Liu Y. , Peters U. , Hsu L. .
Source: Biometrics, 2018 Mar; 74(1), p. 176-184.
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Determining Risk of Colorectal Cancer and Starting Age of Screening Based on Lifestyle, Environmental, and Genetic Factors.
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Strongly enhanced colorectal cancer risk stratification by combining family history and genetic risk score.
Authors: Weigl K. , Chang-Claude J. , Knebel P. , Hsu L. , Hoffmeister M. , Brenner H. .
Source: Clinical Epidemiology, 2018; 10, p. 143-152.
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General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv.
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On Estimation of the Hazard Function from Population-based Case-Control Studies.
Authors: Hsu L. , Gorfine M. , Zucker D.M. .
Source: Journal Of The American Statistical Association, 2018; 113(522), p. 560-570.
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Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.
Authors: Ritchie M.D. , Davis J.R. , Aschard H. , Battle A. , Conti D. , Du M. , Eskin E. , Fallin M.D. , Hsu L. , Kraft P. , et al. .
Source: American Journal Of Epidemiology, 2017-10-01 00:00:00.0; 186(7), p. 771-777.
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Update on the State of the Science for Analytical Methods for Gene-Environment Interactions.
Authors: Gauderman W.J. , Mukherjee B. , Aschard H. , Hsu L. , Lewinger J.P. , Patel C.J. , Witte J.S. , Amos C. , Tai C.G. , Conti D. , et al. .
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Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.
Authors: McAllister K. , Mechanic L.E. , Amos C. , Aschard H. , Blair I.A. , Chatterjee N. , Conti D. , Gauderman W.J. , Hsu L. , Hutter C.M. , et al. .
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Quantifying the genetic correlation between multiple cancer types.
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Source: Cancer Epidemiology, Biomarkers & Prevention : A Publication Of The American Association For Cancer Research, Cosponsored By The American Society Of Preventive Oncology, 2017-06-21 00:00:00.0; , .
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Hypothesis testing in functional linear models.
Authors: Su Y.R. , Di C.Z. , Hsu L. .
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On Estimation Of Time-dependent Attributable Fraction From Population-based Case-control Studies
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Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome.
Authors: Gorfine M. , Berndt S.I. , Chang-Claude J. , Hoffmeister M. , Le Marchand L. , Potter J. , Slattery M.L. , Keret N. , Peters U. , Hsu L. .
Source: Plos One, 2017; 12(8), p. e0181269.
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Enrichment of colorectal cancer associations in functional regions: Insight for using epigenomics data in the analysis of whole genome sequence-imputed GWAS data.
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Genome-wide Interaction Analyses Between Genetic Variants And Alcohol Consumption And Smoking For Risk Of Colorectal Cancer
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A Unified Powerful Set-based Test For Sequencing Data Analysis Of Gxe Interactions
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A Fully Nonparametric Estimator Of The Marginal Survival Function Based On Case-control Clustered Age-at-onset Data
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