Testing for Gene-Environment Interactions Using a Prospective Family Cohort Design: Body Mass Index in Early and Later Adulthood and Risk of Breast Cancer.
Initially submitted February 1, 2016; accepted for publication August 4, 2016. The ability to classify people according to their underlying genetic susceptibility to a disease is increasing with new knowledge, better family data, and more sophisticated risk prediction models, allowing for more effec...
| Published in: | American Journal of Epidemiology Vol. 185; no. 6; pp. 487 - 501 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Oxford University Press / USA
3/15/2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121858172&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121858172 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029262 1X1 jtl: American Journal of Epidemiology issn: 00029262 maglogo: N pubinfo: dt: 3/15/2017 vid: 185 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 121858172 121858172 121858172 10.1093/aje/kww241 121858172 ppf: 487 ppct: 14 formats: tig: atl: Testing for Gene-Environment Interactions Using a Prospective Family Cohort Design: Body Mass Index in Early and Later Adulthood and Risk of Breast Cancer. aug: au: Dite, Gillian S. MacInnis, Robert J. Bickerstaffe, Adrian Dowty, James G. Milne, Roger L. Antoniou, Antonis C. Weideman, Prue Apicella, Carmel Giles, Graham G. Southey, Melissa C. Jenkins, Mark A. Phillips, Kelly-Anne Win, Aung Ko Terry, Mary Beth Hopper, John L. affil: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia sug: subj: Body Mass Index Breast Neoplasms Risk Factors Breast Neoplasms Familial and Genetic Environment Age Factors Australia Body Weight Cox Proportional Hazards Model Human Obesity Prospective Studies Disease Susceptibility Adolescence Young Adult Descriptive Statistics Family History Female Middle Age Questionnaires Risk Assessment Interviews Data Analysis Software P-Value Adult Confidence Intervals Adolescent: 13-18 years Middle Aged: 45-64 years Adult: 19-44 years Female ab: Initially submitted February 1, 2016; accepted for publication August 4, 2016. The ability to classify people according to their underlying genetic susceptibility to a disease is increasing with new knowledge, better family data, and more sophisticated risk prediction models, allowing for more effective prevention and screening. To do so, however, we need to know whether risk associations are the same for people with different genetic susceptibilities. To illustrate one way to estimate such gene-environment interactions, we used prospective data from 3 Australian family cancer cohort studies, 2 enriched for familial risk of breast cancer. There were 288 incident breast cancers in 9,126 participants from 3,222 families. We used Cox proportional hazards models to investigate whether associations of breast cancer with body mass index (BMI; weight (kg)/height (m)2) at age 18-21 years, BMI at baseline, and change in BMI differed according to genetic risk based on lifetime breast cancer risk from birth, as estimated by BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) software, adjusted for age at baseline data collection. Although no interactions were statistically significant, we have demonstrated the power with which gene-environment interactions can be investigated using a cohort enriched for persons with increased genetic risk and a continuous measure of genetic risk based on family history. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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