Detection of gene-environment interactions in a family-based population using SCAD-Reference-Cited by-同舟云学术

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Detection of gene-environment interactions in a family-based population using SCAD

Published:2017-07-13 Issue:22 Volume:36 Page:3547-3559
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statist. Med.

Author:

Kim Gwangsu¹,Lai Chao-Qiang²,Arnett Donna K.³,Parnell Laurence D.²,Ordovas Jose M.²⁴,Kim Yongdai⁵,Kim Joungyoun⁶^ORCID

Affiliation:

1. Data Science for Knowledge Creation Research Center; Seoul National University; Seoul Korea

2. Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging; Tufts University; Boston MA U.S.A.

3. University of Kentucky College of Public Health; Lexington KY U.S.A.

4. Department of Epidemiology and Population Genetics; Centro Nacional Investigacion Cardiovasculares (CNIC) Madrid; Madrid Spain

5. Department of Statistics; Seoul National University; Seoul Korea

6. Department of Information Statistics; Chungbuk National University; Cheongju Chungbuk Korea

Funder

Korean government (MSIP)

U.S. Department of Agriculture Research Service

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

Reference44 articles.

1. Gene-environment interactions in human diseases;Hunter;Nature Reviews Genetics,2005

2. Dietary intake of n-6 fatty acids modulates the effect of apolipoprotein A5 gene on plasma fasting triglycerides, remnant lipoprotein concentrations, and lipoprotein particle size: the Framingham Heart Study;Lai;Circulation,2006

3. A database of gene-environment interactions pertaining to blood lipid traits, cardiovascular disease and type 2 diabetes;Lee;Journal of Data Mining in Genomics & Proteomics,2011

4. Cardiogxe, a catalog of gene-environment Interactions for cardiometabolic traits;Parnell;BioData Mining,2014

5. APOA2, dietary fat, and body mass index: replication of a gene-diet interaction in 3 independent populations;Corella;Archive of International Medicine,2009

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1. BHAFT: Bayesian heredity‐constrained accelerated failure time models for detecting gene‐environment interactions in survival analysis;Statistics in Medicine;2024-07-04

2. Bayesian Approaches in Exploring Gene-environment and Gene-gene Interactions: A Comprehensive Review;Cancer Genomics - Proteomics;2023-11-30

3. A modified least angle regression algorithm for interaction selection with heredity;Statistical Analysis and Data Mining: The ASA Data Science Journal;2022-02-28

4. Gene–environment interaction identification via penalized robust divergence;Biometrical Journal;2021-11

5. Identification of gene–environment interactions with marginal penalization;Genetic Epidemiology;2019-11-14

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