Is the Gene-Environment Interaction Paradigm Relevant to Genome-Wide Studies? The Case of Education and Body Mass Index

Bibliographic Data

ID10971024
AuthorsJ D Boardman (0000-0002-4786-125X, Institute of Behavioral Science, University of Colorado, 1440 15th Street, Boulder, CO 80309-0483, USA, corresponding author), B W Domínguez (0000-0002-3894-9049, Institute of Behavioral Science, University of Colorado, 1440 15th Street, Boulder, CO 80309-0483, USA, corresponding author), Benjamin W Domingue, Casey L Blalock (Institute of Behavioral Science, University of Colorado, 1440 15th Street, Boulder, CO 80309-0483, USA, corresponding author), Brett C Haberstick (Institute for Behavioral Genetics, University of Colorado, 1480 30th Street, Boulder, CO 80303, USA, corresponding author), Kathleen Mullan Harris (0000-0001-9757-1026, Carolina Population Center and Department of Sociology, University of North Carolina, 123 W Franklin Street, Chapel Hill, NC 27516, USA, corresponding author), Matthew B Mcqueen (0000-0002-7035-6112, Institute for Behavioral Genetics, University of Colorado, 1480 30th Street, Boulder, CO 80303, USA, corresponding author)
Year2014
Volume51
Issue1
Pages119-139
Publication date2014-02-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueDemography (JOURNAL)
Journal identifiersISSN: 0070-3370 • E-ISSN: 1533-7790
PublisherDuke University Press (PUBLISHER • US)
DOI10.1007/s13524-013-0259-4
PMID24281739
PMCIDPMC4035460
OpenAlexW1963552091
LanguageEN
Citations received28
References cited62

This study uses data from the Framingham Heart Study to examine the relevance of the gene-environment interaction paradigm for genome-wide association studies (GWAS). We use completed college education as our environmental measure and estimate the interactive effect of genotype and education on body mass index (BMI) using 260,402 single-nucleotide polymorphisms (SNPs). Our results highlight the sensitivity of parameter estimates obtained from GWAS models and the difficulty of framing genome-wide results using the existing gene-environment interaction typology. We argue that SNP-environment interactions across the human genome are not likely to provide consistent evidence regarding genetic influences on health that differ by environment. Nevertheless, genome-wide data contain rich information about individual respondents, and we demonstrate the utility of this type of data. We highlight the fact that GWAS is just one use of genome-wide data, and we encourage demographers to develop methods that incorporate this vast amount of information from respondents into their analyses

Biology · Body mass index · Computational biology · Data science · Disease · Framingham Heart Study · Framingham Risk Score · Gene · Genetic association · Genome · Genome-wide association study · Genotype · Single-nucleotide polymorphism · SNP · Birth, Development, and Health · Computer Science · Genetic Associations and Epidemiology · Genetics · Medicine · Obesity, Physical Activity, Diet

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