Format

Send to

Choose Destination
PLoS Genet. 2017 Jun 14;13(6):e1006812. doi: 10.1371/journal.pgen.1006812. eCollection 2017 Jun.

Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions.

Author information

1
Department of Clinical Sciences, Genetic & Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden.
2
Department of Odontology, Umeå University, Umeå, Sweden.
3
Department of Public Health and Clinical Medicine, Unit of Medicine, Umeå University, Umeå, Sweden.
4
Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA, United States of America.
5
Department of Statistical Sciences, University of Toronto, Toronto, Canada.
6
Novo Nordisk Foundation Center for Protein Research, Translational Disease Systems Biology Group, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
7
Department of Health Sciences, Exercise Physiology Group, Lund University, Lund, Sweden.
8
MRC Epidemiology Unit, University of Cambridge, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.
9
Estonian Genome Center, University of Tartu, Tartu, Estonia.
10
Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.
11
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
12
Department of Biostatistics, University of Liverpool, Liverpool, United Kingdom.
13
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
14
Department of Biobank Research, Umeå University, Umeå, Sweden.
15
Harvard Medical School, Boston, MA, United States of America.
16
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America.
17
Department of Genetic Epidemiology, University of Regensburg, Regensburg, DE, Germany.
18
Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, United States of America.
19
Department of Epidemiology and Public Health, UCL London, United Kingdom.
20
Department of Biostatistics, Boston University School of Public Health, Boston, MA.
21
The NHLBI Framingham Heart Study, Framingham, MA.
22
The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, NY.
23
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
24
The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY.
25
Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
26
Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden.
27
Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, United States of America.
28
The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
29
Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Canada.
30
Department of Nutrition, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States of America.
31
Oxford Centre for Diabetes, Endocrinology & Metabolism, Radcliff Department of Medicine, University of Oxford, Oxford, United Kingdom.

Abstract

Phenotypic variance heterogeneity across genotypes at a single nucleotide polymorphism (SNP) may reflect underlying gene-environment (G×E) or gene-gene interactions. We modeled variance heterogeneity for blood lipids and BMI in up to 44,211 participants and investigated relationships between variance effects (Pv), G×E interaction effects (with smoking and physical activity), and marginal genetic effects (Pm). Correlations between Pv and Pm were stronger for SNPs with established marginal effects (Spearman's ρ = 0.401 for triglycerides, and ρ = 0.236 for BMI) compared to all SNPs. When Pv and Pm were compared for all pruned SNPs, only BMI was statistically significant (Spearman's ρ = 0.010). Overall, SNPs with established marginal effects were overrepresented in the nominally significant part of the Pv distribution (Pbinomial <0.05). SNPs from the top 1% of the Pm distribution for BMI had more significant Pv values (PMann-Whitney = 1.46×10-5), and the odds ratio of SNPs with nominally significant (<0.05) Pm and Pv was 1.33 (95% CI: 1.12, 1.57) for BMI. Moreover, BMI SNPs with nominally significant G×E interaction P-values (Pint<0.05) were enriched with nominally significant Pv values (Pbinomial = 8.63×10-9 and 8.52×10-7 for SNP × smoking and SNP × physical activity, respectively). We conclude that some loci with strong marginal effects may be good candidates for G×E, and variance-based prioritization can be used to identify them.

PMID:
28614350
PMCID:
PMC5489225
DOI:
10.1371/journal.pgen.1006812
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center