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Convergence between biological, behavioural and genetic determinants of obesity

Nature Reviews Genetics volume 18, pages 731748 (2017) | Download Citation


Multiple biological, behavioural and genetic determinants or correlates of obesity have been identified to date. Genome-wide association studies (GWAS) have contributed to the identification of more than 100 obesity-associated genetic variants, but their roles in causal processes leading to obesity remain largely unknown. Most variants are likely to have tissue-specific regulatory roles through joint contributions to biological pathways and networks, through changes in gene expression that influence quantitative traits, or through the regulation of the epigenome. The recent availability of large-scale functional genomics resources provides an opportunity to re-examine obesity GWAS data to begin elucidating the function of genetic variants. Interrogation of knockout mouse phenotype resources provides a further avenue to test for evidence of convergence between genetic variation and biological or behavioural determinants of obesity.

Key points

  • Common genomic variants associated with obesity are interrogated for their potential implications for biological and behavioural mechanisms and their concordance with established risk factors for obesity.

  • An integrative analysis, taking advantage of the recently available large data repositories on tissue-specific gene networks, expression quantitative trait loci (eQTLs) and genome-wide promoter and enhancer location was undertaken, along with a review of evidence for phenotypic relevance through knockout mouse databases.

  • Exploring panels of SNPs (n = 118 SNPs) from three large genome-wide association studies on adult and childhood adiposity confirms that central nervous system (CNS)-related processes dominate human variation in BMI, whereas peripheral signalling pathways are more evident in variability in percentage body fat.

  • Several obesity-associated SNPs function as cis-eQTLs by altering the expression of nearby genes. Conditional analysis of the most significantly associated SNPs suggests that the majority of the obesity-associated SNPs tag other variants that may causally regulate nearby gene expression.

  • A large fraction of obesity-associated SNPs (46 of 118 GWAS variants) are located primarily in non-coding, regulatory domains of the human genome and overlap with at least one promoter- or enhancer-associated histone modification mark, particularly across multiple brain regions.

  • Knocked out genes proximal to the GWAS significant loci were interrogated in mouse databases for their potential convergence with obesity traits. The analysis identified 49 genes that displayed a relationship with more than one obesity trait when knocked out in mice.

  • Overall, common genomic variants tend to occur in genes, pathways and networks influencing brain regulation of energy balance, an observation consistent with the current consensus on the aetiology of obesity. However, at present, these types of variants do not seem to strongly implicate other established determinants of obesity such as hormonal regulation, skeletal muscle metabolism and energy expenditure traits.

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C.B. is partially funded by the John W. Barton Sr Chair in Genetics and Nutrition. S.G. and C.B. are partially supported by the NIH-funded COBRE grant (NIH 81P30GM118430-01). This work was also supported by the National Medical Research Council, Ministry of Health, Singapore (WBS R913200076263) to S.G. We thank X. Chai for help with some data retrieval and steps in the analysis and M. Peterson for assistance with the manuscript.

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  1. Cardiovascular and Metabolic Disorders Program and Center for Computational Biology, Duke–National University of Singapore Graduate Medical School, Singapore 169857, Singapore.

    • Sujoy Ghosh
  2. Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana 70808-4124, USA.

    • Claude Bouchard


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Both authors contributed equally to all aspects of the article.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Sujoy Ghosh or Claude Bouchard.

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    Supplementary information Tables S1–S8



In people of European descent, obesity is defined as a body mass index of 30 kg m−2 or higher. By contrast, overweight refers to a BMI in the range of 25 to 29.9 kg m−2.

Energy balance

The relationship between the calories consumed from food and drink and the calories expended to meet daily energy requirements.

Effect sizes

The magnitude of the difference in allele frequencies between two groups or between group phenotype values. The estimate of effect size is typically expressed as an odds ratio for a case:control GWAS or as a regression coefficient for continuous traits, but there are many other ways to quantify an effect size.

Genetic variance

The contribution of genotypic differences among individuals to phenotypic variation in a population.

Common variants

Single nucleotide variations in genetic sequences where the less prevalent form (minor allele) occurs at a frequency of 1% or greater in the human population under investigation.

Metabolic rate

The rate at which metabolic energy is expended to meet the energy needs of the body. For instance, resting metabolic rate is the rate of calorie expenditure required to maintain the basic biological functions of the body at rest. It is commonly assumed that this rate of energy expenditure can be approximated by the rate of ATP production.


(Genome-wide association study). An approach involving the simultaneous scanning of millions of markers (single nucleotide polymorphisms, SNPs) across the entire genome with the goal of discovering genetic variants that are associated with a particular disease or trait.

Body mass index

(BMI). Also known as the Quetelet Index, the BMI is a person's weight in kilograms divided by the square of their height in metres (kg m−2).

Body fat percentage

A representation of the proportion of total body mass that is stored as fat, primarily in adipose tissue plus small amounts in other tissues and organs. It is calculated as total fat mass divided by total body mass (× 100). Currently, it is most often derived from dual-energy X-ray absorptiometry (DXA) scanning, in which the fat and lean components of body mass are quantified.

Genome-wide significant

A term that typically applies to an association P-value for a single nucleotide polymorphism in a GWAS. A SNP with an association P-value <0.05, after correction for the number of SNPs tested (Bonferroni correction), is considered to be genome-wide significant. For 1 million SNPs tested, this equates to a SNP with nominal P-value of 5 × 10−8.

Expression quantitative trait loci

Regions of the genome containing DNA sequence variants that influence the expression level of one or more genes.

Regulatory marks

Chromatin modifications in gene regulatory regions, primarily involving post-translational modifications of DNA-associated histones (acetylation, methylation, phosphorylation and ubiquitylation).


Refers to the level of fat stored in the adipose tissue of the organism. Most of the lipids are stored in the form of triglycerides in adipose cells. A high level of adiposity implies a large accumulation of fat and is commonly seen in obesity while leanness is associated with a low level of adiposity.


An estimate of the contribution of genetic variation to a phenotype among individuals in a given population.


In genetics, penetrance refers to the likelihood that a particular gene or allele will be expressed. Penetrance can be reduced or complete.

Minor allele frequency

The frequency of the less frequent allele at a given locus and in a given population.

Network analysis

An approach involving the analysis of gene networks. Gene networks are collections of functionally related genes (for example, due to co-expression, protein-protein interactions, gene regulatory networks, etc.) where the topological relationships between the genes are known.


(Data-driven Expression-Prioritized Integration for Complex Traits). An integrative tool that systematically prioritizes the most likely causal genes at associated loci and highlights tissues and pathways enriched for highly expressed loci-associated genes.

Pathway analysis

An approach where the unit of analysis is a gene set, also referred to as a pathway. A pathway is a collection of genes that are related to one another by some functional parameter. For GWAS, the goal of a pathway analysis is to identify gene sets that have a statistically significant excess of polymorphisms compared with random gene collections.

Guilt by association

The process of inferring the function of a molecule by virtue of its association with other molecules of known function. For genetic studies, the association often manifests as transcriptional co-expression or participation in the same transcriptional network.

DNase I hypersensitivity sites

Chromatin regions characterized by increased cleavage as revealed by the endonuclease DNase I. It represents a region of regulatory DNA typically located near transcription start sites, enhancers and silencers.

Quantile–quantile plots

Scatterplots created by plotting two sets of quantiles against one another. In the case of GWAS, this type of plot is often used to compare quantiles of the experimentally observed SNP association P-values versus quantiles calculated from a theoretical (normal) distribution.

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