Genetic studies of body mass index yield new insights for obesity biology

Abstract

Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.

Figures

Extended Data Figure 1. Study design

*The SNP counts reflect sample size filter of n ≥ 50,000. §Counts represent the primary European sex-combined analysis. Please see Extended Data Table 1 for counts for secondary analyses.

Extended Data Figure 2. Genetic characterization of BMI-associated variants

a, Plot of the cumulative phenotypic variance explained by each locus ordered by decreasing effect size. b, The relationship between effect size and allele frequency. Previously identified loci are blue circles and novel loci are red triangles. c, Quantile–quantile (Q–Q) plot of meta-analysis P values for all 1,909 BMI-replication SNPs (blue) and after removing SNPs near the 97 associated loci (green). d, Histogram of cumulative effect of BMI risk alleles. Mean BMI for each bin is shown by the black dots (with standard deviation) and corresponds to the right-hand y axis.

Extended Data Figure 3. Partitioning the variance in and risk prediction from SNP-derived predictor

a, b, The analyses were performed using 2,758 full sibling pairs from the TwinGene cohort (a) and 1,622 pairs from the QIMR cohort (b). The SNP-based predictor was adjusted for the first 20 principal components. The variance of the SNP-based predictor can be partitioned into four components (Vg, Ve, Cg and Ce) using the within-family prediction analysis, in which Vg is the variance explained by real SNP effects, Cg is the covariance between predictors attributed to the real effects of SNPs that are not in LD but correlated due to population stratification, Ve is the accumulated variance due to the errors in estimating SNP effects, and Ce is the covariance between predictors attributed to errors in estimating the effects of SNPs that are correlated due to population stratification. Error bars reflect s.e.m. of estimates. c, The prediction R2 shown on the y axis is the squared correlation between phenotype and SNP-based genetic predictor in unrelated individuals from the TwinGene (n = 5,668) and QIMR (n = 3,953) studies. The number shown in each column is the number of SNPs selected from the GCTA joint and conditional analysis at a range of P-value thresholds. In each case, the predictor was adjusted by the first 20 principal components. The column in orange is the average prediction R2 weighted by sample size over the two cohorts. The dashed grey line is the value inferred from the within-family prediction analyses using this equation R2 = (Vg + Cg)2/(Vg + Ve + Cg + Ce).

Extended Data Figure 4. Comparison of BMI-associated index SNPs across ethnicities

a, b, BMI effects observed in European ancestry individuals (x axes) compared to African ancestry (a) or Asian ancestry (b) individuals (y axes). c, d, Allele frequencies between ancestry groups, as in a and b. e, f, Comparison of the estimates of explained variance. In all plots, novel loci are in red and previously identified loci are in blue.

Extended Data Figure 5. Effects of BMI-associated loci on related metabolic traits

Unsupervised hierarchical clustering of the 97 BMI-associated loci (y axis) on 23 related metabolic traits (x axis). The top row shows the a priori expected relationship with BMI (green is concordant effect direction, purple is opposite). Loci with statistically significant concordant direction of effect are highlighted in green, and significant but opposing effects are in purple. Grey indicates a non-significant relationship and those with no information are in white. The key in the top left corner also shows the count of gene–phenotype pairs in each category (cyan bars).

Extended Data Figure 6. Bubble chart representing the genetic overlap across traits at BMI susceptibility loci

Each bubble represents a trait for which association results were requested for the 97 GWS BMI loci. The size of the bubble is proportional to the number of BMI-increasing loci with a significant association. A line connects each pair of bubbles with thickness proportional to the number of significant loci shared between the traits. Traits tested include the current study BMI SNPs, African-American BMI (AA BMI), hip circumference (HIP), HIP adjusted for BMI (HIPadjBMI), waist circumference (WC), waist circumference adjusted for BMI (WCadjBMI), waist-to-hip ratio (WHR), waist-to-hip ratio adjusted for BMI (WHRadjBMI), height, adiponectin, coronary artery disease (CAD), diastolic blood pressure (DBP), systolic blood pressure (SBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), triglycerides (TG), type 2 diabetes (T2D), fasting glucose (FG), fasting insulin (FI), fasting insulin adjusted for BMI (FIadjBMI), two-hour glucose (Glu2hr), diabetic nephropathy (Diab_Neph), age at menopause (AgeMenopause), and age at menarche (AgeMenarche).

Figure 1. Cumulative variance explained and example of secondary signals

a, The estimated variance in BMI explained by SNPs selected at a range of P values using unrelated individuals from the QIMR (n = 3,924; purple) and TwinGene (n = 5,668; gold), their weighted average (cyan), inferred from within-family prediction (red; Extended Data Fig. 2), and by all HapMap phase III SNPs in 16,275 unrelated individuals from the QIMR, TwinGene and ARIC studies (orange). b, Plot of the region surrounding MC4R (ref. 36). SNP associations from the European sex-combined meta-analysis are plotted with joint conditional P values (Pj) indicated for the three conditionally significant signals. SNPs are shaded and shaped based on the index SNP with which they are in strongest LD (rs6567160 in blue, rs994545 in yellow and rs17066842 in green).

Figure 2. Tissues and reconstituted gene sets significantly enriched for genes within BMI-associated loci

a, DEPICT predicts genes within BMI-associated loci (P < 5 × 10−4) are enriched for expression in the brain and central nervous system. Tissues are sorted by physiological system and significantly enriched tissues are in black; the dotted line represents statistically significant enrichment. b, The gene sets most significantly enriched for BMI-associated loci by DEPICT (P < 10−6, FDR < 4 × 10−4). Nodes represent reconstituted gene sets and are colour-coded by P value. Edge thickness between nodes is proportional to degree of gene overlap as measured by the Jaccard index. Nodes with gene overlap greater than 25% were collapsed into a single ‘meta-node’ (blue border). c, The nodes contained within the most enriched meta-node, ‘clathrin-coated vesicle’, which shares genes with other gene sets relevant to glutamate signalling and synapse biology. d, The ‘generation of a signal involved in cell–cell signalling’ meta-node represents several overlapping gene sets relevant to obesity and energy metabolism (gene sets with P < 4 × 10−3, FDR < 0.05 shown). For the complete list of enriched gene sets refer to Supplementary Table 21a.