Abstract 050: Meta-analysis of Genetic Associations in up to 339,224 Individuals Identify 66 New Loci for Bmi, Confirming a Neuronal Contribution to Body Weight Regulation and Implicating Several Novel Pathways
Authors: Justice Anne , North Kari , Loos Ruth , Vedantam Sailaja , Day Felix , Berndt Sonja , Gustafsson Stefan , Locke Adam , Powell Corey , Bratati Kahali , Croteau-Chonka Damien , Winkler Thomas , Scherag Andre , Barroso Inês , Beckmann Jacqui , Lindgren Cecilia , Pers Tune , Visscher Peter , Willer Cristen , Yang Jian , Mohlke Karen , Hirschorn Joel , Ingelsson Erik , Boehnke Michael , Speliotes Elizabeth 
Published: July 2021

Obesity is a rising global concern as it substantially contributes to cardiovascular disease (CVD) and CVD risk factors (e.g. insulin resistance, dyslipidemia, Type 2 Diabetes). BMI (body mass index) is an easily obtained measure of obesity, which is highly heritable, and often used as a proxy when searching for genetic risk factors. Previous analyses of genome-wide association studies (GWAS) in the GIANT (Genetic Investigation of ANthropometric Traits) Consortium identified 32 loci containing common variants associated with BMI in adults of European ancestry. To enhance discovery of common causal variants for BMI, GIANT has expanded to include 82 studies with GWAS data and 43 studies with Metabochip data in more ancestrally diverse populations including up to 339,224 individuals. We performed a meta-analysis of the study-specific summary statistics for the BMI associations, assuming an additive model and using a fixed-effects inverse variance method. SNPs in 97 loci reached genome-wide significance (P<10-8), of which 31 loci had previously been identified for BMI in European-descent samples. Of the 66 novel BMI loci, three had previously been identified for association with adiposity related traits in specific populations. Many of the 97 loci contain strong biological candidates, and multiple methods were employed to pinpoint the most likely candidate gene(s) within the main signal regions. In addition to manual curation, GRAIL, and MAGENTA, we also employed a newly developed, unbiased computational approach that integrates a variety of data types (i.e. tissue-specific gene expression data, phenotypic information from mouse knockout studies, etc.) to identify potentially causal genes and pathways. Consistent with previous findings, many of these BMI loci contain genes that have a potential neuronal role in regulating appetite (e.g.
MC4R, POMC, GRID1, NAV1
). Our analyses also highlight loci with genes in pathways that were previously less apparent, such as those related to glucose and insulin homeostasis (
TCF7L2
,
GIPR
), lipid metabolism (
APOE
-cluster,
NPC1
,
NR1H3
), the immune system (
TLR4)
, and others. Additionally, many of the newly associated variants are in high LD with previously identified SNPs associated with related phenotypes, including other CVD risk factors (e.g. SNPs nearby
IRS1
associated with T2D, adiposity, HDL, TG, adiponectin levels, and CHD; and SNPs near
NT5C2
associated with CHD and blood pressure variables). This large-scale meta-analysis has greatly increased the number of identified obesity-susceptibility loci and continues to contribute to our understanding of the complex biology of adiposity. Our results have highlighted overlapping GWAS signals and important pathways which connect BMI and other CVD risk factors supporting the importance of pleiotropic effects in the pathogenesis of common complex diseases.