Obesity is a major risk factor for a number of metabolic and cardiovascular complications. However, a substantial proportion of obese individuals are protected from cardiometabolic complications, despite their excess adiposity (the so-called metabolically healthy obese, MHO). Conversely, not all normal weight individuals are metabolically healthy, despite being lean (the metabolically obese normal weight, MONW). The physiological mechanisms that determine why some obese individuals are protected, and why some normal weight individuals are at risk, are poorly understood, because clinical studies are often too small and methods to test presumed pathways involved are too invasive. Identifying genes and the pathways in which they are implicated provides an alternative strategy to elucidate the biology that underlies the MHO and MONW. Genome-wide association studies (GWAS) have so far identified >300 loci for obesity traits. However, these loci have provided only limited insight into the mechanisms that link obesity and its complications, because the GWAS examine each trait in isolation and ignore the fact that obesity is a heterogeneous clinical condition. Therefore, we propose to [1] perform a multi-trait genome-wide search for obesity-increasing loci with protective effects on cardiometabolic traits and vice versa, [2] prioritize candidate genes within identified loci, and [3] functionally characterize prioritized candidate genes in model systems. Specifically, we apply two complementary discovery approaches. In the first approach (Aim 1a), we perform multi-trait correlated meta-analyses that combine summary statistics of adiposity and cardiometabolic GWAS from large-scale genetic consortia and the UK Biobank (Ntotal~840,000).
We aim to identify SNPs simultaneously associated with increased adiposity and a favorable cardiometabolic risk profile, and vice versa. In the second approach (Aim 1b), we use individual-level data from the UKBiobank (N~500,000) to perform GWAS on new outcomes derived from the difference between a cardiometabolic and an adiposity trait. Next, we prioritize the most likely candidate genes in identified loci and the most relevant tissues using functional annotation pipelines (Aim 2a) and high-throughput screens in transgenic CRISPR-Cas9-based zebrafish model systems (Aim 2b). Lastly, we investigate the functional impact of prioritized genes using CRISPR-Cas9 in human-induced pluripotent stem cells (hiPSCs) differentiated into relevant cell types (Aim 3a) and tissue-specific transcriptomic analyses in transgenic zebrafish model systems (Aim 3b). Our focus on obesity-associated loci with protective effects on health (and vice versa) is unique and targets a biology that has not been accessed with single-trait GWAS. Our approaches that use model systems to prioritize and characterize genes are innovative and will provide the critical insights needed for in-depth follow- up in murine models and clinical studies. Some identified genes may point towards actionable targets for the prevention and treatment of obesity and its complications with clinical impact for the general population.
A substantial proportion of obese individuals does not suffer cardiometabolic complications, and not all normal weight individuals are healthy. To elucidate the mechanisms that determine why some obese individuals are protected and why some normal weight individuals are at risk, we will screen the genomes of 800,000 individuals to identify genes that increase obesity risk and protect against cardiometabolic complications. New insights may lead to more precise strategies for the treatment of obesity and its complications.
