Abstract

Obesity is a multifactorial condition influenced by complex interactions between genetic, environmental, and 
socio-behavioural determinants. Recent genomic studies underscore the significance of population and ancestry
related variation in obesity risk, highlighting disparities in the discovery and applicability of obesity-associated 
loci across global populations. While genome-wide association studies (GWAS) have identified over 150 loci 
linked to body mass index (BMI) and adiposity, most findings originate from populations of European ancestry, 
limiting transferability to African, Asian, Hispanic, Latino, and Indigenous groups. Differences in allele frequency, 
linkage disequilibrium patterns, and genetic architecture underscore the necessity of cross-population analyses and 
ancestry-specific polygenic risk models. Environmental and lifestyle factors, including diet, physical activity, 
socioeconomic status, and developmental conditions, further modulate genetic predisposition through gene
environment interactions. Early-life influences, such as intrauterine genetic risk and maternal nutrition, play a 
crucial role in shaping metabolic outcomes and behavioural susceptibility to obesogenic environments. 
Methodological challenges persist, including sampling bias, measurement errors, and limitations in causal 
inference due to over-reliance on self-reported BMI and predominantly European datasets. Advances in 
bioinformatics, rare-variant sequencing, and Mendelian randomisation hold promise for disentangling causality 
and improving prediction models. Addressing ancestry-specific variation, improving study design, and integrating 
environmental context are essential to ensuring equity, accuracy, and applicability in obesity genetics research. 
This integrated approach will advance understanding of the interplay between genetic architecture and 
environmental exposures, promoting more inclusive and effective global strategies for obesity prevention and 
management.