Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications

MO Goodarzi - The lancet Diabetes & endocrinology, 2018 - thelancet.com
MO Goodarzi
The lancet Diabetes & endocrinology, 2018thelancet.com
Genome-wide association studies (GWAS) for BMI, waist-to-hip ratio, and other adiposity
traits have identified more than 300 single-nucleotide polymorphisms (SNPs). Although
there is reason to hope that these discoveries will eventually lead to new preventive and
therapeutic agents for obesity, this will take time because such developments require
detailed mechanistic understanding of how an SNP influences phenotype (and this
information is largely unavailable). Fortunately, absence of functional information has not …
Summary
Genome-wide association studies (GWAS) for BMI, waist-to-hip ratio, and other adiposity traits have identified more than 300 single-nucleotide polymorphisms (SNPs). Although there is reason to hope that these discoveries will eventually lead to new preventive and therapeutic agents for obesity, this will take time because such developments require detailed mechanistic understanding of how an SNP influences phenotype (and this information is largely unavailable). Fortunately, absence of functional information has not prevented GWAS findings from providing insights into the biology of obesity. Genes near loci regulating total body mass are enriched for expression in the CNS, whereas genes for fat distribution are enriched in adipose tissue itself. Gene by environment and lifestyle interaction analyses have revealed that our increasingly obesogenic environment might be amplifying genetic risk for obesity, yet those at highest risk could mitigate this risk by increasing physical activity and possibly by avoiding specific dietary components. GWAS findings have also been used in mendelian randomisation analyses probing the causal association between obesity and its many putative complications. In supporting a causal association of obesity with diabetes, coronary heart disease, specific cancers, and other conditions, these analyses have clinical relevance in identifying which outcomes could be preventable through weight loss interventions.
thelancet.com