Trastornos de la Conducta Alimentaria y Enfermedad de Parkinson - 2: Carga Poblacional, Epidemiología Genética y...

We reviewed the epidemiologic and genomic literature and performed genomic analyses to identify population burdens (Eating Disorders, ED and Parkinson's Disease, PD) and shared genetic risk (Anorexia Nervosa, AN and PD), after we previously demonstrated two-to four-fold relative risks of a family history of Parkinson's Disease in families of individuals with ED.

We reviewed the epidemiology of both disorders and published genome-wide association studies (GWAS), searched PD GWAS findings with AN associated genome-wide significant SNPs and associated genes and regions, and performed conditional/conjunctional false discovery rate genomic analyses of AN and PD summary statistics to identify shared genetics.

The global population burden of ED and PD are similar despite differences in age of onset and sex ratio. GWAS review and linkage disequilibrium analysis showed that genome-wide significant variants from AN GWAS and PD GWAS in the chr3p21.31 region are correlated. We identified a chr3p21.31 variant with joint association with both disorders; this variant has functional linkages to 40 genes.

ED and PD share neuropsychological, neurobiological, and genetic risk factors, including association with the complex chr3p21.31 locus. Translational analyses leveraging disorder-specific research resources may benefit our understanding of the genetics and neuropsychiatric mechanisms of both disorders.

ED and PD have similar age-standardized disability life years (AS-DALYS); the burden of both disorders are expected to increase in the future.Genomic epidemiology literature review reveals AN and PD genetic architectures are highly polygenic (AN >> PD), and, AN variant discoverability is less than PD variant discoverability.AN and PD are jointly associated at rs1352420 at chr3p21.31, a region with multiple AN and PD comorbid and genetically correlated trait associations.Cross-disorder research offers opportunities to identify shared variants and explore mechanistic hypotheses.