Pathway‐based genome analysis of cognitive impairment in a forager‐horticulturalist South American population

Abstract

Abstract Background The genetic etiology of Cognitive Impairment (CI) is poorly understood. This is due, in part, to the complexity of diverse genetic and environmental risk factors in large, heterogeneous study populations. To overcome this obstacle, we examined a population with low genetic heterogeneity and fewer risk factors commonly found in industrialized populations ‐ the Tsimane of Bolivia (N = 353; mean age = 69 years [60–93 years]). The Tsimane are a geographically isolated forager‐horticulturalist population with high levels of physical activity and low prevalence of coronary artery disease, hypertension, and diabetes, even at advanced ages. The absence of these typical Alzheimer’s Disease (AD)‐related comorbidities provides a novel opportunity to isolate aspects of CI that are often intertwined with cardiometabolic and other non‐communicable chronic diseases. Method CI was determined from field evaluations using a locally translated and adapted cognitive battery, mental status examination, informant interview, and neurological evaluation (CI = 31, AD = 1). Genome‐wide variation was characterized using the Infinum Multi‐Ethnic Global Array (1,754,170 SNPs) and evaluated with a novel analytic tool that uses biologic processes as the unit of analysis — Pathways of Distinction Analysis (PoDA). PoDA captures interactions across the multiple genes within a network and assesses significance via empiric resampling. A catalog of 1167 curated networks representing the diversity of biologic function was evaluated. Result The pathway that most significantly distinguishes individuals with CI from those without impairment is the KEGG Alzheimer’s Disease Pathway (Distance Score [DS] = 3.23; FDR‐adjusted pDS<0.001). This pathway is composed of 169 genes that capture the major processes associated with AD. Refined analysis identified a subset of 35 genes that is associated with a 6‐fold difference (OR = 6.26; pOR = 1.21×10 −12 ) in relative risk for prevalent CI vs. no impairment. Those genes are involved in insulin signaling, calcium signaling, and mitochondrial oxidative phosphorylation subcomponents of the AD Pathway. It also includes variation within the AD risk loci APBB1 and MAPT (Figure 1). Conclusion In a population with minimal cardiometabolic disease, pathway‐based genome analysis objectively identifies subcomponents within the AD pathway tied to cellular energy metabolism as important in CI. Following validation in larger, heterogenous populations, these components may provide targets for prevention and intervention.