Evolutionarily derived networks to inform disease pathways

Abstract

Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, Evolutionary Triangulation (ET). ET utilizes the hypothesis that alleles affecting disease risk in multiple populations are distributed consistently with differences in disease prevalence and compares allele frequencies among populations and their relationship to phenotype prevalence. We hypothesized that combining these methods will increase our ability to detect genetic patterns of association in complex diseases. We combined HPN and ET to identify network patterns associated with Type 2 Diabetes Mellitus (T2DM), a leading causes of death worldwide. Fasting glucose, a continuous trait, was used as a proxy for T2DM and differs significantly among continental populations. The combined method identified several diabetes related traits and several phenotypes related to cardiovascular diseases, for which diabetes is a major risk factor. ET-HPN found more phenotypes related to our target and related phenotypes than the application of either method alone. Not only could we detect phenotype connections related to T2DM, but we also identified phenotypes that are distributed in parallel to it, e.g. Amyotrophic Lateral Sclerosis. Our analyses showed that ET filtered HPN provides information that neither technique can individually.