Investigating Shared Genetic Bases between Psychiatric Disorders, Cardiometabolic and Sleep Traits Using K-Means Clustering and Local Genetic Correlation Analysis

Abstract

Psychiatric disorders are among the top leading causes of the global health-related burden. Comorbidity with cardiometabolic and sleep disorders contribute substantially to this burden. While both genetic and environmental factors have been suggested to underlie these comorbidities, the specific molecular underpinnings are not well understood. In this study, we leveraged large datasets from genome-wide association studies (GWAS) on psychiatric disorders, cardiometabolic and sleep-related traits. We computed genetic correlations between pairs of traits using cross-trait linkage disequilibrium (LD) score regression and identified clusters of genetically correlated traits using k-means clustering. We further investigated the identified associations using two-sample mendelian randomization (MR) and tested the local genetic correlation at the identified loci. In the 7-cluster optimal solution, we identified a cluster including insomnia and the psychiatric disorders major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD). MR analysis supported the existence of a bidirectional association between MDD and insomnia and the genetic variants driving this association were found to affect gene expression in different brain regions. Some of the identified loci were further supported by results of local genetic correlation analysis, with body mass index (BMI) and C-reactive protein (CRP) levels suggested to explain part of the observed effects. We discuss how the investigation of the genetic relationships between psychiatric disorders and comorbid conditions might help us to improve our understanding of their pathogenesis and develop improved treatment strategies.

Shared familial risk for type 2 diabetes mellitus and psychiatric disorders: a nationwide multigenerational genetics study

“…This restriction was based on robust Mahalanobis distances of the principal components using the dist_ogk function of the R package bigutilsr (Prive et al, 2020b). Finally, as studies have demonstrated that both T2DM and psychiatric disorders, particularly ADHD, genetically correlate with body mass index (BMI) (Fanelli et al, 2022;Zammarchi, Conversano, & Pisanu, 2022), we assessed the potential confounding or mediating role of BMI genetic liability by repeating the main PRS analyses including as a covariate a polygenic score for BMI based on a GWAS on 700 000 individuals (Yengo et al, 2018). Similarly, as a proxy of genetic susceptibility to low socioeconomic status, we included as a covariate a polygenic score for educational attainment based on a GWAS conducted in 766 345 European-descent individuals (Lee et al, 2018).…”

“…T2DM and psychiatric disorders are heritable conditions, which aggregate in families; results of epidemiological studies document familial aggregation, with higher risk in first-than in second-degree relatives of affected probands, both for T2DM (Hemminki, Li, Sundquist, & Sundquist, 2010;Liao et al, 2022) and for many psychiatric disorders, including obsessivecompulsive disorder (OCD), autism spectrum disorder (ASD), anorexia nervosa (AN), schizophrenia, and attention-deficit/hyperactivity disorder (ADHD) (Chen et al, 2017;Chou et al, 2017;Hansen et al, 2019;Pardue et al, 2014;Steinhausen, Jakobsen, Helenius, Munk-Jorgensen, & Strober, 2015). Genome-wide association studies (GWAS) have confirmed the polygenic nature of each of these conditions (Demontis et al, 2023;Grove et al, 2019;Strom et al, 2021;Trubetskoy et al, 2022;Watson et al, 2019;Xue et al, 2018), and studies using GWAS summary statistics have also demonstrated genetic overlap between T2DM and several psychiatric disorders; this includes genetic correlations of T2DM with ADHD and major depressive disorder (MDD) in the positive direction, and with OCD, AN, and to some extent also schizophrenia in the negative direction (Fanelli et al, 2022;Zammarchi, Conversano, & Pisanu, 2022). Since GWAS-based genetic correlations do not always reflect phenotypic association patterns, using other genetically informative study designs, such as family designs, can help triangulate evidence of how genetic as well as environmental factors contribute to the associations between T2DM and psychiatric disorders.…”