Genetic overlap between Parkinson disease and inflammatory bowel disease

Importance Parkinson disease (PD) and inflammatory bowel disease (IBD) have been associated, implying shared pathophysiology. Characterizing genetic pleiotropy between the two conditions aids the exploration of common etiology. Objective To estimate the genetic correlation between PD and IBD and to identify specific loci influencing both conditions. Design Genetic study with applications of high definition likelihood and conditional false discovery rate (FDR) framework. Setting The study was based on summary statistics of genome-wide association studies (GWAS). Participants The PD GWAS comprised 37,688 cases and 981,372 controls, and the IBD GWAS included 25,042 cases and 34,915 controls. Participants were of mixed ethnicity. Exposures None. Main Outcomes and Measures The main outcomes were a set of single nucleotide polymorphisms (SNPs) identified by conditional FDR analysis as jointly associated with PD and IBD. Results Weak but statistically significant genetic correlations were detected for PD with both Crohn's disease (CD) and ulcerative colitis (UC), the two main subtypes of IBD. A total of 1333 SNPs in 28 genomic loci and 1915 SNPs in 22 loci were jointly associated with PD-CD and PD-UC, respectively, at conjunctional FDR under 0.01. The pleiotropic loci appeared distinctive for PD-CD and PD-UC, are mostly novel and comprise loci with either same or opposing genetic effects on the two phenotypes. Positional and eQTL mapping prioritized 316 PD-CD and 303 PD-UC genes, among which only <10% are differentially expressed in both colon and substantia nigra. The KEGG pathways enriched by all prioritized genes were highly concordant between PD-CD and PD-UC, with the majority being related to immune and/or autoimmune dysfunction. Conclusions and Relevance Overall, we found robust evidence for a genetic link between PD and each subtype of IBD. The identified genetic overlap is complex at the locus and gene levels, indicating the presence of both common etiology and antagonistic pleiotropy. At the functional level, our results highlighted a central role of host immunity and/or autoimmunity in the PD-IBD relationship.

Characterization of the genetic relationship between the domains of sleep and circadian-related behaviors with substance use phenotypes

Sleep problems and substance use frequently cooccur. While substance use can often manifest as specific sleep deficits, genetic pleiotropy could also explain part of the relationship between sleep and substance use. Here we assess the genetic overlap between substance use behaviors and both sleep and circadian-related activity measures by deriving genetic clusters between these domains and testing processes of causality vs. horizontal pleiotropy using the largest publicly available genome-wide summary statistics of substance use behaviors (N= 79,729 - 632,802) and sleep/activity phenotypes/endophenotypes to date (N=85,502 - 449,734). We found 31 genetic correlations between substance use and sleep/activity measures after Bonferroni correction. Two specific genetic clusters explained our patterns of overlap. Genes associated with tobacco use severity (age of first regular tobacco use and smoking cessation) share overlap with elements of sleep health (sleep duration, sleep efficiency, and chronotype). Substance consumption (drinks per day and cigarettes per day) and problematic substance use behaviors (cannabis use disorder, opioid use disorder, and problematic alcohol use) clustered strongly with problematic measures of sleep (insomnia, self-reported short sleep duration, increased number of sleep episodes, increased sleep duration variability, diurnal inactivity) as well as measures of circadian-related activity (L5, M10, and sleep midpoint). Latent causal variable analyses determined that horizontal pleiotropy (rather than causality) underlies a majority of the associations between substance use and sleep/circadian related measures, except one plausible genetically causal relationship for opioid use disorder on self-reported long sleep duration. Results indeed show significant genetic overlap between substance use and sleep/circadian-related activity measures.

Genetic overlap between idiopathic pulmonary fibrosis and COVID−19

Genome-wide association studies (GWAS) of coronavirus disease 2019 (COVID-19) and idiopathic pulmonary fibrosis (IPF) have identified genetic loci associated with both traits, suggesting possible shared biological mechanisms. Using updated GWAS of COVID-19 and IPF, we evaluated the genetic overlap between these two diseases and identified four genetic loci (including one novel) with likely shared causal variants between severe COVID-19 and IPF. Although there was a positive genetic correlation between COVID-19 and IPF, two of these four shared genetic loci had an opposite direction of effect. IPF-associated genetic variants related to telomere dysfunction and spindle assembly showed no association with COVID-19 phenotypes. Together, these results suggest there are both shared and distinct biological processes driving IPF and severe COVID-19 phenotypes.

Potential Genetic Overlap Between Insomnia and Sleep Symptoms in Major Depressive Disorder: A Polygenic Risk Score Analysis

Background: The prevalence of insomnia and hypersomnia in depressed individuals is substantially higher than that found in the general population. Unfortunately, these concurrent sleep problems can have profound effects on the disease course. Although the full biology of sleep remains to be elucidated, a recent genome-wide association (GWAS) of insomnia, and other sleep traits in over 1 million individuals was recently published and provides many promising hits for genetics of insomnia in a population-based sample.Methods: Using data from the largest available GWAS of insomnia and other sleep traits, we sought to test if sleep variable PRS scores derived from population-based studies predicted sleep variables in samples of depressed cases [Psychiatric Genomics Consortium - Major Depressive Disorder subjects (PGC MDD)]. A leave-one-out analysis was performed to determine the effects that each individual study had on our results.Results: The only significant finding was for insomnia, where p-value threshold, p = 0.05 was associated with insomnia in our PGC MDD sample (R2 = 1.75−3, p = 0.006).Conclusion: Our results reveal that &lt;1% of variance is explained by the variants that cover the two significant p-value thresholds, which is in line with the fact that depression and insomnia are both polygenic disorders. To the best of our knowledge, this is the first study to investigate genetic overlap between the general population and a depression sample for insomnia, which has important treatment implications, such as leading to novel drug targets in future research efforts.

Missing genetic links between general factors of brain resting-state functional magnetic resonance imaging, cognition and psychopathology

General factors capturing the shared genetics in psychiatric (genomic p-factor) and cognitive traits (genomic g-factor), and more recently in resting-state functional magnetic resonance imaging-derived brain networks, have contributed to our increased understanding of the etiologies in their respective domains. Yet it remains unclear whether general factors can capture the three-way genetic overlap of psychopathology, cognition and brain function. Here we tested for the presence of this genetic overlap via genetic correlation analyses using summary statistics of genome-wide association studies of the p-factor (N = 162,151 cases and 276,846 controls), the g-factor (N = 269,867), and the two genomic factors estimated from the amplitude in resting-state functional magnetic resonance imaging-derived brain networks (N = 31,688). Unlike hypothesized, only the genetic correlation between the p-factor and the g-factor was significant. We conclude that specific functional brain network constructs may have more potential than their derived general dimensions to capture relevant genetic variation for cognition and psychopathology.

Pleiotropy between language impairment and broader behavioral disorders—an investigation of both common and rare genetic variants

Background Language plays a major role in human behavior. For this reason, neurodevelopmental and psychiatric disorders in which linguistic ability is impaired could have a big impact on the individual’s social interaction and general wellbeing. Such disorders tend to have a strong genetic component, but most past studies examined mostly the linguistic overlaps across these disorders; investigations into their genetic overlaps are limited. The aim of this study was to assess the potential genetic overlap between language impairment and broader behavioral disorders employing methods capturing both common and rare genetic variants. Methods We employ polygenic risk scores (PRS) trained on specific language impairment (SLI) to evaluate genetic overlap across several disorders in a large case-cohort sample comprising ~13,000 autism spectrum disorder (ASD) cases, including cases of childhood autism and Asperger’s syndrome, ~15,000 attention deficit/hyperactivity disorder (ADHD) cases, ~3000 schizophrenia cases, and ~21,000 population controls. We also examine rare variants in SLI/language-related genes in a subset of the sample that was exome-sequenced using the SKAT-O method. Results We find that there is little evidence for genetic overlap between SLI and ADHD, schizophrenia, and ASD, the latter being in line with results of linguistic analyses in past studies. However, we observe a small, significant genetic overlap between SLI and childhood autism specifically, which we do not observe for SLI and Asperger’s syndrome. Moreover, we observe that childhood autism cases have significantly higher SLI-trained PRS compared to Asperger’s syndrome cases; these results correspond well to the linguistic profiles of both disorders. Our rare variant analyses provide suggestive evidence of association for specific genes with ASD, childhood autism, and schizophrenia. Conclusions Our study provides, for the first time, to our knowledge, genetic evidence for ASD subtypes based on risk variants for language impairment.