Genomics in the News

Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). In this manuscript by CGM investigators Jordan Smoller and Tian Ge, Genomic Structural Equation Modeling (Genomic SEM) was used to model this multivariate system of genetic relationships by applying and parsimoniously defining five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. Importantly, these factors were found to align with biologically and functionally relevant parcellations of the cortex. Stratified Genomic SEM was then used to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. These powerful analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex.

Tourette Syndrome (TS) is a complex neurodevelopmental disorder characterized by vocal and motor tics lasting more than a year. It is highly polygenic in nature with both rare and common previously associated variants. Epidemiological studies have shown TS to be correlated with other phenotypes, but large-scale phenome wide analyses in biobank level data have not been performed to date. In this study, by CGM investigator Jeremiah Scharf and colleagues, summary statistics from the latest meta-analysis of TS were used to calculate the polygenic risk score (PRS) of individuals in the UK Biobank data and applied a Phenome Wide Association Study (PheWAS) approach to determine the association of disease risk with a wide range of phenotypes. A total of 57 traits were found to be significantly associated with TS polygenic risk, including multiple psychosocial factors and mental health conditions such as anxiety disorder and depression. Additional associations were observed with complex non-psychiatric disorders such as type 2 diabetes, heart palpitations, and respiratory conditions. This analysis provides further evidence of shared genetic and phenotypic architecture of different complex disorders.

A team of CGM investigators and colleagues led by Pradeep Natarajan uncovered new genetic mechanisms for plasma lipid alterations using whole genome sequencing. The group expanded the allelic heterogeneity of lipids across diverse populations missed by current imputation methods. This work uncovered a new rare non-coding variant model of hyperlipidemia using new methods.

Genome-wide association studies (GWASs) have identified hundreds of loci associated with Crohn’s disease (CD). However, as with all complex diseases, robust identification of the genes dysregulated by noncoding variants typically driving GWAS discoveries has been challenging. Here, to complement GWASs and better define actionable biological targets, CGM investigators Mark Daly, Hailiang Huang, Aarno Palotie and colleagues  analyzed sequence data from more than 30,000 patients with CD and 80,000 population controls. They directly implicate ten genes in general onset CD for the first time to our knowledge via association to coding variation, four of which lie within established CD GWAS loci. In nine instances, a single coding variant is significantly associated, and in the tenth, ATG4C, we see additionally a significantly increased burden of very rare coding variants in CD cases. In addition to reiterating the central role of innate and adaptive immune cells as well as autophagy in CD pathogenesis, these newly associated genes highlight the emerging role of mesenchymal cells in the development and maintenance of intestinal inflammation.

Suicide is among the leading cause of death in children and adolescents. While suicide risk is known to be heritable, statistically well-powered genomics studies of children have been sparse, limiting our understanding of whether, when, and how genetic risk factors of suicide affect children’s suicidal behaviors and risk outcomes. In this study, it was showed that genetic susceptibility to adulthood suicide attempts (SAs), assessed based on the largest GWAS of adult SAs, is associated with suicidal behaviors in 11,878 pre-adolescent US children, after accounting for children’s sociodemographic backgrounds, parental history of suicide, and children’s psychopathology problems. Although the predictive utility of the genetic risk factors is still modest, future efforts may benefit from utilizing genetic data when developing proactive screening and early intervention strategies for mitigating suicide risk in children.

Asthma prevalence varies across populations, but it is unclear how much this is due to genetic factors. CGM PIs, Mark Daly, Alicia Martin, and colleagues report findings from a large-scale GWAS of asthma. In their meta-analysis of 22 biobanks spanning diverse ancestries, they uncovered 179 loci associated with asthma, including 49 novel ones, and observed consistent genetic effects across ancestries and biobanks, suggesting that genetic effects are unlikely to drive most prevalence differences. They also found genetic correlations between asthma subtypes and co-morbidities. The study’s increased ancestral diversity relative to previous analyses improved polygenic risk prediction in non-European populations.