Shared genetic architecture across psychiatric disorders.
paper
Cited
Public
- Authors
- Grotzinger, Andrew D
- Year
- 2021
- Journal
- Psychological medicine
- PMID
- 33729112
- DOI
- 10.1017/S0033291721000829
- PMCID
- PMC9202447
Psychiatric disorders overlap substantially at the genetic level, with family-based methods long pointing toward transdiagnostic risk pathways. Psychiatric genomics has progressed rapidly in the last decade, shedding light on the biological makeup of cross-disorder risk at multiple levels of analysis. Over a hundred genetic variants have been identified that affect multiple disorders, with many more to be uncovered as sample sizes continue to grow. Cross-disorder mechanistic studies build on these findings to cluster transdiagnostic variants into meaningful categories, including in what tissues or when in development these variants are expressed. At the upper-most level, methods have been developed to estimate the overall shared genetic signal across pairs of traits (i.e. single-nucleotide polymorphism-based genetic correlations) and subsequently model these relationships to identify overarching, genomic risk factors. These factors can subsequently be associated with external traits (e.g. functional imaging phenotypes) to begin to understand the makeup of these transdiagnostic risk factors. As psychiatric genomic efforts continue to expand, we can begin to gain even greater insight by including more fine-grained phenotypes (i.e. symptom-level data) and explicitly considering the environment. The culmination of these efforts will help to inform bottom-up revisions of our current nosology.
Levels of Genomic Analysis.Panel A depicts manhattan plots for three hypothetical disorders (Dx1, Dx2, Dx3). Manhattan plots are typically used to depict GWAS results with −log10(p-values) on the y-axis, the chromosomes on the x-axis, and each point reflecting the result for an individual genetic variant. The common factor manhattan plot directly below the individual disorder plots is illustrative of cross-disorder efforts to identify the genetic signal shared across the three disorders. Panel B depicts the goal of mechanistic studies to group together variants based on their functional characteristics. These characteristics can include when in development (e.g., early life, adulthood), specific tissue types (e.g., the hippocampus), and neuronal subtypes or functions (e.g., calcium channel signaling) where the variants are expressed. Panel C depicts the genetic covariance matrix across the three disorders estimated using LDSC, which was then used as input for Genomic SEM to produce the unstandardized estimates for the common factor model shown in the middle. Once a measurement model is identified, psychiatric factors can be associated with relevant external correlates, depicted in the generic form here as trait X in the rightmost panel. Notably, none of these external traits or disorders need to be measured in the same sample in order to be included in the model.
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| Name | Type |
|---|---|
| ADHD | phenotype |
| Alcohol Use Disorder | phenotype |
| All of Us Research Program | cohort |
| anorexia nervosa | phenotype |
| anxiety | phenotype |
| ASD | phenotype |
| attention deficit hyperactivity disorder | phenotype |
| autism spectrum disorder | phenotype |
| autoimmune diseases | phenotype |
| biological sex | phenotype |
| BiP | phenotype |
| bipolar disorder | phenotype |
| candidate genes | cohort |
| CDG2 local | cohort |
| changing diagnoses local | phenotype |
| cognition | phenotype |
| common variants | cohort |
| Comorbid psychiatric presentations local | phenotype |
| copy number variant | variant |
| Crohn's disease | phenotype |
| Danish Biobank Register local | cohort |
| DCC | gene |
| developmental stage | phenotype |
| Earlier age of onset of bipolar disorder local | phenotype |
| early onset disorders local | phenotype |
| early onset SCZ local | phenotype |
| electronic health records local | drug |
| eMERGE network | cohort |
| episodic emotion dysregulation local | phenotype |
| Excitatory genes local | gene |
| GABAergic genes | gene |
| genetic correlation | phenotype |
| genetic correlation between BIP and MDD local | phenotype |
| Genomic Aggregation Project in Sweden local | cohort |
| genomic data local | drug |
| heritability | phenotype |
| HiTOP local | cohort |
| late onset SCZ local | phenotype |
| later onset disorders local | phenotype |
| major depressive disorder | phenotype |
| mixed presentations local | phenotype |
| Mood-congruent psychosis local | phenotype |
| mood disorders | phenotype |
| Mood-incongruent psychosis local | phenotype |
| Nord Trondelag Health Study local | cohort |
| obsessive-compulsive disorder | phenotype |
| Overall functioning local | phenotype |
| pleiotropic variants local | variant |
| Post-Traumatic Stress Disorder | phenotype |
| PsycheMERGE consortium local | cohort |
| psychiatric disorders | phenotype |
| Psychiatric Genomics Consortium | cohort |
| Psychiatric polypharmacy local | phenotype |
| psychiatric traits | phenotype |
| psychosis | phenotype |
| rare CNVs | variant |
| rare variant | cohort |
| RBFOX1 | gene |
| RDoC | cohort |
| rheumatoid arthritis | phenotype |
| risk allele | cohort |
| risk factor | phenotype |
| rs7193263 local | variant |
| rs8084351 local | variant |
| schizophrenia | phenotype |
| SCZ | phenotype |
| selective serotonin reuptake inhibitors | drug |
| sex | phenotype |
| shared symptoms local | phenotype |
| single-nucleotide polymorphism | variant |
| socioeconomic background local | phenotype |
| socioeconomic outcomes local | phenotype |
| super-normal controls local | cohort |
| symptoms | phenotype |
| Tourette syndrome | phenotype |
| transdiagnostic traits local | phenotype |
| type 1 diabetes | phenotype |
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