Research
Our goal is to use a combination of statistical models and large-scale data to understand the nature of psychiatric disorders, with a particular focus on major depressive disorder (MDD).
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Assessing and improving MDD phenotypes in cohorts, biobanks and EHRs
Genome-wide association studies (GWAS) in psychiatric disorders rely heavily on meta-analysis of multiple cohorts to reach sample sizes that would provide adequate sample sizes for statistical significance. Recent studies (since 2016) mostly attempt to meta-analyse as many cohorts as possible, without regard to phenotypic heterogeneity between cohorts that may be due to misdiagnosis from other disorders and other heritable confounders. We use and derive new quantitative genetics metrics to show that different was of collecting and ascertainment data can lead to different genetic findings [1], and develop statistical [2] and deep-learning based methods [3] for imputing and refining poorly collected phenotypes, borrowing information from other phenotypes collected in the same cohort or biobank.
[1] Minimal phenotyping yields genome-wide association signals of low specificity for major depression. Cai, N., Revez, J.A., Adams, M.J. et al. Nat Genet (2020). doi: 10.1038/s41588-020-0594-5
[2] Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder. Dahl, A., Thompson, M., An, U. et al. Nat Genet (2023). doi: 10.1038/s41588-023-01559-9
[3] Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries. An, U., Pazokitoroudi, A., Alvarez, M. et al. Nat Genet (2023). doi: 10.1038/s41588-023-01558-w
Further reading:
The genetic basis of major depressive disorder. Flint, J. Mol Psychiatry (2023). doi: 10.1038/s41380-023-01957-9
Investigating MDD heterogeneity
MDD, like other psychiatric disorders, has long been suspected to be heterogeneous. Genetic evidence of heterogeneity exist, but rely on putative subtypes being defined phenotypically prior to genetic analysis [1,2]. This precludes the identification of new delineations of subtypes based on genetic findings alone. We aim to do the latter in a step-wise manner. Our current work first examines the genetics of endorsing each symptom criteria of MDD [3], then investigates whether they are integral to MDD etiology, and then determines whether genetic risk factors for the integral symptoms of MDD are additively in their contribution to MDD.
[1] Reviewing the genetics of heterogeneity in depression: operationalizations, manifestations and etiologies. Cai, N., Choi KW., Fried EI. Hum Mol Genet (2020). doi: 10.1093/hmg/ddaa115
[2] Genetic heterogeneity and subtypes of major depression. Nguyen, TD., Harder, A., Xiong, Y. et al. Mol Psychiatry (2022). doi: 10.1038/s41380-021-01413-6
[3] Polygenic analyses show important differences between major depressive disorder symptoms measured using various instruments. Huang, L., Tang. S., Rietkerk, J., et al. Biol Psych (2024). doi: 10.1016/j.biopsych.2023.11.021
Further reading:
A robust method uncovers significant context-specific heritability in diverse complex traits. Dahl, AW., Nyugen, K., Cai, N., et al. AJHG (2020). doi: 10.1016/j.ajhg.2019.11.015
Genetic Influences on Disease Subtypes. Dahl, AW. & Zaitlen, N. Annu Rev Genomics Hum Genet (2020). doi: 10.1146/annurev-genom-120319-095026
Identifying cell types and genes of interest for MDD
It is the ultimate goal of genetic association studies to uncover the biology of a disease (in our case, MDD). To do this, we need to identify ways to link genetic effects identified through GWAS or rare-variant association studies (RVAS) to molecular processes, such as regulation of gene expression [1] or affecting particular gene functions [2], either within or across specific cell types or tissues. As MDD is heterogeneous, different cases of (subtypes of) MDD may have pathologies with different tissue and gene involvement. Our work aims to identify the genetic effects, the molecular layers they affect, and the cell or tissue types they act in.
[1] Leveraging eQTLs to identify individual-level tissue of interest for a complex trait. Majumdar, A., Giambartolomei, Cai, N., et al. PLoS Comp Biol (2021). doi: 10.1371/journal.pcbi.1008915
[2] Bayesian Aggregation of Multiple Annotations Enhances Rare Variant Association Testing. Nappi, A., Cai, N., Casale, FP. RECOMB (2025). doi: 10.1007/978-3-031-90252-9_54
Further reading:
Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Finucane, HK., Reshef, YA., Anttila, V., et al. Nat Genet (2018). doi: 10.1038/s41588-018-0081-4
Probabilistic fine-mapping of transcriptome-wide association studies. Mancuso, N., Freund, MK., Johnson R., et al. Nat Genet (2019). doi: 10.1038/s41588-019-0367-1
Understanding psychiatric comorbidity
Psychiatric disorders show a great deal of genetic sharing [1], though it hasn't been shown that this is the reason they are highly comorbid. In fact, how and why psychiatric disorders are often comorbid is unclear, and likely confounded by issues in individual disorder ascertainment and diagnoses [2]. Further, many psychiatric disorders share inclusion (symptom-based) criteria in their diagnostics, compounded with exclusion criteria of entire disorders (e.g. MDD and bipolar disorder (BP) share all the depressive symptom criteria; the latter has further criteria on manic symptoms; MDD excludes individuals with a history of BP). Our on-going work attempts to find out if these inclusion and exclusion criteria between disorders map onto their genetic sharing and differences, as only having done that may we turn empirically derived phenotypic delineations between disorders into biological understanding.
[1] The Landscape of Shared and Divergent Genetic Influences across 14 Psychiatric Disorders. Grotzinger, AD., Werme, J., Peyrout, WJ., et al. medRxiv (2025). doi: 10.1101/2025.01.14.25320574
[2] Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research. Cai, N., Verhulst, B., Andreassen, OA., et al. Mol Psych (2025). doi: 10.1038/s41380-024-02878-x
Further reading:
Impact of diagnostic misclassification on estimation of genetic correlations using genome-wide genotypes. Wray, NR., Lee, SH., Kendler, KS. Eur J Hum Genet (2012). doi:10.1038/ejhg.2011.257
Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits. Grotzinger, AD., Rhemtulla, M., Valming, R., et al. Nat Hum Behav (2019). doi: 10.1038/s41562-019-0566-x
Cross-trait assortative mating is widespread and inflates genetic correlation estimates. Border, R., Athanasiasdis, G., Buil, A., et al. Science (2022). doi: 10.1126/science.abo2059
Effect of chronic stress on neuronal gene expression
We have previously shown stress is a major risk factor to MDD and interact with genetic effects on MDD [1]. We have also previously shown that chronic stress, like MDD, is able cause changes in molecular markers such as mitochondrial DNA copy number increase (mtDNA-CN) and mean leukocyte telomere length decrease in human and mice [2]. We therefore think stress mimics some genetically regulated biological mechanism in its contribution to MDD; studying how stress affects gene expression in the brain (of lab mice) may therefore give us insights into the biological underpinnings of MDD. To do this, we have derived matched single cell RNA sequencing (scRNAseq, 10x Chromium) and spatial sequencing (10x Visium) on mice stressed in different ways [3] and across different periods of time. We use these to investigate how stress affects neuronal gene expression in individual cells, and in groups of cells in relation to their spatio-temporal context.
[1] Molecular genetic analysis subdivided by adversity exposure suggests etiologic heterogeneity in major depression. Peterson, RE., Cai, N., Dahl, AW., et al. AJP (2018). doi: 10.1176/appi.ajp.2017.1706062
[2] Molecular signature of major depression. Cai, N., Chang, S., Li, Yi., et al. Curr Biol (2015). doi: 10.1016/j.cub.2015.03.008
[3] Molecular and neural mechanisms of behavioural integration in the extended-amygdala. Chang, S., Fermani, F., Huang, L., et al. bioRxiv (2024) doi: 10.1101/2024.04.29.591588
Further reading
Tripartite extended amygdala–basal ganglia CRH circuit drives locomotor activation and avoidance behavior. Chang, S., Fermani, F., Lao, C., et al. Science Advances (2022). doi: 10.1126/sciadv.abo1023
Mitochondrial involvement in MDD (and mitochondrial genetics)
We have previously found a replicable increase in mtDNA-CN and mtDNA heteroplasmy [1] in MDD cases, but quickly realised that this result in itself does not make mtDNA-CN or heteroplasmy a good biomarker for MDD, because they are also associated with a lot of other diseases and conditions. We then realised, that to understand the mitochondrial involvement in MDD we first have to understand how mtDNA variations effects on phenotypes in general. We start with molecular phenotypes such as blood metabolites [2] and tissue-specific gene expression [3,4] to lay foundations for expansion of our investigations into complex diseases like MDD.
[1] Genetic control over mtDNA and its relationship to major depressive disorder. Cai, N., Chang, S., Li, Y., et al. Curr Biol (2015). doi: 10.1016/j.cub.2015.10.065
[2] Mitochondrial DNA variants modulate N-formylmethionine, proteostasis and risk of late-onset human diseases. Cai, N., Gomez-Duran, A., Yonova-Doing, E., et al. Nat Med (2021). doi: 10.1038/s41591-021-01441-3
[3] Interplay between mitochondrial and nuclear DNA in gene expression regulation. Giannoulis, X., Wengert, S., Ratajczak, F., et al. bioRxiv (2024). doi: 10.1101/2024.12.10.627680
[4] Tissue-specific apparent mtDNA heteroplasmy and its relationship with ageing and mtDNA gene expression. Wengert, S., Giannoulis, X., Kreitmaier, P., et al. bioRxiv (2024). doi: 10.1101/2024.12.11.627989
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