We study the genetics of severe mental illness. In recent years, genetic studies have achieved tremendous success in identifying variants robustly associated with psychiatric disorders, and the aggregated results of these studies have begun to transform our understanding of disease risk. However, these studies have yet to yield any tools that can readily be applied in clinical practice. With the increased sample sizes in psychiatric genetics as well as the availability of multi-level data (including psychiatric phenotypes, health records and molecular phenotypes) psychiatric genetics is at a point where we can begin to address clinically precise questions using targeted approaches.

In our lab, we aim to characterize and predict psychiatric disease trajectories using genetic and high-dimensional phenotypic data resources, such as electronic health records. We focus on developing techniques to extract psychiatric phenotypic signatures from EHR data and develop meaningful patient representations from such resources. We also study ways  phenotypes from clinical instruments and health records can be harmonized to ultimately aid genetic discovery.

Risk of developing severe mental illness in youth

Psychotic symptoms are not only an important feature of severe neuropsychiatric disorders such as bipolar disorder and schizophrenia, but are also common in the general population, especially in youth. While subclinical psychopathology poses a risk for later development of overt psychiatric illness, only a minority of youth reporting psychotic symptoms will convert to full-blown psychotic disorders. We studied the etiology of these symptoms and their clinical characteristics in a multi-ancestry population youth cohort revealing a significant genetic link with ADHD (and not schizophrenia). However, schizophrenia liability does contribute to conversion to overt psychosis in clinically high risk youth (with Diana Perkins).

References: Olde Loohuis et al. Translational Psychiatry 2021, Perkins et al. American Journal of Psychiatry 2020

Accelerating the diagnosis of bipolar disorder

Bipolar Disorder is a common chronic disorder characterized by episodes of both depression and mania (or hypomania). The diagnosis of bipolar disorder is challenging in clinical practice, with a mean delay between illness onset and diagnosis is 5-10 years. One of the main reasons for this delay is the difficulty of distinguishing bipolar from unipolar depression: many bipolar disorder patients seeking treatment for depression are misdiagnosed with unipolar major depressive disorder, leading to suboptimal treatment and poor outcome. We aim to accelerate the diagnosis of bipolar disorder by identifying genetic and multi-modal phenotypic signatures that are distinctive to bipolar disorder with an onset of depression, which, used together, can differentiate this phenotype from unipolar depression.

In a collaborative PGC study with Roel Ophoff and Thomas Schulze's groups, we genetically and phenotypically characteristized onset of bipolar disorder, including age and type of onset. We observed large differences in these phenotypes across continents and by instruments, hampering genetic analyses. We are also i) investigating genetic differences between bipolar and unipolar depression in ongoing work with Stephan Ripke's group, and ii) evaluating prediction models of conversion to bipolar disorder using genetic data and electronic health records.

References: Kalman, Olde Loohuis, Vreeker et al. 2021. Funded through NIMH K99/R00 award.

Geospatial investigation of severe mental illness

Identifying geographical patterns of disease incidence can enable the identification of specific genetic and environmental variables that contribute to the cause and course of illness. Leveraging geo-located electronic health records (EHRs) from Colombia, we investigate geospatial variations of incidence of severe mental illness and their relation to environmental contributors to psychiatric illness. 


We found a relationship of distance decay between travel time and the incidence for mild (outpatients) but not severe (inpatients) presentations of mental illness. We also found several hotspots of severe mental illness in the region. This is exciting, since each hotspot is a candidate for further research to identify genetic or environmental risk factors for severe mental illness. Check out our preprint



Extracting phenotypes and characterize patient trajectories from electronic health records

We use a variety of natural language processing and network based approaches to capture meaningful longitudinal phenotypic patient characteristics from Electronic Health Records and compare these to those obtained from clinical instruments. 


Here we investigate diagnostic stability of diagnoses and clinical features that affect stability. We find that switching between diagnoses is common, and patients often accumulate comorbidities. We also find that specific symptoms extracted from free text can predict diagnostic switches. 


Molecular signatures of brain-related phenotypes and treatment

One problem that hampers the diagnosis, treatment and study of neuropsychiatric traits is the lack of available biomarkers for diagnosis, severity and treatment response. While the availability of high-quality post-mortem brain tissue is limited, it is feasible to collect blood or cerebrospinal fluid from healthy individuals.

Together with Roel Ophoff, Eleazar Eskin and Serghei Mangul, we have developed a method that examines high quality unmapped sequence reads for taxonomic classification of the microbiome, identifying increased microbial diversity in schizophrenia patients. In another sequencing-based study using whole blood, we studied the effects of lithium on gene expression. To better understand the connection between peripheral systems and the central nervous system, we examined links between central monoamine metabolite levels measured in the CSF and whole blood gene expression. Finally, we have studied molecular aging in schizophrenia. 

References: Olde Loohuis, Mangul et al. 2018, Krebs et al. 2020, Luykx, Olde Loohuis et al. 2016, Ori et al. 2021.