Changing perspectives: Towards detailed phenotyping in genetics

Abstract

Mental health problems are highly prevalent in modern-day society. Despite several decades of intensive research aimed at identifying the underlying biological mechanisms, and potential drug targets, pharmacological treatments still have limited success. Since all traits are at least partially influenced by our genetic makeup, using genetic information to increase our understanding of the biological mechanisms underlying mental health problems might eventually benefit patients. Genome-wide association studies (GWAS) provide an exploratory way to identify genetic variants throughout the genome that are, statistically, associated to a trait of interest. The explosion of GWAS studies since 2005 (https://www.ebi.ac.uk/gwas/diagram) has drastically increased our knowledge of the biology of diseases and identified thousands of variants involved in a wide variety of (disease) traits.Yet for many complex traits, like psychiatric disorders, the identified genetic variants explain only a fraction of the variance in the trait. We argue that this may, in part, be the result of the way in which neuropsychiatric traits are operationalized in genetic studies. Typically, participants are classified as cases (i.e., people that suffer from a given psychiatric disorder) or as controls (i.e., not suffering from that particular disorder). However, people suffering from the same disorder may exhibit different sets of symptoms that may, in turn, be influenced by different genetic variants. In other words, the manner in which phenotypes are operationalized will have consequences for the success of genetic analyses. Therefore, in order to properly study the genetic basis of complex behavior, it is vital to think about the exact nature of the phenotypes used in the analysis, and the way they are operationalized. This thesis uses large-scale genetic data and state-of-the-art methods to study the merits of more detailed phenotyping in uncovering the genetics of complex neuropsychiatric traits.

Comparison of algorithm-based versus single-item diagnostic measures of anxiety and depression disorders in the GLAD and COPING cohorts

“…The questionnaire responses are run through an algorithm based upon diagnostic criteria, such as the Diagnostic Statistical Manual (DSM-5; (6), to assess whether the participant qualifies for a diagnosis. This has been referred to as, variously, either strictly-defined, detailed, or symptom-based phenotyping (7–9). Single-item diagnoses take a contrasting approach and utilise a single question where participants are asked about the presence or absence of a clinical diagnosis from a health professional for a psychiatric disorder across their lifetime.…”