title: Genomic investigations of psychiatric conditions via research domain criteria traits
Creators: name: Ward, Joey
Publisher: name: University of Glasgow
Year: 2020

description: A major challenge of psychiatry is to be able to tell who will respond best to which treatment before they start. This will save time for the patient and be beneficial to wider society. It is widely assumed that an individual’s response to treatment will be due to their genetics. The most common type of genetic analysis in psychiatric genetics is that of comparing those who have a clinical diagnosis of a psychiatric morbidity to controls. The focus of this thesis, however, is to investigate the genetics of phenotypes that are features of several psychiatric diagnoses. This is known as a research domain classification (RDoC) approach. The main benefit to this approach is that is looks at traits that cut across traditional diagnostic boundaries and these traits can also apply to the general population and as such require less effort to obtain larger sample sizes with more detailed phenotyping. The thesis shows a range of differing techniques to identify genomic regions for a variety of traits as well as a range of differing downstream analyses. As the thesis progresses the techniques used become more sophisticated reflecting the progress that is being made in the field of genetic research. The thesis begins with a meta-analysis of three treatment cohorts to determine whether genetic loading for a psychiatric morbidity and a personality trait - Major Depressive Disorder (MDD) and neuroticism, respectively - can be used to predict response to antidepressants (published in PLOS one). The paper uses a polygenic risk scoring (PRS) approach to calculate an individual’s genetic loading for a trait using pruning and thresholding (P&amp;T) methodology to see if higher genetic loading for these traits resulted in poorer outcomes for those taking a selective serotonin reuptake inhibitor (SSRI) antidepressant. The outcome measure is percentage reduction in Hamilton Depression (HAMD) score. The analysis is performed in three cohorts and the results combined using an inverse variance weighted meta-analysis. The results, although largely not statistically significant showed that greater genetic loading for both MDD and neuroticism correlated with poorer response to SSRIs. This leads onto the first genome-wide association study (GWAS). The second and third papers investigate the same mood instability phenotype, that of a single item question on whether the participant thought their mood often goes up and down (published in Translational Psychiatry and Molecular Psychiatry, respectively). Each paper uses a different methodology due to the techniques that were available at the time. Firstly, logistic regression and the then in a BOLT-LMM setting which allows for maximising of the sample size via use of a genetic relationship matrix. The first paper, which identifies 4 loci, uses downstream analytical techniques such as PRS analysis and linkage disequilibrium score regression (LDSR) to validate the use of a simple, easily obtainable mood instability phenotype. The second mood instability paper, due to its larger sample size and identification of forty-six genomic loci, also uses other techniques such as phenotype linkage network (PLN) analysis and expression quantitative trait loci (eQTL) analysis to further contextualise the results and identified a community of genes containing serotonin and melatonin receptors. The fourth paper is an analysis of suicidality and was the first paper to identify areas of the genome that may drive suicidal behaviour (published in EBioMedicine). This paper uses a cumulative link function to analyse an ordered ordinal phenotype that combines self-harm and suicidal behaviours to identify 3 loci. Validation of the phenotype was performed through PRS analysis showing how those who had committed suicide had higher genetic loading for the suicidality phenotype than controls who had reported no suicidal ideation whatsoever. Then the paper explores how loading for this phenotype associates with psychiatric outcomes. The fifth and final paper uses a measure of anhedonia for the genetic analysis and correlates risk scores of this phenotype with brain structure and function. As with the second mood instability GWAS this approach uses BOLT-LMM to maximise statistical power and sample size which led to the identification of 11 independent loci. This paper also uses a newer approach to polygenic risk scoring, that of LDpred. This newer method is superior to that of P&amp;T as only a single risk score is generated and as it makes use of more of the available information from the GWAS summary statistics, generates a more statistically powerful score. These LDpred anhedonia scores correlated with total grey matter volume and the volume of 4 out of 15 regions of interest previously associated with anhedonia as well as two brain integrity measures in those same 15 regions. One of the themes running through the five publications (in addition to the evolution of different methodological approaches) is the potential advantage of studying psychopathological traits rather than formal diagnostic categories. As alluded to by the opening quote in the thesis from a leading psychiatry textbook by Sadock, such an approach may be more useful than identifying genetic variants associated with Diagnostic and Statistical Manual of Mental Disorders (DSM) 5 diagnoses