Investigation of rare genetic variants associated with psychiatric traits
Creators: name: Dattani, Saloni
Publisher: name: Open Science Framework
Year: 2022
Subjects: subject: Psychiatry and Psychology
subject: Medicine and Health Sciences
subject: Life Sciences
subject: Mental and Social Health
subject: Genetics and Genomics

description: In this study, we use imputed genotype data from a sample of 157,358 individuals who responded to the Mental Health Questionnaire of the UK Biobank to explore the role of rare genetic variants in latent traits underlying three psychiatric disorders (major depression, schizophrenia and bipolar disorder). In particular, we seek to identify variants, genes and pathways associated with these traits and to estimate the relationship between allele frequency and effect size. Factor analysis for latent trait estimation Presently, GWAS that aim to identify genetic variants associated with psychiatric disorders tend to measure the severity of individuals' symptoms using a sum-score model, where individuals' scores on items of a questionnaire are summed up and coded into case-control status. This sum-score model rests on several assumptions that, when violated, result in bias and a loss of statistical power to detect true genetic associations. These assumptions include: the existence of only a single latent trait underlying the disorder, the independence of items on the questionnaire, the equivalence of item weightings on the latent trait, and the equivalence of residual variances for all items on the questionnaire. Rather than relying on a typical sum-score model to code symptom severity, we exploit the depth of questions on the Mental Health Questionnaire and use factor analysis to determine the number of latent factors underlying each disorder and to estimate the weightings of items on those latent factors. This model is subsequently used to score individuals on underlying factors, and their scores are used as phenotypes for all further genetic investigations in this study. GWAS, gene-based tests and pathway analysis The UK Biobank uses a multi-step process for imputation. We will use an INFO score threshold of &gt;0.7 for all imputed variants, as recommended by Pistis et al. To deal with genotype variant quality, we use a hard-call threshold of &gt;0.9 (i.e. genotype calls with an uncertainty greater than 0.1 are excluded) and use a conservative MAF threshold in this study for association testing, by excluding SNPs with a MAF &lt; 0.05% (a minor allele count ≈ 67) for GWAS and burden tests. For heritability estimation, we will exclude SNPs with a MAF of &lt; 0.005%. This study aims to identify rare genetic variants associated with latent traits of psychiatric disorders. First, we conduct GWAS by testing the association between individual rare genetic variants and individuals' scores on estimated latent traits using a linear mixed model approach. Second, we conduct gene-based testing to test the association between rare variants and individuals' scores on estimated latent traits. Restricting variants to highly-damaging variants is suggested as an approach to increase statistical power for gene-based tests. Therefore, we will only examine variants that are predicted to cause a loss-of-function in their gene product and collapse these variants according to the gene they are located in. The burden test, a collapsing method used in this study, assumes that variants collapsed into each group have equivalent effects on the phenotype. Additionally, we conduct pathway testing to test the association of gene pathways with individuals' scores on estimated latent traits, using the Gene Ontology resource, in which genes are grouped according to molecular functions, biological processes or presence in cellular components. Enrichment of genes in matched Mendelian disorders Complex disorders are associated with many genetic variants that typically each have a small effect size. In contrast, Mendelian disorders are associated with single genetic variants that, in general, have relatively larger effect sizes. However, genetic variants associated with Mendelian disorders exhibit variable penetrance and expressivity, and the gap between these two extremes spurs the question, of what Mendelian disorders can tell us about complex disorders (and vice versa). More specifically: 1. Are the genes associated with Mendelian disorders more likely to be involved in complex disorders that have similar phenotypes than other genes are? 2. Are both rare and common genetic variants that are associated with a condition more likely to be located in the same genes? In order to probe the second question, we identify Mendelian disorders that exhibit hallmark symptoms that match each of the three psychiatric disorders, using the online database OMIM. We test whether genes that have previously been found to be associated with those Mendelian disorders are more likely to be associated with the latent traits of these psychiatric traits that we have identified, using competitive gene-set analysis with gene-based burden testing. Enrichment of genes in matched common variant GWAS To investigate the third question, we identify recent GWAS of common genetic variants that match the three psychiatric disorders. We then test whether common genetic variants are more likely to be located in the same genes as those containing the rare genetic variants we have identified, using competitive gene-set analysis with gene-based burden testing. Heritability estimation The contribution of rare genetic variants to psychiatric traits remains contested. We use a Bayesian approach called GCTB to estimate the heritability of the latent psychiatric traits we have identified, for both common and rare variants. This approach is especially useful because it explicitly models the relationship between allele frequency and effect size, and estimates the proportion of variants that contribute to the heritability of a trait.