Integrating microarray and sequencing data to identify rare risk and protective genetic variants for schizophrenia.
Fieldrose Charitable Trust PhD Scholarship 2021 - Cardiff University
Supervisors: Professor James Walters and Dr Elliott Rees
Lay Summary:
Schizophrenia is typically a severe psychiatric disorder that can have a dramatic impact on the wellbeing of affected individuals and their families. The most common symptoms experienced in people with schizophrenia include hallucinations, delusions, confused thinking, a lack of motivation and impaired cognition. Although the exact biological and environmental causes of schizophrenia are not fully understood, genetics is known to have an important role in its development. However, not everyone with schizophrenia will share the same genetic risk factors, and you can be at elevated genetic risk without developing the disorder; this complexity has made it challenging to identify which genes are involved in schizophrenia.
This project aims to overcome this challenge by investigating how different genetic factors interact with each other in individuals who have been diagnosed with psychosis and schizophrenia. This will enable new approaches to be developed for identifying genes that increase the chance of developing schizophrenia, as well genes that may help explain why some people in high risk groups do not develop schizophrenia. The research also aims to identify genetic factors that contribute to why some people have symptoms that do not improve with current treatments.
The identification of risk genes for schizophrenia will advance our understanding of the underlying biology that, when combined with psychological and social risk factors, can give rise to the condition. Our hope is that this can lead to the development of better treatments that will have fewer adverse side effects and will help alleviate the distressing symptoms experienced by many people with schizophrenia.
Aims:
The project aims to deliver fundamental insights into the pathophysiology of schizophrenia by discovering novel genes associated with the disorder. The co-action between common and rare variants will be evaluated to test the following hypotheses:
1) Rare schizophrenia risk variants are enriched among cases with low genetic risk for schizophrenia from common variants
2) Rare variants that protect against schizophrenia are enriched among unaffected individuals at high genetic risk for schizophrenia.
Research Student: Sophie Chick
Hello, I’m Sophie, and I will be starting my PhD at the Cardiff MRC Centre for Neuropsychiatric Genetics and Genomics in July 2021, under the supervision of Dr Elliott Rees and Professor James Walters.
I completed my Bachelor’s degree in Genetics at the University of Sheffield, where I developed an interest in psychiatric genetics. I found the complex genetics of psychiatric disorders fascinating, and was drawn to research in this area by the fact that the underlying molecular mechanisms are not yet known. During my Master’s degree in Human Molecular Genetics at Imperial College, I carried out a six-month research project at the University of Cambridge Autism Research Centre. In this project I investigated heterogeneity in autism, by analysing genetic correlations between autistic traits to test whether they are influenced by different sets of common genetic factors.
Analysing common variation as part of this project fostered my interest in using a combined approach to integrate both common and rare variation. I am extremely excited to have the opportunity to develop methods for joint analysis of common and rare variants in schizophrenia, with the aim of improving understanding of its underlying causes and providing new targets for development of treatments. I look forward to working with Dr Rees and Professor Walters, and would like to express my immense gratitude to Mental Health Research UK for funding this project.
Sophie had a paper accepted for presentation at the World Congress of Psychiatric Genetics in Florence. Mental Health Research UK was delighted to be able to financially support her attendance at the Congress. Here is a short video of her experience.
Progress report year 2, 2023
In the second year of my PhD, I have completed my first project and am currently writing up my results for publication.
For this project I analysed a schizophrenia case-control sample sequenced at Cardiff University, constituting 4,661 individuals with schizophrenia and 5,713 without. My supervisor performed processing and quality control and I performed quality control of X chromosome variants. In this new sample, I tested whether schizophrenia is associated with a greater number of rare variants in any gene for 18,321 protein-coding genes. I looked at ultra-rare variants (observed at most 5 times over cases and controls) to enrich for damaging variants, and tested four classes of variant: missense variants (variants which change the protein sequence and damage its function), PTVs (protein-truncating variants which cause loss-of-function of the protein), and two classes combining both at different thresholds, to capture the diversity of genetic variation shown to be associated with schizophrenia.
No gene reached the threshold for statistical significance after correction for 18,321 genes across four classes of variant. I then tested whether schizophrenia is associated with rare variant enrichment across sets of genes previously implicated in schizophrenia. These sets were: constrained genes, where loss-of-function variants have very harmful effects; the ten genes which reached statistical significance for association with schizophrenia in a previous study by the SCHEMA Consortium; and 22 genes which reached suggestive association in that study. In our new sample, individuals with schizophrenia were significantly enriched for ultra-rare PTVs in all gene sets, replicating these findings.
To improve power to discover novel schizophrenia risk genes, I combined the new data with the SCHEMA Consortium data to give a total of 28,909 cases, 103,035 controls and 3,444 trios (trios include two unaffected parents and their affected child, in order to identify newly arising variants). As the SCHEMA dataset is an amalgamation of many case-control samples, I performed a stratified meta-analysis using Cochran-Mantel-Haenszel (CMH) tests, again evaluating ultra-rare variants (observed 5 times across the entire sample). The CMH p-values were combined with trio-based p-values to give final association p-values for each gene. I first applied this to non-damaging variants, which did not produce inflation, showing that this approach is well-controlled. I then applied it to four variant classes as before.
11 genes were significantly associated with schizophrenia for one or more classes of variant after correction for all tests. Three genes are novel, of which two, ZNF136 and MAGEC1, have no previous association with psychiatric disorders. The other gene, STAG1, has previously been implicated in schizophrenia by common variant evidence, supporting its association, and is a cause of intellectual disability. Among the eight significantly or suggestively associated genes in this analysis, two are significantly implicated in autism or developmental disorders, supporting genetic overlap of those disorders with schizophrenia.
I have presented this work at the 2023 Genomics of Brain Disorders and European Society of Human Genetics conferences, and have been invited to present at the World Congress of Human Genetics conference in Oct 2023 following my missed talk last year due to illness. I remain extremely grateful for MHRUK’s support in enabling me to attend these conferences.
Progress Report Year 1, 2022
In the first year of my PhD I have gained familiarity with the software and analysis pipelines used for genetic research and with using the Cardiff Supercomputer. I developed these skills through training workshops and online tutorials, and put them into practice by writing an analysis pipeline replicating published work in a sample of 2,536 cases and 2,543 controls (Purcell et al., 2012). This helped me to understand the range of analyses that are carried out when comparing genetic sequencing data between cases and controls.
For my first PhD project, I was given access to a new sequencing sample of 4,661 cases and 5,713 controls from seven European samples. This sample is too small for any associations to be detected, so I combined it with the largest published sequencing sample in schizophrenia (SCHEMA 2022) for a total of 28,909 cases and 103,035 controls. In this combined sample I used a statistical test called the Cochrane-Mantel-Haenszel test to test whether variants in each gene were associated with schizophrenia, as this test accounts for the subgroups within the combined sample. I found that 11 genes were significantly associated with schizophrenia after correction for multiple testing (the red line shown in the Manhattan plot below). The plot below is for one of the four variant types tested, which are based on predicted consequence for the protein encoded by that gene.
Of the 11 significant genes, 6 were previously identified by SCHEMA (2022), 2 were identified in a pre-print paper by Liu and colleagues, and 3 are novel. One of these novel genes is associated with intellectual development disorder; the other two have no previous associations with psychiatric disorders. Using an alternative multiple testing threshold which is slightly more lenient, 10 additional genes are implicated. I also used another type of statistical test for comparison called the Stouffer’s combined test, and found that this test was less able to detect associations.
Moving forward, my next steps are to look for overlap of these genes with other disorders, and to write up a manuscript in preparation for submission to a journal. I will also be presenting this work at the World Congress of Psychiatric Genetics Conference in Florence in September.