Approaches to predict disease risk in those with rare large effect genetic variants

Neuropsychiatric disorders, including traits like schizophrenia (SCZ) and autism spectrum disorder (ASD), are multifaceted conditions characterised by varying degrees of intellectual disability and behavioural dysfunction. Understanding the causes of neuropsychiatric conditions has important biological and clinical implications. For instance, identifying specific causal genes can guide the development of therapeutic agents, while more broadly, identifying the underlying cell types and mechanisms can shed light on which biological pathways are most relevant. From a clinical perspective, understanding the basis of neuropsychiatric conditions can allow us to predict its occurrence, which is useful as it can identify those who would benefit from additional screening, and/or early intervention. A substantial fraction of the variance of who develops neuropsychiatric conditions in due to genetic causes, or in other words, the different genetic variants we inherit. Some of these variants will always cause the phenotype to occur, which are called monogenic. However, for most neuropsychiatric conditions, each individual genetic variant only partly changes the risk of developing the condition. Broadly, it is helpful to distinguish two classes of genetic variants which contribute risk to neuropsychiatric conditions: rare variants of large effect, like copy number variants, and common variants of small effect, which are aggregated together into a single score called a polygenic risk score (PRS). In this work, we seek to further our understanding of how rare and common variants contribute to neuropsychiatric conditions in various ways, through the aims as listed below. It is the intention of this work to contribute to our understanding of neuropsychiatric conditions, as well as to our ability to predict them. The datasets we will use for this work include genetic data collected from different sources (e.g. different types of genotyping microarrays). There are several ways to construct PRS, which have different trade-offs, from power to bias. To include the largest number of samples in the analyses without bias, we will construct PRS for ASD, IQ and SCZ, and assess their power and bias using multiple genotyping sources. Large rare high effect genetic variants due to deletions or duplications may more radically alter biological pathways than the effect from common low effect genetic variants. Using data from individuals with either 22q11.2 or 7q11.23 CNVs, we will assess whether the genetic effect of a PRS on traits such as IQ and/or SCZ varies depending on the CNV present. In the long term, building on what we learn in this aim, we will also seek to estimate risk for arbitrary CNVs dependent on factors such size and genes intersected. We have previously developed a method that seeks to jointly decompose genome-wide association study results from multiple phenotypes into coming from one or more pathways, where membership of a SNP in a pathway is partly dependent on genome annotations (e.g. nearby a gene expressed in the brain), and SNPs in the same pathways have more consistent effects between the phenotypes (e.g. are more likely to be causal and have similar effects). As a starting point, we would use this methodology to partition SNPs into pathways, and build sub-PRSs. The aim of this work would be to identify sub-PRS that are biologically meaningful, and that are more predictive of sub-types of the phenotype (e.g. repetitive behaviour vs social communication deficits).The proposed aims will increase our understanding of the biology of the phenotypes under study. All aims will increase our understanding of how to predict phenotypes (diseases), Aim 3 should contribute to understanding individual (patient) specific phenotype (illness) prediction. Through the refinement of known phenotypes, Aim 2 should also help improve our understanding of the phenotypes for targeted treatments. This project falls within the EPPSRC "Healthcare technologies

In the same way that bioinformatics has transformed genomic research and clinical practice, health data science will have a dramatic and lasting impact upon the broader fields of medical research, population health, and healthcare delivery. The beneficiaries of the proposed training programme, and of the research that it delivers and enables, will include academia, industry, healthcare, and the broader UK economy.

Academia: Graduates of the training programme will be well placed to start their post-doctoral careers in leading academic institutions, engaging in high-impact multi-disciplinary research, helping to build training and research capacity, sharing their experience within the wider academic community.

Industry: Partner organisations will benefit from close collaboration with leading researchers, from the joint exploration of research priorities, and from the commercialisation of arising intellectual property. Other organisations will benefit from the availability of highly-qualified graduates with skills in big health data analytics.

Healthcare: Healthcare organisations and patients will benefit from the results of enabled and accelerated health research, leading to new treatments and technologies, and an improved ability to identify and evaluate potential improvements in practice through the analysis of real-world health data.

Economy: The life sciences sector is a key component of the UK economy. The programme will provide partner companies with direct access to leading-edge research. Graduates of the programme will be well-qualified to contribute to economic growth - supporting health research and the development of new products and services - and will be able to inform policy and decision making at organisational, regional, and national levels.