Academic research community (GECIP)

We are investigating genetic factors that influence an individual’s response to pharmaceutical products in the context of the COVID-19 pandemic. We aim to consider both therapeutic and preventative interventions to explore whether genetic variation may contribute to how well a medicine works and the risk of adverse reactions.

Life has adapted under the influence of 24-hour day/night cycles, with circadian timekeeping and rhythmicity underpinning many behavioural and physiological mechanisms that anticipate, as well as follow, environment signals. This is also true for annual seasonal cycles but studied to a lesser extent. We will identify which genes demonstrate daily or seasonal periodicity which are also involved with key drug mechanisms. This will enable us to identify which drugs have potential for optimising their administration time of the day or year to achieve the best outcome possible for the patient. Moreover, certain disease mechanisms have been shown to be influenced by circadian mechanisms and these may be exploited to identify novel drug targets or repurpose existing drugs.

This research project is approved, but is not approved for publication. We propose to combine calculation of genetic risk metrics (measuring each participant’s genetic predisposition for a set of obvious, observable, and measurable traits ) with a very large set of patient medical record data to identify characteristics of each disease and the mechanisms, outcomes, and clinical progression associated with each. We propose to investigate in the following areas. Method details are listed in the technical summary section. 1. Compute genome-wide predictions for a panel of relevant traits: Genome-wide Polygenic Risk Scores (PRS) reflect a quantitative measurement of the tendency to develop certain phenotypic traits, e.g., certain blood biomarkers, by aggregating across usually a large number of genetic loci that are known to be related to a specific phenotype in a weighted manner. PRS study can shed light on the disease causal mechanism from the commonly occurring genetic variants. Normally these kinds of genetic loci will have very little impact on the phenotype individually. However, combined together, these loci can help us disentangle the molecular mechanisms behind certain diseases. 2. Analyze the genetic variants/variants that cause a potential loss of function or a gain of function of the protein products within a given disease cohort. While certain genetic variants have minimal functional impact individually (see bullet 1), other variants significantly impact the function of the protein products of disease-relevant genes. These types of variants are predicted Loss of Function variants (pLoF) or Gain of Function variants (pGoF). Individual LoF/GoF variants repeatedly occurring on the same gene or gene pathways across patients with similar disease phenotypes provide evidence of the involvement of those molecular processes in the etiology of disease. PRS analyses (bullet 1) coupled with analysis of pLoF/pGoF variants will give us a robust picture of likely molecular causes of the disease of interest. 3. Focus on somatic tumour mutation profiling results provided by GEL whole genome sequencing data and investigate known and novel patient subgroups based on the small genetic variants, copy number variants and large structural genetic variants currently provided by GEL bioinformatics pipeline. Leveraging the rich patient phenotypic data within GEL, identify the association between patient subgroups discovered in GEL and phenotypic characteristics, to better understand the disease mechanisms and provide insights on actionable drug discovery plans. 4. Take the insights from the 100,000 Genomes Project participant data and combine with insights from other datasets we work with (for instance adding detailed medication information or rich biomarker data, to supplement the genetic data in Genomics England). Pooling all of this together helps us run our advanced algorithms to produce more detailed biological and therapeutic insights related to diseases of interest. Ultimately, the goal of this work is to match the right patients to the right therapies at the right time, and to identify unmet needs and associated potential points of intervention. 5. Engage with patients directly. To bring this to fruition we have engaged and intend to engage with clinical experts and patient groups in critical areas of unmet need. We believe that we must not only identify disease-modifying interventions targeting the mechanisms underlying each patient's specific disease, but that we must also understand the manifestation and impact of these mechanisms on each patient's journey, for instance via devices and patient-reported outcomes, in order to better measure, model, and treat the outcomes that matter most to each person in the context of their lives and their disease.

BridgeBiois developing a gene therapy for congenital adrenal hyperplasia (CAH)andhave been working closely with both the CARES foundation and Adrenal Insufficiency United since the inception of this program. These foundations were integralthroughout the development process providing constant feedback on unmet need and trial design. For example, patient representativesreviewed our clinical trial protocol prior to FDA submission andproposed changes excluding proceduresthat would havepreventedcertain individuals from participating.Theaim of the proposed research project is to understand the impact of CYP21A2variants in the context of other high-impact pathogenic variants in other Mendelian disease genes. The research will be in two phases. First, identify potential cases of CAH in the Genomics England cohort and analyse any relationship between type of mutation and resulting clinical symptoms.Especially focusing on any insights that could be gleaned into the different severity of CAH. Second, analyse if a slight dysfunctionin CYP21A2(ex. 50% loss of CYP21A2 function due to one allele being completely lostvs smaller impact from a slight change in the protein sequence) can modify the severityof otherrare metabolic diseases. Theseanalyses will be able to better elucidatethe clinical impactof certain variants in CYP21A2as well as in relation to other genes. Better understanding of CYP21A2and its interaction with other genes will allow for improvedmodelling of the efficacy ofourCYP21A2gene therapy.Our ultimate hope is to gain insights into whether a specificgenetic subsetexists thatare more likely to benefit from a gene therapy and find potential expansion into other rare metabolic diseases that could benefit from the same therapy.

This research project is approved, but is not approved for publication. Genetic DCM epidemiology data is elusive and confirm the difficulty to know the “real” prevalence of this entity in the overall scope of heart failure (HFrEF) and general population. Our aim is thus to investigate the prevalence and the phenotypic characteristics of patients with DCM of genetic aetiology in general as well as characterise the contributions of the various specific mutation types to the genetic DCM prevalence.

One of the main considerations when conducting any studies using databases (such as Genomics England) is to understand how the patient population being studied in Genomics England may over represent or underrepresent certain characteristics (such as cancer stage at diagnosis) in comparison to the true, underlying population. For example, Genomics England may contain more patients diagnosed with earlier staged cancers than late stage advanced cancers when compared to the prevalence of early stage vs. late stage cancers in the UK. Cancer stage at diagnosis is an important characteristic, since treatment decisions are heavily dictated by whether a patient has early stage vs late stage cancers. Understanding how the spread of stages in the cancer population from Genomics England compare to the spread of stages in cancer from the larger UK population will help researchers interpret and discuss results and findings for studies conducted using the Genomics England cancer data in the proper context. We propose to conduct a study where we look into patients enrolled in the cancer program in Genomics England and understand how many patients are diagnosed with specific stages (stage I, II, III, IV) among the different cancer types.

This research project is approved, but is not approved for publication. Breakthrough Genomics has had a long-standing commitment to helping individuals better understand their genome to empower them on their personal health journey to help them live a healthy and long life. Using its proprietary technology Breakthrough Genomics is able to identify existing and new genes associated with certain illnesses and rare diseases and we can do so faster than most other laboratories. We share GEL’s vision in trying to understand genetic differences that may explain why some people are more severely affected by COVID-19 than others. And we believe that the GEL Research Project has the best quality genetic data and that combined with Breakthrough Genomic’s technology, we can get some critical clues to the answers. In early 2020, the scientific team at Breakthrough Genomics used its proprietary machine learning knowledge base to identify 28 variants encompassing 14 different genes that may be involved in susceptibility to coronavirus. This panel of variants are referred to as the Coronavirus Genetic Susceptibility Assessment Panel (CGSA), the GEL COVID Whole Genome Sequencing data would be useful in determining whether these genes and variants correlate with COVID-19. The GEL COVID WGS data could be run through the Breakthrough Genomic’s cloud-based clinical interpretation software, ENLITER™ while inside the COVID Research Environment and adhering to all the Airlock and governance policies set out by Genomics England. ENLITER™ contains a custom COVID filter for reporting the initial 28 variants already identified so these variants could be analysed immediately. Meta-analysis and heterogeneity testing will be used to assess the strength of association between the gene variants and COVID-19 and COVID-19 complications risks. We will share this information with other GEL partners, the scientific and medical communities to further understand this public health crisis. Our goal is to be able to better manage treatment of SARS-CoV-2 positive patients to minimize severity of COVID and prevent deaths. We also believe that at least some of the clues, may be applicable to other infectious diseases which could impact future treatment strategies for select patients.

Age-related macular degeneration (AMD) is a leading global cause of blindness that affects 8-12% of people over the age of 60 years. It is estimated that by the year 2040, 288 million people will be affected by AMD. As such, it represents one of the biggest economic burdens faced by healthcare services around the world. AMD is a complex condition, caused by a combination of non-modifiable (e.g. increasing age, ethnicity) and modifiable (e.g. obesity, smoking, dietary antioxidant intake) risk factors. Genetic variants also play a significant role in AMD risk, with rare and common polymorphisms in the Complement factor H (CFH) gene accountable for up to 50% increased risk of disease development. Animal and cellular models of AMD are not able to accurately replicate the biological complexities of the condition, thus precluding understanding of the contribution of genetic variants to disease pathogenesis. Consequently, there are very few treatment options available. Early-onset macular degeneration (EOMD) is an important cause of severe and progressive visual loss in young adults that shows very similar disease pathology to AMD. Unlike AMD, EOMD has a much weaker association with life-style related risk factors and is often present in more than one generation of a family suggesting a strong genetic component. We hope to use sophisticated quantitative modelling methodologies to develop scoring system that can be used in clinical settings to calculate individual risk of AMD onset. This information could potentially be used to inform clinical monitoring and treatment strategies to prevent visual loss.

The COVID-19 pandemic is a major public health challenge. A key scientific question is to explain why some individuals become gravely ill while others remain entirely asymptomatic. While the answer is obviously multi-factorial, there is now clear evidence that at least some of the risk of severe response can be explained by genetic factors. Strikingly, a variant on chromosome 3 has been associated with about a three-fold increase in risk of severe COVID-19 response for those with a double copy of it. Other genetic variants are also emerging from latest studies, and these variants can be combined into a polygenic risk score (PRS) to provide better genetic predictors of risk. By combining this PRS with other, non-genetic, risk factors, it will become possible to identify at-risk individuals, with the potential to shelter these individuals or prioritise them for vaccination. Furthermore, each genetic variant associated with disease response has the potential to point to novel therapeutic options. Motivated by these observations, we plan to use the whole genome sequencing data to generate genome-wide association study data for COVID-19 outcomes, validate our COVID-19 response PRS, and integrate these data with our database of genetic associations to point to novel therapeutic options.

This research project is approved, but is not approved for publication. Mayzent® (Siponimod) is the first treatment specifically approved in the US (FDA approval Mar 2019)for patients with active secondary progressive multiple sclerosis (SPMS). It is the only oral treatment specifically indicated for patients with secondary progressive multiple sclerosis (SPMS) with active disease in Europe (EMA approval in Jan 2020). Mayzent addresses an unmet need for SPMS patients with active disease who, until now, did not have an oral treatment, that has been shown to be effective in delaying progression in this patient population. However, Mayzent can be only prescribed to group of patients with particular genetic characteristics which determine how this drug is broken down. In order to identify these patients, Novartis is validating a genetic test (companion diagnostic). For this, they require independent samples of DNA to validate the test.

This research project is approved, but is not approved for publication. We propose to combine calculation of genetic risk metrics (measuring each participant’s genetic predisposition for a set of obvious, observable, and measurable traits ) with a very large set of patient medical record data to identify characteristics of each disease and the mechanisms, outcomes, and clinical progression associated with each. Our project is focused on Parkinson's Disease. First, we will compute genome-wide predictions for a panel of relevant traits. Genome-wide Polygenic Risk Scores (PRS) reflect a quantitative measurement of the tendency to develop certain phenotypic traits, e.g., certain blood biomarkers, by aggregating across usually a large number of genetic loci that are known to be related to a specific phenotype in a weighted manner. PRS study can shed light on the disease causal mechanism from the commonly occurring genetic variants. Normally these kinds of genetic loci will have very little impact on the phenotype individually. However, combined together, these loci can help us disentangle the molecular mechanisms behind certain diseases. Secondly,we will analyze the genetic variant(s) that cause a potential loss of function of the protein products within a given disease cohort. Whilst, as noted, certain genetic variants have minimal functional impact individually, other variants significantly impact the function of the protein products of disease-relevant genes. This type of variants are predicted Loss of Function variants (pLoF). Individual LoF variants repeatedly occurring on the same gene or gene pathways across patients with similar disease phenotypes provide evidence of the involvement of those molecular processes in the etiology of disease. PRS analyses coupled with analysis of pLoF variants will give us a robust picture of likely molecular causes of Parkinson’s Disease. Finally, we will use insights from the 100,000 Genomes Project participant data and combine them with those from other datasets we work with (for instance adding detailed medication information or rich biomarker data, to supplement the genetic data in the NGRL). Pooling all of this together helps us run our advanced algorithms to produce more detailed biological and therapeutic insights related to Parkinson’s disease. Ultimately, the goal of this work is to match the right patients to the right therapies at the right time, and to identify unmet needs and associated potential points of intervention. 4. Engage with patients directly. To bring this to fruition we have engaged and are continuing to engage with clinical experts and patient groups in critical areas of unmet need. We believe that we must not only identify disease-modifying interventions targeting the mechanisms underlying each patient's specific disease, but that we must also understand the manifestation and impact of these mechanisms on each patient's journey--for instance via devices and patient-reported outcomes--in order to better measure, model, and treat the outcomes that matter most to each person in the context of their lives and their disease.

Results from this project will contribute to an improved understanding of the genetic basis of differences in symptom severity of COVID-19 cases. There are likely to be a large number of specific genetic risk factors based on rare variants including copy number variation (CNV) in the human population that confer an increased risk of severe COVID-19 symptoms. It is unclear whether there will be a single (or small number) of highly significant genetic differences with large effects that predispose individuals to an increased risk of severe symptoms. It is however likely that a large number of rare or combinations of rare and common genetic variants may lower an individuals robustness to COVID-19 infection overall. It is entirely possible that commonly observed CNVs may associate with differences in COVID-19 symptom severity and this is an important research area that could have a high impact globally.

Chemotherapy is often prescribed to patients with breast, colorectal and stomach cancers. Chemotherapy describes a range of drugs that can be administered for patients and includes several in the fluoropyrimdine class. For most individuals presecribed these drugs, the outcome is successful. However, in some individuals they can experience drug-related adverse effects such as diarrhoea, an increased risk of infections and in some very rare cases death. These side effects have been shown to be associated with a protein in our bodies called dihydropyrimidine dehydrogenase (DPD) that is responsible for converting the active drug into inactive compounds. Proteins are made from the genetic information that is coded within our DNA, these are called genes. Our DNA is made up of a series of letters in a specific order and changes to these letters can affect the proteins shape and function. Four changes in the DPYD gene, which is responsible for making the DPD protein have been shown to have an association with an increased risk of developing severe side effects, and studies have shown that individuals with these changes have reduced levels of the DPD in their blood. Using the NGRL data will allow individuals with these changes to be identified to determine how many might benefit from a particular test that identifies these four changes in the gene. Furthermore, a particular change seen in individuals of African ancestry will be looked for, this will determine whether it may be useful to identify this change in any test that a healthcare provider may choose to use. Changes in other genes will also be investigated, to assess whether it may be more appropriate to look at a persons whole DNA, as opposed to just the four DPYD changes.

Our research will involve evaluating the performance of polygenic risk scores (PRSs) in the NGRL, and then optimising these PRSs using additional data. PRSs are a way of combining information from a person’s genome to create a single score that can be used to predict that person’s risk of getting a disease. In the evaluation stage, PRSs that have been constructed using data from outside the Genomics England cohort will be calculated on individuals in the Genomics England cohort, and then compared against their known disease status to evaluate their predictive power. In the optimisation stage, genomewide association statistics will be calculated on some individuals both with and without a given disease in the Genomics England cohort. These statistics, which are aggregated and represent group-level rather than individual-level information, will then be used to improve the predictive power of the PRSs.

Genetic data can reveal links between genes and diseases, but the nature of these links are often not well understood, particularly where little is known about the gene or the protein that it encodes. The Structural Genomics Consortium (SGC) aims to bridge this gap through the development of ‘Target Enabling Packages’ for understudied proteins: collections of reagents and knowledge that provide a starting point for exploring the biology of the protein and its link to disease. These packages are made available to the scientific community with the ultimate aim of catalysing drug development. This project aims to explore newly identified genetic variants that are implicated in disease by Genomics England analyses. This will drive work at the SGC to help explain the functional effects of these genetic variants and catalyse new drug discovery programmes.

For particular types of cancer, such as bowel and breast cancer, chemotherapy can be given that includes a class of drug called the fluoropyrimidines. This drug class is effective at helping combat cancer, but can cause severe side effects in some patients, including increasing the risk of developing infections and diarrhoea. Fluoropyrimidines are broken down in the body by an enzyme encoded by a gene called DPYD. Four genetic variants in DPYD have previously been associated with reduced enzyme function and increasing the risk of side effects in patients on fluoropyrimidine chemotherapy. The aim of this project is to test whether these variants are also associated with side effects in a large group of patients within the NGRL that received fluoropyrimidine treatment, and to investigate whether other rare genetic variants also increase the risk of these side effects.

variants are changes to DNA sequence that occur during reproduction at each generation. Some variants that occur can lead to disruption of the function of a protein produced by a gene; these are referred to as loss of function (LoF) variants. The effects of many of these variants that are known to occur in humans are poorly understood. A better understanding of the LoF variants underlying disease can aid diagnostic and screening initiatives, as well as provide insights into therapeutic targets for these diseases. Similarly, understanding the distribution of these variants among healthy individuals can provide a clearer understanding of the compensatory mechanisms by which the effects of these variants can be mitigated in healthy people, potentially also providing insights into therapies. Researchers within this domain will harness the richness of the 100KGP data alongside multi-dimensional publicly available data to develop and validate statistical approaches for better prediction of the effects of LoF variation across the genomes of healthy and diseased people. We will utilise high-throughput screening platforms to better understand the effects of specific LoF variants at the cellular level, providing a better understanding of the biological mechanisms underlying specific diseases. Examining the spectrum of LoF variants at population level will also provide new insights into gene function, and the historical evolutionary forces that have acted on different variants over thousands of years. This work has the potential to vastly improve personalised medicine initiatives by providing a platform to better understand the genesis of rare genetic disease, and interpret genomic variation among diseased and healthy individuals.

This research project is approved, but is not approved for publication. We have identified a common genetic variant that associates with susceptibility to a specific cancer type. By engaging with Genomics England we want to explore this further and ask questions such as how well does the association hold up? Is this association only with this type of cancer or does it expand to other cancer types? Are there other germline and/or somatic variants that associate with cancer? We will use this information to assess whether people who carry the variant should be treated differently to people who do not (patient stratification).

This research project is approved, but is not approved for publication. Dynamo Therapeutics is an early-stage company building a product engine to understand the dynamic evolution of cancer over time. Datasets containing well-annotated clinical and genomics information provide a unique opportunity to gain a better understanding of cancer evolution. By integrating real-world clinical and genomics data from Genomics England, Dynamo Therapeutics aims to identify novel, actionable genetic alterations at the time of cancer diagnosis or at time of relapse to therapies. The initial focus will be in breast cancer, prostate cancer, lung cancer, and hematological malignancies. These novel targets will drive the discovery and development of precision medicines that supplant current standard of care, either as single agents or in novel combinations and sequenced regimes.

This research project is approved, but is not approved for publication. Contributing to drug discovery in immunology, respiratory and neuroscience GSK aims to analyse Genomics England data in two ways. Firstly to find and understand genomic variants that are associated with disease related traits (phenotypes) and to relate these variants to potentially druggable gene targets. Secondly to find other phenotypes that are associated with specific gene targets and to relate these phenotypes to potential drug safety issues or drug repurposing opportunities.