Academic research community (GECIP)

We have identified a new genetic disorder caused by changes in the TUFT1 gene. Affected patients appear to have primarily skin-related problems. We intend to explore the effect of different changes in the TUFT1 gene in a wider cohort of patients who were recruited as part of the 100,000 Genomes Project.

COVID-19 is a complex disease characterized by a variable clinical presentation from very severe cases to those with minimal or no symptoms. Whilst some factors are known to impact COVID-19 severity, such as age, sex, and other existing health conditions, these factors do not fully explain the varaiblity that is seen. Thus, further research is neededto discover unknown factors specifically genetic factors. An Italian National Network (GEN-COVID) aimed at identifying such genetic bases of COVID-19 clinical outcome has been established since March 2020 at the very beginning of the first pandemic wave that has engulfed Italy (IRB approval March 16, 2020, and ClinicalTrials.gov identifier (NCT number): NCT04549831). Currently, whole-exome sequencing has been performed in 3,000 SARS-CoV-2 infected individuals. Whole exome sequencing identifies coding variants within the entire set of genes of an individual genome from commonly seen variants to the very rare ones. But to date the usual approaches to finding these have not uncovered anything that would help explain the cause of such extreme variability. We are proposing using a new approach called “post-Mendelian” that considers common and rare germline variants to be inherited in different ways. Access to data in the NGRL will allow us to validate the model in a cohort of the same ancestry (European) and possibly in cohorts of other ancestries. By better understanding the role of genetic factors in COVID-19 susceptibility, we will be in a stronger position to identify public health measures that will help to curb the impact of the disease on society. Furthermore, such a predictive model should aid not only in the repurposing of already in use therapeutics or developing new interventions, but also in tailoring treatments to the specific needs of individual patients.

Neurological diseases are a surprisingly diverse group of disorders affecting the central and peripheral nervous system. Most debilitating are neurodegenerative pathologies, such as Alzheimer's or Parkinson's disease. Whilst disease causes are variable, progressive damage and loss of nerve cells (neurons) are a shared feature. Axons (the long processes of neurons) are their most vulnerable component. Axon degeneration is common in early disease stages when patients are still asymptomatic but treatment could be most effective. Our group is interested in understanding axon degeneration (more specifically, a type called Wallerian degeneration) and what happens exactly in individuals affected by neurodegeneration. Scientific aim: Many neurological diseases are associated to axon damage. Even when causes are similar (e.g. peripheral nerve damage due to diabetes), patients show great variation regarding the outcomes for neurodegeneration. We are interested in understanding why some people are more vulnerable compared to others. We hypothesize that a contributing factor is individual variability in genes we are studying. What does that mean? Whilst the majority of our genes are the same in all individuals, there are small changes in our genome that account for differences which make us unique. Some of these contribute to disease susceptibility. By comparing thousands of participants' genomes provided by the UK Biobank to their disease history we hope to discover gene patterns that will help identify individuals at greatest risk of axon loss. Altogether, this is an important area for understanding degenerative diseases and increase their therapeutic potential. Public health impact: We propose that rare variants in axon degeneration proteins may contribute to human diseases. Genetic variations is likely to influence axon vulnerability in the human population. Our research will support identification of appropriate patient groups for future clinical trials, and eventual application of drugs stopping axon death using personalised medicine.

Identifying gene-disease associations has the potential to enable new drug target identification and better match patients with treatment. Using the 100,000 Genomes Project’s genomic and medical data held in the NGRL we aim to discover, through case-control association testing, genetic variants associated with a range of diseases, with a focus on cardiovascular, renal, metabolism, respiratory, immunology and oncology therapeutic areas. Results will be used to complement those obtained from in-house genomic and medical data.

We would like to make use of variant data across all tumours sequenced by Genomics England in order to learn more about the patterns of gene alterations across tumour types. This can hopefully increase our understanding in how different cancers develop and highlight the potential value of utilising therapies originally envisaged for one tumour type across additional cancers.

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.

About 2 people in every 100 have intellectual disability (also called learning problems). These people have an IQ under 70 and experience difficulties coping with day to day life because of this. Intellectual disability in children is well studied but it is much less understood in adults. Here we will look at the genetic data for all adults with intellectual disability to find out what the genetic causes are in adulthood. We will also be able to find out what the healthcare needs of adults with intellectual disability are. This will help us give advice to families on the likely adult outcome of intellectual disability of genetic cause diagnosed in children.

Over 300 million people around the world are affected by a rare genetic disease. Patients with rare genetic diseases face on average 5 years between the first onset of symptoms and finally reaching a diagnosis, often undergoing multiple visits, referrals and series of medical tests along this journey. This translates into delayed medical care, affecting quality of life for the patient and high costs for healthcare systems. Exome and genome sequencing can help diagnose patients with rare genetic diseases by identifying the variants in their DNA that are causing their disease. Analysing a genetic test to find disease-causing variants in the patient’s DNA is a crucial step in genetic testing, known as variant interpretation. Variant interpretation is currently a manual process carried out by a specialized (human) analyst, which makes it time-consuming, costly and often delivers inaccurate or uncertain results. As a consequence, patients must often wait several months to receive their genetic test results. Furthermore, less than 50% of patients with rare genetic diseases undergoing exome or genome sequencing receive a clear diagnosis, which is crucial to provide appropriate medical care. We have developed machine learning algorithms to analyse genetic tests to diagnose rare genetic disorders (specifically, to perform variant interpretation for exome and genome sequencing). Our goal is to automate the step of variant interpretation for genetic tests to decrease the costs, turnaround time and other limitations associated with this manual process. We will use the Genomics England dataset to test the performance of these algorithms and ensure they can be implemented in the clinic to reliably automate the analysis of genetic tests. This allows for the analysis of genetic tests to be carried out accurately and in an automated way, lowering costs and turnaround time for tests. For patients with rare genetic diseases, this translates into earlier access to a clear molecular and clinical diagnosis, which is essential to receive appropriate medical care and to make informed medical decisions. Finally, at the population level, shortening the journey from first onset of symptoms to diagnosis leads to better outcomes for patients with rare diseases while lowering costs for health care systems, by reducing the number of patients receiving unnecessary tests and medical interventions.

A fusion gene is a gene made from joining parts of two different genes. Fusion proteins that are made from the 'instructions' given by these fusion genes can lead to development of some cancers. We are investigating a type of fusion gene that is shown to be involved in cancer activity across a variety of cancer types. At this time there is no approved therapy for treatment of patients with this specific type of gene fusion. Through the Genomics England database, we are interested in understanding the proportion of patients whose cancer contains this specific fusion and to learn more about the characteristics of patients that have this fusion (such as sex and cancer type). The study will also identify how many patients have one of two other well-known types of gene fusions that cause cancer, and use this as a “positive control” in order to understand how accurately fusions can be identified in the Genomics England database. This is a descriptive study, a study that describes the characteristics of the population but does not make comparisons and does not look at the causal questions such as risk factor of the disease. The results may help to design future clinical trials of new therapies in cancers that have this particular gene fusion, such as which patients to recruit to the trials and how many patients to recruit.

It has been traditionally assumed that one gene codes for one protein. New research has demonstrated that human genes can in fact code for more than one protein; additional small proteins can also be encoded, called “microproteins”. Over 10,000 microproteins may be encoded by the human genome, but with very limited research having been carried out on them to date. Some of these are likely to be related to, if not causative of, genetic diseases. We have a list of 30,000 human genomic regions that may encode potential microproteins. We would like to analyse these regions to identify genetic variants that arise only in individuals affected by rare disease (not identified in any non-affected family members), which could present novel therapeutic targets or an enhanced understanding of disease biology.

Akin to the caps on the end of shoelaces telomeres are structures that cap the ends of human chromosomes and protect them from damage. When telomeres stop working the chromosomes can become damaged and this helps tumours evolve to become more aggressive and less responsive to treatment. This project aims to use genomic data from breast and blood cancers to understand the mechanism by which dysfunctional telomeres can lead to genetic changes across the cancer genome. Ultimately this may lead to the development of clinical tools that will aid prognosis and prediction of response to treatment

As we get older our blood cells acquire genetic variants. The presence of such blood cells may influence cancer risk and survival. This project aims to find out more about the presence of these abnormal blood cells on cancer risk and survival.

The diagnosis and treatment of certain cancers requires accurate assessment of DNA throughout the whole tumour. New DNA sequencing technology offers the opportunity to improve upon existing technologies to determine the overall DNA structure of the tumour. This project will compare such technologies with the aim of improving the diagnosis and treatment of certain types of cancer including leukaemia.

This study aims to identify the genetic diagnoses associated with Cerebral Visual Impairment (CVI), a disorder which affects the visual pathway within the brain. By using two large UK databases with paediatric rare disease data, this study hopes to be the most comprehensive analysis of the genetics associated with CVI to date. The genetic diagnoses associated with CVI and other clinical features that patients with CVI display will be recorded. This information will then be analysed to determine if there are any trends.

A genetic disease is a health problem caused by faults in an individual's genetic code. By analysing an individual’s genetic code, it is possible to identify the genetic variants that cause disease, and clinicians can use this information to inform appropriate treatment plans. Over the past 10 years, Next Generation Sequencing (NGS) has become a standard analytical technique within the NHS to aid the diagnosis of rare diseases and cancers. Genomic medicine offers rapid and accurate diagnosis, as well as the potential for personalised treatment. The NHS plans to embed whole-genome sequencing and genomic medicine within routine care pathways, to operate at the forefront of healthcare science. For example, the 100,000 Genomes Project was launched in 2013, which sequenced the genomic data from 100,000 participants with cancer and rare diseases. The project aimed to link this genomic data with diagnosis, treatment and patient outcomes. The sequencing phase of the 100,000 Genomes Project was completed in 2018, with 25% of rare disease participants, and 50% of cancer participants, receiving a diagnosis. The variant analysis workflow implemented by Genomics England had a number of limitations that may account for a number of missed actionable variants. Uncovering these previously discounted variants may provide clinicians with the necessary information to inform a much-needed diagnosis for some participants with an undiagnosed rare disease. At Sheffield Teaching Hospitals, one such family failed to receive a diagnosis through the original 100,000 project. It is therefore the aim of this follow-up study to thoroughly scrutinise the wealth of genomic data to uncover variants that may have been missed by the original analysis. This follow up study will use updated decision support software, utilise new research, and identify changes in the genomic data that wasn't originally reported on, such as abnormal chromosomal rearrangements and harmful repeated sections of DNA. Through this follow up project, it is hoped that a diagnosis can be reported, to give the family in question the answers about their help that they deserve.

Genomic medicine has the potential to improve prediction and management of chronic conditions, particular those with a strong genetic component. Thoracic aortic disease (TAD) is one such condition – it is often silent until a catastrophic life-threatening complication occurs (such as dissection or rupture), and genetic information can inform both identification and treatment of the disease at an early stage. There are a number of challenges in assigning causality to genetic variants and or prioritising further studies in TAD. Aortic tissue is not routinely available, and signs and symptms of early disease may be subtle. Aortic vascular smooth muscle cells created from stem cells from patients with Marfan Syndrome, developed in Cambridge, provide a powerful model that lead failure of the aortic wall. These models provide an opportunity to study variants identifed from patients in the 100,000 Genomes Project. In our first aim, we will use stem cell models and techniques to alter DNA we are able to study how disease variants affect structure of smooth muscle cells and then analyse the impact on function. We will then create a platform that will allow assessment of multiple variants at the same time to help characterise if they lead to disease or not. Finally, we will use innovative bioengineering techniques to create blood vessels that will allow us to study these diseases in a 3D form. Ultimately, the aim of this technology is to provide rapid diagnosis to patients with TAD and develop a system that we use to predict how severe disease will be. The work in this project will contribute to creating blood vessels in the lab which one day may be used for bypass operations.

Alport syndrome is a rare genetic disorder that causes damage to small blood vessels in the kidneys. It can also cause hearing loss and eye problems. It is well established that there is considerable variability in the severity of Alport syndrome between patients. This can range from very small amounts of blood in the urine which does not have an impact on health, to severe kidney impairment which requires dialysis or kidney transplantation. This variability is even observed within families with the same genetic cause of disease. We aim to utilise the whole genome sequence and clinical data available in the NGRL, alongside data from our own lab group and external collaborators to better understand this clinical variability.

This project aims to identify new genes which cause epilepsy. This work is part of the largest gene discovery effort focusing on severe epilepsy with associated intellectual disability. Analysis of large cohorts, including that in the NGRL, is required to identify new genes that cause severe, rare forms of epilepsy. Successful delivery of this project will drive up diagnosis rates, showing how useful genomic testing is in the neurology clinic and hopefully encouraging its wider uptake. Expanding the set of known epilepsy genes may inform therapeutic target identification and help develop infrastructure for stratified clinical trials.

Ovarian cancer, like many other cancers, is not in fact a single disease, but a diverse set of different diseases. Further, examination of individual tumours invariably reveals substantial variation within a single cancer, with different cells within a tumour often having different sets of variants. The two types of variation in ovarian cancer, between different ovarian tumours and within a single tumour, both make cancers difficult to treat. We will explore the differences between tumours by classifying them into subtypes based on their genomic characteristics, and will explore variation within tumours by identifying sub-populations of cells within each single tumour. We will also explore how ovarian cancers change over time, which can lead to treatment resistance and spread to distant organs.

Many types of variant in a person’s genes are known to cause rare genetic diseases. In some cases, the effects of these variants are well understood. However, the effects of variants at the far end of genes is often unclear. This is because of the different way in which the far ends of the genes are read and processed in our cells. We want to look at these variants at the far end of genes to find out what makes them different. Hopefully we will be able to find new diagnoses for patients with these variants, and learn more about the biology of rare genetic diseases.