Assessing the pathogenicity, penetrance and expressivity of monogenic disease variants using large-scale population-based cohorts

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Interpreting the medical consequences of genetic variants in individuals is currently extremely challenging. Incorrect interpretation leads to massive overdiagnosis of genetic conditions, resulting in inappropriate treatment of individuals and increased healthcare costs due to unnecessary follow-on tests. Unfortunately, inaccurate genetic variant interpretation is a critical and growing problem because whole genome sequencing is becoming widespread throughout biomedical science and clinical medicine, replacing the standard clinical "disease-first" approach to diagnosis with a faster but less specific "DNA-first" approach. In addition, there has been a substantial increase in direct-to-consumer genetic testing resulting in numerous errors with major clinical implications. We aim to improve the interpretation of rare genetic variants by harnessing a uniquely powerful combination of newly available high-quality genetic data coupled with detailed clinical results on over half a million individuals.There are three main reasons for the incorrect interpretation of genetic variants, caused by historical gaps in the evidence base. First, many genetic variants that have been claimed to cause rare genetic diseases do not, often because the original evidence is now outdated and the variants have since been shown to be too common in the population to cause disease. Second, variants that cause inherited genetic diseases are identified by studying highly-selected, small groups of patients and families with a specific condition; this leads to the conclusion that every individual with the variant will get the condition, which in many cases is untrue. Third, the highly selected nature of the original discovery cohorts means that the complete set of disease symptoms caused by a particular genetic variant is unknown, and can be biased by family history and confounded by other familial diseases.We aim to address this evidence-gap by using newly available large-scale genome-wide sequencing datasets. We will focus on two examples of diseases caused by single rare genetic variants in one of hundreds of specific genes, where we have specific expertise and access to appropriate large-scale disease cohorts. We will compare the prevalence of disease-causing variants in these cohorts to that in a large-scale population cohort. Specifically, we will use datasets from UK Biobank (~500,000 participants), the Exeter-based monogenic diabetes cohort (~15,000 cases), and the UK-wide Deciphering Developmental Disorders Study (~13,500 cases). This enormous collection of high-resolution genetic data coupled with detailed clinical information is unparalleled and uniquely powerful. We will include evaluation of all rare variants linked with these genetic diseases, from the smallest (single base) to the largest (whole chromosome) changes. Based on our prior work, we anticipate producing robust estimates of how likely an individual with a particular disease-causing variant is to develop disease, and to expand and refine the disease symptoms associated with many rare genetic variants. We also expect to refute previous erroneous genetic causes of disease in the literature. Finally, we will test the hypothesis that differences in common genetic factors between the cohorts are responsible for disparities in disease occurrence and severity. This work will inform genetic variant interpretation in the clinic, reduce genetic overdiagnosis particularly from incidental findings, and facilitate the implementation of precision medicine. Our findings will have a direct impact on patients and families affected by genetic diseases, as well as members of the public undergoing genetic testing, and will provide novel insights about the nature of monogenic disease.