Enhanced Interpretation

Domain lay Summary:

Some of the patients and families recruited to the 100,000 Genomes Project have ultra-rare conditions that do not fit into a single group e.g. nerve or heart diseases. These conditions affect a tiny number of individuals and so very large studies like the 100,000 Genomes Project and international collaboration are required to establish the specific genetic cause.
Our previous work indicates that a large number of rare disorders that affect children and are likely to be inherited in a pattern where brothers and sisters are more likely to be affected (autosomal recessive) remain unsolved. We will undertake analyses of the whole genome sequence data to identify the causes of these conditions. This work will determine the specific diagnoses for these conditions removing uncertainty, the need for unnecessary tests, informs reproductive choices and potential treatments.
We plan to work out the best techniques to accurately confirm genetic test results from whole genome sequence data in clinical laboratories. When a single letter is changed in the sequence we may not need extra checks but for complex changes there is a lot of work to do. This will ensure accurate, rapid cheap testing for at risk family members.
Sometimes changes in genes can lead to many different health problems and genes that seem very similar to each other can lead to different problems. Rather than searching for genetic changes in a specific patient group we propose to look at changes in certain selected genes across the entire the 100,000 data and see if these are present in patients with similar/overlapping health problems (genotype-first approach)

The Enhanced Interpretation domain is made up of four sub-domains in the table below. You can also find these in the domain’s full Detailed Reserach Plan.

Sub-domainSub-domain LeadsResearch Aims
Ultra-rare disease:Siddharth Banka
Richard Scott
Sian Ellard
Caroline Wright
Emma Baple
Helen Firth
We will undertake a comprehensive analysis of the clinical features throughcomparison of HPO terms to determine if unrelated individuals could have the same diagnosis. This will allow comparison of whole genome data i.e. to determine if variants in the same gene areshared in unrelated individuals or by analysis of trios (unaffected parents and children) to identifyspontaneous (de novo) causative variants.
CASPARSiddharth Banka
Richard Scott
Sian Ellard
Caroline Wright
Emma Baple
Helen Firth
We will identify families with rare diseases with the following criteria for analysis. Noknown diagnosis following initial 100k Genomes analysis; Affected siblings (multiplex family) ORconsanguineous parents OR a single affected individual with a well-defined phenotype suggestiveof a specific autosomal-recessive disorder. We expect that this will include ~1500 – 1800 casesrecruited across England. We will then prioritise cases for study based on recognition of sharedclinical features. In cases where there is a known clinical diagnosis and only one or no pathogenicvariants in the known causative gene has been identified we will screen the data for intronicvariants and undertake RNA based analysis or determine if structural variants are present. Forconsanguineous families we will prioritise the analysis of novel or ultra rare homozygous variants – initially coding variants but then non-coding variants in or near attractive candidate genes basedon known function.
Validation techniques:Sian Ellard
Emma Baple
Steve Abbs
Dominic McMullan
Hywel Williams
It is clear that genome sequence data is of high quality in detecting singlenucleotide variants (SNVs) and that validation is not technically challenging. However,Working with colleagues across all GMCs we will considercomplex mutational mechanisms e.g.structural variants like inversions, intronic variants that create cryptic splice sites to determine criteria around the optimum approaches for validation. These will result in best practice guidelinesto ensure optimal clinical testing strategies.
Genotype-driven researchSiddharth Banka
Richard Scott
Sian Ellard
Caroline Wright
Helen Firth
Hywel Williams
Previously we and others have demonstrated that systematic re-analysis of the genomic data can help in the identification of novel disorders and reveal newdisease mechanisms. In this project we will analyse genomic data of patients recruited into the rare diseases arm of the 100,000 Genomes project. The patients for this study will be selected onthe basis of their genotype e.g. variants in a specific gene or pathway. The genes will be selectedon the basis of their involvement in a particular pathway, process or knowledge of other related genes associated with inherited disorders. We expect that the phenotypes will be very broad andspan a range of phenotypes across different age ranges and so it is not possible or appropriate to focus on a single clinical participant set. With recruiting physicians, we will undertake reversephenotypic (assess whether certain features perhaps originally not reported are present - reversephenotyping). Where relevant, for selected variants/genes of interest we will interrogate the100,000 Genomes Data to identify additional patients from other GMCs.
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