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Identifying the genetic variants responsible for rare diseases

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If you have a question about this talk, please contact Dr Peter Willeit.

Rare diseases are phenotypically heterogeneous and can be caused by many different genetic variants or combinations thereof. A patient’s phenotype can be coded by selecting a small set of terms from the Human Phenotype Ontology (HPO). The HPO is a useful system for patient coding because it can capture phenotypic abnormalities across many different organ systems with a flexible level of detail.

I will describe a Bayesian method called “phenotype similarity regression” that models the association between an HPO -coded patient phenotype and genotype. This method estimates the probability of an association together with an HPO -coded phenotype characteristic of the disease. I will demonstrate that phenotype similarity regression has the power to detect true associations in a large collection of genome-sequenced and HPO -coded cases with rare diseases.

Methods for a priori filtering of variants based on population allele frequency, conservation or predicted pathogenicity have very low specificity. Thus the vast majority of filtered variants are not pathogenic. I will present a method (“BeviMed”) that models a mixture of pathogenic variants that influence the risk of disease and non-pathogenic variants that do not in a given genomic region under dominant and recessive modes of inheritance. This approach allows individualised weighting of variants and is powerful if good disease labels can be constructed from the phenotypic data. We are exploring how it can be used to identify pathogenic variants in the regulatory non-coding part of the genome.

I will close with a brief description of our new diagnostic platform (“ThromboGenomics”), which we regularly update with our research findings and those of other groups in order to bring benefits to patients without delay.

This talk is part of the Cambridge Cardiovascular Seminar Series series.

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