Using rare disease genetics to understand mechanisms driving neurodegeneration

Many people with rare neurological diseases spend years without a clear genetic diagnosis. Current genetic tests can miss important changes in DNA, including large structural changes, repeated DNA sequences and changes that disrupt how genes are spliced. These “hidden” variants may explain disease in families who currently remain undiagnosed. Finding them could improve diagnosis and, in some cases, guide clinical management and genetic counselling.

This PhD will use long-read DNA and RNA sequencing, which can read much longer stretches of genetic material than conventional methods, together with large-scale genomic datasets. The project will also address an important health inequality: people from minority ethnic backgrounds are underrepresented in many genomic resources. This can make genetic results harder to interpret and may contribute to unequal access to genomic diagnosis. The project will therefore consider population diversity when interpreting genetic variation, with relevance to the diverse communities of East London.

The overarching aims are to (1) identify genetic causes of previously unsolved neurogenetic disorders; (2) determine whether biological pathways causing rare neurological diseases are also involved in common neurodegenerative diseases; and (3) improve the equitable interpretation of genomic variation.

Year 1: analyse existing long-read DNA and RNA data from neuropathologically characterised brain tissue, identify structural variants, repeat expansions and abnormal splicing, and develop skills in genomic data analysis and variant interpretation.

Year 2: integrate functional genomic data from GTEx, the Allen Brain Atlas and other resources to identify genes and pathways associated with neurodegeneration.

Year 3: test links between rare-disease genes and genetic risk for common neurodegenerative diseases using statistical genetics and machine learning.

Year 4: apply the findings to unresolved patient cases, validate promising variants using targeted long-read sequencing, and consolidate results into publications, datasets and clinically relevant resources.