A whole-genome sequencing approach to advance precision medicine and study patient heterogeneity

Lead Supervisor
Professor Ammar Al-Chalabi
Professor of Neurology and Complex Disease Genetics
King’s College London
ammar.al-chalabi@kcl.ac.uk

Co-supervisor
Dr Alfredo Iacoangeli
Research Fellow
Dept. Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London
alfredo.iacoangeli@kcl.ac.uk

Industrial Partners
RawAnalytics (precisionlife), BenevolentAI

Project Details

Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. Recent progress in NGS technologies allows for the sequencing of the whole genome (WGS) of an individual for a few hundred dollars and a turnaround of less than two weeks. As a result, sequencing the whole-genome of people at population scale is being investigated for several uses including as a method for the early diagnosis of genetic diseases as it allows for the testing of all variants in the genome at once. At the same time the low price and increasing availability of commercial services able to provide WGS data, means that an increasing number of patients who are affected by genetic diseases, obtain their genome data without the appropriate ability to interpret the results. There are significant challenges when dealing with NGS data, including interpreting and prioritizing the found variants and setting up the appropriate analysis pipeline to cover the necessary spectrum of genetic factors. These include point mutations, for which established analysis standards exist, but can also include, depending on the disease to be tested, more complex types of variation such as large structural variants and copy number variants for which established methods of detection might not be available.

This project proposes to explore knowledge available of genetic causes of Neurodegenerative disorders (ND), for which we have large sequencing datasets available at King’s College London, as well as state-of-art bioinformatics methods to design a pipeline that can fully exploit the potentialities of WGS data for personalized diagnostic and prognostic genetic profiling. The pipeline will also be used in combination with extensive clinical datasets to investigate the relationship between patients’ genomes and their clinical outcomes. To this end, the use of Machine Learning methods as well as conventional statistical approaches will be explored. The results will be used to identify subgroups of patients with more homogeneous genetic causes and clinical outcomes that can potentially translate into a personalized approach to health care and treatment.

The project will build on the supervisors’ expertise in bioinformatics, genetics of Neurodegeneration and medical informatics, as well as the existing work ongoing within the supervisors’ laboratories, on the development of bioinformatics tools for medical research and the stratification of ND patients based on their genetics and clinical history.

Project Aims:

  1. Develop a bioinformatics pipeline that can fully exploit the potentialities of WGS data by accurately detecting a wide range of genetic variations
  2. Evaluate its applicability in clinical settings for a range of NDs given our current understanding of the genetic causes of Neurodegeneration
  3. Identify subgroups of patients with homogeneous diseases causes and clinical outcomes

Novelty and Importance:

The pipeline, models and results generated by the project will produce novel bioinformatics methods, new insight into the ND genetics and will advance personalised medicine. The project will also generate timely expertise in the translation of our knowledge of the genetic basis of Neurodegeneration into medical practice

Datasets

Large WGS datasets of ALS and FTD patients that we are already available as part of other ongoing project within the supervisors’ laboratories

Keywords

Whole-genome sequencing, Next-generation sequencing, Personalised Medicine, genetics of complex disease, Bioinformatics