The Lek lab was established in January 2018 at Yale School of Medicine and is focused on understanding the genetic mechanism of rare diseases that may lead to rational approaches for therapies. Initially we are aimed at improving the diagnosis of rare neuromuscular diseases through the following approaches:
In recent years, large genomic data sets such as ExAC have been made publicly available and are a powerful resource for methods development. Our goal is to use these resources to build computational methods to improve our interpretation and prioritization of near-coding variants that are captured by exome sequencing but are often filtered or not considered due to difficulties in their interpretation. Further understanding of these classes of variants may contribute to improving the diagnosis rate in rare diseases.
Novel disease gene discovery
In collaboration with the Center of Mendelian Genomics, we aim to build and sequence a large cohort of undiagnosed neuromuscular disease patients to identify novel genes from exome and genome sequencing. These cohorts will be mainly non-European with a particular focus on patients from East Asia and South Asia. As part of these projects, we aim to develop and share tools and methods specific to rare disease analysis.
Detection of non-coding variants associated with disease
We aim to develop a cohort of recessive disease patients with only one pathogenic variant identified and based on several lines of evidence are likely to harbor another pathogenic mutation. Genome sequencing of these patients is likely to reveal non-coding, structural variants or novel mechanisms that were undetected by exome sequencing.
As a complementary approach, we will use saturating mutagenesis (i.e. tiling of sgRNA) to systematically disrupt regions in the vicinity of disease genes to identify non-coding regions that may impact gene function.
Interpretation of variants of unknown significance
A large challenge of rare disease diagnosis is the interpretation or correcting misinterpretations of rare missense variants in well established disease genes. Computational and analytical approaches take several lines of evidence in to consideration such as allele frequency and conservation but rarely physiologically relevant functional assays. Using cells from patients and healthy controls, we will design scalable high-throughput assays that can distinguish pathogenic from benign variants in muscle disease.
The methods and models to better understand the genetic underpinnings of rare disease can then be used to design and test therapies for neuromuscular disease patients.