Gene Discovery

Hunting for New Therapies

There is a treasure trove of valuable information in both the mapped and unmapped reads of the genome.

Gene Discovery involves identifying novel genes implicated in causing rare disease, developing methods to identify patient’s predisposition to a rare disease and building the knowledge base to improve clinical management of novel genetic disease.

At RCIGM, this work is led by Matthew Bainbridge, PhD, Assistant Director of Translational Research. His team develops novel analysis techniques to squeeze every last bit of information from WGS and to attempt to identify uncommon disease mechanisms (such as ALU insertions and deep intronic mutations) in the pediatric patient population. 

Bioinformatic analysis of Whole Genome Sequencing (WGS) data is used to gain a better understanding of the mechanisms by which pathogenic genomic variants contribute to the development of rare diseases.

Traditional wet-lab modeling of novel diseases is used to functionalize variants of uncertain significance.

Research Projects

Several grant funded research projects are currently under Dr. Bainbridge’s direction:

  • Oligogenic Models of Cardiomyopathy
    The goal is to identify synergistic and modifier mutations that impact structural cardiomyopathies.  Bioinformatically identified variants are prioritized and then functionally tested by Dr. Neil Chi at UC San Diego. Learn More

Matthew Bainbridge, PhD

RCIGM Assistant Director of Translational Research

Publications

Sci Rep. 2021 Aug 24;11(1):17115. doi: 10.1038/s41598-021-96374-9.

ABSTRACT

Heat shock proteins are involved in the response to stress including activation of the immune response. Elevated circulating heat shock proteins are associated with spontaneous preterm birth (SPTB). Intracellular heat shock proteins act as multifunctional molecular chaperones that regulate activity of nuclear hormone receptors. Since SPTB has a significant genetic predisposition, our objective was to identify genetic and transcriptomic evidence of heat shock proteins and nuclear hormone receptors that may affect risk for SPTB. We investigated all 97 genes encoding members of the heat shock protein families and all 49 genes encoding nuclear hormone receptors for their potential role in SPTB susceptibility. We used multiple genetic and genomic datasets including genome-wide association studies (GWASs), whole-exome sequencing (WES), and placental transcriptomics to identify SPTB predisposing factors from the mother, infant, and placenta. There were multiple associations of heat shock protein and nuclear hormone receptor genes with SPTB. Several orthogonal datasets supported roles for SEC63, HSPA1L, SACS, RORA, and AR in susceptibility to SPTB. We propose that suppression of specific heat shock proteins promotes maintenance of pregnancy, whereas activation of specific heat shock protein mediated signaling may disturb maternal-fetal tolerance and promote labor.

PMID:34429451 | DOI:10.1038/s41598-021-96374-9

Brief Bioinform. 2021 Aug 19:bbab323. doi: 10.1093/bib/bbab323. Online ahead of print.

ABSTRACT

DNA methylation may be regulated by genetic variants within a genomic region, referred to as methylation quantitative trait loci (mQTLs). The changes of methylation levels can further lead to alterations of gene expression, and influence the risk of various complex human diseases. Detecting mQTLs may provide insights into the underlying mechanism of how genotypic variations may influence the disease risk. In this article, we propose a methylation random field (MRF) method to detect mQTLs by testing the association between the methylation level of a CpG site and a set of genetic variants within a genomic region. The proposed MRF has two major advantages over existing approaches. First, it uses a beta distribution to characterize the bimodal and interval properties of the methylation trait at a CpG site. Second, it considers multiple common and rare genetic variants within a genomic region to identify mQTLs. Through simulations, we demonstrated that the MRF had improved power over other existing methods in detecting rare variants of relatively large effect, especially when the sample size is small. We further applied our method to a study of congenital heart defects with 83 cardiac tissue samples and identified two mQTL regions, MRPS10 and PSORS1C1, which were colocalized with expression QTL in cardiac tissue. In conclusion, the proposed MRF is a useful tool to identify novel mQTLs, especially for studies with limited sample sizes.

PMID:34414410 | DOI:10.1093/bib/bbab323

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