Approved Research

Prioritizing Mendelian variants using GWAS and exome sequencing data

Lay summary

We intend to improve the understanding of genetic diseases for patients with severe clinical syndromes using data from individuals with common or less severe forms of each disorder. We will use common genetic variants which carry a very small disease risk to identify adjacent genes which might be related to more severe forms of each disorder if damaged. For example, if a patient has very high cholesterol, we would try to determine whether the patient carries a common coding variant nearby which is related a nearby gene which might be important for cholesterol metabolism. Along the same lines, we are also interested in determining whether we can use variant and disease status data to improve clinical risk assessment for patients in established disease genes.

Scope extension:

We aim to improve predictions of Mendelian disease variants and Mendelian disease gene discovery using data from related complex disease and population health phenotypes. For each Mendelian syndrome, we will use related complex disease association summary statistics, exome sequencing burden data, and clinical data related to each gene for our analysis.

For variant assessment in known genes, we will specifically focus on the "ACMG 59", genes related to dominant, severe Mendelian disorders, including cardiomyopathies, cancer predisposition, and arrhythmias. For gene discovery and validation efforts, we will focus on developing methods to identify shared associations between complex and Mendelian lipid disorders, diabetes, cardiomyopathies, hearing loss, renal disorders, and cancer predisposition.

New Scope: We would like to add additional clarification to the scope -- in addition to the ACMG 59 genes, we are interested in developing and testing methods to predict variant functional effects and individual-level clinical risk across genes with existing clinical associations (e.g. through ClinVar/HGMD/LSDBs) as well as in those identified through high-throughput functional screens or GWAS studies as having a promising association with disease. This would include burden analyses, rare variant analyses, and individual variant functional assessments. These necessarily cross a broad range of phenotypes and we are requesting data access to additional phenotypes at present.