Stanford Medicine

Genes and Genomes

Whole Genome Sequencing

LVNC echo

Pedigree

Gene finding using high-throughput sequencing. We use a combination of advanced cardiovascular phenotyping (top) and family-based sequencing (bottom).

We have an active interest in fundamental genetics and the application of high-throughput genetic techniques to cardiovascular disease genetics. Through collaborations with Steve Quake, Atul Butte, Russ Altman, Carlos Bustamante and Mike Snyder, we have developed tools for genetic risk assessment using whole genome sequence data in healthy and diseased individuals. We have recently developed a framework for advanced analysis of familial whole genome sequence data that includes ethnicity-aware genetic variant identification algorithms, novel phasing and phased disease risk estimation methods, recombination site mapping, and an integrated variant annotation pipeline.

We currently use exome sequencing and whole genome sequencing to investigate possible genetic causes of cardiomyopathies such as left ventricular non-compaction, hypertrophic, dilated, and arrhythmogenic right ventricular cardiomyopathy, and ion channelopathies. Using a translational approach that encompasses comprehensive familial genetic analysis, cardiovascular phenotyping, and molecular phenotyping of model systems, we aim to elucidate the genetic basis for Mendelian cardiovascular diseases. To this end we have integrated our genetic discovery pipeline with cellular phenotyping, small animal physiology, and isolated heart experiments to uncover phenotype-genotype relationships. In an exciting collaboration with Dr. Joseph Wu, we are also utilizing induced pluripotent stem cell-derived cardiomyocytes from individuals with and without potentially causative genetic mutations to investigate the effects of these mutations on human cardiac cells. These “experiments of nature” allow us to explore in detail the hypothesis generated by our high-throughput genetic analysis.

Phasing

Gene finding using high-throughput sequencing using novel variant phasing algorithms to identify disease-associated gene loci.