A major resulting challenge facing contemporary genomic medicine is the clinical community’s desire for yes/no
answers to the nuanced issue of whether a specific genetic variant will produce a meaningful phenotype.
The current framework used to assess the significance of these variants classifies them from likely pathogenic/
pathogenic to likely benign/benign, with most stuck as variant of uncertain significance. Here we present a
data-driven estimate of disease penetrance along side raw data for interpreting variants in KCNQ1, KCNH2, and SCN5A.
To estimate penetrance of disease, heuristically, we found that the innate diagnostic information one learns
about a variant from three-dimensional variant location,
in vitro functional data, and
in silico
predictors is equivalent to the diagnostic information one learns about that same variant by clinically
phenotyping around 10-20 heterozygotes. These results are published in 2020 in
PLOS Genetics, 2021 in
Circ. Gen, and 2022 in
Genetics in Medicine.
We present the results from these analyses in the form of searchable tables.
A Bayesian method to estimate disease penetrance from genetic variant properties
We leveraged our previously collected NaV1.5 (SCN5A) dataset, supplemented with variants published within the last year, to assess the ability of functional, structural, and sequence-based predictive features to estimate Brugada syndrome penetrance. Scripts and data can be found on GitHub
(link)
Predicting changes in NaV1.5 (SCN5A) and KV7.1 (KCNQ1) function
We leveraged our previously collected NaV1.5 (SCN5A) and KV7.1 (KCNQ1) datasets to assess the ability of structure- and sequence-based predictive features to predict changes in channel function. Scripts and data can be found on GitHub
(link)
Deep Mutational Scan of KCNH2
We developed a method to assay the trafficking perturbation induced by missense variants in KCNH2 in high-throughput, capable of collecting these data for 1,000s of variants at a time. Scripts and data used can be found on GitHub
(link)