Using multiplexed functional data to reduce variant classification inequities in underrepresented populations

Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Authors

Institutions

Publication Details

Journal
Genome Medicine
Published
2024-12-03
DOI
https://doi.org/10.1186/s13073-024-01392-7
Citations
19
Primary Topic
Genetic Associations and Epidemiology
Type
article
Field-Weighted Citation Impact
10.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using multiplexed functional data to reduce variant classification inequities in underrepresented populations

Douglas M. Fowler, James R. Lupski, Alan F. Rubin, Jennifer E. Posey et al.
19 citations
Genome Medicine
Genetic Associations and Epidemiology
10.46
article

Using multiplexed functional data to reduce variant classification inequities in underrepresented populations

Douglas M. Fowler, James R. Lupski, Alan F. Rubin, Jennifer E. Posey, Shawn Fayer, Sharon E. Plon, Lara A. Muffley, Karynne Patterson, Sriram Pendyala, Carla Daniela Robles‐Espinoza, Willow Coyote‐Maestas, Eric Venner, Lea M. Starita, Moez Dawood, Irene Gallego Romero, Mason Post, Richard A. Gibbs, Divya Kalra
article en
19 citations

Abstract

Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Genome MedicineVol. 16(1)
The University of Melbourne (AU), University of California, San Francisco (US), Baylor College of Medicine (US), Walter and Eliza Hall Institute of Medical Research (AU), University of Washington (US), Wellcome Sanger Institute (GB), Quantitative BioSciences (US), Brotman Baty Institute (US), Texas Children's Hospital (US), Baylor Genetics (US), University of Tartu (EE), St Vincents Institute of Medical Research (AU), Australian Catholic University (AU), Universidad Nacional Autónoma de México (MX)
Reduced inequalities
Openalex Percentile: Top 1%
Genetic Associations and Epidemiology
10.46
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.