Detection of distant relatedness in biobanks to identify undiagnosed cases of Mendelian disease as applied to Long QT syndrome

Megan C. Lancaster, Hung-Hsin Chen, M. Benjamin Shoemaker, Matthew R. Fleming, Teresa L. Strickland, James T. Baker, Grahame F. Evans, Hannah G. Polikowsky, David C. Samuels, Chad D. Huff, Dan M. Roden and Jennifer E. Below ()
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Megan C. Lancaster: Vanderbilt University Medical Center
Hung-Hsin Chen: Vanderbilt University Medical Center
M. Benjamin Shoemaker: Vanderbilt University Medical Center
Matthew R. Fleming: Vanderbilt University Medical Center
Teresa L. Strickland: Vanderbilt University Medical Center
James T. Baker: Vanderbilt University Medical Center
Grahame F. Evans: Vanderbilt University Medical Center
Hannah G. Polikowsky: Vanderbilt University Medical Center
David C. Samuels: Vanderbilt University
Chad D. Huff: University of Texas MD Anderson Cancer Center
Dan M. Roden: Vanderbilt University Medical Center
Jennifer E. Below: Vanderbilt University Medical Center

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Rare genetic diseases are typically studied in referral populations, resulting in underdiagnosis and biased assessment of penetrance and phenotype. To address this, we develop a generalizable method of genotype inference based on distant relatedness and deploy this to identify undiagnosed Type 5 Long QT Syndrome (LQT5) rare variant carriers in a non-referral population. We identify 9 LQT5 families referred to a single specialty clinic, each carrying p.Asp76Asn, the most common LQT5 variant. We uncover recent common ancestry and a single shared haplotype among probands. Application to a non-referral population of 69,819 BioVU biobank subjects identifies 22 additional subjects sharing this haplotype, which we confirm to carry p.Asp76Asn. Referral and non-referral carriers have prolonged QT interval corrected for heart rate (QTc) compared to controls, and, among carriers, the QTc polygenic score is independently associated with QTc prolongation. Thus, our innovative analysis of shared chromosomal segments identifies undiagnosed cases of genetic disease and refines the understanding of LQT5 penetrance and phenotype.

Date: 2024
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DOI: 10.1038/s41467-024-51977-4

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