Additional file 8 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

Additional file 8 of Unsupervised feature extraction using deep learning empowers discovery of genetic determinants of the electrocardiogram

Additional file 8: Polyfun likely causal variants FDR. This file contains the additional likely causal variants for the genomic regions selected at the relaxed significance threshold of 5% FDR.

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Bibliographic Details
Main Author: Ewa Sieliwonczyk (22406334) (author)
Other Authors: Arunashis Sau (12914977) (author), Konstantinos Patlatzoglou (22406337) (author), Kathryn A. McGurk (22406340) (author), Libor Pastika (22406343) (author), Prisca K. Thami (4811598) (author), Massimo Mangino (87550) (author), Sean L. Zheng (22406346) (author), George Powell (833319) (author), Lara Curran (17464035) (author), Rachel J. Buchan (22406349) (author), Pantazis Theotokis (17273105) (author), Nicholas S. Peters (8363781) (author), Bart Loeys (526781) (author), Daniel B. Kramer (7460285) (author), Jonathan W. Waks (22406352) (author), Fu Siong Ng (6547454) (author), James S. Ware (7759880) (author)
Published: 2025
Subjects:
Genetics
GWAS
ECG
Deep learning
PheWAS
Burden testing
Variational autoencoder
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