Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
30
01
2023
accepted:
04
09
2023
revised:
26
09
2023
medline:
4
10
2023
pubmed:
14
9
2023
entrez:
14
9
2023
Statut:
epublish
Résumé
The development of high-throughput next-generation sequencing technologies and large-scale genetic association studies produced numerous advances in the biostatistics field. Various aggregation tests, i.e. statistical methods that analyze associations of a trait with multiple markers within a genomic region, have produced a variety of novel discoveries. Notwithstanding their usefulness, there is no single test that fits all needs, each suffering from specific drawbacks. Selecting the right aggregation test, while considering an unknown underlying genetic model of the disease, remains an important challenge. Here we propose a new ensemble method, called Excalibur, based on an optimal combination of 36 aggregation tests created after an in-depth study of the limitations of each test and their impact on the quality of result. Our findings demonstrate the ability of our method to control type I error and illustrate that it offers the best average power across all scenarios. The proposed method allows for novel advances in Whole Exome/Genome sequencing association studies, able to handle a wide range of association models, providing researchers with an optimal aggregation analysis for the genetic regions of interest.
Identifiants
pubmed: 37708232
doi: 10.1371/journal.pcbi.1011488
pii: PCOMPBIOL-D-23-00145
pmc: PMC10522036
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1011488Informations de copyright
Copyright: © 2023 Boutry et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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