Statistical learning for sparser fine-mapped polygenic models: The prediction of LDL-cholesterol.
UK Biobank
boosting
polygenic score
stochastic search
variable selection
Journal
Genetic epidemiology
ISSN: 1098-2272
Titre abrégé: Genet Epidemiol
Pays: United States
ID NLM: 8411723
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
revised:
03
06
2022
received:
24
08
2021
accepted:
07
06
2022
pubmed:
9
8
2022
medline:
19
11
2022
entrez:
8
8
2022
Statut:
ppublish
Résumé
Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models.
Substances chimiques
Cholesterol, LDL
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
589-603Informations de copyright
© 2022 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.
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