Trade-offs of Linear Mixed Models in Genome-Wide Association Studies.
J Comput Biol
; 29(3): 233-242, 2022 03.
Article
in En
| MEDLINE
| ID: mdl-35230156
ABSTRACT
Motivated by empirical arguments that are well known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS. First, we study the sensitivity of LMMs to the inclusion of a candidate single nucleotide polymorphism (SNP) in the kinship matrix, which is often done in practice to speed up computations. Our results shed light on the size of the error incurred by including a candidate SNP, providing a justification to this technique to trade off velocity against veracity. Second, we investigate how mixed models can correct confounders in GWAS, which is widely accepted as an advantage of LMMs over traditional methods. We consider two sources of confounding factors-population stratification and environmental confounding factors-and study how different methods that are commonly used in practice trade off these two confounding factors differently.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Genome-Wide Association Study
/
Models, Genetic
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
J Comput Biol
Journal subject:
BIOLOGIA MOLECULAR
/
INFORMATICA MEDICA
Year:
2022
Document type:
Article
Affiliation country:
United States