Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.
Genet Epidemiol
; 43(2): 189-206, 2019 Mar.
Article
en En
| MEDLINE
| ID: mdl-30537345
ABSTRACT
We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Carácter Cuantitativo Heredable
/
Estudios de Asociación Genética
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Secuenciación de Nucleótidos de Alto Rendimiento
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Modelos Genéticos
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Año:
2019
Tipo del documento:
Article