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Genet Epidemiol. 2019 Sep 10. doi: 10.1002/gepi.22254. [Epub ahead of print]

Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.

Author information

1
Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee.
2
Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia.
3
Department de Mathematiques et de Statistique, Universite Laval, Quebec, Quebec, Canada.
4
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.
5
Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland.
6
Department of Biostatistics, Human Genetics Center, University of Texas-Houston, Houston, Texas.

Abstract

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this study direction. For unrelated population data, functional regression (FR) models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests. In this paper, we extend this approach to analyze censored traits for family data or related samples using FR based mixed effect Cox models (FamCoxME). The FamCoxME model effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

KEYWORDS:

association study; common variants; complex diseases; functional data analysis; mixed effect Cox models; rare variants

PMID:
31502722
DOI:
10.1002/gepi.22254

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