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Gene-level association analysis of ordinal traits with functional ordinal logistic regressions.
Chiu, Chi-Yang; Wang, Shuqi; Zhang, Bingsong; Luo, Yutong; Simpson, Claire; Zhang, Wei; Wilson, Alexander F; Bailey-Wilson, Joan E; Agron, Elvira; Chew, Emily Y; Zhang, Jun; Xiong, Momiao; Fan, Ruzong.
Afiliación
  • Chiu CY; Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Wang S; Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA.
  • Zhang B; Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.
  • Luo Y; Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.
  • Simpson C; Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.
  • Zhang W; Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Wilson AF; Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
  • Bailey-Wilson JE; Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA.
  • Agron E; Computational and Statistical Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland, USA.
  • Chew EY; National Eye Institute, National Institute of Health, Bethesda, Maryland, USA.
  • Zhang J; National Eye Institute, National Institute of Health, Bethesda, Maryland, USA.
  • Xiong M; Department of Computer Science and Engineering Technology, University of Maryland Eastern Shore, Princess Anne, Maryland, USA.
  • Fan R; Human Genetics Center, University of Texas-Houston, Houston, Texas, USA.
Genet Epidemiol ; 46(5-6): 234-255, 2022 07.
Article en En | MEDLINE | ID: mdl-35438198
In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions. The FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pruebas Genéticas / Modelos Genéticos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Pruebas Genéticas / Modelos Genéticos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos