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Structured testing of genetic association with mixed clinical outcomes.
Liu, Meiling; Su, Yu-Ru; Liu, Yang; Hsu, Li; He, Qianchuan.
Afiliación
  • Liu M; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
  • Su YR; Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
  • Liu Y; Department of Mathematics and Statistics, Wright State University, Dayton, Ohio, USA.
  • Hsu L; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
  • He Q; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
Genet Epidemiol ; 2024 Apr 12.
Article en En | MEDLINE | ID: mdl-38606632
ABSTRACT
Genetic factors play a fundamental role in disease development. Studying the genetic association with clinical outcomes is critical for understanding disease biology and devising novel treatment targets. However, the frequencies of genetic variations are often low, making it difficult to examine the variants one-by-one. Moreover, the clinical outcomes are complex, including patients' survival time and other binary or continuous outcomes such as recurrences and lymph node count, and how to effectively analyze genetic association with these outcomes remains unclear. In this article, we proposed a structured test statistic for testing genetic association with mixed types of survival, binary, and continuous outcomes. The structured testing incorporates known biological information of variants while allowing for their heterogeneous effects and is a powerful strategy for analyzing infrequent genetic factors. Simulation studies show that the proposed test statistic has correct type I error and is highly effective in detecting significant genetic variants. We applied our approach to a uterine corpus endometrial carcinoma study and identified several genetic pathways associated with the clinical outcomes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Genet Epidemiol Asunto de la revista: EPIDEMIOLOGIA / GENETICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos