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Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework

Author

Listed:
  • Zheng Xu

    (Department of Mathematics and Statistics, Wright State University, Dayton, OH 45324, USA)

  • Song Yan

    (Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Deceased author.)

  • Cong Wu

    (Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68508, USA)

  • Qing Duan

    (Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA)

  • Sixia Chen

    (Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA)

  • Yun Li

    (Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
    Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

Abstract

To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been proposed by us using a linear model framework which can handle continuous responses. In this paper, we extend our linear model-based framework to a generalized linear model-based framework so that the methods can handle other types of responses especially binary responses which is a common problem in association studies. To evaluate the performance of various estimators and compare them we performed simulation studies. We found that all methods have Type I errors controlled, and our NGS data-based methods have better performance than genotype-based methods for other types of responses, including binary responses (logistics regression) and count responses (Poisson regression), especially when sequencing depth is low. We have extended our previous linear model (LM) framework to a generalized linear model (GLM) framework and derived NGS data-based methods for a group of genetic variables. Compared with our previously proposed LM-based methods, the new GLM-based methods can handle more complex responses (for example, binary responses and count responses) in addition to continuous responses. Our methods have filled the literature gap and shown advantage over their corresponding genotype-based methods in the literature.

Suggested Citation

  • Zheng Xu & Song Yan & Cong Wu & Qing Duan & Sixia Chen & Yun Li, 2023. "Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2560-:d:1163047
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    References listed on IDEAS

    as
    1. Iuliana Ionita-Laza & Joseph D Buxbaum & Nan M Laird & Christoph Lange, 2011. "A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease," PLOS Genetics, Public Library of Science, vol. 7(2), pages 1-6, February.
    2. Vincent Plagnol & Jason D Cooper & John A Todd & David G Clayton, 2007. "A Method to Address Differential Bias in Genotyping in Large-Scale Association Studies," PLOS Genetics, Public Library of Science, vol. 3(5), pages 1-9, May.
    3. Zheng Xu, 2023. "Association Testing of a Group of Genetic Markers Based on Next-Generation Sequencing Data and Continuous Response Using a Linear Model Framework," Mathematics, MDPI, vol. 11(6), pages 1-32, March.
    4. Dajiang J Liu & Suzanne M Leal, 2010. "A Novel Adaptive Method for the Analysis of Next-Generation Sequencing Data to Detect Complex Trait Associations with Rare Variants Due to Gene Main Effects and Interactions," PLOS Genetics, Public Library of Science, vol. 6(10), pages 1-14, October.
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