Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a...

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Association testing is crucial for understanding genetic variant-phenotype relationships.
• Genotype-based methods, common in association studies, can be limited by genotype calling accuracy.
• Next-generation sequencing (NGS) data offers opportunities for direct association testing without prior genotype calling.

Purpose of the Study:

• To extend existing linear model-based NGS association testing frameworks to a generalized linear model (GLM) framework.
• To develop and evaluate NGS data-based group testing methods for diverse response types, including binary and count data.
• To compare the performance of the new GLM-based NGS methods against traditional genotype-based methods.

Main Methods:

• Extension of a linear model (LM) framework to a generalized linear model (GLM) framework for association testing.
• Development of NGS data-based group testing methods capable of handling continuous, binary (logistic regression), and count (Poisson regression) responses.
• Extensive simulation studies to assess Type I error control and performance comparison with genotype-based methods.

Main Results:

• All tested methods demonstrated controlled Type I errors.
• NGS data-based testing methods showed superior performance compared to corresponding genotype-based methods.
• The advantage of NGS methods was particularly evident for binary and count responses, especially under low sequencing depth conditions.

Conclusions:

• The developed GLM-based framework effectively extends NGS association testing to accommodate complex response variables beyond continuous data.
• These novel NGS data-based methods offer significant advantages over conventional genotype-based approaches, particularly for binary and count phenotypes.
• The study addresses a gap in the literature by providing robust NGS association testing tools for various data types.