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Related Experiment Video

Updated: Oct 1, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression

Shijing Li1,2, Shiqin Li3, Shaoqiang Su1

  • 1College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China.

Frontiers in Genetics
|March 10, 2022
PubMed
Summary

A new method, longitudinal functional data association test (LFDAT), analyzes longitudinal traits and gene regions. LFDAT accurately identifies gene switching and offers computational advantages for genetic studies.

Keywords:
association testingfunctional data analysisgene regionlongitudinal traitsrare variants

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Longitudinal traits are crucial for understanding biological development and genetic mechanisms.
  • Ultra-high-density sequencing presents significant statistical challenges for association analysis.

Purpose of the Study:

  • To propose a novel statistical method, longitudinal functional data association test (LFDAT), for analyzing longitudinal quantitative traits and gene regions.
  • To address the challenges posed by high-density sequencing data in genetic association studies.

Main Methods:

  • Developed LFDAT based on a function-on-function regression model.
  • Treated phenotypic traits and marker information as continuum variables.
  • Integrated micro-effects of multiple variants and utilized entire gene region information.

Main Results:

  • LFDAT demonstrated robust performance in both linkage equilibrium and disequilibrium simulations.
  • The method showed superior performance across various variant types (common, low-frequency, rare) within gene regions.
  • LFDAT accurately identified gene switching during growth and development stages.
  • Analysis of *Oryza sativa* projected shoot area revealed computational efficiency.

Conclusions:

  • LFDAT provides a feasible and efficient approach for studying the formation and expression of longitudinal traits.
  • The method effectively analyzes associations between longitudinal traits and gene regions, including complex variant patterns.
  • LFDAT offers advantages in computational speed and accuracy for genetic association studies.