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Region-Based Association Test for Familial Data under Functional Linear Models.

Gulnara R Svishcheva1, Nadezhda M Belonogova1, Tatiana I Axenovich2

  • 1Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.

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Summary
This summary is machine-generated.

This study enhances region-based association analysis for gene mapping in related individuals by incorporating random polygene effects. The new functional linear model (FLM) method demonstrates increased statistical power for quantitative trait association studies, outperforming existing approaches.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Region-based association analysis offers greater power for gene mapping than individual variant testing, especially for rare variants.
  • Functional data analysis (FDA) is a powerful approach for regional mapping, treating genomic regions as stochastic functions.
  • Existing FDA methods are limited to independent samples, excluding valuable family data.

Purpose of the Study:

  • To extend functional data analysis-based region-based association analysis to samples of related individuals.
  • To develop a novel statistical method that incorporates random polygene effects into functional linear models for family data.
  • To compare the statistical power of the new method against existing burden-based and kernel-based methods.

Main Methods:

  • Developed a functional linear model incorporating random polygene effects for association testing in related individuals.
  • Utilized Genetic Analysis Workshop 17 mini-exome family data and extensive simulation scenarios for evaluation.
  • Implemented the method as an R-function 'famFLM' using B-spline and Fourier basis functions.

Main Results:

  • The proposed method significantly increases the power of regional association analysis for quantitative traits compared to burden-based and kernel-based methods.
  • The advantage in statistical power is maintained even for genomic regions with a small number of genetic variants.
  • Models using Fourier basis functions demonstrated superior speed and power compared to B-spline or combined basis function models.

Conclusions:

  • The new functional linear model approach effectively extends powerful region-based association analysis to related individuals.
  • The 'famFLM' R-package provides a valuable, freely available tool for genetic association studies in family data.
  • Fourier basis functions offer an advantageous implementation for speed and statistical power in this context.