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We developed a new Bayesian Gaussian process method to estimate dynamic single-nucleotide polymorphism (SNP)-heritability using longitudinal data. This approach provides more precise estimates with narrower credible intervals than traditional methods.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Estimating single-nucleotide polymorphism (SNP)-heritability is crucial for understanding trait inheritance.
  • Longitudinal data are essential for growth and development traits due to their time-dependent nature.
  • Existing methods for dynamic SNP-heritability estimation with full uncertainty quantification are limited.

Purpose of the Study:

  • To introduce a novel, tuning-free Bayesian Gaussian process (GP)-based approach for dynamic variance component and heritability estimation.
  • To enable full uncertainty quantification using a modern Markov Chain Monte Carlo method.
  • To assess the performance and scalability of the proposed method.

Main Methods:

  • A completely tuning-free Bayesian Gaussian process (GP)-based approach.
  • Markov Chain Monte Carlo (MCMC) for parameter estimation and uncertainty quantification.
  • Joint estimation of variance components across time points to 'borrow strength'.

Main Results:

  • The proposed joint estimation method yields significantly narrower 95% credible intervals compared to a two-stage baseline method.
  • Quantitative comparisons show superior performance against random regression models (MTG2, BLUPF90).
  • The method demonstrates scalability for large datasets with up to tens of thousands of individuals and thousands of time points.

Conclusions:

  • The developed Bayesian GP method offers a robust and accurate approach for estimating dynamic SNP-heritability from longitudinal data.
  • This method provides improved uncertainty quantification and outperforms existing models.
  • The implementation is publicly available, facilitating its application in genetic research.