Paper Info
Title
Estimation of dynamic SNP-heritability with Bayesian Gaussian process models.
Abstract
Motivation: Improved DNA technology has made it practical to estimate single-nucleotide polymorphism (SNP)-heritability among distantly related individuals with unknown relationships. For growth- and development-related traits, it is meaningful to base SNP-heritability estimation on longitudinal data due to the time-dependency of the process. However, only few statistical methods have been developed so far for estimating dynamic SNP-heritability and quantifying its full uncertainty. Results: We introduce a completely tuning-free Bayesian Gaussian process (GP)-based approach for estimating dynamic variance components and heritability as their function. For parameter estimation, we use a modern Markov Chain Monte Carlo method which allows full uncertainty quantification. Several datasets are analysed and our results clearly illustrate that the 95% credible intervals of the proposed joint estimation method (which 'borrows strength' from adjacent time points) are significantly narrower than of a two-stage baseline method that first estimates the variance components at each time point independently and then performs smoothing. We compare the method with a random regression model using MTG2 and BLUPF90 software and quantitative measures indicate superior performance of our method. Results are presented for simulated and real data with up to 1000 time points. Finally, we demonstrate scalability of the proposed method for simulated data with tens of thousands of individuals.
Year
DOI
Venue
2020
10.1093/bioinformatics/btaa199
BIOINFORMATICS
DocType
Volume
Issue
Journal
36
12
ISSN
Citations 
PageRank 
1367-4803
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Arttu Arjas100.34
Andreas Hauptmann2183.62
Mikko J Sillanpää3103.47