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

SUBMITTER: Arjas A 

PROVIDER: S-EPMC7672693 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature