A Bayesian mixed modeling approach for estimating heritability
A Bayesian mixed modeling approach for estimating heritability
Blog Article
Abstract Background A Bayesian mixed model approach using integrated nested Laplace approximations (INLA) allows us to construct flexible models that can account for pedigree structure.Using these models, we estimate genome-wide patterns of Deep learning-assisted diagnosis of chronic atrophic gastritis in endoscopy DNA methylation heritability (h 2 ), which are currently not well understood, as well as h 2 of blood lipid measurements.Methods We included individuals from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study with Infinium 450 K cytosine-phosphate-guanine (CpG) methylation and blood lipid data pre- and posttreatment with fenofibrate in families with up to three-generation pedigrees.
For genome-wide patterns, we constructed 1 model per CpG with methylation as the response variable, with a random effect to model kinship, and age and gender as fixed effects.Results In total, 425,791 CpG sites pre-, but only 199,027 CpG sites posttreatment were found to have nonzero heritability.Across these CpG sites, the distributions of h 2 estimates are similar in pre- and posttreatment (pre: median = 0.
31, interquartile range [IQR] = 0.16; post: median = 0.34, IQR = 0.
20).Blood lipid h 2 estimates were similar pre- and posttreatment with overlapping 95% credibility intervals.Heritability was nonzero for treatment effect, that is, the difference between pre- and posttreatment blood lipids.
Estimates for triglycerides h 2 are 0.48 (pre), 0.42 (post), and 0.
21 (difference); likewise for high-density lipoprotein cholesterol h 2 the estimates are Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data 0.61, 0.68, and 0.
10.Conclusions We show that with INLA, a fully Bayesian approach to estimate DNA methylation h 2 is possible on a genome-wide scale.This provides uncertainty assessment of the estimates, and allows us to perform model selection via deviance information criterion (DIC) to identify CpGs with strong evidence for nonzero heritability.