A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks.

ABSTRACT: Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for

SUBMITTER: Tsai YT 

PROVIDER: S-EPMC11140211 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature