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

## TL;DR

The paper introduces a new method to compute confidence intervals for genetic correlations, especially useful for large-scale studies of protein-protein interactions.

## Contribution

A parametric bootstrap procedure is proposed to improve confidence interval estimation for genetic correlations in large omics datasets.

## Key findings

- The parametric bootstrap method outperforms asymptotic approaches in small samples or at parameter boundaries.
- The method is demonstrated on a proteomics dataset with tens of thousands of protein pairs.
- The procedure uses Haseman-Elston regression and kinship matrices to estimate genetic correlations.

## 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 genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

The authors developed a method to estimate CIs for genetic correlations. The method is particularly useful when applied on a large panel of omics assays. The authors demonstrate its application to study genetic correlations between tens of thousands of protein pairs.