PT  - JOURNAL ARTICLE
AU  - Mai, The Tien
TI  - Reliable genetic correlation estimation via multiple sample splitting and smoothing
AID  - 10.1101/2023.01.15.524097
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2023.01.15.524097
4099  - http://biorxiv.org/content/early/2023/01/18/2023.01.15.524097.short
4100  - http://biorxiv.org/content/early/2023/01/18/2023.01.15.524097.full
AB  - In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model to relate a trait to genetic factors. To estimate the genetic correlation, we develop a generic strategy that combines the use of sparse penalization methods and multiple sample splitting approaches. The final estimate is determined by taking the median of the calculations, resulting in a smoothed and reliable estimate. Through simulations, we demonstrate that our proposed approach is reliable and accurate in comparison to naive plug-in methods. To further illustrate the advantages of our method, we apply it to a real-world example of a bacterial GWAS dataset, specifically to estimate the genetic correlation between antibiotic resistant traits in Streptococus pneumoniae. This application not only validates the effectiveness of our method but also highlights its potential in real-world applications.Competing Interest StatementThe authors have declared no competing interest.