Due to their low frequency, estimating the effect of rare variants is challenging. Here, we propose RareEffect, a method that first estimates gene or region-based heritability and then each variant effect size using an empirical Bayesian approach. Our method uses a variance component model, popular in rare variant tests, and is designed to provide two levels of effect sizes, gene/region-level and variant-level, which can provide better interpretation. To adjust for the case-control imbalance in phenotypes, our approach uses a fast implementation of the Firth bias correction. We demonstrate the accuracy and computational efficiency of our method through extensive simulations and the analysis of UK Biobank whole exome sequencing data for five continuous traits and five binary disease phenotypes. Additionally, we show that the effect sizes obtained from our model can be leveraged to improve the performance of polygenic scores.

The authors have declared no competing interest.

This research was supported by the Brain Pool Plus (BP+) Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (2020H1D3A2A03100666) and the grants funded by the Ministry of Food and Drug Safety, Republic of Korea (Grant Number: 23212MFDS202).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This research was conducted using the UK Biobank Resource under application number 45227.

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The analysis results for 5 quantitative and 5 binary phenotypes of UKB WES data analysis results are available at: https://storage.googleapis.com/leelabsg/RareEffect/RareEffect_effect_size.zip (variant-level effect size) and https://storage.googleapis.com/leelabsg/RareEffect/RareEffect_h2.zip (gene-level signed heritability).