| Summary: | Population attributable fraction (PAF) has been widely used to quantify the proportion of disease risk in a population that can be attributed to risk factors in epidemiological studies. However, the use of PAF has been limited in assessing the contribution of genetic variants. Most notably, the PAF estimate is typically much larger than other commonly used measures, such as heritability, thereby raising the concern that PAF may overestimate the genetic contribution. In this paper, we show that PAF is a one-to-one function of heritability, and explain why PAF is larger than heritability. Further, we present an estimation procedure based on the summary statistics from genome-wide association studies (GWAS) to estimate the PAF of multiple correlated genetic variants for a binary outcome. Currently available estimation procedures only apply to a single variant or to multiple genetic variants that are independent from each other. Our simulation studies verified the relationship between PAF and heritability, and showed that the proposed estimation procedure for these two measures performed well. Finally, we applied the proposed method to the published data of two lung cancer GWAS to estimate the PAF and heritability of several newly identified variants. Our results demonstrate that the PAF estimate is a useful measure of the genetic contribution to the development of the disease. We hope this paper serves as an advocate for a wider use of PAF in genetic studies.
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