![]() ![]() Since such information is routinely available in the summary results from genome-wide association studies (GWAS), the PAF of a single genetic variant can be estimated without making use of the individual genetic data ( Witte et al., 2014). In order to estimate the PAF associated with a genetic variant, information on the allele frequency and its association with disease risk is required. Therefore, we show that the one-to-one relationship is applied to h L 2 as well. As h 2 is dependent on the prevalence of the disease in the population, it is often transformed to the underlying liability scale (denoted as h L 2 in this paper) under the assumption of a classical liability threshold model ( Falconer, 1965). Specifically, we first establish a one-to-one function between the PAF and heritability on the observed scale (denoted as h 2 in this paper), which explains why the PAF estimate is often larger than h 2. In this paper, we aim to link the PAF estimate and the heritability estimate. However, the relationship between them has not been examined. As such, heritability and PAF measure the different aspects of genetic contribution. On the other hand, PAF measures the effects from genetic variants on the mean level of risk, i.e., the proportion of disease that can be potentially prevented if effective interventions are available. Heritability is the most commonly used measure of genetic contribution to the risk of a disease, which quantifies the effects on the variability of risk at the population level. In addition, heritability is considered to be more meaningful because it explains an individual’s “genetic variation in risk." The primary concern is that the PAF may overestimate the genetic contribution because its estimate is typically much larger than other measures, such as heritability, sibling recurrence risk, and the proportion of area under the curve ( Witte et al., 2014). Several concerns have been raised for the use of PAF as a measure to assess the contribution of genetic variants to a disease. When a risk factor is a genetic risk allele, the PAF infers the proportion of disease that is “explained" by this allele ( Moonesinghe et al., 2012). Since its introduction by Levin ( Levin, 1953), the PAF has been widely used to quantify the proportion of disease risk in a population that can be attributed to a risk factor or a set of risk factors in epidemiological studies ( Rockhill et al., 1998 Lim et al., 2012 Burnett et al., 2014 Flegal et al., 2015). Population attributable fraction (PAF) is defined as the reduction in average disease risk by eliminating the exposure(s) of interest from the population, while the other risk factors in the population remain unchanged. We hope this paper serves as an advocate for a wider use of PAF in genetic studies. Our results demonstrate that the PAF estimate is a useful measure of the genetic contribution to the development of the disease. 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 simulation studies verified the relationship between PAF and heritability, and showed that the proposed estimation procedure for these two measures performed well. Currently available estimation procedures only apply to a single variant or to multiple genetic variants that are independent from each other. 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. In this paper, we show that PAF is a one-to-one function of heritability, and explain why PAF is larger than heritability. 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. ![]() However, the use of PAF has been limited in assessing the contribution of genetic variants. 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. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, The Bronx, NY, United States. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |