Mortality differences by APOE genotype estimated from demographic synthesis

Abstract

The 4 allele of apolipoprotein E (APOE) is associated with increased risk of two major causes of death in low-mortality populations: ischemic heart disease and Alzheimer's disease. It is less common among centenarians than at younger ages. Therefore, it is likely that it is associated with excess risk of death. This article extends demographic models that estimate relative mortality risks from changes in gene frequencies with age. The resulting demographic synthesis combines gene frequencies with data on mortality by genotype from cohort studies. The model was applied to data from Denmark, Finland, France, Italy, Sweden, and the United States. Near age 50, the 3/4 genotype is associated with a risk of death of 1.34 times that of the 3/3 (95% CI 1.18-1.67). The relative risk for 4/4 is the square of the relative risk for 3/4, 1.81. The 2/3 genotype is protective with a relative risk of 0.84 (0.68-0.93) near age 50. These relative risks move toward 1.0 at the oldest ages and APOE genotype is associated with little variation in mortality over age 100. There are no significant differences in the relative risks by sex. There is little evidence of differences within Europe in the effects of APOE. This approach can be generalized to combine data on genetic risk factors for disease from a wide variety of study designs and sample characteristics.

Effects of the APOE ε4 allele and education on cognitive function in Japanese centenarians

“…First, the number of participants which analyzed in the previous centenarian studies were small (N = 33, 103, and 179, respectively, Asada et al 1996;Choi et al 2003;Sobel et al 1995). Second, the APOE gene has been associated with longevity; centenarians tend not to have the APOE ε4 allele (Ewbank 2002;Farrer et al 1997). The selective survival effect for APOE ε4-negative individuals makes it difficult to evaluate the effect of APOE on outcomes in very late life population.…”

Power of non‐parametric linkage analysis in mapping genes contributing to human longevity in long‐lived sib‐pairs

“…As shown in Table I, heterogeneity has a profound effect on the power of a non-parametric approach. Several authors have tried to estimate the parameters using a gamma frailty model Tan et al, 2001a;Ewbank, 2002] in fitting survival functions to individual genotype data but found that the estimated variance of the unobserved frailty varies considerably in different studies, perhaps due to small sample sizes used. Based on our experience in fitting survival distributions to large population data, we set s 2 to 0.1 in our simulation to ensure a reliable result.…”

“…Another significance of heterogeneity is relevant to association studies. Given the existence of competing risk factors or heterogeneity, the risk for any particularly observed factor (including genetic factors) will tend to decline [Ewbank, 2002]. In our simulation, we set the risk of the allele constant at the individual level.…”

“…Here s 0 ðxÞ is the baseline survival corresponding to m 0 ðxÞ: Assuming risk of the allele is additive on the log hazard of death, we have r i ¼ r i when r is the relative risk for the heterozygous genotype Tan et al, 2002b;Ewbank, 2002]. s 2 has been estimated when applying the above model to genotype data in marker-longevity association studies Tan et al, 2001a;Ewbank, 2002] but with large variations due to small sample sizes.…”

“…s 2 has been estimated when applying the above model to genotype data in marker-longevity association studies Tan et al, 2001a;Ewbank, 2002] but with large variations due to small sample sizes. A higher s 2 is expected to bring about larger variation in the unobserved frailty and thus more uncertainty in the individuals' life spans, even if they carry the same genotype.…”