Population effects on individual systolic blood pressure: A multilevel analysis of the World Health Organization MONICA project

Individuals from the same population share a number of contextual circumstances that may condition a common level of blood pressure over and above individual characteristics. Understanding this population effect is relevant for both etiologic research and prevention strategies. Using multilevel regression analyses, the authors quantified the extent to which individual differences in systolic blood pressure (SBP) could be attributed to the population level. They also investigated possible cross-level interactions between the population in which a person lived and pharmacological (antihypertensive medication) and nonpharmacological (body mass index) effects on individual SBP. They analyzed data on 23,796 men and 24,986 women aged 35-64 years from 39 worldwide Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) study populations participating in the final survey of this World Health Organization project (1989-1997). SBP was positively associated with low educational achievement, high body mass index, and use of antihypertensive medication and, for women, was negatively associated with smoking. About 7-8% of all SBP differences between subjects were attributed to the population level. However, this population effect was particularly strong (i.e., 20%) in antihypertensive medication users and overweight women. This empirical evidence of a population effect on individual SBP emphasizes the importance of developing population-wide strategies to reduce individual risk of hypertension.

The v-MFG test: Investigating maternal, offspring and maternal-fetal genetic incompatibility effects on disease and viability

The MFG test is a family-based association test that detects genetic effects contributing to disease in offspring, including offspring allelic effects, maternal allelic effects and MFG incompatibility effects. Like many other family-based association tests, it assumes that the offspring survival and the offspring-parent genotypes are conditionally independent provided the offspring is affected. However, when the putative disease-increasing locus can affect another competing phenotype, for example, offspring viability, the conditional independence assumption fails and these tests could lead to incorrect conclusions regarding the role of the gene in disease. We propose the v-MFG test to adjust for the genetic effects on one phenotype, e.g., viability, when testing the effects of that locus on another phenotype, e.g., disease. Using genotype data from nuclear families containing parents and at least one affected offspring, the v-MFG test models the distribution of family genotypes conditional on offspring phenotypes. It simultaneously estimates genetic effects on two phenotypes, viability and disease. Simulations show that the v-MFG test produces accurate genetic effect estimates on disease as well as on viability under several different scenarios. It generates accurate type-I error rates and provides adequate power with moderate sample sizes to detect genetic effects on disease risk when viability is reduced. We demonstrate the v-MFG test with HLA-DRB1 data from study participants with rheumatoid arthritis (RA) and their parents, we show that the v-MFG test successfully detects an MFG incompatibility effect on RA while simultaneously adjusting for a possible viability loss.