Genetic Analysis of Age-at-Onset for Cardiovascular Risk Factors in a Brazilian Family Study

Abstract

Background/Aims: Statistical analysis of age-at-onset involving family data is particularly complicated because there is a correlation pattern that needs to be modeled and also because there are measurements that are censored. In this paper, our main purpose was to evaluate the effect of genetic and shared family environmental factors on age-at-onset of three cardiovascular risk factors: hypertension, diabetes and high cholesterol. Methods: The mixed-effects Cox model proposed by Pankratz et al. [2005] was used to analyze the data from 81 families, involving 1,675 individuals from the village of Baependi, in the state of Minas Gerais, Brazil. Results: The analyses performed showed that the polygenic effect plays a greater role than the shared family environmental effect in explaining the variability of the age-at-onset of hypertension, diabetes and high cholesterol. The model which simultaneously evaluated both effects indicated that there are individuals which may have risk of hypertension due to polygenic effects 130% higher than the overall average risk for the entire sample. For diabetes and high cholesterol the risks of some individuals were 115 and 45%, respectively, higher than the overall average risk for the entire population. Conclusions: Results showed evidence of significant polygenic effects indicating that age-at-onset is a useful trait for gene mapping of the common complex diseases analyzed. In addition, we found that the polygenic random component might absorb the effects of some covariates usually considered in the risk evaluation, such as gender, age and BMI.

Genetic analyses of smoking initiation, persistence, quantity, and age-at-onset of regular cigarette use in Brazilian families: the Baependi Heart Study

“…From the polygenic model, we obtained an estimate of polygenic variance component equal to 0.21 for males, with a 95% confidence interval of 0.01 - 0.54. Considering that polygenic variance estimates can be interpreted as measures of familial aggregation [18], this result suggests an intermediate degree of familial aggregation associated with the age-at-onset of regular cigarette use and shows that the individual relative risk of cigarette use due to polygenic effects among males are on average exp(√0.21) = 1.58. Among females, we observed a moderate polygenic variance estimate of 0.40 (95% CI, 0.11 - 0.78) indicating a higher level of familial aggregation compared to males and a risk of cigarette use which may be 88% (exp(√0.40) = 1.88) higher than the average risk of this sample.…”

“…However, the variance components estimated may be interpreted as measures of familial aggregation [18]. The relative risk of the smoking behavior that corresponds to the random effect is obtained by exponentiation of the square root of the variance component.…”

Cohort profile: the Baependi Heart Study—a family-based, highly admixed cohort study in a rural Brazilian town

“…The initial publication 19 determined the extent to which unmeasured genetic factors and measured environmental and lifestyle factors contributed to variation in a large panel of cardiovascular-related traits. Subsequent studies identified that age-at-onset is a useful trait for gene mapping of common complex diseases 21 and reported that heterogeneity in trait variances needs to be accounted for in the design and analyses of trait orientated gene finding studies. 22 Lifestyle factors, including physical activity 23 and smoking 24 have also been a focus of the study.…”

Potential Modifying Loci Associated With Primary Lens Luxation, Pedal Hyperkeratosis, and Ocular Phenotypes in Miniature Bull Terriers

“…This comprehensive model has been extended to include components of the Cox model, random effects, and familial relationship (kinship or identity by descent [IBD] matrix in families) in the Cox model survival analysis. [21][22][23] Kinship IBD matrixes were created, using the genotypic data in kinship software, and Cox model survival analysis was performed on each SNP using the age at diagnosis of PLL and the disease status, whereas the structure of individual random effect was adjusted using the kinship IBD matrixes. Association of the genome-wide SNPs with the disease risk was estimated using the Wald test v 2 for each SNP.…”