Longitudinal variance components models for systolic blood pressure, fitted using Gibbs sampling

This paper describes an analysis of systolic blood pressure (SBP) in the Genetic Analysis Workshop 13 (GAW13) simulated data. The main aim was to assess evidence for both general and specific genetic effects on the baseline blood pressure and on the rate of change (slope) of blood pressure with time. Generalized linear mixed models were fitted using Gibbs sampling in WinBUGS, and the additive polygenic random effects estimated using these models were then used as continuous phenotypes in a variance components linkage analysis. The first-stage analysis provided evidence for general genetic effects on both the baseline and slope of blood pressure, and the linkage analysis found evidence of several genes, again for both baseline and slope

Genome-wide linkage analysis of longitudinal phenotypes using σ(2)(A )random effects (SSARs) fitted by Gibbs sampling

The study of change in intermediate phenotypes over time is important in genetics. In this paper we explore a new approach to phenotype definition in the genetic analysis of longitudinal phenotypes. We utilized data from the longitudinal Framingham Heart Study Family Cohort to investigate the familial aggregation and evidence for linkage to change in systolic blood pressure (SBP) over time. We used Gibbs sampling to derive sigma-squared-A-random-effects (SSARs) for the longitudinal phenotype, and then used these as a new phenotype in subsequent genome-wide linkage analyses. Additive genetic effects (σ(2)(A.time)) were estimated to account for ~9.2% of the variance in the rate of change of SBP with age, while additive genetic effects (σ(2)(A)) were estimated to account for ~43.9% of the variance in SBP at the mean age. The linkage results suggested that one or more major loci regulating change in SBP over time may localize to chromosomes 2, 3, 4, 6, 10, 11, 17, and 19. The results also suggested that one or more major loci regulating level of SBP may localize to chromosomes 3, 8, and 14. Our results support a genetic component to both SBP and change in SBP with age, and are consistent with a complex, multifactorial susceptibility to the development of hypertension. The use of SSARs derived from quantitative traits as input to a conventional linkage analysis appears to be valuable in the linkage analysis of genetically complex traits. We have now demonstrated in this paper the use of SSARs in the context of longitudinal family data

Socioeconomic position in young adulthood is associated with BMI in Australian families

BACKGROUND: Low socioeconomic position (SEP) is associated with increased cardiovascular (CV) disease risk, but the relative importance of SEP in childhood and adulthood, and of changes in SEP between these two life stages, remains unclear. Studies of families may help clarify these issues. We aimed to assess whether SEP in young adulthood, or change in SEP from childhood to young adulthood, was associated with five continuously measured CV risk factors. METHODS: We used data from 286 adult Australian families from the Victorian Family Heart Study (VFHS), in which some offspring have left home (n = 364) and some remained at home (n = 199). SEP (defined as the Index of Relative Socioeconomic Disadvantage) was matched to addresses. We fitted variance components models to test whether young adult SEP and/or change in SEP was associated with systolic blood pressure, diastolic blood pressure, body mass index (BMI), total cholesterol or high-density lipoprotein cholesterol, after adjustment for parental SEP and within-family correlation. RESULTS: An increase in SEP of 100 SEIFA units from childhood to adulthood was associated with a lower BMI (β = -0.49 kg/m(2), P < 0.01) only. CONCLUSIONS: These results suggest that a change in SEP in young adulthood is an important predictor of BMI, independent of childhood SEP

Is the association between obesity and hip osteoarthritis surgery explained by familial confounding?

Background: Familial confounding is confounding due to genetics or environmental exposures shared by family members. We aimed to study whether familial confounding explains the association between body mass index (BMI) and severe hip osteoarthritis (OA). Methods: We linked data from the Norwegian Arthroplasty Registry with the Norwegian Twin Registry on the National ID-number in 2014, generating a population-based prospective cohort study of same-sex twins born between 1915 and 1960 (53.4% females). BMI was calculated from self-reported height/weight. The outcome was incident hip arthroplasty due to OA (follow-up time, 1987-2014; 424 914 person-years). We performed sex-specific co-twin control analyses of dizygotic (N = 5,226) and monozygotic (MZ, N = 3,803) twin pairs using Cox regression models and explored reasons for any familial confounding using bivariate twin models. Results: The mean (SD) BMI was 22.6 (2.96), peak lifetime BMI 25.6 (2.61), and N = 614 had hip surgery due to OA. In cohort analyses, BMI was associated with hip OA for women and men (hazard ratio [HR] = 1.09, 95% confidence intervals [CIs] = 1.06 to 1.11 and HR = 1.08, 95% CI = 1.04 to 1.12, respectively). When adjusting for familial confounding within MZ twins, the association got stronger for women (HR = 1.19; 95% CI = 1.05 to 1.36) but weaker for men (HR = 0.93; 95% CI = 0.75 to 1.16). There was no genetic overlap between BMI and hip OA, yet the familial confounding in men provides suggestive evidence that the association could be explained by shared environmental factors. Conclusion: The association between BMI and hip OA may be explained by familial confounding for men. For women, there was no evidence for familial confounding, consistent with a causal association. See video abstract at, http://links.lww.com/EDE/B336