Deakin University
Browse
- No file added yet -

Bivariate variance-component analysis, with application to systolic blood pressure and total cholesterol levels in the Framingham Heart Study

Download (315.98 kB)
conference contribution
posted on 2002-01-01, 00:00 authored by Jisheng Cui, L Sheffield
Background :
The correlations between systolic blood pressure (SBP) and total cholesterol levels (CHOL) might result from genetic or environmental factors that determine variation in the phenotypes and are shared by family members. Based on 330 nuclear families in the Framingham Heart Study, we used a multivariate normal model, implemented in the software FISHER, to estimate genetic and shared environmental components of variation and genetic and shared environmental correlation between the phenotypes. The natural logarithm of the phenotypes measured at the last visit in both Cohort 1 and 2 was used in the analyses. The antihypertensive treatment effect was corrected before adjustment of the systolic blood pressure for age, sex, and cohort.
Results :
The univariate correlation coefficient was statistically significant for sibling pairs and parent-offspring pairs, but not significant for spouse pairs. In the bivariate analysis, the cross-trait correlation coefficients were not statistically significant for all relative pairs. The shared environmental correlation was statistically significant, but the genetic correlation was not significant.
Conclusion :
There is no significant evidence for a close genetic correlation between systolic blood pressure and total cholesterol levels. However, some shared environmental factors may determine the variation of both phenotypes.

History

Pagination

1 - 5

Location

New Orleans, La.

Open access

  • Yes

Start date

2002-11-11

End date

2002-11-14

ISSN

1471-2156

Language

eng

Notes

Appears in BMC Genetics 2003, 4(Suppl 1):S81

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2003, The Authors

Title of proceedings

GAW13 2002 : Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors