This article is part of the supplement: Genetic Analysis Workshop 13: Analysis of Longitudinal Family Data for Complex Diseases and Related Risk Factors
Genome-wide linkage analysis of longitudinal phenotypes using σ2A random effects (SSARs) fitted by Gibbs sampling
1 Channing Laboratory, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
2 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA
3 Department of Epidemiology and Public Health and Institute of Genetics, University of Leicester, United Kingdom
BMC Genetics 2003, 4(Suppl 1):S12 doi:10.1186/1471-2156-4-S1-S12Published: 31 December 2003
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 (σ2A.time) were estimated to account for ~9.2% of the variance in the rate of change of SBP with age, while additive genetic effects (σ2A) 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.