Genetic analysis of phenotypes derived from longitudinal data: Presentation Group 1 of Genetic Analysis Workshop 13

Abstract

The participants of Presentation Group 1 used the GAW13 data to derive new phenotypes, which were then analyzed for linkage and, in one case, for association to the genetic markers. Since the trait measurements ranged over longer time periods, the participants looked at the time dependence of particular traits in addition to the trait itself. The phenotypes analyzed with the Framingham data can be roughly divided into 1) body weight-related traits, which also include a type 2 diabetes progression trait, and 2) traits related to systolic blood pressure. Both trait classes are associated with metabolic syndrome. For traits related to body weight, linkage was consistently identified by at least two participating groups to genetic regions on chromosomes 4, 8, 11, and 18. For systolic blood pressure, or its derivatives, at least two groups obtained linkage for regions on chromosomes 4, 6, 8, 11, 14, 16, and 19. Five of the 13 participating groups focused on the simulated data. Due to the rather sparse grid of microsatellite markers, an association analysis for several traits was not successful. Linkage analysis of hypertension and body mass index using LODs and heterogeneity LODs (HLODs) had low power. For the glucose phenotype, a combination of random coefficient regression models and variance component linkage analysis turned out to be strikingly powerful in the identification of a trait locus simulated on chromosome 5. Haseman-Elston regression methods, applied to the same phenotype, had low power, but the above-mentioned chromosome 5 locus was not included in this analysis.

The sumLINK statistic for genetic linkage analysis in the presence of heterogeneity

“…Others who analyzed these data reported that the simulated obesity-related genes, particularly those genes affecting change over time, were very difficult to find. [Strauch, et al 2003; Yoo, et al 2003] The data creators intentionally made many of the genes challenging and perhaps even impossible to find. [Daw, et al 2003] Although the power was low, the sumLINK and sumLOD statistics consistently outperformed the HLOD in identifying the minor genes.…”

Flexible Semiparametric Analysis of Longitudinal Genetic Studies by Reduced Rank Smoothing

“…However, this approach ignores rich information in the longitudinal structure and may not detect genes affecting the time varying features of a trait. Strauch et al (2003) reviewed several two-step methods: the first step is either to take the average of trait measurements on a subject or to fit a longitudinal model without consideration of genetic markers or family structures; the second step is to perform genetic analysis on one or more summary statistics derived from the first step. This method may be improved by a joint approach that fits longitudinal and genetic parameters simultaneously.…”

Heritability of blood pressure through latent curve trajectories in families from the Gubbio population study

“…Heritability of BP traits has been shown to be age-dependent too [6,12]. Only a few studies deal with BP longitudinal heritability analysis [12,[23][24][25][26][27][28][29][30][31][32][33][34]; the family dataset of these studies is mainly derived from the Framingham Heart Study. The largest numbers of studies dealing with BP heritability estimates are based on cross-sectional family studies.…”