Note that although the likelihood ratio of S k c is lower than that of S k , since the null distributions differ S k c can sometimes give smaller P -values. Univariate, repeatability Re , RR, and full multivariate models were used to analyze the data simulated under the simulation models described above. A number of other methods for allowing analyses of multivariate data have been proposed. Analysis of the inheritance, selection and evolution of growth trajectories. To fit the RR model the full multivariate model is reparameterized.

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First, the QTL was modeled under a repeatability model with the same QTL effect same variance across the five time points simulation model A.

Multiple phenotype modeling in gene-mapping studies of quantitative traits: Multipoint quantitative-trait linkage analysis mcagregor general pedigrees. Longitudinal data analysis in pedigree studies. We also note that it is important to m15 either a polygenic or a QTL effect or both to evaluate the null distribution; in the univariate case the null distribution when neither effect is simulated appears to be cf.

For real data, suitable fixed effects e. These results are summarized in Table 7.

Furthermore, it is unclear how to keep the significance level at the desired level when there are multiple tests. Given that the trivariate data in these articles de A ndrade et al. Genetic linkage analysis in complex pedigrees.

Received Mar 30; Accepted Jul 7. No mqcgregor effects were simulated but note that a single polygenic effect was estimated in all analysis methods. For the univariate tests of multiple time points the maximum 2 ln LR test statistic from the five time points and from the mean of the five trait values was used statistic S uni ; also computed was a Bonferroni-corrected version, S uni b.


Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees

Genomewide significance can be dealt with when using the methods we describe here. While these allow the extraction of information from univariate data one trait measure per individualtechniques for QTL mapping when there are multiple trait measures are less well developed.

A low-degree macgrgeor may be adequate to model the change in variance over time but inadequate for approximating the covariance structure or vice versa. These increase in complexity from model A to model C.

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Some longitudinal traits will be relatively highly correlated across multiple measures of the same trait compared with nonlongitudinal multivariate measures e. Genetic analysis workshop This follows because there are two variance terms and these are on the boundary of the parameter space under the null.

The covariance function-based approach may have considerably more utility than the full multivariate model as it can reduce the number of parameters in the model. Macregor the case where the first-degree Macregor was compared with the Re model, the asymptotic result was validated by simulation Figure 1. This ad hoc approach has been shown to give results that are indistinguishable from those obtained from SOLAR which does not require the inverse of the IBD matrix in simplified univariate cases M acgregor and was hence used in all the simulations described here.

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An illustration of the application of the RR methodology to three-generation extended families and genome scan data are given in our GAW13 article M acgregor et al. While this is unlikely to be true for all but the most strongly related traits, this model may allow parameter estimation in cases in which there are limited amounts of data.

S uni b Bonferroni-corrected S uni. Nonetheless, even the first-degree RR is substantially better than the repeatability model. The covariance structure of such a model is.

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The procedure outlined above was used to determine the best-fitting model for the data. Support Center Support Center.

Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. Abstract There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index.

The simulations also ignore one of the benefits of the RR procedure compared with full multivariate and repeatability analysesnamely the ability of the RR method to analyze data with phenotypes measured at different ages in different individuals.

Analyses macgrgor these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. S uni univariate QTL mcgregor.