Genetical Research

Mapping interacting QTL for count phenotypes using hierarchical Poisson and binomial models: an application to reproductive traits in mice.

Publication Date: 2010 Mar 4 PMID: 20199696
Authors: Li, J. - Reynolds, R. - Pomp, D. - Allison, D. B. - Yi, N.
Journal: Genet Res

SummaryWe proposed hierarchical Poisson and binomial models for mapping multiple interacting quantitative trait loci (QTLs) for count traits in experimental crosses. We applied our methods to two counted reproductive traits, live fetuses (LF) and dead fetuses (DF) at 17 days gestation, in an F2 female mouse population. We treated observed number of corpora lutea (ovulation rate) as the baseline and the total trials in our Poisson and binomial models, respectively. We detected more than 10 QTLs for LF and DF, most having epistatic and pleiotropic effects. The epistatic effects were larger, involved more QTLs, and explained a larger proportion of phenotypic variance than the main effects. Our analyses revealed a complex network of multiple interacting QTLs for the reproductive traits, and increase our understanding of the genetic architecture of reproductive characters. The proposed statistical models and methods provide valuable tools for detecting multiple interacting QTLs for complex count phenotypes.

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Composite interval mapping to identify quantitative trait loci for point-mass mixture phenotypes.

Publication Date: 2010 Mar 3 PMID: 20196895
Authors: Taylor, S. L. - Pollard, K. S.
Journal: Genet Res

SummaryIncreasingly researchers are conducting quantitative trait locus (QTL) mapping in metabolomics and proteomics studies. These data often are distributed as a point-mass mixture, consisting of a spike at zero in combination with continuous non-negative measurements. Composite interval mapping (CIM) is a common method used to map QTL that has been developed only for normally distributed or binary data. Here we propose a two-part CIM method for identifying QTLs when the phenotype is distributed as a point-mass mixture. We compare our new method with existing normal and binary CIM methods through an analysis of metabolomics data from Arabidopsis thaliana. We then conduct a simulation study to further understand the power and error rate of our two-part CIM method relative to normal and binary CIM methods. Our results show that the two-part CIM has greater power and a lower false positive rate than the other methods when a continuous phenotype is measured with many zero observations.

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An optimal strategy for functional mapping of dynamic trait loci.

Publication Date: 2010 Mar 3 PMID: 20196894
Authors: Jin, T. - Li, J. - Guo, Y. - Zhou, X. - Yang, R. - Wu, R.
Journal: Genet Res

SummaryAs an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values.

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Genetic architecture of rainbow trout survival from egg to adult.

Publication Date: 2010 Mar 3 PMID: 20196893
Authors: Vehvilainen, H. - Kause, A. - Koskinen, H. - Paananen, T.
Journal: Genet Res

SummarySurvival from birth to a reproductive adult is a challenge that only robust individuals resistant to a variety of mortality factors will overcome. To assess whether survival traits share genetic architecture throughout the life cycle, we estimated genetic correlations for survival within fingerling stage, and across egg, fingerling and grow-out stages in farmed rainbow trout. Genetic parameters of survival at three life cycle stages were estimated for 249 166 individuals originating from ten year classes of a pedigreed population. Despite being an important fitness component, survival traits harboured significant but modest amount of genetic variation (h2=0.07-0.27). Weak associations between survival during egg-fry and fingerling periods, between early and late fingerling periods (rG=0.30) and generally low genetic correlations between fingerling and grow-out survival (mean rG=0.06) suggested that life-stage specific survival traits are best regarded as separate traits. However, in the sub-set of data with detailed time of death records, positive genetic correlations between early and late fingerling survival (rG=0.89) showed that during certain years the best genotypes in the early period were also among the best in the late period. That survival across fingerling period can be genetically the same, trait was indicated also by only slightly higher heritability (h2=0.15) estimated with the survival analysis of time to death during fingerling period compared to the analysis treating fingerling survival as a binary character (h2=0.11). The results imply that (1) inherited resistance against unknown mortality factors exists, but (2) ranking of genotypes changes across life stages.

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