Academic journal article Genetics

Mapping Quantitative Trait Loci from a Single-Tail Sample of the Phenotype Distribution Including Survival Data

Academic journal article Genetics

Mapping Quantitative Trait Loci from a Single-Tail Sample of the Phenotype Distribution Including Survival Data

Article excerpt

ABSTRACT

A new effective Bayesian quantitative trait locus (QTL) mapping approach for the analysis of single-tail selected samples of the phenotype distribution is presented. The approach extends the affected-only tests to single-tail sampling with quantitative traits such as the log-normal survival time or censored/selected traits. A great benefit of the approach is that it enables the utilization of multiple-QTL models, is easy to incorporate into different data designs (experimental and outbred populations), and can potentially be extended to epistatic models. In inbred lines, the method exploits the fact that the parental mating type and the linkage phases (haplotypes) are known by definition. In outbred populations, two-generation data are needed, for example, selected offspring and one of the parents (the sires) in breeding material. The idea is to statistically (computationally) generate a fully complementary, maximally dissimilar, observation for each offspring in the sample. Bayesian data augmentation is then used to sample the space of possible trait values for the pseudoobservations. The benefits of the approach are illustrated using simulated data sets and a real data set on the survival of F^sub 2^ mice following infection with Listeria monocytogenes.

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QUANTITATIVE trait locus (QTL) mapping methods often assume that the trait, conditionally on the effects of the QTL, follows a normal distribution. However, nonrandom missing data patterns resulting from single-tail sampling may violate this assumption. The target in single-tail sampling is to increase the expected genotype-phenotype correlation of a sample with respect to the original population parameters. By sampling (ascertaining) individuals from the right tail of the phenotype distribution, the genotype frequencies for QTL with positive phenotype effects are potentially enriched. Similarly, sampling individuals from the left tail of the phenotype distribution can increase our chances to find QTL with negative effects. Single-tail sampling may also arise from censoring or if a quantitative trait exhibits measurable values only for a portion of the individuals, i.e., there is a spike in thephenotype distribution (Broman 2003). However, due to single-tail sampling, the phenotypic variation of a sample may becometoo small for standardQTLmapping methods to work properly, i.e., the signal is totally masked by the error. Therefore current approaches toQTLmapping of data resulting from singletail sampling of the phenotype distribution consider the deviation of the allele- (or genotype-) frequency distribution at the marker loci from their Mendelian expectation, use logistic regression-based analysis strategies, or combine both of these approaches (Henshall and Goddard 1999; Beasley et al. 2004; Tenesaet al. 2005). Alternatively one can apply nonparametric/semiparametric methods, rank-based statistical procedures, or a robust mixture model to analyze such data (Kruglyak and Lander 1995; Zou et al. 2002, 2003; Broman 2003; FeenstraandSkovgaard2004).Adisadvantage of these approaches is that a single-QTL model is implicitly assumed, since only a single chromosomal position is tested at a time.

As stated in Luo et al. (2005), the viability (survival) of an individual can be simply defined as a binary phenotype indicating whether an individual has survived (y = 1) or not (y = 0). For continuous survival (or failure) time data, such as time to tumor or time to death (measured in logarithmic scale), the single-tail sampling approach can be considered (Broman 2003). Alternatively, methods exist for survival phenotypes (Diao et al. 2004; Moreno et al. 2005). In controlled crosses, several methods have been designed specially to map viability loci, the gene positions that have an influence on the fitness or the survival of an individual (e.g., Vogl and Xu 2000; Luo and Xu 2003; Luo et al. 2005; Nixon 2006). …

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