By means of complex segregation analysis we studied the inheritance of litter size in two large pedigrees of captive-bred colonies of the Brazilian grass mouse Akodon cursor. Genetic analysis has revealed a highly significant influence of genetic factors on the variation of litter size (heritability, h2, was estimated as 0.44). The inheritance followed the classical polygene model: neither the major-gene model nor the polygene with unequal contribution model described the data significantly better.

Sponsored by: The Genetics Society, Promega (UK) Ltd and B&K Universal Group Ltd

The properties of alleles at quantitative trait loci (QTLs) contributing to variation in lifespan should be described to determine the mechanisms of evolution of life length and to predict its future changes. Previously, we and others conducted genome-wide screens for QTLs that segregate among one panel of recombinant inbred lines (RILs) using a dense molecular marker map. In non-stressful conditions, QTLs effecting the lifespans of virgin females and males were frequently sex specific. In an unrelated panel of RILs, the effects of QTLs in flies maintained in cages with mixed sexes were similar in both sexes. Here, we re-measured the lifespans of the former panel of RILs in cages with mixed sex cohorts. Lifespan declined owing to mating. The amount of decline correlated between sexes within lines. QTLs mapping to the intervals 15A–19C, 50B–57C, 63A–65A, and 96F–99B had similar effects on the lifespans of both males and females. These QTLs have previously been detected in virgin flies surveys and had sex- and/or environment-specific effects.

Founder-origin probability methods are used to trace specific chromosomal segments in individual offspring. A haplotypic method was developed for calculating founder-origin probabilities in three-generation outbred pedigrees suited to quantitative trait locus (QTL) analysis. Estimators for expected founder-origin proportions were derived for a linkage group segment, an entire linkage group and a complete haplotype. If the founders are truly outbred, the haplotypic method gives a close approximation when compared with the Haley et al. (1994) method that simultaneously uses all marker information for QTL analysis, and it is less computationally demanding. The chief limitation of the haplotypic method is that some information in two-allele intercross marker-type configurations is ignored. Informativeness of marker arrays is discussed in the framework of founder-origin probabilities and proportions. The haplotypic method can be extended to more complex pedigrees with additional generations.

Accurate and rapid methods for the detection of quantitative trait loci (QTLs) and evaluation of consequent allelic effects are required to implement marker-assisted selection in outbred populations. In this study, we present a simple deterministic method for estimating identity-by-descent (IBD) coefficients in full- and half-sib families that can be used for the detection of QTLs via a variance-component approach. In a simulated dataset, IBD coefficients among sibs estimated by the simple deterministic and Markov chain Monte Carlo (MCMC) methods with three or four alleles at each marker locus exhibited a correlation of greater than 0·99. This high correlation was also found in QTL analyses of data from an outbred pig population. Variance component analysis used both the simple deterministic and MCMC methods to estimate IBD coefficients. Both procedures detected a QTL at the same position and gave similar test statistics and heritabilities. The MCMC method, however, required much longer computation than the simple method. The conversion of estimated QTL genotypic effects into allelic effects for use in marker-assisted selection is also demonstrated.

Finite polygenic models (FPM) might be an alternative to the infinitesimal model (TIM) for the genetic evaluation of pedigreed multiple-generation populations for multiple quantitative traits. I present a general flexible Bayesian method that includes the number of genes in the FPM as an additional random variable. Markov-chain Monte Carlo techniques such as Gibbs sampling and the reversible jump sampler are used for implementation. Sampling of genotypes of all genes in the FPM is done via the use of segregation indicators. A broad range of FPM models, some combined with TIM, are empirically tested for the estimation of variance components and the number of genes in the FPM. Four simulation scenarios were studied, including genetic models with 5 or 50 additive independent diallelic genes affecting the traits, and random selection or selection on one of the traits was performed. The results in this study were based on ten replicates per simulation scenario. In the case of random selection, uniform priors on additive gene effects led to posterior mean estimates of genetic variance that were positively correlated with the number of genes fitted in the FPM. In the case of trait selection, assuming normal priors on gene effects also led to genetic variance estimates for the selected trait that were negatively correlated with the number of genes in the FPM. This negative correlation was not observed for the unselected trait. Treating the number of genes in the FPM as random revealed a positive correlation between prior and posterior mean estimates of this number, but the prior hardly affected the posterior estimates of genetic variance. Posterior inferences about the number of genes should be considered to be indicative where trait selection seems to improve the power of distinguishing between TIM and FPM. Based on the results of this study, I suggest not replacing TIM by the FPM, but combining TIM and FPM with the number of genes treated as random, to facilitate a highly flexible and thereby robust method for variance component estimation in pedigreed populations. Further study is required to explore the full potential of these models under different genetic model assumptions.