A full-sib intercross line (FSIL) is constructed in an outcrossing species by mating two parents and intercrossing their progeny to form a large intercross line. For given statistical power, a FSIL design requires only slightly more individuals than an F2 design derived from inbred line cross, but 6- to 10-fold fewer than a half-sib or full-sib design. Due to population-wide linkage disequilibrium, a FSIL is amenable to analysis by selective DNA pooling. In addition, a FSIL is maintained by continued intercrossing so that DNA samples and phenotypic information are accumulated across generations. Continued intercrossing also leads to map expansion and thus to increased mapping accuracy in the later generations. A FSIL can thus provide a bridge to positional cloning of quantitative trait loci (QTL) and marker-assisted selection in outcrossers; and is particularly effective in exploiting the QTL mapping potential of crosses between selection lines or phenotypically differentiated populations that differ in frequency, but are not at fixation, for alternative QTL alleles. In the course of the power analyses, it is shown that for F2 and FSIL designs, power is a function of Nd2 alone, where N is the total size of the mapping population and d is the standardized gene effect; while for half-sib and full-sib populations, power is a function of Nd2 and of the number of families included in the mapping population. This provides a convenient means of estimating power for a wide variety of mapping designs.

A mixture model approach is employed for the mapping of quantitative trait loci (QTL) for the situation where individuals, in an outbred population, are selectively genotyped. Maximum likelihood estimation of model parameters is obtained from an Expectation-Maximization (EM) algorithm facilitated by Monte Carlo sampling using a Gibbs sampler. All individuals with phenotypes, whether genotyped or not, are included in the analysis where both putative QTLs and missing marker genotypes are sampled conditional on known marker information and phenotype. A simulation of a half-sib family structure demonstrates that this mixture model approach will yield unbiased estimates of the allelic effects of a QTL affecting the trait on which selective genotyping is based. Unbiased estimates were also obtained for the QTL effect on a correlated trait provided both traits were analysed jointly in a bivariate model. The procedure is also compared with a standard linear model approach. The application of these methods is demonstrated for bovine chromosome six, the data arising from two Holstein–Friesian families selectively genotyped for protein yield in a daughter design.

Unconditionally deleterious mutations could be an important source of variation in quantitative traits. Deleterious mutations should be rare (segregating at low frequency in the population) and at least partially recessive. In this paper, I suggest that the contribution of rare, partially recessive alleles to quantitative trait variation can be assessed by comparing the relative magnitudes of two genetic variance components: the covariance of additive and homozygous dominance effects (Cad) and the additive genetic variance (Va). If genetic variation is due to rare recessives, then the ratio of Cad to Va should be equal to or greater than 1. In contrast, Cad/Va should be close to zero or even negative if variation is caused by alleles at intermediate frequencies. The ratio of Cad to Va can be estimated from phenotypic comparisons between inbred and outbred relatives, but such estimates are likely to be highly imprecise. Selection experiments provide an alternative estimator for Cad/Va, one with favourable statistical properties. When combined with other biometrical analyses, the ratio test can provide an incisive test of the deleterious mutation model.