The effects of reproductive compensation on the population genetics of sex-linked recessive lethal mutations are investigated. Simple equations are presented which describe these effects, and so complement existing population genetic theory. More importantly, this type of mutation is responsible for several severe human genetic diseases such as Duchenne muscular dystrophy. It is argued that the applications of three modern reproductive technologies – effective family planning, in utero diagnosis with termination, and embryo sexing – will lead to reproductive compensation. The adoption of any of these technologies may rapidly elevate the frequencies of those mutations which are lethal in childhood. This increase is large, in the order of 33% upwards, and occurs rapidly over two to five generations. It also depends on the source of mutations, the effect being larger if most mutations are paternal. In utero diagnosis and/or embryo sexing increase the frequency of the mutation, but simultaneously decrease disease incidence by preventing the birth of affected offspring. In contrast, effective family planning may rapidly increase both mutation frequency and disease incidence.

We propose a method that minimizes the rate of inbreeding (ΔF) for small unselected populations with overlapping generations and several reproductive age classes. It minimizes the increase in coancestry of parents and optimizes the contribution of each selection candidate. The carrying capacity of the population is limited to a fixed number of animals per year. When survival rate equalled 100%, only animals from the oldest age class were selected, which maximized the number of parents per generation, slowed down the turnover of generations and minimized the increase of coancestry across sublines. However, the population became split into sublines separated by age classes, which substantially increased inbreeding within sublines. Sublines were prevented by a restriction of selecting at least one sire and one dam from the second-oldest age class, which resulted in an L times lower ΔF, where L equals the average generation interval of sires and dams. Minimum coancestry mating resulted in lower levels of inbreeding than random mating, but ΔF was approximately the same. For schemes where the oldest animals were selected, ΔF increased by 18–52% compared with the proposed method.

A method is proposed to infer genetic parameters within a cohort, using data from all individuals in an experiment. An application is the study of changes in additive genetic variance over generations, employing data from all generations. Inferences about the genetic variance in a given generation are based on its marginal posterior distribution, estimated via Markov chain Monte Carlo methods. As defined, the additive genetic variance within the group is directly related to the amount of selection response to be expected if parents are chosen within the group. Results from a simulated selection experiment are used to illustrate properties of the method. Four sets of data are analysed: directional selection with and without environmental trend, and random selection, with and without environmental trend. In all cases, posterior credibility intervals of size 95% assign relatively high density to values of the additive genetic variance and heritability in the neighbourhood of the true values. Properties and generalizations of the method are discussed.

A major obstacle to the positional cloning of quantitative trait loci (QTLs) lies in resolving genetic factors whose allelic effects are blurred by environmental and background genetic variation. We investigate a fine-mapping approach that combines the use of an interval-specific congenic strain with progeny testing of recombinants for markers flanking a QTL. We apply the approach to map a murine QTL with an approximately 20% effect on growth rate by progeny testing 39 recombinants in a 12 cM region of the X chromosome. We use a likelihood analysis in an attempt to maximize the information on QTL map location and effect. The major X-linked effect is mapped to an approximately 2 cM region flanked by markers about 5 cM apart, outside which LOD support for the QTL drops extremely steeply by about 80. Nearly unambiguous assignment of the QTL genotypic state is obtained for each recombinant. The resolution of individual recombinants in the region is therefore sufficiently high to facilitate the positional cloning of the locus, although progress has been hampered because the genomic region containing the QTL shows an exceptionally low level of polymorphism in comparison with recent studies.

Many studies of QTL locations record several different traits on the same population, but most analyses look at this information on a trait-by-trait basis. In this paper we show how the regression approach to QTL mapping of Haley & Knott (1992) may be extended to a multi-trait analysis via multivariate regression, easily programmed in statistical packages. A procedure for identifying QTL locations using forward selection and bootstrapping is proposed. The method is applied to examine the locations for QTLs for six yield characters (the number of fertile stems, the grain number of the main stem, the main stem grain weight, the single plant yield, the plot yield and the thousand grain weight) in a doubled haploid population of spring barley. Several chromosomal locations with effects on more than one trait are found. The method is also suitable for examining a single trait measured in different years or environments, and is used here to examine data on heading date, a highly heritable trait, and plot yield, a trait with moderate heritability and showing QTL–environment interactions.

Interval mapping by simple regression is a powerful method for the detection of quantitative trait loci (QTLs) in line crosses such as F2 populations. Due to the ease of computation of the regression approach, relatively complex models with multiple fixed effects, interactions between QTLs or between QTLs and fixed effects can easily be accommodated. However, polygenic effects, which are not targeted in QTL analysis, cannot be treated as random effects in a least squares analysis. In a cross between true inbred lines this is of no consequence, as the polygenic effect contributes just to the residual variance. In a cross between outbred lines, however, if a trait has high polygenic heritability, the additive polygenic effect has a large influence on variation in the population. Here we extend the fixed model for the regression interval mapping method to a mixed model using an animal model. This makes it possible to use not only the observations from progeny (e.g. F2), but also those from the parents (F1) to evaluate QTLs and polygenic effects. We show how the animal model using parental observations can be applied to an outbred cross and so increase the power and accuracy of QTL analysis. Three estimation methods, i.e. regression and an animal model either with or without parental observations, are applied to simulated data. The animal model using parental observations is shown to have advantages in estimating QTL position and additive genotypic value, especially when the polygenic heritability is large and the number of progeny per parent is small.

We examined the genetic architecture of four fitness-related traits (reproductive success, ovariole number, body size and early fecundity) in a panel of 98 Oregon-R × 2b3 recombinant inbred lines (RILs). Highly significant genetic variation was observed in this population for female, but not male, reproductive success. The cross-sex genetic correlation for reproductive success was 0·20, which is not significantly different from zero. There was significant genetic variation segregating in this cross for ovariole number, but not for body size or early fecundity. The RILs were genotyped for cytological insertion sites of roo transposable elements, yielding 76 informative markers with an average spacing of 3·2 cM. Quantitative trait loci (QTL) affecting female reproductive success and ovariole number were mapped using a composite interval mapping procedure. QTL for female reproductive success were located at the tip of the X chromosome between markers at cytological locations 1B and 3E; and on the left arm of chromosome 2 in the 30D–38A cytological region. Ovariole number QTL mapped to cytological intervals 62D–69D and 98A–98E, both on the third chromosome. The regions harbouring QTL for female reproductive success and ovariole number were also identified as QTL for longevity in previous studies with these lines.