A triosephosphate isomerase (TPI) mutant, Tpi1a-m6Neu, with approximately 57% residual enzyme activity in blood compared with wild-type was detected among offspring of triethylenemelamine-treated male mice. Homozygous mutants with about 13% residual enzyme activity were recovered in progeny of inter se matings of heterozygotes. The loss of TPI activity was evident both in blood and in other tissue extracts. Values for haematocrit, haemoglobin, number of red blood cells (RBC), mean corpuscular volume of RBC, mean corpuscular haemoglobin concentration and spleen weight show significant differences between wild-type animals and homozygous mutants. Sequence analysis revealed a substitution (c.A149G) in the Tpi1 gene. This mutation results in an Asp to Gly substitution at codon 49 in exon 2 at a highly conserved position located in the functional domain of the TPI protein which is responsible for the correct dimerization of the subunits. As a potential animal model, Tpi1a-m6Neu represents the only available TPI-deficient homozygous viable mouse mutation.

This paper calls attention to an overlooked logical difficulty that has impeded the directed mutation debate for over half a century. It further suggests that the random mutation hypothesis be regarded at present as a null hypothesis in evolutionary biology.

A linkage–linkage disequilibrium map that describes the pattern and extent of linkage dis-equilibrium (LD) decay with genomic distance has now emerged as a viable tool to unravel the genetic structure of population differentiation and fine-map genes for complex traits. The prerequisite for constructing such a map is the simultaneous estimation of the linkage and LD between different loci. Here, we develop a computational algorithm for simultaneously estimating the recombination fraction and LD in a natural outcrossing population with multilocus marker data, which are often estimated separately in most molecular genetic studies. The algorithm is founded on a commonly used progeny test with open-pollinated offspring sampled from a natural population. The information about LD is reflected in the co-segregation of alleles at different loci among parents in the population. Open mating of parents will reveal the genetic linkage of alleles during meiosis. The algorithm was constructed within the polynomial-based mixture framework and implemented with the Expectation–Maximization (EM) algorithm. The by-product of the derivation of this algorithm is the estimation of outcrossing rate, a parameter useful to explore the genetic diversity of the population. We performed computer simulation to investigate the influences of different sampling strategies and different values of parameters on parameter estimation. By providing a number of testable hypotheses about population genetic parameters, this algorithmic model will open a broad gateway to understand the genetic structure and dynamics of an outcrossing population under natural selection.

The value of a new crop species is usually judged by the overall performance of multiple traits. Therefore, in most quantitative trait locus (QTL) mapping experiments, researchers tend to collect phenotypic records for multiple traits. Some traits may vary continuously and others may vary in a discrete fashion. Although mapping QTLs jointly for multiple traits is more efficient than mapping QTLs separately for individual traits, the latter is still commonly practised in QTL mapping. This is primarily due to the lack of efficient statistical methods and computer software packages to implement the methods. Mapping multiple QTLs simultaneously in a single multivariate model has not been available, especially when categorical traits are involved. In the present study, we developed a Bayesian method to map QTLs of the entire genome for multiple traits with continuous, discrete or both types of phenotypic distribution. Instead of using the reversible jump Markov chain Monte Carlo (MCMC) for model selection, we adopt a parameter shrinkage approach to estimate the genetic effects of all marker intervals. We demonstrate the method by analysing a set of simulated data with both continuous and discrete traits. We also apply the method to mapping QTLs responsible for multiple disease resistances to the blast fungus of rice. A computer program written in SAS/IML that implements the method is freely available, on request, to academic researchers.

Susceptibility to scrapie is known to be associated with polymorphisms at the prion protein (PrP) gene, and this association is the basis of current selective programmes implemented to control scrapie in many countries. However, these programmes might have unintended consequences for other traits that might be associated with PrP genotype. The objective of this study was to investigate the relationship between PrP genotype and coat colour characteristics in two UK native sheep breeds valued for their distinctive coat colour patterns. Coat colour pattern, darkness and spotting and PrP genotype records were available for 11 674 Badgerfaced Welsh Mountain and 2338 Shetland sheep. The data were analysed with a log–linear model using maximum likelihood. Results showed a strong significant association of PrP genotype with coat colour pattern in Badgerfaced Welsh Mountain and Shetland sheep and with the presence of white spotting in Shetland sheep. Animals with the ARR/ARR genotype (the most scrapie resistant) had higher odds of having a light dorsum and a dark abdomen than the reverse pattern. The implication of these associations is that selection to increase resistance to scrapie based only on PrP genotype could result in change in morphological diversity and affect other associated traits such as fitness.

Dense marker genotypes allow the construction of the realized relationship matrix between individuals, with elements the realized proportion of the genome that is identical by descent (IBD) between pairs of individuals. In this paper, we demonstrate that by replacing the average relationship matrix derived from pedigree with the realized relationship matrix in best linear unbiased prediction (BLUP) of breeding values, the accuracy of the breeding values can be substantially increased, especially for individuals with no phenotype of their own. We further demonstrate that this method of predicting breeding values is exactly equivalent to the genomic selection methodology where the effects of quantitative trait loci (QTLs) contributing to variation in the trait are assumed to be normally distributed. The accuracy of breeding values predicted using the realized relationship matrix in the BLUP equations can be deterministically predicted for known family relationships, for example half sibs. The deterministic method uses the effective number of independently segregating loci controlling the phenotype that depends on the type of family relationship and the length of the genome. The accuracy of predicted breeding values depends on this number of effective loci, the family relationship and the number of phenotypic records. The deterministic prediction demonstrates that the accuracy of breeding values can approach unity if enough relatives are genotyped and phenotyped. For example, when 1000 full sibs per family were genotyped and phenotyped, and the heritability of the trait was 0·5, the reliability of predicted genomic breeding values (GEBVs) for individuals in the same full sib family without phenotypes was 0·82. These results were verified by simulation. A deterministic prediction was also derived for random mating populations, where the effective population size is the key parameter determining the effective number of independently segregating loci. If the effective population size is large, a very large number of individuals must be genotyped and phenotyped in order to accurately predict breeding values for unphenotyped individuals from the same population. If the heritability of the trait is 0·3, and Ne=1000, approximately 5750 individuals with genotypes and phenotypes are required in order to predict GEBVs of un-phenotyped individuals in the same population with an accuracy of 0·7.

In this study, we attempted a molecular characterization of the 5S rDNA in two closely related species of carcharhiniform sharks, Rhizoprionodon lalandii and Rhizoprionodon porosus, as well as a further comparative analysis of available data on lampreys, several fish groups and other vertebrates. Our data show that Rhizoprionodon sharks carry two 5S rDNA classes in their genomes: a short repeat class (termed class I) composed of ~185 bp repeats, and a large repeat class (termed class II) arrayed in ~465 bp units. These classes were differentiated by several base substitutions in the 5S coding region and by completely distinct non-transcribed spacers (NTS). In class II, both species showed a similar composition for both the gene coding region and the NTS region. In contrast, class I varied extensively both within and between the two shark species. A comparative analysis of 5S rRNA gene sequences of elasmobranchs and other vertebrates showed that class I is closely related to the bony fishes, whereas the class II gene formed a separate cartilaginous clade. The presence of two variant classes of 5S rDNA in sharks likely maintains the tendency for dual ribosomal classes observed in other fish species. The present data regarding the 5S rDNA organization provide insights into the dynamics and evolution of this multigene family in the fish genome, and they may also be useful in clarifying aspects of vertebrate genome evolution.

We compute an accurate approximation to the probability of fixation for a beneficial mutation in a population fluctuating with a stationary distribution of population size. The population dynamics are described by the theta-logistic model with environmental variance, assuming that the population size is large enough to ignore demographic variance. We show that stochastic fluctuations of population size reduce the probability of fixation. However, it is not the magnitude of the population fluctuations per se that creates this reduction. Only the environmental variance has a substantial effect on the probability of fixation. The strength of density dependence (or expected return time to equilibrium) and the functional form of density-regulation, given by the parameter θ in the theta-logistic model, have little effect on the fixation probability. Effective population size based on harmonic mean population size will therefore underestimate the expected fixation rate of beneficial mutations in fluctuating populations.