[Observation on the distribution of nerve fibers and neural cells morphology in Aspidogaster conchiola].

OBJECTIVE: To understand the distribution of nerve fibers and the types of neural cells in Aspidogaster conchiola. METHODS: Whole worms were subjected to silver staining, histochemical staining and hematoxylin-eosin (HE) staining, and the nervous systems of the worms were observed. RESULTS: There were 3 types of neural cells in the worm head near the cerebral ganglion, including unipolar, bipolar and multipolar neurons, which were divided into 7 types according to the morphology. There was a nerve network on the surface of pharynx and intestinal tract, as well as the reproductive organ, including testis, ovary, lower uterus and penis sac. The nerve network was consisted of circular and longitudinal nerve fibers, and the structure of the nerve network around the mouth was similar to central nerve. CONCLUSIONS: The structure of the A. conchiola central nervous system is very complicated, and the neural networks may be associated with the physiologic activity of the worm. Different neural cells may have diverse functions.

Heterozygosity for genes influencing a quantitative trait.

Choosing families to sample for a quantitative trait locus mapping experiment is a critical component of experimental design because only heterozygous families contribute information to the analysis. Additive genetic variance of a paternal half-sib family can be partitioned into two parts: a variance component of maternal source that is constant across different families and a variance component of paternal source that is defined as an index of heterozygosity of a sire. This index is shown to be an upper limit of variance among marker genotypes of a half-sib family and can be used to identify highly heterozygous sires, thus improving the power of detecting QTL in detection studies. Simulated progeny phenotypic data were used to estimate sire's heterozygosity index via an ANOVA method, and accuracy of the estimation was evaluated with the correlation coefficient between the true and estimated index summarized both as the correlation and by the correct ranking of results as measured by the ratio of the true average heterozygosity index of experimentally selected parents to average heterozygosity of all sires. Positive but small correlation can be achieved in the estimation of a sire's heterozygosity when based on the daughters' phenotypic data, and accuracy was improved when progeny-tested sons were used to estimate their grandsire's heterozygosity index, depending on the genetic model of a trait and the size and structure of families.

A note on algorithms for genotype and allele elimination in complex pedigrees with incomplete genotype data.

Elimination of genotypes or alleles for each individual or meiosis, which are inconsistent with observed genotypes, is a component of various genetic analyses of complex pedigrees. Computational efficiency of the elimination algorithm is critical in some applications such as genotype sampling via descent graph Markov chains. We present an allele elimination algorithm and two genotype elimination algorithms for complex pedigrees with incomplete genotype data. We modify all three algorithms to incorporate inheritance restrictions imposed by a complete or incomplete descent graph such that every inconsistent complete descent graph is detected in any pedigree, and every inconsistent incomplete descent graph is detected in any pedigree without loops with the genotype elimination algorithms. Allele elimination requires less CPU time and memory, but does not always eliminate all inconsistent alleles, even in pedigrees without loops. The first genotype algorithm produces genotype lists for each individual, which are identical to those obtained from the Lange-Goradia algorithm, but exploits the half-sib structure of some populations and reduces CPU time. The second genotype elimination algorithm deletes more inconsistent genotypes in pedigrees with loops and detects more illegal, incomplete descent graphs in such pedigrees.

Estimation of additive, dominance and epistatic variance components using finite locus models implemented with a single-site Gibbs and a descent graph sampler.

In a previous contribution, we implemented a finite locus model (FLM) for estimating additive and dominance genetic variances via a Bayesian method and a single-site Gibbs sampler. We observed a dependency of dominance variance estimates on locus number in the analysis FLM. Here, we extended the FLM to include two-locus epistasis, and implemented the analysis with two genotype samplers (Gibbs and descent graph) and three different priors for genetic effects (uniform and variable across loci, uniform and constant across loci, and normal). Phenotypic data were simulated for two pedigrees with 6300 and 12,300 individuals in closed populations, using several different, non-additive genetic models. Replications of these data were analysed with FLMs differing in the number of loci. Simulation results indicate that the dependency of non-additive genetic variance estimates on locus number persisted in all implementation strategies we investigated. However, this dependency was considerably diminished with normal priors for genetic effects as compared with uniform priors (constant or variable across loci). Descent graph sampling of genotypes modestly improved variance components estimation compared with Gibbs sampling. Moreover, a larger pedigree produced considerably better variance components estimation, suggesting this dependency might originate from data insufficiency. As the FLM represents an appealing alternative to the infinitesimal model for genetic parameter estimation and for inclusion of polygenic background variation in QTL mapping analyses, further improvements are warranted and might be achieved via improvement of the sampler or treatment of the number of loci as an unknown.

Haplotype construction of sires with progeny genotypes based on an exact likelihood.

A maximum likelihood method is presented that can be used to construct parental haplotypes based on their progeny genotypes. The exact error rate and choice of family size in haplotype construction were evaluated through mathematical expressions and numerical examples. Numerical results suggest that, if two markers are tightly linked (< or = 10 cM) and each has intermediate allele frequencies, a difference of one between progeny receiving parental and recombinant gametes is sufficient for constructing sire linkage phase; a difference of two or more progeny is required with two markers 30 cM apart. When each of two adjacent markers has two alleles with equal allelic frequencies, genotyping 10 and 50 progeny are needed to achieve a power of 0.85 for constructing a sire linkage phase of two tightly (10 cM) and moderately tightly linked (30 cM) markers, respectively. The family size is reduced by approximately half when both markers have three alleles with equal frequencies. Results suggest that, when an experiment requiring haplotype determination of a parent is being designed, researches should choose the appropriate threshold and family size in the context of marker allelic frequencies and recombination fractions.

A two-stage half-sib design for mapping quantitative trait loci in food animals.

Daughter and granddaughter half-sib designs for mapping quantitative trait loci were modified to increase experimental power. This new design includes a two-stage procedure, in contrast to conventional one-step half-sib designs. In stage 1, a few progeny of each sire are genotyped for marker loci. Based on the analyses of stage 1 data, some sires are chosen to continue genotyping more progeny for stage 2. When multiple chromosomes are under investigation, chromosomes and sires for stage 2 are selected based on the analysis of stage 1 data. Sire selection results in increased frequency of heterozygous genotypes of interest in stage 2 if the markers are linked to those genes. Chromosome selection can increase the proportion of chromosomes with segregating quantitative trait loci in stage 2 if not all of the chromosomes evaluated in stage 1 have segregating quantitative trait loci. Numerical results indicated that two-stage half-sib designs are generally more powerful than conventional designs when 1) the noncentrality parameter is moderate or larger, 2) larger quantitative trait loci are mapped using tightly linked markers in larger families, and 3) variation is large in numbers and sizes of segregating quantitative trait loci among the chromosomes evaluated in stage 1.