Introgression of the avian naked neck gene assisted by DNA fingerprints

Theoretical predictions suggest that DNA markers can be useful tools for genomic selection in gene introgression programmes. An experiment was carried out to evaluate the efficiency of using multi-locus DNA markers in an introgression programme designed to transfer the naked neck gene from a donor to a recipient chicken line. The donor line was a commercial egg layer chicken stock heterozygous at the naked neck locus (Na/na⁺), while the recipients were from a Cornish broiler line. These two lines differ markedly in their average body weight, a quantitative trait that can also represent the comprehensive differences between the genomes of the two lines involved. Three groups of naked neck BC₁ individuals were selected according to the following criteria: (i) low band-sharing with their donor grandsires evaluated by multi-locus DNA markers, (ii) high body weight at six weeks of age, and (iii) selection at random as a control group. Birds from each of these groups were mated at random to individuals from the heavier Cornish line to produce three groups of BC₂ individuals whose body weights were recorded weekly from three to seven weeks of age. Results indicated that BC₂ birds obtained from BC₁ parents selected for band-sharing levels and those selected for body weight, performed equally well at 4–7 weeks of age; both were 3.1–3.9% heavier than birds from the randomly selected group. The additional genome recovery of the heavier broiler line, obtained by DNA markers, was found to be in agreement with theoretically predicted values.     

Diallel analysis for sex-linked and maternal effects

Genetic models including sex-linked and maternal effects as well as autosomal gene effects are described. Monte Carlo simulations were conducted to compare efficiencies of estimation by minimum norm quadratic unbiased estimation (MINQUE) and restricted maximum likelihood (REML) methods. MINQUE(1), which has 1 for all prior values, has a similar efficiency to MINQUE(), which requires prior estimates of parameter values. MINQUE(1) has the advantage over REML of unbiased estimation and convenient computation. An adjusted unbiased prediction (AUP) method is developed for predicting random genetic effects. AUP is desirable for its easy computation and unbiasedness of both mean and variance of predictors. The jackknife procedure is appropriate for estimating the sampling variances of estimated variances (or covariances) and of predicted genetic effects. A t-test based on jackknife variances is applicable for detecting significance of variation. Worked examples from mice and silkworm data are given in order to demonstrate variance and covariance estimation and genetic effect prediction.     

Predicting badger sett numbers: evaluating methods in East Sussex

Abstract. One way in which a species' numbers may be estimated without direct counting is to predict their dispersion and density from more readily available habitat measures, such as landscape variables measured from maps or vegetation variables measured in the field. We compare the power of ordination and regression techniques for predicting badger ( Meles meles L.) numbers at a local scale, using a land class system, map-read landscape variables and field-derived vegetation variables. Sett density was used as a surrogate of badger density. Multiple linear regression using vegetation and landscape variables together gave the most accurate prediction of sett density, while ordination techniques were of lesser value. The addition of vegetation variables to landscape variables did not substantially improve the power of ordination. Outlier Sett Density was predicted more accurately, and by different variables, to Main Sett Density. The relationship between badger ecology and habitat variables that were useful in predicting sett density is discussed.     

Affinity chromatography—dependent selection （ACDS） of genomic DNA fragments bound specifically to bacterial synthesized Myc／Myn proteins

This paper describes an approach to seek for mouse c-Myc/Myn proteins-bound specific sequences among genomic DNA.cDNA fragment of myn gene was obtained through RT-PCR technique from RNA of NIH3T3 cells.DNA fragments encoding BR/HLH/LZ structure of Myc and Myn proteins were cloned in frame into pGEX-2T vector respectively.Fusion GST-Myc and GST-Myn synthesized in E.coli hosts showed affinity to CACGTG E-box DNA and subsequently interacted with genomic fragments prepared through whole-genome-PCR.A PCR-assisted procedure which combines protein-DNA interaction and affinity chromatography was designed to enrich Myc/Myn bound DNA.At least two genomic DNA fragments obtained exhibit specifical binding capacity to Myc/Myn complex but not to GST alone.Significance of the work and of the technique itself as well asidentification of the DNAs are discussed.     

Sequence homology and structure predictions of the creatine kinase isoenzymes

Comparisons of the protein sequences and gene structures of the known creatine kinase isoenzymes and other guanidino kinases revealed high homology and were used to determine the evolutionary relationships of the various guamidino kinases. A CK framework is defined, consisting of the most conserved sequence blocks, and diagnostic boxes are identified which are characteristic for anyone creatine kinase isoenzyme (e.g. for vertebrate B-CK) and which may serve to distinguish this isoenzyme from all others (e.g. from M-CKs and Mi-CKs). Comparison of the guanidino kinases by near-UV and far-UV circular dichroism further indicates pronounced conservation of secondary structure as well as of aromatic amino acids that are involved in catalysis.Abbreviations GuaK guanidino kinase - CK creatine kinase - B-and M-CK brain and muscle cytosolic CK isoenzyme - Mi-CK mitochondrial CK isoenzyme - ArgK arginine kinase - Cr creatine - PCr phosphorylcreatine - PArg phosphorylarginine     

Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis

Multilocation trials are often used to analyse the adaptability of genotypes in different environments and to find for each environment the genotype that is best adapted; i.e. that is highest yielding in that environment. For this purpose, it is of interest to obtain a reliable estimate of the mean yield of a cultivar in a given environment. This article compares two different statistical estimation procedures for this task: the Additive Main Effects and Multiplicative Interaction (AMMI) analysis and Best Linear Unbiased Prediction (BLUP). A modification of a cross validation procedure commonly used with AMMI is suggested for trials that are laid out as a randomized complete block design. The use of these procedure is exemplified using five faba bean datasets from German registration trails. BLUP was found to outperform AMMI in four of five faba bean datasets.