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EN
PIT1 was chosen as a candidate gene to investigate its associations with growth, meat quality and carcass composition traits in the pig. PIT1 is known as the pituitary-specific activator of the growth hormone in several mammals. Furthermore, PIT1 is a positive regulatory factor of prolactin and thyroid-stimulating hormone b. PIT1 is a member of the POU-domain family of genes and is located on porcine chromosome 13. Two informative three-generation families of the University of Hohenheim were used for the presented investigations. The families were based on crosses of the European Wild boar (W) ? Pietrain (P) and Meishan (M) ? Pietrain (P). Each family included 310 F2 animals. A RsaI (PCR) RFLP described by YU et al. (1994) was used for genotyping the animals. Altogether over 50 parameters of growth, meat quality, carcass composition and stress susceptibility were evaluated concerning their associations with RsaI PCR-RFLP. The statistical model of association analyses was used including fixed effects of sex, family, PIT1 genotypes and covariate age at slaughter. Taking the significance level of p < 0.05 as the basis, fourteen traits of growth and carcass composition were associated with PIT1 genotypes in family W ? P. Results from this study suggest that there are contributions of PIT1 gene to variations in the analysed performance traits in pigs. The influence of PIT1 genotypes could not be confirmed under the supposition of a genome-wide test limit.
EN
Wheat head blight caused mainly by Fusarium graminearum, is an important wheat disease, causing yield and quality losses. The breeding of resistant varieties is the key measure to control this disease, but the conventional breeding method is of low efficiency. The marker-assisted selection (MAS) can significantly improve the breeding efficiency. In this study, four RAPD (randomly amplified polymorphic DNA) markers linked to FHB resistance were obtained and one was cloned and sequenced. F7 recombinant inbred lines (RILs) were derived from the F1 of the cross Ning894037 (resistant)/Alondra (susceptible) by the single-seed descent method. Scab resistance of F7 RILs was evaluated in the greenhouse by injecting conidiospores into a central floret. Scab symptoms were evaluated on the 21st day after inoculation. Disease severity was assessed as the percentage of infected spikelets/spike. The F7 RIL population displayed a normal distribution, transgressive segregation and significant variation for FHB severity. DNA from resistant and susceptible parents was analyzed with 520 RAPD primers. Four markers (S1384-640,S1360-600, S1319-350,S1319-820) linked to FHB resistance were obtained. DNA of S1384-640 was recovered, subjected to re-amplification by using S1384 primer and the same protocol as for RAPD analysis and identified the rightness. The PCR product of S1384-640 was ligated into the pUCm-T vector, and cloned into fresh competent cells of Escherichia coli strain DH5 RAPD anlysis showed that the inserts of the recombinant plasmids were DNA of S1384-640. The sequencing result showed that the cloned fragment was 648 bp.
EN
Current information on barley resistance genes available from scientific papers and on-line databases is summarised. The recent literature contains information on 107 major resistance genes (R genes) against fungal pathogens (excluding powdery mildew), pathogenic viruses and aphids identified in Hordeum vulgare accessions. The highest number of resistance genes was identified against Puccinia hordei, Rhynchosporium secalis, and the viruses BaYMV and BaMMV, with 17, 14 and 13 genes respectively. There is still a lot of confusion regarding symbols for R genes against powdery mildew. Among the 23 loci described to date, two regions Mla and Mlo comprise approximately 31 and 25 alleles. Over 50 R genes have already been localised and over 30 mapped on 7 barley chromosomes. Four barley R genes have been cloned recently: Mlo, Rpg1, Mla1 and Mla6, and their structures (sequences) are available. The paper presents a catalogue of barley resistance gene symbols, their chromosomal location and the list of available DNA markers useful in characterising cultivars and breeding accessions.
EN
Extensive genetic variations of low-molecular-weight glutenin subunits (LMW-GS) and their coding genes were found in the wild diploid A- and D-genome donors of common wheat. In this study, we reported the isolation and characterization of 8 novel LMW-GS genes from Ae.longissima Schweinf. & Muschl., a species of the section Sitopsis of the genus Aegilops, which is closely related to the B genome of common wheat. Based on the N-terminal domain sequences, the 8 genes were divided into 3 groups. A consensus alignment of the extremely conserved domains with known gene groups and the subsequent cluster analysis showed that 2 out of the 3 groups of LMW-GS genes were closely related to those from the B genome, and the remaining was related to those from A and D genomes of wheat and Ae. tauschii. Using 3 sets of gene-group-specific primers, PCRs in diploid, tetraploid and hexaploid wheats and Ae. tauschii failed to obtain the expected products, indicating that the 3 groups of LMW-GS genes obtained in this study were new members of LMW-GS multi-gene families. These results suggested that the Sitopsis species of the genus Aegilops with novel gene variations could be used as valuable gene resources of LMW-GS. The 3 sets of group-specific primers could be utilized as molecular markers to investigate the introgression of novel alien LMW-GS genes from Ae. longissima into wheat.
EN
This paper described a method for predicting additive effects of a cluster of tightly linked QTLs for outbred populations of animals in the situation where the QTLs are located on a chromosome segment surrounded by multiple linked DNA markers. We present a mixed model method for best linear unbiased prediction (conditional to the marker data) of the additive effects of the QTL-cluster and of the remaining QTLs unlinked to the marker linkage group. This method takes into consideration the identity-by-descent proportion (IBDP) for the particular chromosomal segment, in contrast to some other methods which use IBD probabilities at one specific location. In this method, fully informative data on different flanking markers is used to calculate the values of the expectations of the IBDPs (EIBDPs) between gametes for animals to be evaluated. Then the expected values are used as the elements of the gametic relationship matrix required in the best linear unbiased prediction. Giving a small numerical example, we illustrate how the present method can be used for the prediction of the QTL-cluster effects and for genetic evaluation of animals in outbred populations. A computational strategy is discussed on the basis of the calculation of the EIBDPs and the inverted gametic relationship matrix in complex pedigrees.
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