Background: Inbreeding in seed orchards is expected to increase with the advancement of breeding cycles, which results in the delivery of crops with suboptimal genetic gain, reduced genetic diversity, and lower seed set. Here, a genetic distance-dependent method for clonal spatial deployment in seed orchards was developed and demonstrated, which reduced the inbreeding levels. The method's main evaluation parameter of inbreeding is the genetic distance among individuals and the deployment method used an improved adaptive parallel genetic algorithm (IAPGA) based on Python language. Using inbreeding-prone Chinese Mongolian pine breeding population material originating from a single natural population, the proposed method was compared to a traditional orchard design and a distance-based design; namely, complete randomized block (RCB) and optimum neighborhood (ONA) designs, respectively. Results: With the advancement of selective breeding cycles, group separation among orchard related individuals is expected to increase. Based on the genetic distance among individuals, the IAPGA design was superior in significantly reducing the inbreeding level as compared to the two existing designs, confirming its suitability to advanced-generation orchards where relatedness among parents is common. In the 1st, 2nd, and mixed generations clonal deployment schemes, the IAPGA design produced lower inbreeding with 87.22%, 81.49%, and 87.23% of RCB, and 92.78%, 91.30%, and 91.67% of ONA designs, respectively. Conclusions: The IAPGA clonal deployment proposed in this study has the obvious advantage of controlling inbreeding, and it is expected to be used in clonal deployment in seed orchards on a large-scale. Further studies are needed to focus on the actual states of pollen dispersal and mating in seed orchards, and more assumptions should be taken into account for the optimized deployment method.

ABSTRACT. Random amplified polymorphic DNA technology was used to analyze the genetic diversity of 25 salt-tolerant alfalfa varieties using 30 different primers. Results showed that the percentage of polymorphic loci between single-plant DNA was 81.52%, and that between mixed DNA of various varieties was 61.65%. Compared to the mixed DNA samples, single-plant DNA samples can better reveal the level of genetic variation among and between alfalfa varieties. The gene differentiation coefficients of 18 Chinese salt-tolerant alfalfa varieties and 7 American salt-tolerant alfalfa varieties were 0.271 and 0.152, respectively, showing that the exchange of genes between Chinese salttolerant alfalfa germplasms was more frequent than that of American germplasms. As a topical cross-pollinated plant, the genetic structure of biological populations of alfalfa was directly linked to its breeding system. According to the analysis of genetic distance (GD), 25 varieties can be divided into 9 groups, among which, the GD of Tumu No. 1 and Tumu No. 2 was the shortest (0.148), and the GD of Jieda No. 1 and Tumu was the longest (0.786). The analysis of genetic diversity 4439 ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 14 (2): 4438-4447 (2015) RAPD analysis of alfalfa salt-tolerant germplasms of salt-tolerant alfalfa germplasms provided a theoretical basis for the creation of an alfalfa salt-tolerant core germplasm repository and for the selection and breeding of new salt-tolerant varieties.

The maintenance of genetic diversity across seed orchard generations is an important management objective. Here, we used Pinus tabuliformis as a model to explore the extent of genetic diversity across the species’ breeding activities through their corresponding seed orchards generations. We utilized a large number of SSR markers selected from <i></i>Pinus tabuliformis<i></i> transcriptomic data, and then assessed the effect of marker number on genetic diversity and individuals’ genetic relationships across orchards’s generations. In total, we designed 125 simple sequence repeat (SSR) markers, from which 39 SSRs were polymorphic and used in the present study. The genetic diversity and genetic distance parameters tended to increase with thean increase ofin markerloci numbers and a stable trend was reached at 24 SSRs. The selected optimal 24 SSR markers were further used to assess the genetic diversity across seed orchards’s generations, and a decreasing trend was detected with the advancement of orchards’s generations. Genetic distance analysis indicated that individuals in the 2nd generation orchard was more closely related as compared to those of the 1st- and 1.5-generations. This study provided valuable information on the effect of selection and breeding on genetic diversity and highlighted its role for effective seed orchards management.

ABSTRACT. The purpose of this study was to identify genes and pathways for osteoarthritis (OA) diagnosis and therapy. We downloaded the gene expression profile of OA from Gene Expression Omnibus (GEO) database including 10 early OA, 9 late OA, and 5 normal control samples. Next, we screened differentially expressed genes (DEGs) between early-and late-stage OA samples comparing with healthy control samples. Then, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software was used to construct proteinprotein interaction (PPI) network, which was to predict the proteins that may interact with DEGs. The Gene Ontology (GO)-enrichment method was used to analyze the function of genes in the PPI networks. Meanwhile network module analysis was performed using Cytoscape. A total of 24 and 29 DEGs were identified for the early and late OA, respectively. TAC1 showed the highest degree in the PPI network. Functional annotation of the TAC1 network module indicated that this gene is associated with the G protein-coupled signal transduction pathway. In summary, TAC1, together with G protein-coupled receptors, appear to play a role in the biogenesis and progress of OA. Further analysis of this gene and pathway could therefore provide a potential target for the diagnosis and treatment of OA.

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