Northeast Agricultural University

About: Northeast Agricultural University is a education organization based out in Harbin, China. It is known for research contribution in the topics: Gene & Population. The organization has 14428 authors who have published 9850 publications receiving 126705 citations. The organization is also known as: Dōngběi Nóngyè Dàxué.

Genome-wide association study for wool production traits in a Chinese Merino sheep population.

Abstract: Genome-wide association studies (GWAS) provide a powerful approach for identifying quantitative trait loci without prior knowledge of location or function. To identify loci associated with wool production traits, we performed a genome-wide association study on a total of 765 Chinese Merino sheep (JunKen type) genotyped with 50 K single nucleotide polymorphisms (SNPs). In the present study, five wool production traits were examined: fiber diameter, fiber diameter coefficient of variation, fineness dispersion, staple length and crimp. We detected 28 genome-wide significant SNPs for fiber diameter, fiber diameter coefficient of variation, fineness dispersion, and crimp trait in the Chinese Merino sheep. About 43% of the significant SNP markers were located within known or predicted genes, including YWHAZ, KRTCAP3, TSPEAR, PIK3R4, KIF16B, PTPN3, GPRC5A, DDX47, TCF9, TPTE2, EPHA5 and NBEA genes. Our results not only confirm the results of previous reports, but also provide a suite of novel SNP markers and candidate genes associated with wool traits. Our findings will be useful for exploring the genetic control of wool traits in sheep.

A Functional Allele of CsFUL1 Regulates Fruit Length through Repressing CsSUP and Inhibiting Auxin Transport in Cucumber.

Abstract: Fruit length is a prominent agricultural trait during cucumber (Cucumis sativus) domestication and diversifying selection; however, the regulatory mechanisms of fruit elongation remain elusive. We identified two alleles of the FRUITFULL (FUL)–like MADS-box gene CsFUL1 with 3393C/A Single Nucleotide Polymorphism variation among 150 cucumber lines. Whereas CsFUL1A was specifically enriched in the long-fruited East Asian type cucumbers (China and Japan), the CsFUL1C allele was randomly distributed in cucumber populations, including wild and semiwild cucumbers. CsFUL1A knockdown led to further fruit elongation in cucumber, whereas elevated expression of CsFUL1A resulted in significantly shorter fruits. No effect on fruit elongation was detected when CsFUL1C expression was modulated, suggesting that CsFUL1A is a gain-of-function allele in long-fruited cucumber that acts as a repressor during diversifying selection of East Asian cucumbers. Furthermore, CsFUL1A binds to the CArG-box in the promoter region of SUPERMAN, a regulator of cell division and expansion, to repress its expression. Additionally, CsFUL1A inhibits the expression of auxin transporters PIN-FORMED1 (PIN1) and PIN7, resulting in decreases in auxin accumulation in fruits. Together, our work identifies an agriculturally important allele and suggests a strategy for manipulating fruit length in cucumber breeding that involves modulation of CsFUL1A expression.

An improved genetic algorithm for numerical function optimization

Abstract: To avoid problems such as premature convergence and falling into a local optimum, this paper proposes an improved real-coded genetic algorithm (RCGA-rdn) to improve the performance in solving numerical function optimization. These problems are mainly caused by the poor search ability of the algorithm and the loss of population diversity. Therefore, to improve the search ability, the algorithm integrates three specially designed operators: ranking group selection (RGS), direction-based crossover (DBX) and normal mutation (NM). In contrast to the traditional strategy framework, RCGA-rdn introduces a new step called the replacement operation, which periodically performs a local initialization operation on the population to increase the population diversity. In this paper, comparisons with several advanced algorithms were performed on 21 complex constrained optimization problems and 10 high-dimensional unconstrained optimization problems to verify the effectiveness of RCGA-rdn. Based on the results, to further verify the feasibility of the algorithm, it was applied to a series of practical engineering optimization problems. The experimental results show that the proposed operations can effectively improve the performance of the algorithm. Compared with the other algorithms, the improved algorithm (RCGA-rdn) has a better search ability, faster convergence speed and can maintain a certain population diversity.