• 한국균학회소식:학술대회논문집
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• 2015.05a
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• pp.41-41
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• 2015

BSA-seq technologies, combined Bulked Segregant Analysis (BSA) and Next-Generation Sequencing (NGS), are making it faster and more efficient to establish the association of agronomic traits with molecular markers or candidate genes, which is the requirement for marker-assisted selection in molecular breeding. Clubroot disease, caused by Plasmodiophora brassicae, is a serious threat to Brassica crops. Even we have breed new clubroot resistant varieties of Chinese cabbage (B. rapa ssp. pekinesis), the underlying genetic mechanism is unclear. In this study, an $F_2$ population of 340 plants were inoculated with P. brassicae from Xinye (Pathotype 2 on the differentials of Williams). Resistance phenotype segregation ratio for the populations fit a 3:1 (R:S) segregation model, consistent with a single dominant gene model. Super-BSA, using re-sequencing the parents, extremely R and S DNA pools with each 50 plants, revealed 3 potential candidate regions on the chromosome A03, with the most significant region falling between 24.30 Mb and 24.75 Mb. A linkage map with 31 markers in this region was constructed with several closely linked markers identified. A Major QTL for clubroot resistance, CRq, which was identified with the peak LOD score at 169.3, explaining 89.9% of the phenotypic variation. And we developed a new co-segregated InDel marker BrQ-2. Joint BSA-seq and traditional QTL analysis delimited CRq to an 250 kb genomic region, where four TIR-NBS-LRR genes (Bra019409, Bra019410, Bra019412 and Bra019413) clustered. The CR gene CRq and closely linked markers will be highly useful for breeding new resistant Chinese cabbage cultivars.

• Journal of The Korean Society of Grassland and Forage Science
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• v.38 no.2
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• pp.106-111
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• 2018

This study was conducted to investigate the relationship between changes of rumen microflora and bloat in Jersey cow. Jersey cows (control age: 42 months, control weight: 558kg; treatment age: 29 months, treatment weight 507kg) were fed on the basis of dairy feeding management at dairy science division in National Institute of Animal Science. The change of microbial population in rumen was analyzed by using next generation sequencing (NGS) technologies due to metabolic disease. The diversity of Ruminococcus bromii, Bifidobacterium pseudolongum, Bifidobacterium merycicum and Butyrivibrio fibrisolvens known as major starch fermenting bacteria was increased more than 36-fold in bloated Jersey, while cellulolytic bacteria community such as Fibrobacter succinogenes, Ruminococcus albus and Ruminococcus flavefaciens was increased more than 12-fold in non-bloated Jersey. The proportion of bacteroidetes and firmicutes was 33.4% and 39.6% in non-bloated Jersey's rumen, while bacteroidetes and firmicutes were 24.9% and 55.1% in bloated Jersey's. In conclusion, the change of rumen microbial community, in particular the increase in starch fermenting bacteria, might have an effect to occur the bloat in Jersey cow.

• Journal of Life Science
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• v.28 no.11
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• pp.1255-1261
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• 2018

Whole genome analysis have been made possible with the development of DNA sequencing technologies and discovery of many single nucleotide polymorphisms (SNPs). Large number of SNP can be analyzed with SNP chips, since SNPs of human as well as livestock genomes are available. Among the various missing nucleotide imputation programs, Minimac3 software is suggested to be highly accurate, with a simplified workflow and relatively fast. In the present study, we used Minimac3 program to perform genomic missing value substitution 1,226 animals 770K SNP chip and imputing missing SNPs with next generation sequencing data from 311 animals. The accuracy on each chromosome was about 94~96%, and individual sample accuracy was about 92~98%. After imputation of the genotypes, SNPs with R Square ($R^2$) values for three conditions were 0.4, 0.6, and 0.8 and the percentage of SNPs were 91%, 84%, and 70% respectively. The differences in the Minor Allele Frequency gave $R^2$ values corresponding to seven intervals (0, 0.025), (0.025, 0.05), (0.05, 0.1), (0.1, 0.2), (0.2, 0.3). (0.3, 0.4) and (0.4, 0.5) of 64~88%. The total analysis time was about 12 hr. In future SNP chip studies, as the size and complexity of the genomic datasets increase, we expect that genomic imputation using Minimac3 can improve the reliability of chip data for Hanwoo discrimination.