• Genomics & Informatics
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• v.18 no.2
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• pp.14.1-14.11
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• 2020

Phenotyping is a major issue for wheat agriculture to meet the challenges of adaptation of wheat varieties to climate change and chemical input reduction in crop. The need to improve the reuse of observations and experimental data has led to the creation of reference ontologies to standardize descriptions of phenotypes and to facilitate their comparison. The scientific literature is largely under-exploited, although extremely rich in phenotype descriptions associated with cultivars and genetic information. In this paper we propose the Wheat Trait Ontology (WTO) that is suitable for the extraction and management of scientific information from scientific papers, and its combination with data from genomic and experimental databases. We describe the principles of WTO construction and show examples of WTO use for the extraction and management of phenotype descriptions obtained from scientific documents.

• Genomics & Informatics
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• v.14 no.4
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• pp.211-215
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• 2016

The alteration of alternative splicing patterns has an effect on the quantification of functional proteins, leading to phenotype variation. The splicing quantitative trait locus (sQTL) is one of the main genetic elements affecting splicing patterns. Here, we report the results of genome-wide sQTLs across 141 strains of Arabidopsis thaliana with publicly available next generation sequencing datasets. As a result, we found 1,694 candidate sQTLs in Arabidopsis thaliana at a false discovery rate of 0.01. Furthermore, among the candidate sQTLs, we found 25 sQTLs that overlapped with the list of previously examined trait-associated single nucleotide polymorphisms (SNPs). In summary, this sQTL analysis provides new insight into genetic elements affecting alternative splicing patterns in Arabidopsis thaliana and the mechanism of previously reported trait-associated SNPs.

• Korean Journal of Breeding Science
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• v.40 no.3
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• pp.223-227
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• 2008

In rice, tillering is an important trait determining yield. To study tillering at the agricultural and molecular aspects, we have examined a spontaneous rice mutant that showed reduction in the number of culms. The mutant was derived from a $F^6$ line of the cross of Junambyeo*4 / IR72. It could produce, on average, 4 tillers per hill in the paddy field while wild-type plants usually have 15. Except the reduced culm numbers, they also show pale green phenotypes. The phenotypes of this mutant were co-segregated as the monogenic Mendelian ratio (${\chi}^b=0.002$, p=0.969). In order to locate a gene responsible for the rcn phenotype, the mutant with the japonica genetic background was crossed with Milyang21 of the indica background. Bulked segregant analysis was used for rapid determination of chromosomal location. Three SSR markers (RM551, RM8213, and RM16467) on chromosome 4 were genetically associated with the mutant phenotype. Each of the 217 $F_2$ plants was genotyped with simple sequence length polymorphisms. The data showed that RM16572 on chromosome 4 was the closest marker that showed perfect co-segregation among the $F_2$ population. We suggest the new rcn gene studied here name as $rcn10^t$ because there was no report which exhibit a rcn phenotype with a pleiotropic effect of pale green (chlorophyll deficiency), and mapped at same position on chromosome 4.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.5
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• pp.83-90
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• 2020

In this paper, a web-based seed phenotype visualization support system is proposed to extract and visualize data such as the surface color, length, area, perimeter and compactness of seed, which is phenotype information from the image of soybean/rice seeds. This system systematically stores data extracted from seeds in databases, and provides a web-based user interface that facilitates the analysis of data by researchers using data tables and charts. Conventional seed characteristic studies have been manually measured by humans, but the system developed in this paper allows researchers to simply upload seed images for analysis and obtain seed's numerical data after image processing. It is expected that the proposed system will be able to obtain time efficiency and remove spatial restriction, if it is used in seed characterization research, and it will be easy to analyze through systematic management of research results and visualization of the phenotype characteristics.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.4
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• pp.358-362
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• 2004

In order to develop molecular markers related to stay green phenotype, AFLP analysis was conducted using near-isogenic lines for either stay green or non stay green trait. Both lines have characteristics of multi-ear and tillers (MET). Two out of 64 primer combinations of selective amplification identified three reproducible polymorphic fragments in MET corn with stay green. Both of E+AGC/M+CAC and E+AAG/M+CAA primer combinations produced two and one specific polymorphic fragments linked to stay green trait, respectively. For the conversion of AFLPs to sequence tag sites (STSs), primers were designed form both end sequences of each two polymorphic fragments. One fragment, which was amplified with E+AAG/M+CAA primer combinations, possessed 298 bp long and showed a $91\%$ homology with maize retrotransposon Cinful-l. One out of two polymorphic fragments produced with E+AGC/M+CAC primer combination had 236 bp long and matched a $96\%$ homology with an intron region of 22kDa alpha zein gene cluster in Zea mays. One out of two PCR fragments amplified with MET2 primer set in the stay green MET was not produced in the non-stay green MET. The developed AFLP and STS marker could be used as an efficient tool for selection of the stay green trait in the MET inbred.

• Genomics & Informatics
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• v.14 no.4
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• Genomics & Informatics
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• v.20 no.2
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• pp.17.1-17.11
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• 2022

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.13-13
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• 2022

Recently, stable crop production is threatened by the effects of climate change. In particular, it is difficult to consistently maintain agricultural policies due to large price fluctuations depending on the difference in total domestic rice production from year to year. For stable rice production amid changes in the crop growing environment, development of varieties with improved disease resistance and abiotic stress stability is becoming more important. In here, drought and cold tolerant trait have been studied. First, for the development of drought tolerant varieties, we analyzed which agricultural traits are mainly affected by domestic drought conditions. As a result, it was observed that drought caused by the lack of water during transplanting season inhibits the development of the number of tiller and reduces the yield. 'Samgang' was selected as a useful genetic resource with strong drought tolerant and stable tiller number development even under drought conditions by phenotype screening. Three of drought tolerant QTLs were identified using doubled haploid (DH) population derived from a cross between Nacdong and Samgang, a drought sensitive and a tolerant, respectively. Among these QTLs, when qVDT2 and qVDTl1 were integrated, it was investigated that the tiller number development was relatively stable in the rainfed paddy field conditions. It is known that the high-yielding Tongil-type cultivars are severely affected by cold stress throughout the entire growth stage. In this study, we established conditions that can test the cold tolerance phenotype with alternate temperature to treat low temperatures in indoor growth conditions similar to those in field conditions at seedling stage. Three cold tolerant QTLs were explored using population derived from a cross between Hanareum2 (cold sensitive variety, Tongil-type) and Unkwang (cold tolerant variety, Japonica). Among these QTLs, qSCT12 showed strong cold tolerant phenotype, and when all of three QTLs were integrated, it was investigated that cold tolerant score was relatively similar to its donor parent, Unkwang, in our experimental conditions. We are performing that development of new variety with improved cold tolerant through the introduction of these QTLs.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.7
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• pp.926-935
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• 2015

The efficiency of genome-wide association analysis (GWAS) depends on power of detection for quantitative trait loci (QTL) and precision for QTL mapping. In this study, three different strategies for GWAS were applied to detect QTL for carcass quality traits in the Korean cattle, Hanwoo; a linkage disequilibrium single locus regression method (LDRM), a combined linkage and linkage disequilibrium analysis (LDLA) and a $BayesC{\pi}$ approach. The phenotypes of 486 steers were collected for weaning weight (WWT), yearling weight (YWT), carcass weight (CWT), backfat thickness (BFT), longissimus dorsi muscle area, and marbling score (Marb). Also the genotype data for the steers and their sires were scored with the Illumina bovine 50K single nucleotide polymorphism (SNP) chips. For the two former GWAS methods, threshold values were set at false discovery rate <0.01 on a chromosome-wide level, while a cut-off threshold value was set in the latter model, such that the top five windows, each of which comprised 10 adjacent SNPs, were chosen with significant variation for the phenotype. Four major additive QTL from these three methods had high concordance found in 64.1 to 64.9Mb for Bos taurus autosome (BTA) 7 for WWT, 24.3 to 25.4Mb for BTA14 for CWT, 0.5 to 1.5Mb for BTA6 for BFT and 26.3 to 33.4Mb for BTA29 for BFT. Several candidate genes (i.e. glutamate receptor, ionotropic, ampa 1 [GRIA1], family with sequence similarity 110, member B [FAM110B], and thymocyte selection-associated high mobility group box [TOX]) may be identified close to these QTL. Our result suggests that the use of different linkage disequilibrium mapping approaches can provide more reliable chromosome regions to further pinpoint DNA makers or causative genes in these regions.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.2
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• pp.162-172
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• 2011

In this paper, simulation was used to determine accuracies of genomic breeding values for polygenic traits associated with many thousands of markers obtained from high density genome scans. The statistical approach was based upon stochastically simulating a pedigree with a specified base population and a specified set of population parameters including the effective and noneffective marker distances and generation time. For this population, marker and quantitative trait locus (QTL) genotypes were generated using either a single linkage group or multiple linkage group model. Single nucleotide polymorphism (SNP) was simulated for an entire bovine genome (except for the sex chromosome, n = 29) including linkage and recombination. Individuals drawn from the simulated population with specified marker and QTL genotypes were randomly mated to establish appropriate levels of linkage disequilibrium for ten generations. Phenotype and genomic SNP data sets were obtained from individuals starting after two generations. Genetic prediction was accomplished by statistically modeling the genomic relationship matrix and standard BLUP methods. The effect of the number of linkage groups was also investigated to determine its influence on the accuracy of breeding values for genomic selection. When using high density scan data (0.08 cM marker distance), accuracies of breeding values on juveniles were obtained of 0.60 and 0.82, for a low heritable trait (0.10) and high heritable trait (0.50), respectively, in the single linkage group model. Estimates of 0.38 and 0.60 were obtained for the same cases in the multiple linkage group models. Unexpectedly, use of BLUP regression methods across many chromosomes was found to give rise to reduced accuracy in breeding value determination. The reasons for this remain a target for further research, but the role of Mendelian sampling may play a fundamental role in producing this effect.