第774回 千葉医学会例会・第二内科例会 54.

InterProScan results. InterProScan [34] queries sequences against 16 member databases enabling protein classification by family as well as by conserved domains; the number of annotations and the percent of sequences receiving annotations varied across the different databases. (XLSX 32 kb

Image_4_Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).tif

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164–0.498 and a predictive ability of 0.018–0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86–5.10, and against the square root of heritability was 0.19–0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.</p

Image_1_Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).tif

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164–0.498 and a predictive ability of 0.018–0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86–5.10, and against the square root of heritability was 0.19–0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.</p

Image_3_Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).tif

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164–0.498 and a predictive ability of 0.018–0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86–5.10, and against the square root of heritability was 0.19–0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.</p

Image_2_Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).tif

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164–0.498 and a predictive ability of 0.018–0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86–5.10, and against the square root of heritability was 0.19–0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.</p

Table_1_Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).pdf

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164–0.498 and a predictive ability of 0.018–0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86–5.10, and against the square root of heritability was 0.19–0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.</p

Additional file 1: of Transcriptome analysis and identification of genes associated with ω-3 fatty acid biosynthesis in Perilla frutescens (L.) var. frutescens

Table S1. Fatty acid composition of perilla developing seeds and leaves. All data are averages of three measurements ± SE. Table S2. Statistical summary of de novo transcriptome assembly and annotation. Table S3. Annotation of 54,079 perilla representative transcripts and their expression in developing seeds and leaf. Transcripts represents as Locus. Raw read count and normalized read count were represented as expression level for each developing seeds and leaves samples. Table S4. Nucleotide sequences of 54,079 perilla representative transcripts in FASTA format. Table S5. Gene ontology analysis of DEG cluster 3, 4, 5 and 10 transcripts of perilla. Table S6. List of gene products for the top 50 DEGs in Cluster 3, 4, 5 and 10. Table S7. Number of identified genes involved in acyl-lipid metabolism of perilla transcriptome. Table S8. List of expressed genes involved in fatty acid and TAG biosynthesis in perilla. Table S9. Information of perilla gene primers (5′ → 3′) used in the qPCR analysis and cDNA cloning. (ZIP 27440 kb

Distribution of sequencing depth among reads.

<p>(a) Similar univariate distributions of overall depth between root and leaf of sequence reads mapped to the <i>G</i>. <i>max</i> unigenes. (b) Random distribution of depths of reads for specific unigenes compared between leaf and root sequence data. (c) Venn diagram illustrating amount of overlap in <i>G</i>. <i>max</i> unigenes to which leaf and root reads mapped at average read depth > 0.5 in both tissues.</p

Characterization of the Transcriptome of the Xerophyte <i>Ammopiptanthus mongolicus</i> Leaves under Drought Stress by 454 Pyrosequencing

<div><p>Background</p><p><i>Ammopiptanthus mongolicus</i> (Maxim. Ex Kom.) Cheng f., an endangered ancient legume species, endemic to the Gobi desert in north-western China. As the only evergreen broadleaf shrub in this area, <i>A</i>. <i>mongolicus</i> plays an important role in the region’s ecological-environmental stability. Despite the strong potential of <i>A</i>. <i>mongolicus</i> in providing new insights on drought tolerance, sequence information on the species in public databases remains scarce. To both learn about the role of gene expression in drought stress tolerance in <i>A</i>. <i>mongolicus</i> and to expand genomic resources for the species, transcriptome sequencing of stress-treated <i>A</i>. <i>mongolicus</i> plants was performed.</p><p>Results</p><p>Using 454 pyrosequencing technology, 8,480 and 7,474 contigs were generated after <i>de novo</i> assembly of RNA sequences from leaves of untreated and drought-treated plants, respectively. After clustering using TGICL and CAP3 programs, a combined assembly of all reads produced a total of 11,357 putative unique transcripts (PUTs). Functional annotation and classification of the transcripts were conducted by aligning the 11,357 PUTs against the public protein databases and nucleotide database (Nt). Between control and drought-treated plants, 1,620 differentially expressed genes (DEGs) were identified, of which 1,106 were up-regulated and 514 were down-regulated. The differential expression of twenty candidate genes in metabolic pathways and transcription factors families related to stress-response were confirmed by quantitative real-time PCR. Representatives of several large gene families, such as WRKY and P5CS, were identified and verified in <i>A</i>. <i>mongolicus</i> for the first time.</p><p>Conclusions</p><p>The additional transcriptome resources, gene expression profiles, functional annotations, and candidate genes provide a more comprehensive understanding of the stress response pathways in xeric-adapted plant species such as <i>A</i>. <i>mongolicus</i>.</p></div

GO annotation of DEGs.

<p>From GO annotation results for all of the PUTs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0136495#pone.0136495.s001" target="_blank">S1 Fig</a>), annotations of DEGs were summarized in the three main categories of biological process, cellular component and molecular function. The Histogram in the small insert box shows only the DEGs related to abiotic stress.</p