Chih‐Wei Tung

National Taiwan University

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Co-Authors

Susan R. McCouch

Cornell University

Chia-Hui Chou

National Taiwan University

Chia‐Lin Chung

National Taiwan University

Mark H. Wright

University of Alberta

Szu‐Yu Chen

National Taiwan University

Hsun-Shih Lin

National Taiwan University

Chen-Hsin Wen

National Taiwan University

Chen‐Tuo Liao

National Taiwan University

Fang‐Yu Chang

National Chung Hsing University

Dong‐Hong Wu

Taiwan Agricultural Research Institute Council of Agriculture, Executive Yuan

Cooperative Institutions

Cornell University

National Taiwan University

International Rice Research Institute

Cold Spring Harbor Laboratory

United States Department of Agriculture

National Cheng Kung University

Agricultural Research Service

Council of Agriculture

University of Illinois Urbana-Champaign

National Chung Hsing University

Author Statistics

Papers

Citation

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EHPGS: A Statistical Package for Evaluation of Hybrid Performance in Plant Breeding via Genomic Selection

Figshare (2023)

Szu-Ping Chen Chih‐Wei Tung Pei-Hsien Wang Chen‐Tuo Liao

The dataset was used in the article "EHPGS: A Statistical Package for Evaluation of Hybrid Performance in Plant Breeding via Genomic Selection". In the dataset, the file name containing TP is the training population file, and the file name containing BP is the breeding population file. The pumpkin dataset is originally from Wu et al. (2019) [1] ,and maize dataset and wheat dataset are originally from Guo et al. (2019) [2].
1. Wu, PY, Tung, CW, Lee, CY, and Liao, CT (2019) Genomic prediction of pumpkin hybrid performance. Plant Genome 12: 180082. 2. Guo, T, Yu, X, Li, X, Zhang, H, Zhu, C, Flint-Garcia, S. Michael, D, McMullen, JB, Holland, SJ, Szalma, RJ, and Yu, WJ (2019) Optimal Designs for Genomic Selection in Hybrid Crops. Mol Plant 12: 390-401.

Genomic Selection

R package

Plant Breeding

10.6084/m9.figshare.22359883.v2

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Development of genome-wide SNP assays for rice

Breeding Science (2010)

Susan R. McCouch Keyan Zhao Mark H. Wright Chih‐Wei Tung Kaworu Ebana

Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation in eukaryotic genomes. SNPs may be functionally responsible for specific traits or phenotypes, or they may be informative for tracing the evolutionary history of a species or the pedigree of a variety. As genetic markers, SNPs are rapidly replacing simple sequence repeats (SSRs) because they are more abundant, stable, amenable to automation, efficient, and increasingly cost-effective. The integration of high throughput SNP genotyping capability promises to accelerate genetic gain in a breeding program, but also imposes a series of economic, organizational and technical hurdles. To begin to address these challenges, SNP-based resources are being developed and made publicly available for broad application in rice research. These resources include large SNP datasets, tools for identifying informative SNPs for targeted applications, and a suite of custom-designed SNP assays for use in marker-assisted and genomic selection, association and QTL mapping, positional cloning, pedigree analysis, variety identification and seed purity testing. SNP resources also make it possible for breeders to more efficiently evaluate and utilize the wealth of natural variation that exists in both wild and cultivated germplasm with the aim of improving the productivity and sustainability of agriculture.

SNP genotyping

SNP

Germ plasm

Identification

SNP array

10.1270/jsbbs.60.524

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Citations (206)

Identification of qBK2.1, a novel QTL controlling rice resistance against Fusarium fujikuroi

Botanical studies (2023)

Szu‐Yu Chen Ming‐Hsin Lai Yi-Ling Chu Dong‐Hong Wu Chih‐Wei Tung

Abstract Background Bakanae disease caused by Fusarium fujikuroi is an increasing threat to rice production. The infected plants show symptoms such as elongation, slenderness, chlorosis, a large leaf angle, and even death. Bakanae disease is traditionally managed by seed treatment. However, fungicide-resistant F. fujikuroi isolates have emerged in several Asian areas, including Taiwan. This study aimed to identify new bakanae resistance quantitative trait loci (QTLs) and provide molecular markers to assist future breeding. Results A population of F 2:9 recombinant inbred lines (RILs) was derived from the cross between an elite japonica Taiwanese cultivar ‘Taikeng 16 (TK16)’ and an indica variety ‘Budda’. ‘Budda’ was found highly resistant to all 24 representative isolates of the F. fujikuroi population in Taiwan. For the RIL population, 6,492 polymorphic single nucleotide polymorphisms (SNPs) spanning the rice genome were obtained by genotyping-by-sequencing (GBS) technique, and the disease severity index (DSI) was evaluated by inoculation with a highly virulent F. fujikuroi isolate Ff266. Trait-marker association analysis of 166 RILs identified two QTLs in ‘Budda’. qBK2.1 (21.97–30.15 Mb) is a novel and first bakanae resistance QTL identified on chromosome 2. qBK1.8 (5.24–8.66 Mb) partially overlaps with the previously reported qBK1 .3 (4.65–8.41 Mb) on chromosome 1. The log of odds (LOD) scores of qBK1.8 and qBK2.1 were 4.75 and 6.13, accounting for 4.9% and 8.1% of the total phenotypic variation, respectively. 64 RILs carrying both qBK1.8 and qBK2.1 showed lower DSI (7%) than the lines carrying only qBK1.8 (15%), only qBK2.1 (13%), or none of the two QTLs (21%). For the future application of identified QTLs, 11 KBioscience competitive allele-specific PCR (KASP) markers and 3 insertion-deletion (InDel) markers were developed. Conclusions Compared to other important rice diseases, knowledge of bakanae resistance has been insufficient, which limited the development and deployment of resistant cultivars. The discovery of qBK2.1 has provided a new source of bakanae resistance. The resistant RILs inheriting good plant type, good taste, and high yield characteristics from ‘TK16’ can be used as good resistance donors. Our newly developed markers targeting qBK2.1 and qBK1.8 can also serve as an important basis for future fine-mapping and resistance breeding.

Marker-Assisted Selection

10.1186/s40529-023-00375-y

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Citations (2)

PtrA, Piz-t, and a novel minor-effect QTL (qBR12_3.3–4.4) collectively contribute to the durable blast-resistance of rice cultivar Tainung 84

Botanical studies (2024)

Sheng-Shan Wang W. Chang Ming-Chien Hsieh Szu‐Yu Chen Dah-Jing Liao

Rice blast caused by Pyricularia oryzae is a major threat to rice production worldwide. Tainung 84 (TNG84) is an elite japonica rice cultivar developed through the traditional pedigree method. It has maintained superior blast resistance since its release in 2010. This study aimed to investigate the genetic factors underlying the durable resistance of TNG84 in Taiwan.

10.1186/s40529-024-00444-w

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Additional file 15 of Patterns of genetic variation and QTLs controlling grain traits in a collection of global wheat germplasm revealed by high-quality SNP markers

Figshare (2022)

Chia-Hui Chou Hsun-Shih Lin Chen-Hsin Wen Chih‐Wei Tung

Additional file 15.

Germ plasm

Grain Quality

SNP array

SNP

10.6084/m9.figshare.21185118

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Genome-Wide Single Nucleotide Polymorphism Discovery and the Construction of a High-Density Genetic Map for Melon (Cucumis melo L.) Using Genotyping-by-Sequencing

Frontiers in Plant Science (2017)

Che-Wei Chang Yuhua Wang Chih‐Wei Tung

Although genotyping-by-sequencing (GBS) enables the efficient and low-cost generation of large numbers of markers, the utility of resultant genotypes are limited, because they are enormously error-prone and contain high proportions of missing data. In this study, we generated single nucleotide polymorphism (SNP) markers for 109 recombinant inbred lines (RILs) of melon (Cucumis melo L.) using the GBS approach and ordered them according to their physical position on the draft double haploid line DHL92 genome. Next, by investigating associations between these SNPs, we discovered that some segments on the physical map conflict with linkage relationships. Therefore, to filter out error-prone loci, 4,110 SNPs in which we have a high degree of confidence were selected as anchors to test independence with respect to unselected markers, and the resultant dataset was then analyzed using the Full-sib Family Haplotype (FSFHap) algorithm in the software TASSEL 5.2. On the basis of this analysis, 22,933 loci that have an average rate of missing data of 0.281% were used to construct a genetic map, which spans 1,088.3 cM across 12 chromosomes and has a maximum spacing of 6.0 cM. Use of this high-quality linkage map enabled the identification of several quantitative trait loci (QTL) known to control traits in fruit and validated our approach. This study highlights the utility of GBS markers for the identification of trait-associated QTLs in melon and facilitates further investigation of genome structure.

Melon

Cucumis

Genetic linkage

SNP genotyping

10.3389/fpls.2017.00125

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Citations (27)

MOESM1 of Genomic and phenotypic evaluation of rice susceptible check TN1 collected in Taiwan

Figshare (2019)

Yi Li Yung‐Fen Huang Shou‐Horng Huang Yun‐Hung Kuang Chih‐Wei Tung

Additional file 1: Table S1. SNPs across four rice accessions: Qingliu, TN1-NPGRC, TN1-TDARES, and TN1-NTU.

10.6084/m9.figshare.9751187

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Additional file 20 of Patterns of genetic variation and QTLs controlling grain traits in a collection of global wheat germplasm revealed by high-quality SNP markers

Figshare (2022)

Chia-Hui Chou Hsun-Shih Lin Chen-Hsin Wen Chih‐Wei Tung

Additional file 20.

Germ plasm

Grain Quality

SNP array

SNP

10.6084/m9.figshare.21185136

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ALCHEMY: a reliable method for automated SNP genotype calling for small batch sizes and highly homozygous populations

Bioinformatics (2010)

Mark H. Wright Chih‐Wei Tung Keyan Zhao Andy Reynolds Susan R. McCouch

Abstract Motivation: The development of new high-throughput genotyping products requires a significant investment in testing and training samples to evaluate and optimize the product before it can be used reliably on new samples. One reason for this is current methods for automated calling of genotypes are based on clustering approaches which require a large number of samples to be analyzed simultaneously, or an extensive training dataset to seed clusters. In systems where inbred samples are of primary interest, current clustering approaches perform poorly due to the inability to clearly identify a heterozygote cluster. Results: As part of the development of two custom single nucleotide polymorphism genotyping products for Oryza sativa (domestic rice), we have developed a new genotype calling algorithm called ‘ALCHEMY’ based on statistical modeling of the raw intensity data rather than modelless clustering. A novel feature of the model is the ability to estimate and incorporate inbreeding information on a per sample basis allowing accurate genotyping of both inbred and heterozygous samples even when analyzed simultaneously. Since clustering is not used explicitly, ALCHEMY performs well on small sample sizes with accuracy exceeding 99% with as few as 18 samples. Availability: ALCHEMY is available for both commercial and academic use free of charge and distributed under the GNU General Public License at http://alchemy.sourceforge.net/ Contact: mhw6@cornell.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Replicate

10.1093/bioinformatics/btq533

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Citations (49)

Challenges to Agriculture Production Under Climate Change

Warren Hwa-Jen Kuo Chang-Jo Chung Kai‐Wei Juang Chih‐Wei Tung Liyu Liu

10.1007/978-3-031-80912-5_2

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