Identifying barley pan-genome sequence anchors using genetic mapping and machine learning

Journal article

Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji. (2020). Identifying barley pan-genome sequence anchors using genetic mapping and machine learning. Theoretical and Applied Genetics. 133(9), pp. 2535-2544. https://doi.org/10.1007/s00122-020-03615-y

Publication dates
Authors	Gao, Shang, Wu, Ryan, Stiller, Jiri, Zheng, Zhi, Zhou, Meixue, Wang, You-Gan and Liu, Chunji
Abstract	We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, building more comprehensive pan-genomes is of great importance in genetic research and breeding. Obtaining a thousand-genotype scale pan-genome using deep-sequencing data is currently impractical for species like barley which has a huge and highly repetitive genome. To this end, we attempted to identify barley pan-genome sequence anchors from a large quantity of genotype-by-sequencing (GBS) datasets by combining genetic mapping and machine learning algorithms. Based on the GBS sequences from 11,166 domesticated and 1140 wild barley genotypes, we identified 1.844 million pan-genome sequence anchors. Of them, 532,253 were identified as presence/absence variation (PAV) tags. Through aligning these PAV tags to the genome of hulless barley genotype Zangqing320, our analysis resulted in a validation of 83.6% of them from the domesticated genotypes and 88.6% from the wild barley genotypes. Association analyses against flowering time, plant height and kernel size showed that the relative importance of the PAV and non-PAV tags varied for different traits. The pan-genome sequence anchors based on GBS tags can facilitate the construction of a comprehensive pan-genome and greatly assist various genetic studies including identification of structural variation, genetic mapping and breeding in barley.
Keywords	Algorithms; Chromosome Mapping; Plant Genome; Genotype; Hordeum genetics; Linkage Disequilibrium; Machine Learning
Year	01 Jan 2020
Journal	Theoretical and Applied Genetics
Journal citation	133 (9), pp. 2535-2544
Publisher	Springer
ISSN	0040-5752
Digital Object Identifier (DOI)	https://doi.org/10.1007/s00122-020-03615-y
PubMed ID	32448920
Web address (URL)	https://link.springer.com/article/10.1007/s00122-020-03615-y
Open access	Published as non-open access
Research or scholarly	Research
Page range	2535-2544
Publisher's version	License All rights reserved File Access Level Controlled
Output status	Published
Print	24 May 2020
Publication process dates
Accepted	16 May 2020
Deposited	11 Jan 2023
Additional information	© Springer-Verlag GmbH Germany, part of Springer Nature 2020.
Place of publication	Germany

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Wei, Chen, Hu, Zhi-Hua and Wang, You-Gan. (2020). Exact algorithms for energy-efficient virtual machine placement in data centers. Future Generation Computer Systems. 106, pp. 77-91. https://doi.org/10.1016/j.future.2019.12.043

A working likelihood approach for robust regression

Fu, Liya, Wang, You-Gan and Cai, Fengjing. (2020). A working likelihood approach for robust regression. Statistical Methods in Medical Research. 29(12), pp. 3641-3652. https://doi.org/10.1177/0962280220936310

Maritime convection and fluctuation between Vietnam and China : A data-driven study

Hu, Zhi-Hua, Liu, Chan-Juan, Chen, Wanting, Wang, You-Gan and Wei, Chen. (2020). Maritime convection and fluctuation between Vietnam and China : A data-driven study. Research in Transportation Business and Management. 34, pp. 1-15. https://doi.org/10.1016/j.rtbm.2019.100414

Natural mortality estimation using tree-based ensemble learning models

Liu, Chanjuan, Zhou, Shijie, Wang, You-Gan and Hu, Zhi-Hua. (2020). Natural mortality estimation using tree-based ensemble learning models. ICES Journal of Marine Science. 77(4), pp. 1414-1426. https://doi.org/10.1093/icesjms/fsaa058

Profile-guided three-phase virtual resource management for energy efficiency of data centers

Ding, Zhe, Tian, Yu-Chu, Tang, Maolin, Li, Yuefeng, Wang, You-Gan and Zhou, Chunjie. (2020). Profile-guided three-phase virtual resource management for energy efficiency of data centers. IEEE Transactions on Industrial Electronics. 67(3), pp. 2460-2468. https://doi.org/10.1109/TIE.2019.2902786

Response of water quality to land use and sewage outfalls in different seasons

Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Mo, Yuming, Wang, You-Gan and Li, Ling. (2019). Response of water quality to land use and sewage outfalls in different seasons. Science of the Total Environment. 696, p. Article 134014. https://doi.org/10.1016/j.scitotenv.2019.134014

A new hybrid model to predict the electrical load in five states of Australia

Wu, Jinran, Cui, Zhesen, Chen, Yanyan, Kong, Demeng and Wang, You-Gan. (2019). A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, pp. 598-609. https://doi.org/10.1016/j.energy.2018.10.076

Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere

Jones, Andy, Lavery, Shane D., Le Port, Agnès, Wang, You-Gan, Blower, Dean and Ovenden, Jennifer. (2019). Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite “tiny” Ne/N ratios elsewhere. Molecular Ecology. 28(12), pp. 2986-2995. https://doi.org/10.1111/mec.15130

A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all?

Paul, Sudhir, Wang, You-Gan and Ullah, Insha. (2019). A review of the Behrens–Fisher problem and some of its analogs : Does the same size fit all? Revstat Statistical Journal. 17(4), p. 563–597.

Incorporating social objectives in evaluating sustainable fisheries harvest strategy

Wu, Jiafeng, Wang, Na, Hu, Zhi-Hua, Hong, Zhenjie and Wang, You-Gan. (2019). Incorporating social objectives in evaluating sustainable fisheries harvest strategy. Environmental Modeling and Assessment. 24(4), pp. 381-386. https://doi.org/10.1007/s10666-019-9651-9

Significance tests for analyzing gene expression data with small sample sizes

Ullah, Insha, Paul, Sudhir, Hong, Zhenjie and Wang, You-Gan. (2019). Significance tests for analyzing gene expression data with small sample sizes. Bioinformatics. 35(20), pp. 3996-4003. https://doi.org/10.1093/bioinformatics/btz189

Robust Estimation Using Modified Huber’s Functions With New Tails

Jiang, Yunlu, Wang, You-Gan, Fu, Liya and Wang, Xueqin. (2019). Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics. 61(1), pp. 111-122. https://doi.org/10.1080/00401706.2018.1470037

Variable selection in rank regression for analyzing longitudinal data

Fu, Liya and Wang, You-Gan. (2018). Variable selection in rank regression for analyzing longitudinal data. Statistical Methods in Medical Research. 27(8), pp. 2447-2458. https://doi.org/10.1177/0962280216681347

Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control

Xu, Jing, Jin, Guangqiu, Tang, Hongwu, Zhang, Pei, Wang, Shen, Wang, You-Gan and Li, Ling. (2018). Assessing temporal variations of Ammonia Nitrogen concentrations and loads in the Huaihe River Basin in relation to policies on pollution source control. Science of the Total Environment. 642, pp. 1386-1395. https://doi.org/10.1016/j.scitotenv.2018.05.395

Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods

Li, Bo, Zhang, Nanxi, Wang, You-Gan, George, Andrew W., Reverter, Antonio and Li, Yutao. (2018). Genomic prediction of breeding values using a subset of SNPs identified by three machine learning methods. Frontiers in Genetics. 9, p. Article 237. https://doi.org/10.3389/fgene.2018.00237

Dividend growth and equity premium predictability

Zhu, Min, Chen, Rui, Du, Ke and Wang, You-Gan. (2018). Dividend growth and equity premium predictability. International Review of Economics and Finance. 56, pp. 125-137. https://doi.org/10.1016/j.iref.2017.10.020

Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations

Wang, Na, Wang, You-Gan, Hu, Shuwen, Hu, Zhi-Hua, Xu, Jing, Tang, Hongwu and Jin, Guangqiu. (2018). Robust Regression with Data-Dependent Regularization Parameters and Autoregressive Temporal Correlations. Environmental Modeling and Assessment. 23(6), pp. 779-786. https://doi.org/10.1007/s10666-018-9605-7

Analysis of spatial data with a nested correlation structure

Adegboye, Oyelola, Leung, Denis and Wang, You-Gan. (2018). Analysis of spatial data with a nested correlation structure. Journal of the Royal Statistical Society Series C: Applied Statistics. 67(2), pp. 329-354. https://doi.org/10.1111/rssc.12230

Working correlation structure selection in generalized estimating equations

Fu, Liya, Hao, Yangyang and Wang, You-Gan. (2018). Working correlation structure selection in generalized estimating equations. Computational Statistics. 33(2), pp. 983-996. https://doi.org/10.1007/s00180-018-0800-4

Selection of working correlation structure in generalized estimating equations

Wang, You-Gan and Fu, Liya. (2017). Selection of working correlation structure in generalized estimating equations. Statistics in Medicine. 36(14), pp. 2206-2219. https://doi.org/10.1002/sim.7262

Blockwise AICc for model selection in generalized linear models

Song, Guofeng, Dong, Xiaogang, Wu, Jiafeng and Wang, You-Gan. (2017). Blockwise AICc for model selection in generalized linear models. Environmental Modeling and Assessment. 22(6), pp. 523-533. https://doi.org/10.1007/s10666-017-9552-8

A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”

Zhu, Min, Liu, Chang and Wang, You-Gan. (2017). A comment on Koh’s “The optimal design of fallible organizations : Invariance of optimal decision threshold and uniqueness of hierarchy and polyarchy structures”. Social Choice and Welfare. 48(2), pp. 385-392. https://doi.org/10.1007/s00355-016-1009-5

The Buckley–James estimator and induced smoothing

Wang, You-Gan, Zhao, Yudong and Fu, Liya. (2016). The Buckley–James estimator and induced smoothing. Australian and New Zealand Journal of Statistics. 58(2), pp. 211-225. https://doi.org/10.1111/anzs.12155

Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data

Kienzle, Marco, Sterling, David, Zhou, Shijie and Wang, You-Gan. (2016). Maximum likelihood estimation of natural mortality and quantification of temperature effects on catchability of brown tiger prawn (penaeus esculentus) in Moreton Bay (Australia) using logbook data. Ecological Modelling. 322, pp. 1-9. https://doi.org/10.1016/j.ecolmodel.2015.11.008

Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment

Zischke, Mitchell T., Litherland, Lenore, Tilyard, Benjamin R., Stratford, Nicholas J., Jones, Ebony L. and Wang, You-Gan. (2016). Otolith morphology of four mackerel species (Scomberomorus spp.) in Australia : Species differentiation and prediction for fisheries monitoring and assessment. Fisheries Research. 176, pp. 39-47. https://doi.org/10.1016/j.fishres.2015.12.003

Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data

Fu, Liya and Wang, You-Gan. (2016). Efficient parameter estimation via Gaussian copulas for quantile regression with longitudinal data. Journal of Multivariate Analysis. 143, pp. 492-502. https://doi.org/10.1016/j.jmva.2015.07.004

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Lloyd-Jones, Luke R., Nguyen, Hien D., McLachlan, Geoffrey J., Sumpton, Wayne and Wang, You-Gan. (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics. 72(4), pp. 1255-1265. https://doi.org/10.1111/biom.12531

Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas

Purcell, Steven W., Piddocke, Toby P., Dalton, Steven J. and Wang, You-Gan. (2016). Movement and growth of the coral reef holothuroids Bohadschia argus and Thelenota ananas. Marine Ecology Progress Series. 551, pp. 201-214. https://doi.org/10.3354/meps11720

Improved confidence intervals for the linkage disequilibrium method for estimating effective population size

Jones, A. T., Ovenden, J. R. and Wang, Y.-G.. (2016). Improved confidence intervals for the linkage disequilibrium method for estimating effective population size. Heredity. 117(4), pp. 217-223. https://doi.org/10.1038/hdy.2016.19