MicroRNAs (miRNAs) play an essential role as non‐coding‐RNA‐type epigenetic regulators in response to high‐temperature stress in plants. There are crucial roles for global transcriptional regulation under SUMO (small ubiquitin‐related MOdifier) stress response (SSR). However, the molecular mechanisms underlying its downstream regulation remain unclear. In this study, SUMO‐specific chromatin immunoprecipitation sequencing analysis detected specific binding in the promoter region of miRNAs under high‐temperature stress. A correlation analysis between this binding and miRNA profiling revealed that the location of SUMO on the chromosome was correlated with the expression pattern of miRNAs, particularly miR398a and miR824a. In contrast, knockout mutants of the SSR‐dependent SUMO E3 ligase SAP AND MIZ 1 in Arabidopsis exhibited opposing trends in target gene expression for the SUMO‐related miRNAs compared to the wild type. Multi‐omics correlation analyses identified 34 SUMO‐candidate proteins that might be involved in the regulation of miRNA response to high‐temperature stress. Therefore, we propose a potential model whereby high‐temperature exposure induces nuclear entry of SUMO molecules, modifying specific transcription factors that bind to miRNA gene promoters and potentially regulate miRNA expression.
微RNA（miRNA）作为非编码RNA类型的表观遗传调节因子，在植物应对高温胁迫的过程中发挥着重要作用。在 SUMO（小泛素相关修饰因子）胁迫响应（SSR）中，miRNAs 对全局转录调控起着至关重要的作用。然而，其下游调控的分子机制仍不清楚。在本研究中，SUMO 特异性染色质免疫沉淀测序分析检测到了高温胁迫下 miRNA 启动子区域的特异性结合。这种结合与 miRNA 图谱之间的相关性分析表明，SUMO 在染色体上的位置与 miRNA 的表达模式相关，尤其是 miR398a 和 miR824a。相比之下，拟南芥中依赖 SSR 的 SUMO E3 连接酶 SAP AND MIZ 1 的基因敲除突变体与野生型相比，在 SUMO 相关 miRNA 的靶基因表达方面表现出相反的趋势。多组学相关分析发现了 34 个可能参与调控 miRNA 对高温胁迫响应的 SUMO 候选蛋白。因此，我们提出了一个潜在的模型，即高温暴露诱导 SUMO 分子进入细胞核，从而修饰与 miRNA 基因启动子结合的特定转录因子，并可能调控 miRNA 的表达。
{"title":"Functional characterization of protein SUMOylation in the miRNA transcription regulation during heat stress in Arabidopsis","authors":"Simin Xia, Yue Chen, Jianbin Lai, Zhonghui Zhang, Chengwei Yang, Danlu Han","doi":"10.1002/tpg2.20511","DOIUrl":"https://doi.org/10.1002/tpg2.20511","url":null,"abstract":"MicroRNAs (miRNAs) play an essential role as non‐coding‐RNA‐type epigenetic regulators in response to high‐temperature stress in plants. There are crucial roles for global transcriptional regulation under SUMO (small ubiquitin‐related MOdifier) stress response (SSR). However, the molecular mechanisms underlying its downstream regulation remain unclear. In this study, SUMO‐specific chromatin immunoprecipitation sequencing analysis detected specific binding in the promoter region of miRNAs under high‐temperature stress. A correlation analysis between this binding and miRNA profiling revealed that the location of SUMO on the chromosome was correlated with the expression pattern of miRNAs, particularly miR398a and miR824a. In contrast, knockout mutants of the SSR‐dependent SUMO E3 ligase <jats:italic>SAP AND MIZ 1</jats:italic> in Arabidopsis exhibited opposing trends in target gene expression for the SUMO‐related miRNAs compared to the wild type. Multi‐omics correlation analyses identified 34 SUMO‐candidate proteins that might be involved in the regulation of miRNA response to high‐temperature stress. Therefore, we propose a potential model whereby high‐temperature exposure induces nuclear entry of SUMO molecules, modifying specific transcription factors that bind to miRNA gene promoters and potentially regulate miRNA expression.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"1 1","pages":"e20511"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dipendra Shahi,Jia Guo,Md Ali Babar,Sumit Pradhan,Muhsin Avci,Naeem Khan,Jordan McBreen,Smita Rayamajhi,Zhao Liu,Guihua Bai,Paul St Amand,Amy Bernardo,Matthew Reynolds,Gemma Molero,Sivakumar Sukumaran,John Foulkes,Jahangir Khan
Wheat (Triticum aestivum L.) production must be doubled in the next 25 years to meet the global food demand. Harvest index (HI) is an important indicator of efficient partitioning of photosynthetic assimilates to grains. Reducing competition from alternative sinks, such as stems, and deviating assimilates toward grain increase grain number (GN), HI, and grain yield (GY). Novel partitioning traits have great potential to be utilized in wheat breeding programs to increase HI. In this study, we evaluated 236 US facultative soft wheat genotypes for multiple stem and spike partitioning traits at 7 days after anthesis, and for GN, HI, and GY in two locations of Florida in 2016-2017 and 2017-2018 wheat growing seasons. The panel was genotyped with 20,706 single nucleotide polymorphisms generated by genotype-by-sequencing approach. Spike partitioning index (SPI) showed negative significant correlations with lamina partitioning index and true stem partitioning index. Internode 2 and 3 lengths and partitioning indices had significant negative correlations with SPI and HI. The results indicate enhanced competition for assimilates between spikes and second and third internodes during stem elongation. Genome-wide association study (GWAS) identified 114 unique significant marker-trait associations (MTAs) for 12 traits, and 58 MTAs were found within genes that encode different proteins related to biotic/abiotic stress tolerance and other functions. Significant MTAs identified in the GWAS were converted into kompetitive allele specific PCR markers. Some of the markers were validated and can be effectively employed in marker-assisted selection to improve HI, GY, and GN.
{"title":"Deciphering the genetic basis of novel traits that discriminate useful and non-useful biomass to enhance harvest index in wheat.","authors":"Dipendra Shahi,Jia Guo,Md Ali Babar,Sumit Pradhan,Muhsin Avci,Naeem Khan,Jordan McBreen,Smita Rayamajhi,Zhao Liu,Guihua Bai,Paul St Amand,Amy Bernardo,Matthew Reynolds,Gemma Molero,Sivakumar Sukumaran,John Foulkes,Jahangir Khan","doi":"10.1002/tpg2.20512","DOIUrl":"https://doi.org/10.1002/tpg2.20512","url":null,"abstract":"Wheat (Triticum aestivum L.) production must be doubled in the next 25 years to meet the global food demand. Harvest index (HI) is an important indicator of efficient partitioning of photosynthetic assimilates to grains. Reducing competition from alternative sinks, such as stems, and deviating assimilates toward grain increase grain number (GN), HI, and grain yield (GY). Novel partitioning traits have great potential to be utilized in wheat breeding programs to increase HI. In this study, we evaluated 236 US facultative soft wheat genotypes for multiple stem and spike partitioning traits at 7 days after anthesis, and for GN, HI, and GY in two locations of Florida in 2016-2017 and 2017-2018 wheat growing seasons. The panel was genotyped with 20,706 single nucleotide polymorphisms generated by genotype-by-sequencing approach. Spike partitioning index (SPI) showed negative significant correlations with lamina partitioning index and true stem partitioning index. Internode 2 and 3 lengths and partitioning indices had significant negative correlations with SPI and HI. The results indicate enhanced competition for assimilates between spikes and second and third internodes during stem elongation. Genome-wide association study (GWAS) identified 114 unique significant marker-trait associations (MTAs) for 12 traits, and 58 MTAs were found within genes that encode different proteins related to biotic/abiotic stress tolerance and other functions. Significant MTAs identified in the GWAS were converted into kompetitive allele specific PCR markers. Some of the markers were validated and can be effectively employed in marker-assisted selection to improve HI, GY, and GN.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"203 1","pages":"e20512"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ashish Kumar, Yogesh Dashrath Naik, Vedant Gautam, Sunanda Patil, Vinod Valluri, Sonal Channale, Jayant Bhatt, Stuti Sharma, R. S. Ramakrishnan, Radheshyam Sharma, Himabindu Kudapa, Rebecca S. Zwart, Somashekhar M. Punnuri, Rajeev K. Varshney, Mahendar Thudi
Root‐lesion nematodes (RLN) pose a significant threat to chickpea (Cicer arietinum L.) by damaging the root system and causing up to 25% economic losses due to reduced yield. Worldwide commercially grown chickpea varieties lack significant genetic resistance to RLN, necessitating the identification of genetic variants contributing to natural resistance. This study identifies genomic loci responsible for resistance to the RLN, Pratylenchus thornei Sher & Allen, in chickpea by utilizing high‐quality single nucleotide polymorphisms from whole‐genome sequencing data of 202 chickpea accessions. Phenotypic evaluations of the genetically diverse set of chickpea accessions in India and Australia revealed a wide range of responses from resistant to susceptible. Genome‐wide association studies (GWAS) employing Fixed and Random Model Circulating Probability Unification (FarmCPU) and Bayesian‐Information and Linkage‐Disequilibrium Iteratively Nested Keyway (BLINK) models identified 44 marker‐trait associations distributed across all chromosomes except Ca1. Crucially, genomic regions on Ca2 and Ca5 consistently display significant associations across locations. Of 25 candidate genes identified, five genes were putatively involved in RLN resistance response (glucose‐6‐phosphate dehydrogenase, heat shock proteins, MYB‐like DNA‐binding protein, zinc finger FYVE protein and pathogenesis‐related thaumatin‐like protein). One notably identified gene (Ca_10016) presents four haplotypes, where haplotypes 1–3 confer moderate susceptibility, and haplotype 4 contributes to high susceptibility to RLN. This information provides potential targets for marker development to enhance breeding for RLN resistance in chickpea. Additionally, five potential resistant genotypes (ICC3512, ICC8855, ICC5337, ICC8950, and ICC6537) to P. thornei were identified based on their performance at a specific location. The study's significance lies in its comprehensive approach, integrating multiple‐location phenotypic evaluations, advanced GWAS models, and functional genomics to unravel the genetic basis of P. thornei resistance. The identified genomic regions, candidate genes, and haplotypes offer valuable insights for breeding strategies, paving the way for developing chickpea varieties resilient to P. thornei attack.
{"title":"Genome‐wide association mapping reveals novel genes and genomic regions controlling root‐lesion nematode resistance in chickpea mini core collection","authors":"Ashish Kumar, Yogesh Dashrath Naik, Vedant Gautam, Sunanda Patil, Vinod Valluri, Sonal Channale, Jayant Bhatt, Stuti Sharma, R. S. Ramakrishnan, Radheshyam Sharma, Himabindu Kudapa, Rebecca S. Zwart, Somashekhar M. Punnuri, Rajeev K. Varshney, Mahendar Thudi","doi":"10.1002/tpg2.20508","DOIUrl":"https://doi.org/10.1002/tpg2.20508","url":null,"abstract":"Root‐lesion nematodes (RLN) pose a significant threat to chickpea (<jats:italic>Cicer arietinum</jats:italic> L.) by damaging the root system and causing up to 25% economic losses due to reduced yield. Worldwide commercially grown chickpea varieties lack significant genetic resistance to RLN, necessitating the identification of genetic variants contributing to natural resistance. This study identifies genomic loci responsible for resistance to the RLN, <jats:italic>Pratylenchus thornei</jats:italic> Sher & Allen, in chickpea by utilizing high‐quality single nucleotide polymorphisms from whole‐genome sequencing data of 202 chickpea accessions. Phenotypic evaluations of the genetically diverse set of chickpea accessions in India and Australia revealed a wide range of responses from resistant to susceptible. Genome‐wide association studies (GWAS) employing Fixed and Random Model Circulating Probability Unification (FarmCPU) and Bayesian‐Information and Linkage‐Disequilibrium Iteratively Nested Keyway (BLINK) models identified 44 marker‐trait associations distributed across all chromosomes except Ca1. Crucially, genomic regions on Ca2 and Ca5 consistently display significant associations across locations. Of 25 candidate genes identified, five genes were putatively involved in RLN resistance response (glucose‐6‐phosphate dehydrogenase, heat shock proteins, MYB‐like DNA‐binding protein, zinc finger FYVE protein and pathogenesis‐related thaumatin‐like protein). One notably identified gene (<jats:italic>Ca_10016</jats:italic>) presents four haplotypes, where haplotypes 1–3 confer moderate susceptibility, and haplotype 4 contributes to high susceptibility to RLN. This information provides potential targets for marker development to enhance breeding for RLN resistance in chickpea. Additionally, five potential resistant genotypes (ICC3512, ICC8855, ICC5337, ICC8950, and ICC6537) to <jats:italic>P. thornei</jats:italic> were identified based on their performance at a specific location. The study's significance lies in its comprehensive approach, integrating multiple‐location phenotypic evaluations, advanced GWAS models, and functional genomics to unravel the genetic basis of <jats:italic>P. thornei</jats:italic> resistance. The identified genomic regions, candidate genes, and haplotypes offer valuable insights for breeding strategies, paving the way for developing chickpea varieties resilient to <jats:italic>P. thornei</jats:italic> attack.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muyideen Yusuf, Michael D. Miller, Thomas R. Stefaniak, Darrin Haagenson, Jeffrey B. Endelman, Asunta L. Thompson, Laura M. Shannon
Potato (Solanum tuberosum L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such as shape and skin color, making these traits important targets for breeders. Achieving and evaluating genetic gain is facilitated by precise and accurate trait measures. Historically, quality traits have been measured using visual rating scales, which are subject to human error and necessarily lump individuals with distinct characteristics into categories. Image analysis offers a method of generating quantitative measures of quality traits. In this study, we use TubAR, an image‐analysis R package, to generate quantitative measures of shape and skin color traits for use in genomic prediction. We developed and compared different genomic models based on additive and additive plus non‐additive relationship matrices for two aspects of skin color, redness, and lightness, and two aspects of shape, roundness, and length‐to‐width ratio for fresh market red and yellow potatoes grown in Minnesota between 2020 and 2022. Similarly, we used the much larger chipping potato population grown during the same time to develop a multi‐trait selection index including roundness, specific gravity, and yield. Traits ranged in heritability with shape traits falling between 0.23 and 0.85, and color traits falling between 0.34 and 0.91. Genetic effects were primarily additive with color traits showing the strongest effect (0.47), while shape traits varied based on market class. Modeling non‐additive effects did not significantly improve prediction models for quality traits. The combination of image analysis and genomic prediction presents a promising avenue for improving potato quality traits.
{"title":"Genomic prediction for potato (Solanum tuberosum) quality traits improved through image analysis","authors":"Muyideen Yusuf, Michael D. Miller, Thomas R. Stefaniak, Darrin Haagenson, Jeffrey B. Endelman, Asunta L. Thompson, Laura M. Shannon","doi":"10.1002/tpg2.20507","DOIUrl":"https://doi.org/10.1002/tpg2.20507","url":null,"abstract":"Potato (<jats:italic>Solanum tuberosum</jats:italic> L.) is the most widely grown vegetable in the world. Consumers and processors evaluate potatoes based on quality traits such as shape and skin color, making these traits important targets for breeders. Achieving and evaluating genetic gain is facilitated by precise and accurate trait measures. Historically, quality traits have been measured using visual rating scales, which are subject to human error and necessarily lump individuals with distinct characteristics into categories. Image analysis offers a method of generating quantitative measures of quality traits. In this study, we use TubAR, an image‐analysis R package, to generate quantitative measures of shape and skin color traits for use in genomic prediction. We developed and compared different genomic models based on additive and additive plus non‐additive relationship matrices for two aspects of skin color, redness, and lightness, and two aspects of shape, roundness, and length‐to‐width ratio for fresh market red and yellow potatoes grown in Minnesota between 2020 and 2022. Similarly, we used the much larger chipping potato population grown during the same time to develop a multi‐trait selection index including roundness, specific gravity, and yield. Traits ranged in heritability with shape traits falling between 0.23 and 0.85, and color traits falling between 0.34 and 0.91. Genetic effects were primarily additive with color traits showing the strongest effect (0.47), while shape traits varied based on market class. Modeling non‐additive effects did not significantly improve prediction models for quality traits. The combination of image analysis and genomic prediction presents a promising avenue for improving potato quality traits.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"53 1","pages":"e20507"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drought is a significant factor that causes yield loss in essential cereal crops such as sorghum [Sorghum bicolor (L.) Moench], necessitating the development of drought‐tolerant varieties adaptable to various water conditions. This study aimed to pinpoint drought‐tolerant sorghum lines and genomic regions for tolerance by utilizing 216 sorghum accessions in stressed and non‐stressed environments at two locations. Genetic diversity was evident among accessions in terms of grain yield under different watering regimes. Drought stress indices such as the stress tolerance index, mean productivity, geometric mean productivity, harmonic mean productivity, yield stability index, and yield index were identified as effective measures for selecting drought‐tolerant sorghum. Cluster analysis classified genotypes into four groups based on their association with grain yield, highlighting Acc. #28546 and Acc. #216739 as highly drought tolerant across environments. This study identified 32 and 22 quantitative trait nucleotides (QTNs) for drought indices and grain yield under stress and non‐stress conditions, respectively, at two locations, with five common QTNs linked to multiple drought indices. Colocation analysis revealed that these QTNs were associated with known stay‐green‐related quantitative trait loci (QTLs), and 47 putative genes near these QTNs potentially influenced drought tolerance traits. It is suggested that accession selection considers multiple indices for robust evaluation. Understanding the identified genes and their functions provides insights into the genetic mechanisms governing plant responses to drought stress, offering prospects for developing improved drought‐resistant sorghum varieties through further genetic research.
{"title":"Multi‐locus genome‐wide association study for grain yield and drought tolerance indices in sorghum accessions","authors":"Yirgalem Tsehaye, Temesgen M. Menamo, Fetien Abay, Taye Tadesse, Kassahun Bantte","doi":"10.1002/tpg2.20505","DOIUrl":"https://doi.org/10.1002/tpg2.20505","url":null,"abstract":"Drought is a significant factor that causes yield loss in essential cereal crops such as sorghum [<jats:italic>Sorghum bicolor</jats:italic> (L.) Moench], necessitating the development of drought‐tolerant varieties adaptable to various water conditions. This study aimed to pinpoint drought‐tolerant sorghum lines and genomic regions for tolerance by utilizing 216 sorghum accessions in stressed and non‐stressed environments at two locations. Genetic diversity was evident among accessions in terms of grain yield under different watering regimes. Drought stress indices such as the stress tolerance index, mean productivity, geometric mean productivity, harmonic mean productivity, yield stability index, and yield index were identified as effective measures for selecting drought‐tolerant sorghum. Cluster analysis classified genotypes into four groups based on their association with grain yield, highlighting Acc. #28546 and Acc. #216739 as highly drought tolerant across environments. This study identified 32 and 22 quantitative trait nucleotides (QTNs) for drought indices and grain yield under stress and non‐stress conditions, respectively, at two locations, with five common QTNs linked to multiple drought indices. Colocation analysis revealed that these QTNs were associated with known stay‐green‐related quantitative trait loci (QTLs), and 47 putative genes near these QTNs potentially influenced drought tolerance traits. It is suggested that accession selection considers multiple indices for robust evaluation. Understanding the identified genes and their functions provides insights into the genetic mechanisms governing plant responses to drought stress, offering prospects for developing improved drought‐resistant sorghum varieties through further genetic research.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"20 1","pages":"e20505"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hawlader A. Al‐Mamun, Monica F. Danilevicz, Jacob I. Marsh, Cedric Gondro, David Edwards
The surge in high‐throughput technologies has empowered the acquisition of vast genomic datasets, prompting the search for genetic markers and biomarkers relevant to complex traits. However, grappling with the inherent complexities of high dimensionality and sparsity within these datasets poses formidable hurdles. The immense number of features and their potential redundancy demand efficient strategies for extracting pertinent information and identifying significant markers. Feature selection is important in large genomic data as it helps in enhancing interpretability and computational efficiency. This study focuses on addressing these challenges through a comprehensive investigation into genomic feature selection methodologies, employing a rich soybean (Glycine max L. Merr.) dataset comprising 966 lines with over 5.5 million single nucleotide polymorphisms. Emphasizing the “small n large p” dilemma prevalent in contemporary genomic studies, we compared the efficacy of traditional genome‐wide association studies (GWAS) with two prominent machine learning tools, random forest and extreme gradient boosting, in pinpointing predictive features. Utilizing the expansive soybean dataset, we assessed the performance of these methodologies in selecting features that optimize predictive modeling for various phenotypes. By constructing predictive models based on the selected features, we ascertain the comparative prediction accuracies, thereby illuminating the strengths and limitations of these feature selection methodologies in the realm of genomic data analysis.
高通量技术的迅猛发展为获取庞大的基因组数据集提供了可能，促使人们寻找与复杂性状相关的遗传标记和生物标志物。然而，要解决这些数据集固有的高维性和稀疏性等复杂问题，却面临着巨大的障碍。大量的特征及其潜在的冗余性要求采用高效的策略来提取相关信息并识别重要标记。特征选择在大型基因组数据中非常重要，因为它有助于提高可解释性和计算效率。本研究通过对基因组特征选择方法的全面调查，采用丰富的大豆（Glycine max L. Merr.）数据集，包括 966 个品系和 550 多万个单核苷酸多态性，重点解决这些挑战。我们强调了当代基因组研究中普遍存在的 "小 n 大 p "困境，比较了传统的全基因组关联研究（GWAS）与随机森林和极端梯度提升这两种著名的机器学习工具在确定预测特征方面的功效。利用广阔的大豆数据集，我们评估了这些方法在选择优化各种表型预测模型的特征方面的性能。通过基于所选特征构建预测模型，我们确定了预测准确率的比较，从而阐明了这些特征选择方法在基因组数据分析领域的优势和局限性。
{"title":"Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large‐scale soybean dataset","authors":"Hawlader A. Al‐Mamun, Monica F. Danilevicz, Jacob I. Marsh, Cedric Gondro, David Edwards","doi":"10.1002/tpg2.20503","DOIUrl":"https://doi.org/10.1002/tpg2.20503","url":null,"abstract":"The surge in high‐throughput technologies has empowered the acquisition of vast genomic datasets, prompting the search for genetic markers and biomarkers relevant to complex traits. However, grappling with the inherent complexities of high dimensionality and sparsity within these datasets poses formidable hurdles. The immense number of features and their potential redundancy demand efficient strategies for extracting pertinent information and identifying significant markers. Feature selection is important in large genomic data as it helps in enhancing interpretability and computational efficiency. This study focuses on addressing these challenges through a comprehensive investigation into genomic feature selection methodologies, employing a rich soybean (<jats:italic>Glycine max</jats:italic> L. Merr.) dataset comprising 966 lines with over 5.5 million single nucleotide polymorphisms. Emphasizing the “<jats:italic>small n large p</jats:italic>” dilemma prevalent in contemporary genomic studies, we compared the efficacy of traditional genome‐wide association studies (GWAS) with two prominent machine learning tools, random forest and extreme gradient boosting, in pinpointing predictive features. Utilizing the expansive soybean dataset, we assessed the performance of these methodologies in selecting features that optimize predictive modeling for various phenotypes. By constructing predictive models based on the selected features, we ascertain the comparative prediction accuracies, thereby illuminating the strengths and limitations of these feature selection methodologies in the realm of genomic data analysis.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"36 1","pages":"e20503"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sibel Bahadır, Mohamed Farah Abdulla, Karam Mostafa, Musa Kavas, Safa Hacıkamiloğlu, Orhan Kurt, Kubilay Yıldırım
Plants produce numerous fatty acid derivatives, and some of these compounds have significant regulatory functions, such as governing effector‐induced resistance, systemic resistance, and other defense pathways. This study systematically identified and characterized eight FAT genes (Acyl‐acyl carrier protein thioesterases), four in the Solanum lycopersicum and four in the Solanum tuberosum genome. Phylogenetic analysis classified these genes into four distinct groups, exhibiting conserved domain structures across different plant species. Promoter analysis revealed various cis‐acting elements, most of which are associated with stress responsiveness and growth and development. Micro‐RNA (miRNA) analysis identified specific miRNAs, notably miRNA166, targeting different FAT genes in both species. Utilizing clustered regularly interspaced short palindromic repeats/CRISPR‐associated protein 9 (CRISPR/Cas9)‐mediated knockout, mutant lines for SlFATB1 and SlFATB3 were successfully generated and exhibited diverse mutation types. Biochemical evaluation of selected mutant lines revealed significant changes in fatty acid composition, with linoleic and linolenic acid content variations. The study also explored the impact of FAT gene knockout on tomato leaf architecture through scanning electron microscopy, providing insights into potential morphological alterations. Knocking out of FAT genes resulted in a significant reduction in both trichome and stoma density. These findings contribute to a comprehensive understanding of FAT genes in Solanaceous species, encompassing genetic, functional, and phenotypic aspects.
{"title":"Exploring the role of FAT genes in Solanaceae species through genome‐wide analysis and genome editing","authors":"Sibel Bahadır, Mohamed Farah Abdulla, Karam Mostafa, Musa Kavas, Safa Hacıkamiloğlu, Orhan Kurt, Kubilay Yıldırım","doi":"10.1002/tpg2.20506","DOIUrl":"https://doi.org/10.1002/tpg2.20506","url":null,"abstract":"Plants produce numerous fatty acid derivatives, and some of these compounds have significant regulatory functions, such as governing effector‐induced resistance, systemic resistance, and other defense pathways. This study systematically identified and characterized eight FAT genes (Acyl‐acyl carrier protein thioesterases), four in the <jats:italic>Solanum lycopersicum</jats:italic> and four in the <jats:italic>Solanum tuberosum</jats:italic> genome. Phylogenetic analysis classified these genes into four distinct groups, exhibiting conserved domain structures across different plant species. Promoter analysis revealed various cis‐acting elements, most of which are associated with stress responsiveness and growth and development. Micro‐RNA (miRNA) analysis identified specific miRNAs, notably miRNA166, targeting different FAT genes in both species. Utilizing clustered regularly interspaced short palindromic repeats/CRISPR‐associated protein 9 (CRISPR/Cas9)‐mediated knockout, mutant lines for <jats:italic>SlFATB1</jats:italic> and <jats:italic>SlFATB3</jats:italic> were successfully generated and exhibited diverse mutation types. Biochemical evaluation of selected mutant lines revealed significant changes in fatty acid composition, with linoleic and linolenic acid content variations. The study also explored the impact of FAT gene knockout on tomato leaf architecture through scanning electron microscopy, providing insights into potential morphological alterations. Knocking out of FAT genes resulted in a significant reduction in both trichome and stoma density. These findings contribute to a comprehensive understanding of FAT genes in <jats:italic>Solanaceous</jats:italic> species, encompassing genetic, functional, and phenotypic aspects.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"281 1","pages":"e20506"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Insights into changes in genome base composition underlying crop domestication can be gained by using comparative genomics. With this approach, previous studies have reported that crop genomes during domestication accumulate more nucleotides adenine (A) and thymine (T) (termed as [AT]‐increase) across polymorphic sites. However, the potential influence of the environment or its factors, for example, solar ultraviolet (UV) radiation and temperature, on the [AT]‐increase has not been well elucidated. Here, we investigated the [AT]‐increase in barley (Hordeum vulgare L.) and rice (Oryza sativa L.) and the association with natural environments, where accessions are distributed. With 12,798,376 and 2,861,535 single‐nucleotide polymorphisms from 368 barley and 1375 rice accessions, respectively, we discovered that [AT] increases from wild accessions to improved cultivars, and genomic regions with larger [AT]‐increase tend to have higher UV‐related motif frequencies, suggesting solar UV radiation as a potential factor in driving genome variation. To link [AT] change with the geographic distribution, we gathered georeferenced accessions and examined their local environments. Interestingly, negative correlations between [AT] and environmental factors were observed (r = −0.39 ∼ −0.75) and modern accessions with higher [AT] values, as compared with wild relatives, are from the environments with lower solar UV radiation or lower temperature. With [AT] and environmental factors as phenotypes, genome‐wide association mapping identified three candidate genes that have the potential to contribute to [AT] variation under the effect of environmental conditions. Our findings provide genomic and environmental insights into evolutionary pattern of DNA base composition and underlying mechanisms.
{"title":"Evolutionary patterns of DNA base composition at polymorphic sites highlight the role of the environment in shaping barley and rice genomes","authors":"Xiangjian Gou, Yang Shao, Xiao Wang, Haoran Shi, Jianming Yu, Xianran Li, Tingting Guo","doi":"10.1002/tpg2.20456","DOIUrl":"https://doi.org/10.1002/tpg2.20456","url":null,"abstract":"Insights into changes in genome base composition underlying crop domestication can be gained by using comparative genomics. With this approach, previous studies have reported that crop genomes during domestication accumulate more nucleotides adenine (A) and thymine (T) (termed as [AT]‐increase) across polymorphic sites. However, the potential influence of the environment or its factors, for example, solar ultraviolet (UV) radiation and temperature, on the [AT]‐increase has not been well elucidated. Here, we investigated the [AT]‐increase in barley (<jats:italic>Hordeum vulgare</jats:italic> L.) and rice (<jats:italic>Oryza sativa</jats:italic> L.) and the association with natural environments, where accessions are distributed. With 12,798,376 and 2,861,535 single‐nucleotide polymorphisms from 368 barley and 1375 rice accessions, respectively, we discovered that [AT] increases from wild accessions to improved cultivars, and genomic regions with larger [AT]‐increase tend to have higher UV‐related motif frequencies, suggesting solar UV radiation as a potential factor in driving genome variation. To link [AT] change with the geographic distribution, we gathered georeferenced accessions and examined their local environments. Interestingly, negative correlations between [AT] and environmental factors were observed (<jats:italic>r</jats:italic> = −0.39 ∼ −0.75) and modern accessions with higher [AT] values, as compared with wild relatives, are from the environments with lower solar UV radiation or lower temperature. With [AT] and environmental factors as phenotypes, genome‐wide association mapping identified three candidate genes that have the potential to contribute to [AT] variation under the effect of environmental conditions. Our findings provide genomic and environmental insights into evolutionary pattern of DNA base composition and underlying mechanisms.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140835620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Carl VanGessel, Brian Rice, Terry J. Felderhoff, Jean Rigaud Charles, G. Pressoir, V. Nalam, Geoffrey P. Morris
Durable host plant resistance (HPR) to insect pests is critical for sustainable agriculture. Natural variation exists for aphid HPR in sorghum (Sorghum bicolor), but the genetic architecture and phenotype have not been clarified and characterized for most sources. In order to assess the current threat of a sorghum aphid (Melanaphis sorghi) biotype shift, we characterized the phenotype of Resistance to Melanaphis sorghi 1 (RMES1) and additional HPR architecture in globally admixed populations selected under severe sorghum aphid infestation in Haiti. We found RMES1 reduces sorghum aphid fecundity but not bird cherry-oat aphid (Rhopalosiphum padi) fecundity, suggesting a discriminant HPR response typical of gene-for-gene interaction. A second resistant gene, Resistance to Melanaphis sorghi 2 (RMES2), was more frequent than RMES1 resistant alleles in landraces and historic breeding lines. RMES2 contributes early and mid-season aphid resistance in a segregating F2 population; however, RMES1 was only significant with mid-season fitness. In a fixed population with high sorghum aphid resistance, RMES1 and RMES2 were selected for demonstrating a lack of severe antagonistic pleiotropy. Associations with resistance colocated with cyanogenic glucoside biosynthesis genes support additional HPR sources. Globally, therefore, an HPR source vulnerable to biotype shift via selection pressure (RMES1) is bolstered by a second common source of resistance in breeding programs (RMES2), which may be staving off a biotype shift and is critical for sustainable sorghum production.
{"title":"Globally deployed sorghum aphid resistance gene RMES1 is vulnerable to biotype shifts but is bolstered by RMES2.","authors":"Carl VanGessel, Brian Rice, Terry J. Felderhoff, Jean Rigaud Charles, G. Pressoir, V. Nalam, Geoffrey P. Morris","doi":"10.1002/tpg2.20452","DOIUrl":"https://doi.org/10.1002/tpg2.20452","url":null,"abstract":"Durable host plant resistance (HPR) to insect pests is critical for sustainable agriculture. Natural variation exists for aphid HPR in sorghum (Sorghum bicolor), but the genetic architecture and phenotype have not been clarified and characterized for most sources. In order to assess the current threat of a sorghum aphid (Melanaphis sorghi) biotype shift, we characterized the phenotype of Resistance to Melanaphis sorghi 1 (RMES1) and additional HPR architecture in globally admixed populations selected under severe sorghum aphid infestation in Haiti. We found RMES1 reduces sorghum aphid fecundity but not bird cherry-oat aphid (Rhopalosiphum padi) fecundity, suggesting a discriminant HPR response typical of gene-for-gene interaction. A second resistant gene, Resistance to Melanaphis sorghi 2 (RMES2), was more frequent than RMES1 resistant alleles in landraces and historic breeding lines. RMES2 contributes early and mid-season aphid resistance in a segregating F2 population; however, RMES1 was only significant with mid-season fitness. In a fixed population with high sorghum aphid resistance, RMES1 and RMES2 were selected for demonstrating a lack of severe antagonistic pleiotropy. Associations with resistance colocated with cyanogenic glucoside biosynthesis genes support additional HPR sources. Globally, therefore, an HPR source vulnerable to biotype shift via selection pressure (RMES1) is bolstered by a second common source of resistance in breeding programs (RMES2), which may be staving off a biotype shift and is critical for sustainable sorghum production.","PeriodicalId":501653,"journal":{"name":"The Plant Genome","volume":"33 30","pages":"e20452"},"PeriodicalIF":0.0,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140671841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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