Unraveling growth molecular mechanisms inPinus taedawith GWAS, machine learning, and gene coexpression networks DOI Creative Commons
Alexandre Hild Aono, Stephanie Karenina Bajay, Felipe Roberto Francisco

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Dec. 10, 2022

Abstract Pinus taeda (loblolly pine [LP]) is a long-lived tree species and one of the most economically significant forest species. Among growth traits, volume widely considered trait in improvement programs. However, deciphering genetic variants responsible for variations conifers, such as LP, particularly challenging due to vast size intricate complexity genomes. We present comprehensive analysis focusing on markers associated with stem variation, elucidate molecular mechanisms governing high-performance phenotypes. used population 1,692 individuals phenotyped genotyped these using sequence capture probes. To conduct genome-wide associations, we utilized both association study (GWAS) machine learning (ML) approaches. The identified were found be linked genes assembled from three distinct transcriptomes. These subsequently construct gene coexpression networks, through topological evaluations, key potential regulatory roles within configurations. Using set 31,589 SNPs, defined 7 GWAS-associated SNPs 128 ML-associated markers, all which correlated multiple involved diverse biological functions. Gene revealed group 270 potentially regulation material. Key directly implicated response stress identified, inferences about their impact development elucidated. Our not only offers insights into but also elucidates subset characterized by unique features. findings significantly advance our understanding factors influencing reveal candidate future functional studies, contribute broader comprehension architecture underlying traits LP.

Language: Английский

Factors affecting the production of sugarcane yield and sucrose accumulation: suggested potential biological solutions DOI Creative Commons

Faisal Mehdi,

Zhengying Cao,

Shuzhen Zhang

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: May 13, 2024

Environmental stresses are the main constraints on agricultural productivity and food security worldwide. This issue is worsened by abrupt severe changes in global climate. The formation of sugarcane yield accumulation sucrose significantly influenced biotic abiotic stresses. Understanding biochemical, physiological, environmental phenomena associated with these essential to increase crop production. review explores effect factors content highlights negative effects insufficient water supply, temperature fluctuations, insect pests, diseases. article also explains mechanism reactive oxygen species (ROS), role different metabolites under stresses, function stress-related resistance genes sugarcane. further discusses improvement approaches, a focus endophytic consortium endophyte application plants. Endophytes vital plant defense; they produce bioactive molecules that act as biocontrol agents enhance immune systems modify responses through interaction provides an overview internal mechanisms growth offers new ideas for improving fitness productivity.

Language: Английский

Citations

13
Genomic Exploration for the Sucrose Content in Sugarcane DOI

N. Aswini,

J. Moniusha,

S. Keerthana

et al.

Tropical Plant Biology, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 28, 2025

Language: Английский

Citations

0
Genomics‐based plant disease resistance prediction using machine learning DOI Creative Commons
Shriprabha R. Upadhyaya, Monica F. Danilevicz, Aria Dolatabadian

et al.

Plant Pathology, Journal Year: 2024, Volume and Issue: 73(9), P. 2298 - 2309

Published: Aug. 29, 2024

Abstract Plant disease outbreaks continuously challenge food security and sustainability. Traditional chemical methods used to treat diseases have environmental health concerns, raising the need enhance inherent plant resistance mechanisms. Traits, including resistance, can be linked specific loci in genome identifying these markers facilitates targeted breeding approaches. Several methods, genome‐wide association studies genomic selection, been identify important select varieties with desirable traits. However, traditional approaches may not fully capture non‐linear characteristics of effect variation on Machine learning, known for its data‐mining abilities, offers an opportunity accuracy existing trait It has found applications predicting various agronomic traits across several species. use prediction remains limited. This review highlights potential machine learning as a complementary tool genetic contributing pathogen resistance. We provide overview summarize machine‐learning applications, address challenges opportunities associated learning‐based crop prediction.

Language: Английский

Citations

2
Sugarcane DOI
Marcos César Gonçalves, Luciana Rossini Pinto, Ricardo José Gonzaga Pimenta

et al.

Elsevier eBooks, Journal Year: 2023, Volume and Issue: unknown, P. 193 - 205

Published: Sept. 22, 2023

Language: Английский

Citations

2
Produtos ecológicos para a Indústria da Construção Civil DOI Open Access
Rafael Rodrigo Ferreira de Lima

Cerâmica Industrial, Journal Year: 2024, Volume and Issue: 29, P. e0529 - e0529

Published: Jan. 1, 2024

A indústria da construção civil é um importante e necessário segmento industrial presente em todos os países nas mais recônditas localidades, promovendo a viabilidade vida humana nos diferentes ambientes. No entanto, reconhecidamente uma de alto impacto no meio ambiente, requerendo, conforme descrito Objetivos Desenvolvimento Sustentável, ações para mitigar efeitos deletérios dos seus processos produtivos. Dessa maneira, o fomento intersetorial entre canavicultura pode gerar meios viáveis manutenção das atividades desses setores enquanto produtos são adequados às necessidades emergentes sustentabilidade. Neste artigo, objetiva-se, portanto, revisar as características físico-químicas do resíduo cana-de-açúcar suas possíveis aplicações na forma contribuir elucidação clara objetiva atual estágio desse processo inovação servir base continuidade busca sustentáveis envolvendo intersetorialidade agricultura.

Citations

0
Multiomic insights into sucrose accumulation in sugarcane DOI Creative Commons
Alexandre Hild Aono, Ricardo José Gonzaga Pimenta, Jéssica Faversani Diniz

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 19, 2024

Abstract Sugarcane ( Saccharum spp.) holds significant economic importance in sugar and biofuel production. Despite extensive research, understanding highly quantitative traits, such as sucrose content, remains challenging due to the complex genomic landscape of crop. In this study, we conducted a multiomic investigation elucidate genetic architecture molecular mechanisms governing accumulation sugarcane. Using biparental cross (IACSP95-3018 × IACSP93-3046) genetically diverse collection sugarcane genotypes, evaluated soluble solids (Brix) content (POL) across various years environments. Both populations were genotyped using genotyping-by-sequencing (GBS) approach, with single nucleotide polymorphisms (SNPs) identified via bioinformatics pipelines. Genotype‒phenotype associations established combination traditional linear mixed-effect models machine learning algorithms. Furthermore, an RNA sequencing (RNA-Seq) experiment on genotypes exhibiting distinct Brix POL profiles different developmental stages. Differentially expressed genes (DEGs) potentially associated variations identified. All findings integrated through comprehensive gene coexpression network analysis. Strong correlations among characteristics observed, estimates modest high heritabilities. By leveraging broad set SNPs for both populations, several linked phenotypic variance. Our examination close these markers facilitated association DEGs contrasting levels. Through integration results network, delineated involved regulatory sugarcane, collectively contributing definition critical phenotype. constitute resource biotechnology plant breeding initiatives. our genotype‒phenotype hold promise application selection, offering valuable insights into underpinnings

Language: Английский

Citations

0
Unraveling growth molecular mechanisms inPinus taedawith GWAS, machine learning, and gene coexpression networks DOI Creative Commons
Alexandre Hild Aono, Stephanie Karenina Bajay, Felipe Roberto Francisco

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Dec. 10, 2022

Abstract Pinus taeda (loblolly pine [LP]) is a long-lived tree species and one of the most economically significant forest species. Among growth traits, volume widely considered trait in improvement programs. However, deciphering genetic variants responsible for variations conifers, such as LP, particularly challenging due to vast size intricate complexity genomes. We present comprehensive analysis focusing on markers associated with stem variation, elucidate molecular mechanisms governing high-performance phenotypes. used population 1,692 individuals phenotyped genotyped these using sequence capture probes. To conduct genome-wide associations, we utilized both association study (GWAS) machine learning (ML) approaches. The identified were found be linked genes assembled from three distinct transcriptomes. These subsequently construct gene coexpression networks, through topological evaluations, key potential regulatory roles within configurations. Using set 31,589 SNPs, defined 7 GWAS-associated SNPs 128 ML-associated markers, all which correlated multiple involved diverse biological functions. Gene revealed group 270 potentially regulation material. Key directly implicated response stress identified, inferences about their impact development elucidated. Our not only offers insights into but also elucidates subset characterized by unique features. findings significantly advance our understanding factors influencing reveal candidate future functional studies, contribute broader comprehension architecture underlying traits LP.

Language: Английский

Citations

0