Searching the Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
Root system plays an essential role in water and nutrient acquisition in plants. Understanding the genetic basis of root development will be beneficial for breeding new cultivars with efficient root system to enhance resource use efficiency in maize. Here, the natural variation of 13 root and 3 shoot traits was evaluated in 297 maize inbred lines and genome-wide association mapping was conducted to identify SNPs associated with target traits. All measured traits exhibited 2.02- to 21.36-fold variations. A total of 34 quantitative trait loci (QTLs) were detected for 13 traits, and each individual QTL explained 5.7% to 15.9% of the phenotypic variance. Three pleiotropic QTLs involving five root traits were identified; SNP_2_104416607 was associated with lateral root length (LRL), root surface area (RA), root length between 0 and 0.5mm in diameter (RL005), and total root length (TRL); SNP_2_184016997 was associated with RV and RA, and SNP_4_168917747 was associated with LRL, RA and TRL. The expression levels of candidate genes in root QTLs were evaluated by RNA-seq among three long-root lines and three short-root lines. A total of five genes that showed differential expression between the long- and short-root lines were identified as promising candidate genes for the target traits. These QTLs and the potential candidate genes are important source data to understand root development and genetic improvement of root traits in maize.
Pubmed ID: 31581635
Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.
A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)
View all literature mentionsOriginal SAMTOOLS package has been split into three separate repositories including Samtools, BCFtools and HTSlib. Samtools for manipulating next generation sequencing data used for reading, writing, editing, indexing,viewing nucleotide alignments in SAM,BAM,CRAM format. BCFtools used for reading, writing BCF2,VCF, gVCF files and calling, filtering, summarising SNP and short indel sequence variants. HTSlib used for reading, writing high throughput sequencing data.
View all literature mentionsGenetic variant annotation and effect prediction software toolbox that annotates and predicts effects of variants on genes (such as amino acid changes). By using standards, such as VCF, SnpEff makes it easy to integrate with other programs.
View all literature mentionsCollection of data related to crop plant and model organism Zea mays. Used to synthesize, display, and provide access to maize genomics and genetics data, prioritizing mutant and phenotype data and tools, structural and genetic map sets, and gene models and to provide support services to the community of maize researchers. Data stored at MaizeGDB was inherited from the MaizeDB and ZmDB projects. Sequence data are from GenBank. Data are searchable by phenotype, traits, Pests, Gel Pattern, and Mutant Images.
View all literature mentionsSoftware package which performs a variety of genetic analyses including association mapping, diversity estimation and calculating linkage disequilibrium. The association analysis between genotypes and phenotypes can be performed by either a general linear model or a mixed linear model. The general linear model now allows users to analyze complex field designs, environmental interactions, and epistatic interactions. The mixed model is specially designed to handle polygenic effects at multiple levels of relatedness including pedigree information. These new analyses should permit association analysis in a wide range plant and animal species. (entry from Genetic Analysis Software)
View all literature mentionsA read summarization program, which counts mapped reads for the genomic features such as genes and exons.
View all literature mentionsGraph-based alignment of next generation sequencing reads to a population of genomes.
View all literature mentions