Identification and validation of quantitative trait loci for fertile spikelet number per spike and grain number per fertile spikelet in bread wheat (Triticum aestivum L.)

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

A major and stable QTL for fertile spikelet number per spike and grain number per fertile spikelet identified in a 4.96-Mb interval on chromosome 2A was validated in different genetic backgrounds. Fertile spikelet number per spike (FSN) and grain number per fertile spikelet (GNFS) contribute greatly to wheat yield improvement. To detect quantitative trait loci (QTL) associated with FSN and GNFS, we used a recombinant inbred line population crossed by Zhongkemai 13F10 and Chuanmai 42 in eight environments. Two Genomic regions associated with FSN were detected on chromosomes 2A and 6A using bulked segregant exome sequencing analysis. After the genetic linkage maps were constructed, four QTL QFsn.cib-2A, QFsn.cib-6A, QGnfs.cib-2A and QGnfs.cib-6A were identified in three or more environments. Among them, two major QTL QFsn.cib-2A (LOD = 4.67-9.34, PVE = 6.66-13.05%) and QGnfs.cib-2A (LOD = 5.27-11.68, PVE = 7.95-16.71%) were detected in seven and six environments, respectively. They were co-located in the same region, namely QFsn/Gnfs.cib-2A. The developed linked Kompetitive Allele Specific PCR (KASP) markers further validated this QTL in a different genetic background. QFsn/Gnfs.cib-2A showed pleiotropic effects on grain number per spike (GNS) and spike compactness (SC), and had no effect on grain weight. Since QFsn/Gnfs.cib-2A might be a new locus, it and the developed KASP markers can be used in wheat breeding. According to haplotype analysis, QFsn/Gnfs.cib-2A was identified as a target of artificial selection during wheat improvement. Based on haplotype analysis, sequence differences, spatiotemporal expression patterns, and gene annotation, the potential candidate genes for QFsn/Gnfs.cib-2A were predicted. These results provide valuable information for fine mapping and cloning gene(s) underlying QFsn/Gnfs.cib-2A.

Related collections

Most cited references 58

Record: found
Abstract: found
Article: found

Is Open Access

fastp: an ultra-fast all-in-one FASTQ preprocessor

Shifu Chen, Yanqing Zhou, Yaru Chen … (2018)

Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

0 comments Cited 6505 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

TBtools - an integrative toolkit developed for interactive analyses of big biological data

Chengjie Chen, Hao Chen, Yi Zhang … (2020)

The rapid development of high-throughput sequencing techniques has led biology into the big-data era. Data analyses using various bioinformatics tools rely on programming and command-line environments, which are challenging and time-consuming for most wet-lab biologists. Here, we present TBtools (a Toolkit for Biologists integrating various biological data-handling tools), a stand-alone software with a user-friendly interface. The toolkit incorporates over 130 functions, which are designed to meet the increasing demand for big-data analyses, ranging from bulk sequence processing to interactive data visualization. A wide variety of graphs can be prepared in TBtools using a new plotting engine ("JIGplot") developed to maximize their interactive ability; this engine allows quick point-and-click modification of almost every graphic feature. TBtools is platform-independent software that can be run under all operating systems with Java Runtime Environment 1.6 or newer. It is freely available to non-commercial users at https://github.com/CJ-Chen/TBtools/releases.

0 comments Cited 3635 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

Kai Wang, Mingyao Li, Hakon Hakonarson (2010)

High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .