Variation block-based genomics method for crop plants

ggComp enables dissection of germplasm resources and construction of a multiscale germplasm network in wheat

Accurate germplasm characterization is a vital step for accelerating crop genetic improvement, which remains largely infeasible for crops such as bread wheat (Triticum aestivum L.), which has a complex genome that undergoes frequent introgression and contains many structural variations. Here, we propose a genomic strategy called ggComp, which integrates resequencing data with copy number variations and stratified single-nucleotide polymorphism densities to enable unsupervised identification of pairwise germplasm resource-based Identity-By-Descent (gIBD) blocks. The reliability of ggComp was verified in wheat cultivar Nongda5181 by dissecting parental-descent patterns represented by inherited genomic blocks. With gIBD blocks identified among 212 wheat accessions, we constructed a multi-scale genomic-based germplasm network. At the whole-genome level, the network helps to clarify pedigree relationship, demonstrate genetic flow, and identify key founder lines. At the chromosome level, we were able to trace the utilization of 1RS introgression in modern wheat breeding by hitchhiked segments. At the single block scale, the dissected germplasm-based haplotypes nicely matched with previously identified alleles of "Green Revolution" genes and can guide allele mining and dissect the trajectory of beneficial alleles in wheat breeding. Our work presents a model-based framework for precisely evaluating germplasm resources with genomic data. A database, WheatCompDB (http://wheat.cau.edu.cn/WheatCompDB/), is available for researchers to exploit the identified gIBDs with a multi-scale network.

Development of 50 InDel-based barcode system for genetic identification of tartary buckwheat resources

Tartary buckwheat (Fagopyrum tataricum Gartn.) is a highly functional crop that is poised to be the target of many future breeding efforts. The reliable ex situ conservation of various genetic resources is essential for the modern breeding of tartary buckwheat varieties. We developed PCR-based co-dominant insertion/deletion (InDel) markers to discriminate tartary buckwheat genetic resources. First, we obtained the whole genome from 26 accessions across a superscaffold-scale reference genome of 569.37 Mb for tartary buckwheat cv. "Daegwan 3-7." Next, 171,926 homogeneous and 53,755 heterogeneous InDels were detected by comparing 26 accessions with the "Daegwan 3-7" reference sequence. Of these, 100 candidate InDels ranging from 5-20 bp in length were chosen for validation, and 50 of them revealed polymorphisms between the 26 accessions and "Daegwan 3-7." The validated InDels were further tested through the assessment of their likelihood to give rise to a single or a few PCR products in 50 other accessions, covering most tartary buckwheat genome types. The major allele frequencies ranged from 0.5616 at the TB42 locus to 0.9863 at the TB48 locus, with the average PIC value of 0.1532 with a range of 0.0267-0.3712. To create a user-friendly system, the homology of the genotypes between and among the accessions were visualized in both one- (1D) and two-dimensional (2D) barcode types by comparing amplicon polymorphisms with the reference variety, "Daegwan 3-7." A phylogenetic tree and population structure of the 76 accessions according to amplicon polymorphisms for the 50 InDel markers corresponded to those using non-synonymous single nucleotide polymorphism variants, indicating that the barcode system based on the 50 InDels was a useful tool to improve the reliability of identification of tartary buckwheat accessions in the germplasm stocks.

Barcode System for Genetic Identification of Soybean [Glycine max (L.) Merrill] Cultivars Using InDel Markers Specific to Dense Variation Blocks

For genetic identification of soybean [Glycine max (L.) Merrill] cultivars, insertions/deletions (InDel) markers have been preferred currently because they are easy to use, co-dominant and relatively abundant. Despite their biological importance, the investigation of InDels with proven quality and reproducibility has been limited. In this study, we described soybean barcode system approach based on InDel makers, each of which is specific to a dense variation block (dVB) with non-random recombination due to many variations. Firstly, 2,274 VBs were mined by analyzing whole genome data in six soybean cultivars (Backun, Sinpaldal 2, Shingi, Daepoong, Hwangkeum, and Williams 82) for transferability to dVB-specific InDel markers. Secondly, 73,327 putative InDels in the dVB regions were identified for the development of soybean barcode system. Among them, 202 dVB-specific InDels from all soybean cultivars were selected by gel electrophoresis, which were converted as 2D barcode types according to comparing amplicon polymorphisms in the five cultivars to the reference cultivar. Finally, the polymorphism of the markers were assessed in 147 soybean cultivars, and the soybean barcode system that allows a clear distinction among soybean cultivars is also detailed. In addition, the changing of the dVBs in a chromosomal level can be quickly identified due to investigation of the reshuffling pattern of the soybean cultivars with 27 maker sets. Especially, a backcross-inbred offspring, "Singang" and a recurrent parent, "Sowon" were identified by using the 27 InDel markers. These results indicate that the soybean barcode system enables not only the minimal use of molecular markers but also comparing the data from different sources due to no need of exploiting allele binning in new varieties.

A Genome-Wide Association Study for Agronomic Traits in Soybean Using SNP Markers and SNP-Based Haplotype Analysis

Mapping quantitative trait loci through the use of linkage disequilibrium (LD) in populations of unrelated individuals provides a valuable approach for dissecting the genetic basis of complex traits in soybean (Glycine max). The haplotype-based genome-wide association study (GWAS) has now been proposed as a complementary approach to intensify benefits from LD, which enable to assess the genetic determinants of agronomic traits. In this study a GWAS was undertaken to identify genomic regions that control 100-seed weight (SW), plant height (PH) and seed yield (SY) in a soybean association mapping panel using single nucleotide polymorphism (SNP) markers and haplotype information. The soybean cultivars (N = 169) were field-evaluated across four locations of southern Brazil. The genome-wide haplotype association analysis (941 haplotypes) identified eleven, seventeen and fifty-nine SNP-based haplotypes significantly associated with SY, SW and PH, respectively. Although most marker-trait associations were environment and trait specific, stable haplotype associations were identified for SY and SW across environments (i.e., haplotypes Gm12_Hap12). The haplotype block 42 on Chr19 (Gm19_Hap42) was confirmed to be associated with PH in two environments. These findings enable us to refine the breeding strategy for tropical soybean, which confirm that haplotype-based GWAS can provide new insights on the genetic determinants that are not captured by the single-marker approach.

Recent breeding programs enhanced genetic diversity in both desi and kabuli varieties of chickpea (Cicer arietinum L.)

In order to understand the impact of breeding on genetic diversity and gain insights into temporal trends in diversity in chickpea, a set of 100 chickpea varieties released in 14 countries between 1948 and 2012 were re-sequenced. For analysis, the re-sequencing data for 29 varieties available from an earlier study was also included. Copy number variations and presence absence variations identified in the present study have potential to drive phenotypic variations for trait improvement. Re-sequencing of a large number of varieties has provided opportunities to inspect the genetic and genomic changes reflecting the history of breeding, which we consider as breeding signatures and the selected loci may provide targets for crop improvement. Our study also reports enhanced diversity in both desi and kabuli varieties as a result of recent chickpea breeding efforts. The current study will aid the explicit efforts to breed for local adaptation in the context of anticipated climate changes.

InDel marker detection by integration of multiple softwares using machine learning techniques

Background

In the biological experiments of soybean species, molecular markers are widely used to verify the soybean genome or construct its genetic map. Among a variety of molecular markers, insertions and deletions (InDels) are preferred with the advantages of wide distribution and high density at the whole-genome level. Hence, the problem of detecting InDels based on next-generation sequencing data is of great importance for the design of InDel markers. To tackle it, this paper integrated machine learning techniques with existing software and developed two algorithms for InDel detection, one is the best F-score method (BF-M) and the other is the Support Vector Machine (SVM) method (SVM-M), which is based on the classical SVM model.

Results

The experimental results show that the performance of BF-M was promising as indicated by the high precision and recall scores, whereas SVM-M yielded the best performance in terms of recall and F-score. Moreover, based on the InDel markers detected by SVM-M from soybeans that were collected from 56 different regions, highly polymorphic loci were selected to construct an InDel marker database for soybean.

Conclusions

Compared to existing software tools, the two algorithms proposed in this work produced substantially higher precision and recall scores, and remained stable in various types of genomic regions. Moreover, based on SVM-M, we have constructed a database for soybean InDel markers and published it for academic research.

PopGeV: a web-based large-scale population genome browser

MOTIVATION

The development of high-throughput sequencing technology has made it possible for more and more researchers to use population sequencing data to mine genes associated with specific traits. However, the massive amounts of sequencing data have also brought new challenges to the researchers. The question of how to browse population genomic data in an easy and intuitive manner must be addressed. Web-based genome browsers allow user to conveniently view the results of genomic analyses, but heavy usage can reduce the response speed of the webpage, which limits its usefulness in the display of large-scale genome data. IndexedDB technology is a good solution to this problem; it supports web browsers and so creates local databases. In this way, data can be read from the local storage, achieving a smooth display of population genomic data.

RESULTS

PopGeV has the following characteristics. First, it uses a new encoding method for compression of population SNP and INDEL data. IndexedDB technology is used to download the results to local storage so that users can browse the results smoothly even when the network traffic is heavy. Second, PopGeV identify similar genomic regions between two individuals based on SNP data. Population diversity indexes are calculated when comparing two populations. Third, user defined annotation information can be integrated for user-friendly mining of gene functions. Simulation shows that PopGeV can smoothly display analysis results of population genome containing over 500 individuals with 2 millions SNP data.

AVAILABILITY AND IMPLEMENTATION

PopGeV is available at www.soyomics.com/popgev/

CONTACT

yuanxh@iga.ac.cn.

Introgression browser: high-throughput whole-genome SNP visualization

Breeding by introgressive hybridization is a pivotal strategy to broaden the genetic basis of crops. Usually, the desired traits are monitored in consecutive crossing generations by marker-assisted selection, but their analyses fail in chromosome regions where crossover recombinants are rare or not viable. Here, we present the Introgression Browser (iBrowser), a bioinformatics tool aimed at visualizing introgressions at nucleotide or SNP (Single Nucleotide Polymorphisms) accuracy. The software selects homozygous SNPs from Variant Call Format (VCF) information and filters out heterozygous SNPs, multi-nucleotide polymorphisms (MNPs) and insertion-deletions (InDels). For data analysis iBrowser makes use of sliding windows, but if needed it can generate any desired fragmentation pattern through General Feature Format (GFF) information. In an example of tomato (Solanum lycopersicum) accessions we visualize SNP patterns and elucidate both position and boundaries of the introgressions. We also show that our tool is capable of identifying alien DNA in a panel of the closely related S. pimpinellifolium by examining phylogenetic relationships of the introgressed segments in tomato. In a third example, we demonstrate the power of the iBrowser in a panel of 597 Arabidopsis accessions, detecting the boundaries of a SNP-free region around a polymorphic 1.17 Mbp inverted segment on the short arm of chromosome 4. The architecture and functionality of iBrowser makes the software appropriate for a broad set of analyses including SNP mining, genome structure analysis, and pedigree analysis. Its functionality, together with the capability to process large data sets and efficient visualization of sequence variation, makes iBrowser a valuable breeding tool.