About the Journal

The Plant Genome is a gold open access journal that provides the latest advances and breakthroughs in plant genomics research, with special attention to innovative genomic applications that advance our understanding of plant biology that may have applications to crop improvement.


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Featured Article

Two blossoms with purple and white leaves
Sequencing the USDA core soybean collection reveals gene loss during domestication and breeding

The gene content of plants varies between individuals of the same species due to gene presence/absence variation, and selection can alter the frequency of specific genes in a population. Selection during domestication and breeding will modify the genomic landscape, though the nature of these modifications is only understood for specific genes or on a more general level (e.g., by a loss of genetic diversity). In a paper scheduled for the upcoming special issue ‘Pangenomics in Crop Improvement’, Bayer et al. assembled and analyzed a soybean pangenome representing more than 1,000 soybean accessions derived from the USDA Soybean Germplasm Collection, including both wild and cultivated lineages, to assess genomewide changes in gene and allele frequency during domestication and breeding.  Read more

Browse Articles

Open access

Selection signatures in the CIMMYT International Elite Spring and Semi‐arid Wheat Yield Trials

  •  8 November 2021

Core Ideas

  • Genomic changes may be tracked across internationally distributed CIMMYT wheat lines.
  • Structure analysis revealed admixture among elite lines targeted to different environments.
  • Genome scans identified large linkage blocks in several chromosomes.
  • A higher percentage of selection signatures resulted from selection over time than over environments.

Open access

Identification and evolutionary analysis of the metal‐tolerance protein family in eight Cucurbitaceae species

  •  6 November 2021

Core Ideas

  • A total of 142 metal-tolerance proteins (MTPs) were identified in eight Cucurbitaceae species.
  • Cucurbitaceae MTPs were under strong purification selection.
  • Cucurbitaceae MTPs showed tissues and treatment-specific expression patterns.
  • Cucumber MTPs were induced by metal ions.

Open access

Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs

  •  1 November 2021

Core Ideas

  • Machine learning models can increase accuracy but can be inconsistent in individual years.
  • Combining training populations over environments can increase the accuracy of complex traits.
  • Breeding lines can increase accuracy over mapping populations within breeding programs.
  • Parametric models can be used efficiently to predict complex traits in breeding programs.
  • Choice of model depends on structure of training population.

Open access

Detection of rare nematode resistance Rhg1 haplotypes in Glycine soja and a novel Rhg1 α‐SNAP

  •  30 October 2021

Core Ideas

  • Germplasm carrying useful alleles can be identified using SNP data for other genes.
  • Glycine soja accessions with potentially valuable SCN resistance were identified.
  • A novel variant of the Rhg1 α-SNAP SCN resistance protein was identified and characterized.
  • The unusual Rhg1 multigene copy number variation structure arose prior to the domestication of soybean.

Open access

Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa

  •  26 October 2021

Core Ideas

  • Common bean varied in grain yield, cooking time, iron, and zinc in East African field trials.
  • Significant genomic heritability was present for all traits in an African bean panel.
  • Favorable genetic correlations were found between high grain yield and high iron and zinc.
  • Favorable genetic correlations were found between short cooking time and high iron and zinc.
  • High genetic gains were predicted with genomic selection and optimal contributions selection.

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Open access

Status and prospects of genome‐wide association studies in plants

Core Ideas

  • GWAS dissect complex traits by testing genome-wide SNPs across an assembled population.
  • Unified mixed-model GWAS control for both population structure and kinship.
  • New GWAS methods build on this widely adopted mixed model foundation.
  • Ongoing challenges call for the further development of GWAS methods and software.

Open access

Systems biology for crop improvement

Core Ideas

  • Multiomics data enable understanding and prediction of plant phenotype with higher precision.
  • Systems biology approach involves multiomics data integration and modeling.
  • Integrative systems biology aids better prediction of cellular functions of complex traits.
  • Advancements, challenges, and opportunities in systems biology research are reviewed.
  • Prospects of systems biology to crop improvement are presented.

Open access

Introgression of “QTL‐hotspot” region enhances drought tolerance and grain yield in three elite chickpea cultivars

Core Ideas

  • Sixty-one backcross progenies with >90% recurrent parent genome recovery were developed.
  • Six superior lines with enhanced drought tolerance and yield performance were nominated for national yield trials in India.
  • Pusa Chickpea 10216, the first molecular breeding variety for drought tolerance, was released in India.

Open access

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

Abstract

Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR), which is equivalent to best linear unbiased prediction (BLUP) when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identification of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was significantly higher than RR for wheat (Triticum aestivum L.) grain yield but equivalent for several maize (Zea mays L.) traits.

Open access

Status and Prospects of Association Mapping in Plants

Abstract

There is tremendous interest in using association mapping to identify genes responsible for quantitative variation of complex traits with agricultural and evolutionary importance. Recent advances in genomic technology, impetus to exploit natural diversity, and development of robust statistical analysis methods make association mapping appealing and affordable to plant research programs. Association mapping identifies quantitative trait loci (QTLs) by examining the marker-trait associations that can be attributed to the strength of linkage disequilibrium between markers and functional polymorphisms across a set of diverse germplasm. General understanding of association mapping has increased significantly since its debut in plants. We have seen a more concerted effort in assembling various association-mapping populations and initiating experiments through either candidate-gene or genome-wide approaches in different plant species. In this review, we describe the current status of association mapping in plants and outline opportunities and challenges in complex trait dissection and genomics-assisted crop improvement.

Open access

Genotyping‐by‐Sequencing for Plant Breeding and Genetics

Abstract

Rapid advances in “next-generation” DNA sequencing technology have brought the US$1000 human (Homo sapiens) genome within reach while providing the raw sequencing output for researchers to revolutionize the way populations are genotyped. To capitalize on these advancements, genotyping-by-sequencing (GBS) has been developed as a rapid and robust approach for reduced-representation sequencing of multiplexed samples that combines genome-wide molecular marker discovery and genotyping. The flexibility and low cost of GBS makes this an excellent tool for many applications and research questions in plant genetics and breeding. Here we address some of the new research opportunities that are becoming more feasible with GBS. Furthermore, we highlight areas in which GBS will become more powerful with the continued increase of sequencing output, development of reference genomes, and improvement of bioinformatics. The ultimate goal of plant biology scientists is to connect phenotype to genotype. In plant breeding, the genotype can then be used to predict phenotypes and select improved cultivars. Furthering our understanding of the connection between heritable genetic factors and the resulting phenotypes will enable genomics-assisted breeding to exist on the scale needed to increase global food supplies in the face of decreasing arable land and climate change.

Open access

Genomic Selection in Wheat Breeding using Genotyping‐by‐Sequencing

Abstract

Genomic selection (GS) uses genomewide molecular markers to predict breeding values and make selections of individuals or breeding lines prior to phenotyping. Here we show that genotyping-by-sequencing (GBS) can be used for de novo genotyping of breeding panels and to develop accurate GS models, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS we discovered 41,371 single nucleotide polymorphisms (SNPs) in a set of 254 advanced breeding lines from CIMMYT's semiarid wheat breeding program. Four different methods were evaluated for imputing missing marker scores in this set of unmapped markers, including random forest regression and a newly developed multivariate-normal expectation-maximization algorithm, which gave more accurate imputation than heterozygous or mean imputation at the marker level, although no significant differences were observed in the accuracy of genomic-estimated breeding values (GEBVs) among imputation methods. Genomic-estimated breeding value prediction accuracies with GBS were 0.28 to 0.45 for grain yield, an improvement of 0.1 to 0.2 over an established marker platform for wheat. Genotyping-by-sequencing combines marker discovery and genotyping of large populations, making it an excellent marker platform for breeding applications even in the absence of a reference genome sequence or previous polymorphism discovery. In addition, the flexibility and low cost of GBS make this an ideal approach for genomics-assisted breeding.

Open access

GAPIT Version 2: An Enhanced Integrated Tool for Genomic Association and Prediction

Abstract

Core Ideas

  • Genome-wide association study
  • Genomic prediction
  • Simulation and experimental design

Most human diseases and agriculturally important traits are complex. Dissecting their genetic architecture requires continued development of innovative and powerful statistical methods. Corresponding advances in computing tools are critical to efficiently use these statistical innovations and to enhance and accelerate biomedical and agricultural research and applications. The genome association and prediction integrated tool (GAPIT) was first released in 2012 and became widely used for genome-wide association studies (GWAS) and genomic prediction. The GAPIT implemented computationally efficient statistical methods, including the compressed mixed linear model (CMLM) and genomic prediction by using genomic best linear unbiased prediction (gBLUP). New state-of-the-art statistical methods have now been implemented in a new, enhanced version of GAPIT. These methods include factored spectrally transformed linear mixed models (FaST-LMM), enriched CMLM (ECMLM), FaST-LMM-Select, and settlement of mixed linear models under progressively exclusive relationship (SUPER). The genomic prediction methods implemented in this new release of the GAPIT include gBLUP based on CMLM, ECMLM, and SUPER. Additionally, the GAPIT was updated to improve its existing output display features and to add new data display and evaluation functions, including new graphing options and capabilities, phenotype simulation, power analysis, and cross-validation. These enhancements make the GAPIT a valuable resource for determining appropriate experimental designs and performing GWAS and genomic prediction. The enhanced R-based GAPIT software package uses state-of-the-art methods to conduct GWAS and genomic prediction. The GAPIT also provides new functions for developing experimental designs and creating publication-ready tabular summaries and graphs to improve the efficiency and application of genomic research.

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