Connecting genome structural variation with complex traits in crop plants

Gabur, Iulian; Chawla, Harmeet Singh; Snowdon, Rod J.; Parkin, Isobel A. P.

doi:10.1007/s00122-018-3233-0

Cited by 112 publications

(102 citation statements)

References 157 publications

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“…The recognition that both small (copy number, presence/absence) and large (chromosomal rearrangements) structural variation (SV) plays an important role in controlling key agronomic traits is gaining traction 43 ; yet deciphering such variation with short-read data has proved problematic 44 . Long-read sequencing technologies have distinct advantages in predicting SVs 45 , however the current limitation is developing and training software to accurately identify such variants.…”

Section: Discussionmentioning

confidence: 99%

High contiguity long read assembly ofBrassica nigraallows localization of active centromeres and provides insights into the ancestralBrassicagenome

Perumal

Koh

Jin

et al. 2020

Preprint

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Section: Discussionmentioning

confidence: 99%

High contiguity long read assembly ofBrassica nigraallows localization of active centromeres and provides insights into the ancestralBrassicagenome

Perumal

Koh

Jin

et al. 2020

Preprint

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“…One clear example of such adaptive evolution is presented by the weed species A. palmeri, which rapidly became resistant to a widely used herbicide through amplification of the EPSPS gene, resulting in increased expression (Gaines et al, 2010). Similar relationships between CNV and adaptation were found in domesticated crop species (Gabur et al, 2019), indicating that CNV may offer a pool of genetic variation that can be used to improve crop cultivars.…”

Section: Introductionmentioning

confidence: 90%

“…Given the increasing interest of the plant research community in CNV (Żmieńko et al, 2014;Gabur et al, 2019;Lye and Purugganan, 2019), the question arises whether current methods accurately detect copy number variants (CNVs) in plants. Currently, CNVs are mainly analyzed by whole genome sequencing (WGS).…”

Section: Introductionmentioning

confidence: 99%

Hecaton: reliably detecting copy number variation in plant genomes using short read sequencing data

Wijfjes

Smit

Ridder

2019

Preprint

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Copy number variation (CNV) is thought to actively contribute to adaptive evolution of plant species. While many computational algorithms are available to detect copy number variation from whole genome sequencing datasets, the typical complexity of plant data likely introduces false positive calls.To enable reliable and comprehensive detection of CNV in plant genomes, we developed Hecaton, a novel computational workflow tailored to plants, that integrates calls from multiple state-of-the-art algorithms through a machine-learning approach. In this paper, we demonstrate that Hecaton outperforms current methods when applied to short read sequencing data of A. thaliana, rice, maize, and tomato. Moreover, it correctly detects dispersed duplications, a type of CNV commonly found in plant species, in contrast to several state-of-the-art tools that erroneously represent this type of CNV as overlapping deletions and tandem duplications. Finally, Hecaton scales well in terms of memory usage and running time when applied to short read datasets of domesticated and wild tomato accessions. Hecaton provides a robust method to detect CNV in plants.We expect it to be of immediate interest to both applied and fundamental research on the relationship between genotype and phenotype in plants.

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“…The availability of sequence information has given rise to more efficient breeding strategies, and Varshney et al (2018) provide valuable insight into sequencingbased breeding with emphasis on legumes crops. Connecting genome structural variation with complex traits represents a new challenge emerging from more detailed knowledge about complex crop genomes, and Gabur et al (2018) present some innovative strategies to address this issue in polyploids. Voss-Fels et al (2018) review concepts to further improve the power and efficiency of genomic selection technologies, whilst Rembe et al (2018) present a strong case on how reciprocal recurrent genomic selection strategies could assist hybrid breeding in wheat.…”

Section: Introductionmentioning

confidence: 99%