Structural variants in the barley gene pool: precision and sensitivity to detect them using short-read sequencing and their association with gene expression and phenotypic variation.
Journal
TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
ISSN: 1432-2242
Titre abrégé: Theor Appl Genet
Pays: Germany
ID NLM: 0145600
Informations de publication
Date de publication:
Oct 2022
Oct 2022
Historique:
received:
11
05
2022
accepted:
03
08
2022
pubmed:
28
8
2022
medline:
1
10
2022
entrez:
27
8
2022
Statut:
ppublish
Résumé
Structural variants (SV) of 23 barley inbreds, detected by the best combination of SV callers based on short-read sequencing, were associated with genome-wide and gene-specific gene expression and, thus, were evaluated to predict agronomic traits. In human genetics, several studies have shown that phenotypic variation is more likely to be caused by structural variants (SV) than by single nucleotide variants. However, accurate while cost-efficient discovery of SV in complex genomes remains challenging. The objectives of our study were to (i) facilitate SV discovery studies by benchmarking SV callers and their combinations with respect to their sensitivity and precision to detect SV in the barley genome, (ii) characterize the occurrence and distribution of SV clusters in the genomes of 23 barley inbreds that are the parents of a unique resource for mapping quantitative traits, the double round robin population, (iii) quantify the association of SV clusters with transcript abundance, and (iv) evaluate the use of SV clusters for the prediction of phenotypic traits. In our computer simulations based on a sequencing coverage of 25x, a sensitivity > 70% and precision > 95% was observed for all combinations of SV types and SV length categories if the best combination of SV callers was used. We observed a significant (P < 0.05) association of gene-associated SV clusters with global gene-specific gene expression. Furthermore, about 9% of all SV clusters that were within 5 kb of a gene were significantly (P < 0.05) associated with the gene expression of the corresponding gene. The prediction ability of SV clusters was higher compared to that of single-nucleotide polymorphisms from an array across the seven studied phenotypic traits. These findings suggest the usefulness of exploiting SV information when fine mapping and cloning the causal genes underlying quantitative traits as well as the high potential of using SV clusters for the prediction of phenotypes in diverse germplasm sets.
Identifiants
pubmed: 36029318
doi: 10.1007/s00122-022-04197-7
pii: 10.1007/s00122-022-04197-7
pmc: PMC9519679
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3511-3529Subventions
Organisme : Deutsche Forschungsgemeinschaft (DFG)
ID : EXC 2048/1
Organisme : Deutsche Forschungsgemeinschaft (DFG)
ID : Project ID: 390686111
Informations de copyright
© 2022. The Author(s).
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