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Different structural variant prediction tools yield considerably different results in Caenorhabditis elegans.

ABSTRACT: The accurate characterization of structural variation is crucial for our understanding of how large chromosomal alterations affect phenotypic differences and contribute to genome evolution. Whole-genome sequencing is a popular approach for identifying structural variants, but the accuracy of popular tools remains unclear due to the limitations of existing benchmarks. Moreover, the performance of these tools for predicting variants in non-human genomes is less certain, as most tools were developed and benchmarked using data from the human genome. To evaluate the use of long-read data for the validation of short-read structural variant calls, the agreement between predictions from a short-read ensemble learning method and long-read tools were compared using real and simulated data from Caenorhabditis elegans. The results obtained from simulated data indicate that the best performing tool is contingent on the type and size of the variant, as well as the sequencing depth of coverage. These results also highlight the need for reference datasets generated from real data that can be used as 'ground truth' in benchmarks.

SUBMITTER: Lesack K

PROVIDER: S-EPMC9803319 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Different structural variant prediction tools yield considerably different results in Caenorhabditis elegans.

Lesack Kyle K Mariene Grace M GM Andersen Erik C EC Wasmuth James D JD

PloS one 20221230 12

The accurate characterization of structural variation is crucial for our understanding of how large chromosomal alterations affect phenotypic differences and contribute to genome evolution. Whole-genome sequencing is a popular approach for identifying structural variants, but the accuracy of popular tools remains unclear due to the limitations of existing benchmarks. Moreover, the performance of these tools for predicting variants in non-human genomes is less certain, as most tools were develope ...[more]

PMID: 36584177

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Project description:Over long evolutionary timescales, major changes to the copy number, function, and genomic organization of genes occur, however, our understanding of the individual mutational events responsible for these changes is lacking. In this report, we study the genetic basis of adaptation of two strains of C. elegans to laboratory food sources using competition experiments on a panel of 89 recombinant inbred lines (RIL). Unexpectedly, we identified a single RIL with higher relative fitness than either of the parental strains. This strain also displayed a novel behavioral phenotype, resulting in higher propensity to explore bacterial lawns. Using bulk-segregant analysis and short-read resequencing of this RIL, we mapped the change in exploration behavior to a spontaneous, complex rearrangement of the rcan-1 gene that occurred during construction of the RIL panel. We resolved this rearrangement into five unique tandem inversion/duplications using Oxford Nanopore long-read sequencing. rcan-1 encodes an ortholog to human RCAN1/DSCR1 calcipressin gene, which has been implicated as a causal gene for Down syndrome. The genomic rearrangement in rcan-1 creates two complete and two truncated versions of the rcan-1 coding region, with a variety of modified 5' and 3' non-coding regions. While most copy-number variations (CNVs) are thought to act by increasing expression of duplicated genes, these changes to rcan-1 ultimately result in the reduction of its whole-body expression due to changes in the upstream regions. By backcrossing this rearrangement into a common genetic background to create a near isogenic line (NIL), we demonstrate that both the competitive advantage and exploration behavioral changes are linked to this complex genetic variant. This NIL strain does not phenocopy a strain containing an rcan-1 loss-of-function allele, which suggests that the residual expression of rcan-1 is necessary for its fitness effects. Our results demonstrate how colonization of new environments, such as those encountered in the laboratory, can create evolutionary pressure to modify gene function. This evolutionary mismatch can be resolved by an unexpectedly complex genetic change that simultaneously duplicates and diversifies a gene into two uniquely regulated genes. Our work shows how complex rearrangements can act to modify gene expression in ways besides increased gene dosage.

Dataset Information

Different structural variant prediction tools yield considerably different results in Caenorhabditis elegans.

Publications

Different structural variant prediction tools yield considerably different results in Caenorhabditis elegans.

Similar Datasets

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