Structural variants (SVs) are a major source of genetic and phenotypic variation, but remain challenging to accurately type and are hence poorly characterized in most species. We present an approach for reliable SV discovery in non-model species using whole genome sequencing and report 15,483 high-confidence SVs in 492 Atlantic salmon (Salmo salar L.) sampled from a broad phylogeographic distribution. These SVs recover population genetic structure with high resolution, include an active DNA transposon, widely affect functional features, and overlap more duplicated genes retained from an ancestral salmonid autotetraploidization event than expected. Changes in SV allele frequency between wild and farmed fish indicate polygenic selection on behavioural traits during domestication, targeting brain-expressed synaptic networks linked to neurological disorders in humans. This study offers novel insights into the role of SVs in genome evolution and the genetic architecture of domestication traits, along with resources supporting reliable SV discovery in non-model species.
1Structural variants (SVs) are a major source of genetic and phenotypic variation, but remain challenging to 2 accurately type and are hence poorly characterized in most species. We present an approach for reliable SV 3 discovery in non-model species using whole genome sequencing and report 15,483 high-confidence SVs in 4 492 Atlantic salmon (Salmo salar L.) sampled from a broad phylogeographic distribution. These SVs 5 recover population genetic structure with high resolution, include an active DNA transposon, widely affect 6 functional features, and overlap more duplicated genes retained from an ancestral salmonid 7 autotetraploidization event than expected. Changes in SV allele frequency between wild and farmed fish 8 indicate polygenic selection on behavioural traits during domestication, targeting brain-expressed synaptic 9 networks linked to neurological disorders in humans. This study offers novel insights into the role of SVs 10 in genome evolution and the genetic architecture of domestication traits, along with resources supporting 11 reliable SV discovery in non-model species. 12Main 13Modern genetics remains primarily focused on single nucleotide polymorphism (SNP) analyses, with a 14 growing recognition of the importance of larger structural variants (SVs) including inversions, insertions, 15 deletions and copy number variations (CNVs) (defined here as variants ≥100 bp), among others 1 . SVs 16 affect a larger proportion of bases in human genomes than SNPs 4 , are not always reliably tagged by SNPs 5 , 17 more frequently have regulatory impacts 6 , and have been shown to alter the structure, presence, number, 18 dosage, and regulation of many genes 1 . Nonetheless, SVs remain challenging to accurately type using 19 whole genome sequence data 2-3 , limiting our understanding of their biological roles and exploitation as 20 genetic markers. Consequently, there is a need for reliable SV detection approaches to fully exploit the fast-21 accumulating genome sequencing datasets in both model and non-model species, allowing for more 22 complete genetics investigations. Many tools exist for SV discovery using short-read sequencing data, but 23 all suffer from high false discovery rates (10-89%) 2,3,7 . This poses a challenge for truly de novo SV 24 detection in previously unstudied species lacking 'gold standard' reference SVs to help distinguish true 25 from false calls. Most studies rely on combining an ensemble of signals from different SV detection 26 methods, although this strategy does not reliably improve performance and can in some cases aggravate 27 false discovery 3 . Researchers therefore often apply independent experimental 8-9 or visualization methods 10 28 to validate a subset of SV calls. Overall, there remains an unsatisfactory lack of consensus on how to 29 validate the quality of de novo SV datasets in most species 3 . 31Salmonids have the highest combined economic, ecological and scientific importance among all fish 32 lineages, and have consequently been subject to hundreds of genetics stu...
A novel, reliable method of measuring flesh tensile strength in salmon, provides data of relevance to gaping.
Accurate SNP (single nucleotide polymorphism) genotype information is critical for a wide range of selective breeding applications in aquaculture, including parentage assignment, marker-assisted, and genomic selection. However, the sampling of tissue for genetic analysis can be invasive for juvenile animals or taxa where sampling tissue is difficult or may cause mortality (e.g. bivalve mollusks). Here, we demonstrate a novel, non-invasive technique for sampling DNA based on the collection of environmental DNA using European Flat Oysters (Ostrea edulis) as an example. The live animals are placed in individual containers until sufficient genetic material is released into the seawater which is then recovered by filtration. We compared the results of tissue and eDNA derived SNP genotype calls using a PCR based genotyping platform. We found that 100% accurate genotype calls from eDNA are possible, but depend on appropriate filtration and the dilution of the sample throughout the workflow. We also developed an additional low-cost DNA extraction technique which provided >99% correct SNP genotype calls in comparison to tissue. It was concluded that eDNA sampling can be used in hatchery and selective breeding programs applicable to any aquatic organism for which direct tissue sampling may result in animal welfare concerns or mortality.
Accurate discrimination of two morphologically similar species of Patella limpets has been facilitated by using qPCR amplification of species-specific mitochondrial genomic regions. Cost-effective and non-destructive sampling is achieved using a mucus swab and simple sample lysis and dilution to create a PCR template. Results show 100% concurrence with dissection and microscopic analysis, and the technique has been employed successfully in field studies. The use of highly sensitive DNA barcoding techniques such as this hold great potential for improving previously challenging field assessments of species abundance.
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