Genome re-sequencing projects have revealed substantial amounts of genetic variation between individuals extending beyond single nucleotide polymorphisms (SNPs) and short indels. Structural variants (SVs) such as deletions, insertions, duplications, inversions and translocations litter genomes and are often associated with gene expression changes and severe phenotypes (ie. genetic diseases in humans). Recent studies on the functional aspects of different types of SVs have unveiled several cases of adaptive evolution. For example, inversions have been associated with ecological adaptations and may facilitate speciation. Due to their prevalent nature, SVs arguably have a large impact on genome evolution and should not be neglected when studying the genetics of adaptation and speciation.
SVs were classically defined as chromosomal rearrangements larger than 1kb, but due to a higher resolution of new detection methods, smaller variants (between 50 and 1000 base pairs) can now be accurately assessed. Besides various methods of detection in next generation sequencing data (paired end mapping, split reads, and depth of coverage), array-based approaches have proven to be particularly useful for detecting copy number variations (CNVs). These technologies have enabled researchers to catalog a wide spectrum of SVs in many organisms and infer the effects of selection shaping their evolutionary trajectories.
To gain further insights into the ecological and evolutionary implications of genomic SVs, we stress the need for a more comprehensive account of SVs in a wide range of organisms sampled in their natural habitat. Collecting data on SVs across various species and amongst populations with different divergence times will further add to the data available on the evolutionary turnover rates of SVs. Achieving these goals would greatly benefit from further development of methods for SV detection, particularly for next generation sequencing data (due to its applicability in non-model organisms). An essential step will be to collect more validation data to evaluate and improve methodologies. The determination of exact breakpoints and genotypes, which is currently challenging in diploid organisms, will allow the use of SV polymorphism data within a population genetic framework. This will aid in disentangling genetic drift and demographic effects from selection - the evolutionary processes shaping the patterns of SV diversity.
In this research topic we would like to bring together articles (1) that describe bioinformatics and statistical solutions for detecting, genotyping and analyzing structural variations on a genome scale, (2) that focus on the impact of structural variation on phenotypic variation, especially in the context of ecology and adaptation, and (3) that utilize comparative molecular and genomic approaches to improve our understanding of structural variation formation, frequency and maintenance across populations.
Genome re-sequencing projects have revealed substantial amounts of genetic variation between individuals extending beyond single nucleotide polymorphisms (SNPs) and short indels. Structural variants (SVs) such as deletions, insertions, duplications, inversions and translocations litter genomes and are often associated with gene expression changes and severe phenotypes (ie. genetic diseases in humans). Recent studies on the functional aspects of different types of SVs have unveiled several cases of adaptive evolution. For example, inversions have been associated with ecological adaptations and may facilitate speciation. Due to their prevalent nature, SVs arguably have a large impact on genome evolution and should not be neglected when studying the genetics of adaptation and speciation.
SVs were classically defined as chromosomal rearrangements larger than 1kb, but due to a higher resolution of new detection methods, smaller variants (between 50 and 1000 base pairs) can now be accurately assessed. Besides various methods of detection in next generation sequencing data (paired end mapping, split reads, and depth of coverage), array-based approaches have proven to be particularly useful for detecting copy number variations (CNVs). These technologies have enabled researchers to catalog a wide spectrum of SVs in many organisms and infer the effects of selection shaping their evolutionary trajectories.
To gain further insights into the ecological and evolutionary implications of genomic SVs, we stress the need for a more comprehensive account of SVs in a wide range of organisms sampled in their natural habitat. Collecting data on SVs across various species and amongst populations with different divergence times will further add to the data available on the evolutionary turnover rates of SVs. Achieving these goals would greatly benefit from further development of methods for SV detection, particularly for next generation sequencing data (due to its applicability in non-model organisms). An essential step will be to collect more validation data to evaluate and improve methodologies. The determination of exact breakpoints and genotypes, which is currently challenging in diploid organisms, will allow the use of SV polymorphism data within a population genetic framework. This will aid in disentangling genetic drift and demographic effects from selection - the evolutionary processes shaping the patterns of SV diversity.
In this research topic we would like to bring together articles (1) that describe bioinformatics and statistical solutions for detecting, genotyping and analyzing structural variations on a genome scale, (2) that focus on the impact of structural variation on phenotypic variation, especially in the context of ecology and adaptation, and (3) that utilize comparative molecular and genomic approaches to improve our understanding of structural variation formation, frequency and maintenance across populations.