Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato. - Alonge, Michael; Wang, Xingang; Benoit, Matthias; Soyk, Sebastian; Pereira, Lara; Zhang, Lei; Suresh, Hamsini; Ramakrishnan, Srividya; Maumus, Florian; Ciren, Danielle; Levy, Yuval; Harel, Tom Hai; et al. (Cell, 2020)
Structural variants (SVs) underlie important crop improvement and domestication traits. However, resolving the extent, diversity, and quantitative impact of SVs has been challenging. We used long-read nanopore sequencing to capture 238,490 SVs in 100 diverse tomato lines. This panSV genome, along with 14 new reference assemblies, revealed large-scale intermixing of diverse genotypes, as well as thousands of SVs intersecting genes and cis-regulatory regions. Hundreds of SV-gene pairs exhibit subtle and significant expression changes, which could broadly influence quantitative trait variation. By combining quantitative genetics with genome editing, we show how multiple SVs that changed gene dosage and expression levels modified fruit flavor, size, and production. In the last example, higher order epistasis among four SVs affecting three related transcription factors allowed introduction of an important harvesting trait in modern tomato. Our findings highlight the underexplored role of SVs in genotype-to-phenotype relationships and their widespread importance and utility in crop improvement.
Keywords: Alleles; Crops, Agricultural; Cytochrome P-450 Enzyme System; Ecotype; Epistasis, Genetic; Fruit; Gene Duplication; Gene Expression Regulation, Plant; Genome, Plant; Genomic Structural Variation; Genotype; Inbreeding; Lycopersicon esculentum; Molecular Sequence Annotation; Phenotype; Plant Breeding; Quantitative Trait Loci

Mapping and characterization of structural variation in 17,795 human genomes. - Abel, Haley J; Larson, David E; Regier, Allison A; Chiang, Colby; Das, Indraniel; Kanchi, Krishna L; Layer, Ryan M; Neale, Benjamin M; Salerno, William J; Reeves, Catherine; Buyske, Steven; Matise, Tara C; et al. (Nature, 2020)
A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.
Keywords: Alleles; Case-Control Studies; Continental Population Groups; Epigenesis, Genetic; Female; Gene Dosage; Genetic Variation; Genetics, Population; Genome, Human; High-Throughput Nucleotide Sequencing; Humans; Male; Molecular Sequence Annotation; Quantitative Trait Loci; Software; Whole Genome Sequencing

denovo-db: a compendium of human de novo variants. - Turner, Tychele N; Yi, Qian; Krumm, Niklas; Huddleston, John; Hoekzema, Kendra; F Stessman, Holly A; Doebley, Anna-Lisa; Bernier, Raphael A; Nickerson, Deborah A; Eichler, Evan E (Nucleic Acids Res, 2017)
Whole-exome and whole-genome sequencing have facilitated the large-scale discovery of de novo variants in human disease. To date, most de novo discovery through next-generation sequencing focused on congenital heart disease and neurodevelopmental disorders (NDDs). Currently, de novo variants are one of the most significant risk factors for NDDs with a substantial overlap of genes involved in more than one NDD. To facilitate better usage of published data, provide standardization of annotation, and improve accessibility, we created denovo-db (http://denovo-db.gs.washington.edu), a database for human de novo variants. As of July 2016, denovo-db contained 40 different studies and 32,991 de novo variants from 23,098 trios. Database features include basic variant information (chromosome location, change, type); detailed annotation at the transcript and protein levels; severity scores; frequency; validation status; and, most importantly, the phenotype of the individual with the variant. We included a feature on our browsable website to download any query result, including a downloadable file of the full database with additional variant details. denovo-db provides necessary information for researchers to compare their data to other individuals with the same phenotype and also to controls allowing for a better understanding of the biology of de novo variants and their contribution to disease.
Keywords: Computational Biology; Databases, Nucleic Acid; Genetic Association Studies; Genetic Variation; Germ-Line Mutation; Humans; Molecular Sequence Annotation; Polymorphism, Single Nucleotide; Web Browser

A general framework for estimating the relative pathogenicity of human genetic variants. - Kircher, Martin; Witten, Daniela M; Jain, Preti; O'Roak, Brian J; Cooper, Gregory M; Shendure, Jay (Nat Genet, 2014)
Current methods for annotating and interpreting human genetic variation tend to exploit a single information type (for example, conservation) and/or are restricted in scope (for example, to missense changes). Here we describe Combined Annotation-Dependent Depletion (CADD), a method for objectively integrating many diverse annotations into a single measure (C score) for each variant. We implement CADD as a support vector machine trained to differentiate 14.7 million high-frequency human-derived alleles from 14.7 million simulated variants. We precompute C scores for all 8.6 billion possible human single-nucleotide variants and enable scoring of short insertions-deletions. C scores correlate with allelic diversity, annotations of functionality, pathogenicity, disease severity, experimentally measured regulatory effects and complex trait associations, and they highly rank known pathogenic variants within individual genomes. The ability of CADD to prioritize functional, deleterious and pathogenic variants across many functional categories, effect sizes and genetic architectures is unmatched by any current single-annotation method.
Keywords: Genetic Variation; Genome, Human; Genome-Wide Association Study; Humans; INDEL Mutation; Models, Genetic; Molecular Sequence Annotation; Polymorphism, Single Nucleotide; Selection, Genetic; Support Vector Machine