Large-scale comparative small RNA analyses reveal genomic structural variants in driving expression dynamics and differential selection pressures on distinct small RNA classes during tomato domestication

Tomato has undergone extensive selections during domestication. Recent progress has shown that genomic structural variants (SVs) have contributed to gene expression dynamics during tomato domestication, resulting in changes of important traits. Here, through comprehensive analyses of small RNAs (sRNAs) from nine representative tomato accessions, we demonstrate that SVs substantially contribute to the dynamic expression of the three major classes of plant sRNAs: microRNAs (miRNAs), phased secondary short interfering RNAs (phasiRNAs), and 24-nt heterochromatic siRNAs (hc-siRNAs). Changes in the abundance of phasiRNAs and 24-nt hc-siRNAs likely contribute to the alteration of mRNA gene expression during recent evolution of tomato, particularly for genes associated with biotic and abiotic stress tolerance. We also observe that miRNA expression dynamics are associated with imprecise processing, alternative miRNA-miRNA* selections, and SVs. SVs mainly affect the expression of less-conserved miRNAs that do not have established regulatory functions or low abundant members in highly expressed miRNA families, highlighting different selection pressures on miRNAs compared to phasiRNAs and 24-nt hc-siRNAs. Our findings provide insights into plant sRNA evolution as well as SV-based gene regulation during crop domestication. Furthermore, our dataset provides a rich resource for mining the sRNA regulatory network in tomato.

Genomic structural variants constrain and facilitate adaptation in natural populations of Theobroma cacao, the chocolate tree

Genomic structural variants (SVs) can play important roles in adaptation and speciation. Yet the overall fitness effects of SVs are poorly understood, partly because accurate population-level identification of SVs requires multiple high-quality genome assemblies. Here, we use 31 chromosome-scale, haplotype-resolved genome assemblies of Theobroma cacao—an outcrossing, long-lived tree species that is the source of chocolate—to investigate the fitness consequences of SVs in natural populations. Among the 31 accessions, we find over 160,000 SVs, which together cover eight times more of the genome than single-nucleotide polymorphisms and short indels (125 versus 15 Mb). Our results indicate that a vast majority of these SVs are deleterious: they segregate at low frequencies and are depleted from functional regions of the genome. We show that SVs influence gene expression, which likely impairs gene function and contributes to the detrimental effects of SVs. We also provide empirical support for a theoretical prediction that SVs, particularly inversions, increase genetic load through the accumulation of deleterious nucleotide variants as a result of suppressed recombination. Despite the overall detrimental effects, we identify individual SVs bearing signatures of local adaptation, several of which are associated with genes differentially expressed between populations. Genes involved in pathogen resistance are strongly enriched among these candidates, highlighting the contribution of SVs to this important local adaptation trait. Beyond revealing empirical evidence for the evolutionary importance of SVs, these 31 de novo assemblies provide a valuable resource for genetic and breeding studies in T. cacao.

Somatic structural variant formation is guided by and influences genome architecture

The occurrence and formation of genomic structural variants (SV) is known to be influenced by the 3D chromatin architecture , but the extent and magnitude has been challenging to study. Here, we apply Hi-C to study chromatin organization before and after induction of chromothripsis in human cells. We use Hi-C to manually assemble the derivative chromosomes following the massive complex rearrangements, which allowed us to study the sources of SV formation and their consequences on gene regulation. We observe an action-reaction interplay whereby the 3D chromatin architecture directly impacts on the location and formation of SVs. In turn, the SVs reshape the chromatin organization to alter the local topologies, replication timing and gene regulation in cis. We show that genomic compartments and replication timing are important determinants for juxtaposing distant loci to form SVs across 30 different cancer types with a pronounced abundance of SVs between early replicating regions in uterine cancer. We find that SVs frequently occur at 3D loop-anchors, cause compartment switching and changes in replication timing, and that this is a major source of SV-mediated effects on nearby gene expression changes.

PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

Network-based analysis of allele frequency distribution among multiple populations identifies adaptive genomic structural variants

Structural variants have a considerable impact on human genomic diversity. However, their evolutionary history remains mostly unexplored. Here, we developed a new method to identify potentially adaptive structural variants based on a network-based analysis that incorporates genotype frequency data from 26 populations simultaneously. Using this method, we analyzed 57,629 structural variants and identified 577 structural variants that show high population distribution. We further showed that 39 and 20 of these putatively adaptive structural variants overlap with coding sequences or are significantly associated with GWAS traits, respectively. Closer inspection of the haplotypic variation associated with these putatively adaptive and functional structural variants reveals deviations from neutral expectations due to (i) population differentiation of rapidly evolving multi-allelic variants, (ii) incomplete sweeps, and (iii) recent population-specific negative selection. Overall, our study provides new methodological insights, documents hundreds of putatively adaptive variants, and introduces evolutionary models that may better explain the complex evolution of structural variants.

SVFX: a machine learning framework to quantify the pathogenicity of structural variants

There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity scores to somatic and germline SVs. In particular, we generate somatic and germline training models, which include genomic, epigenomic, and conservation-based features, for SV call sets in diseased and healthy individuals. We then apply SVFX to SVs in cancer and other diseases; SVFX achieves high accuracy in identifying pathogenic SVs. Predicted pathogenic SVs in cancer cohorts are enriched among known cancer genes and many cancer-related pathways.

Optical Mapping Uncovers Multiple Novel Genomic Structural Variants in Patient Leukemias

Leukemias are a diverse collection of hematopoietic cancers with limited chemotherapeutic treatment options. Patients unsuitable or unable for bone marrow transplantation have a dismal prognosis. Although previous studies have shown that there are only a limited number of potential driver mutations (3-8) per leukemia, there is an extensive heterogeneity of subtypes of disease. Using optical mapping with the Saphyr genome imaging system we confirm what other laboratories have found; that leukemias have additionally approximately 30-70 genomic structural variants per patient. This suggests that these SVs could ascribe the observed heterogeneity and could be responsible for individual pathogenesis of disease. Furthermore, we show that of many these SVs involve genes with functions associated with cellular processes relevant to cancer. Whilst known SVs, such asBCR-ABLtranslocations, are readily detected, multiple novel variants are also uncovered by Saphyr. Here we demonstrate the utility of this technique to uncover potential driver SV events and provide examples of such, some of which are associated with sensitivity and resistance to chemotherapeutics, including the standard of care drug Idarubicin (Idamycin). Finally, optical genome mapping shows 100% concordance with extant cytogenetic analyses, yet with a more streamlined methodology, a greater resolution, and higher sensitivity of detection. Figure Disclosures Hong: Bionano Genomics:Current Employment.Lee:Bionano Genomics, San Diego:Current Employment.Pang:Bionano Genomics:Current Employment.Lai:Bionano Genomics:Current Employment.Hastie:Bionano Genomics:Current Employment.Vuori:Bionano Genomics:Membership on an entity's Board of Directors or advisory committees.

LongAGE: defining breakpoints of genomic structural variants through optimal and memory efficient alignments of long reads

Summary Defining the precise location of structural variations (SVs) at single-nucleotide breakpoint resolution is a challenging problem due to large gaps in alignment. Previously, Alignment with Gap Excision (AGE) enabled us to define breakpoints of SVs at single-nucleotide resolution; however, AGE requires a vast amount of memory when aligning a pair of long sequences. To address this, we developed a memory-efficient implementation—LongAGE—based on the classical Hirschberg algorithm. We demonstrate an application of LongAGE for resolving breakpoints of SVs embedded into segmental duplications on Pacific Biosciences (PacBio) reads that can be longer than 10 kb. Furthermore, we observed different breakpoints for a deletion and a duplication in the same locus, providing direct evidence that such multi-allelic copy number variants (mCNVs) arise from two or more independent ancestral mutations. Availability and implementation LongAGE is implemented in C++ and available on Github at https://github.com/Coaxecva/LongAGE. Supplementary information Supplementary data are available at Bioinformatics online.