June 1, 2021  |  

Highly accurate read mapping of third generation sequencing reads for improved structural variation analysis

Characterizing genomic structural variations (SV) is vital for understanding how genomes evolve. Furthermore, SVs are known for playing a role in a wide range of diseases including cancer, autism, and schizophrenia. Nevertheless, due to their complexity they remain harder to detect and less understood than single nucleotide variations. Recently, third-generation sequencing has proven to be an invaluable tool for detecting SVs. The markedly higher read length not only allows single reads to span a SV, it also enables reliable mapping to repetitive regions of the genome. These regions often contain SVs and are inaccessible to short-read mapping. However, current sequencing technologies like PacBio show a raw read error rate of 10% or more consisting mostly of insertions and deletions. Especially in repetitive regions the high error rate causes current mapping methods to fail finding exact borders for SVs, to split up large deletions and insertions into several small ones, or in some cases, like inversions, to fail reporting them at all. Furthermore, for complex SVs it is not possible to find one end-to-end alignment for a given read. The decision of when to split a read into two or more separate alignments without knowledge of the underlying SV poses an even bigger challenge to current read mappers. Here we present NextGenMap-LR for long single molecule PacBio reads which addresses these issues. NextGenMap-LR uses a fast k-mer search to quickly find anchor regions between parts of a read and the reference and evaluates them using a vectorized implementation of the Smith-Waterman (SW) algorithm. The resulting high-quality anchors are then used to determine whether a read spans an SV and has to be split or can be aligned contiguously. Finally, NextGenMap-LR uses a banded SW algorithm to compute the final alignment(s). In this last step, to account for both the sequencing error and real genomic variations, we employ a non-affine gap model that penalizes gap extensions for longer gaps less than for shorter ones. Based on simulated as well as verified human breast cancer SV data we show how our approach significantly improves mapping of long reads around SVs. The non-affine gap model is especially effective at more precisely identifying the position of the breakpoint, and the enhanced scoring scheme enables subsequent variation callers to identify SVs that would have been missed otherwise.


June 1, 2021  |  

Candidate gene screening using long-read sequencing

We have developed several candidate gene screening applications for both Neuromuscular and Neurological disorders. The power behind these applications comes from the use of long-read sequencing. It allows us to access previously unresolvable and even unsequencable genomic regions. SMRT Sequencing offers uniform coverage, a lack of sequence context bias, and very high accuracy. In addition, it is also possible to directly detect epigenetic signatures and characterize full-length gene transcripts through assembly-free isoform sequencing. In addition to calling the bases, SMRT Sequencing uses the kinetic information from each nucleotide to distinguish between modified and native bases.


June 1, 2021  |  

Screening and characterization of causative structural variants for bipolar disorder in a significantly linked chromosomal region onXq24-q27 in an extended pedigree from a genetic isolate

Bipolar disorder (BD) is a phenotypically and genetically complex and debilitating neurological disorder that affects 1% of the worldwide population. There is compelling evidence from family, twin and adoption studies supporting the involvement of a genetic predisposition in BD with estimated heritability up to ~ 80%. The risk in first-degree relatives is ten times higher than in the general population. Linkage and association studies have implicated multiple putative chromosomal loci for BP susceptibility, however no disease genes have been identified to date.


June 1, 2021  |  

Joint calling and PacBio SMRT Sequencing for indel and structural variant detection in populations

Fast and effective variant calling algorithms have been crucial to the successful application of DNA sequencing in human genetics. In particular, joint calling – in which reads from multiple individuals are pooled to increase power for shared variants – is an important tool for population surveys of variation. Joint calling was applied by the 1000 Genomes Project to identify variants across many individuals each sequenced to low coverage (about 5-fold). This approach successfully found common small variants, but broadly missed structural variants and large indels for which short-read sequencing has limited sensitivity. To support use of large variants in rare disease and common trait association studies, it is necessary to perform population-scale surveys with a technology effective at detecting indels and structural variants, such as PacBio SMRT Sequencing. For these studies, it is important to have a joint calling workflow that works with PacBio reads. We have developed pbsv, an indel and structural variant caller for PacBio reads, that provides a two-step joint calling workflow similar to that used to build the ExAC database. The first stage, discovery, is performed separately for each sample and consolidates whole genome alignments into a sparse representation of potentially variant loci. The second stage, calling, is performed on all samples together and considers only the signatures identified in the discovery stage. We applied the pbsv joint calling workflow to PacBio reads from twenty human genomes, with coverage ranging from 5-fold to 80-fold per sample for a total of 460-fold. The analysis required only 102 CPU hours, and identified over 800,000 indels and structural variants, including hundreds of inversions and translocations, many times more than discovered with short-read sequencing. The workflow is scalable to thousands of samples. The ongoing application of this workflow to thousands of samples will provide insight into the evolution and functional importance of large variants in human evolution and disease.


April 21, 2020  |  

A robust benchmark for germline structural variant detection

New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls =50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.


April 21, 2020  |  

Schizophrenia risk variants influence multiple classes of transcripts of sorting nexin 19 (SNX19).

Genome-wide association studies (GWAS) have identified many genomic loci associated with risk for schizophrenia, but unambiguous identification of the relationship between disease-associated variants and specific genes, and in particular their effect on risk conferring transcripts, has proven difficult. To better understand the specific molecular mechanism(s) at the schizophrenia locus in 11q25, we undertook cis expression quantitative trait loci (cis-eQTL) mapping for this 2 megabase genomic region using postmortem human brain samples. To comprehensively assess the effects of genetic risk upon local expression, we evaluated multiple transcript features: genes, exons, and exon-exon junctions in multiple brain regions-dorsolateral prefrontal cortex (DLPFC), hippocampus, and caudate. Genetic risk variants strongly associated with expression of SNX19 transcript features that tag multiple rare classes of SNX19 transcripts, whereas they only weakly affected expression of an exon-exon junction that tags the majority of abundant transcripts. The most prominent class of SNX19 risk-associated transcripts is predicted to be overexpressed, defined by an exon-exon splice junction between exons 8 and 10 (junc8.10) and that is predicted to encode proteins that lack the characteristic nexin C terminal domain. Risk alleles were also associated with either increased or decreased expression of multiple additional classes of transcripts. With RACE, molecular cloning, and long read sequencing, we found a number of novel SNX19 transcripts that further define the set of potential etiological transcripts. We explored epigenetic regulation of SNX19 expression and found that DNA methylation at CpG sites near the primary transcription start site and within exon 2 partially mediate the effects of risk variants on risk-associated expression. ATAC sequencing revealed that some of the most strongly risk-associated SNPs are located within a region of open chromatin, suggesting a nearby regulatory element is involved. These findings indicate a potentially complex molecular etiology, in which risk alleles for schizophrenia generate epigenetic alterations and dysregulation of multiple classes of SNX19 transcripts.


April 21, 2020  |  

Genetic Variation, Comparative Genomics, and the Diagnosis of Disease.

The discovery of mutations associated with human genetic dis- ease is an exercise in comparative genomics (see Glossary). Although there are many different strategies and approaches, the central premise is that affected persons harbor a significant excess of pathogenic DNA variants as com- pared with a group of unaffected persons (controls) that is either clinically defined1 or established by surveying large swaths of the general population.2 The more exclu- sive the variant is to the disease, the greater its penetrance, the larger its effect size, and the more relevant it becomes to both disease diagnosis and future therapeutic investigation. The most popular approach used by researchers in human genetics is the case–control design, but there are others that can be used to track variants and disease in a family context or that consider the probability of different classes of mutations based on evolutionary patterns of divergence or de novo mutational change.3,4 Although the approaches may be straightforward, the discovery of patho- genic variation and its mechanism of action often is less trivial, and decades of research can be required in order to identify the variants underlying both mendelian and complex genetic traits.


April 21, 2020  |  

DNA Methylation at the Schizophrenia and Intelligence GWAS-Implicated MIR137HG Locus May Be Associated with Disease and Cognitive Functions

The largest genome-wide association studies have identified schizophrenia and intelligence associated variants in the MIR137HG locus containing genes encoding microRNA-137 and microRNA-2682. In the present study, we investigated DNA methylation in the MIR137HG intragenic CpG island (CGI) in the peripheral blood of 44 patients with schizophrenia and 50 healthy controls. The CGI included the entire MIR137 gene and the region adjacent to the 5′-end of MIR2682. The aim of the study was to examine the relationship of the CGI methylation with schizophrenia and cognitive functioning. The methylation level of 91 CpG located in the selected region was established for each participant by means of single-molecule real-time bisulfite sequencing. All subjects completed the battery of neuropsychological tests. We found that the CGI was hypomethylated in both groups, except for one site—CpG (chr1: 98?511?049), with significant interindividual variability in methylation. A higher level of methylation of this CpG was seen in male patients and was associated with a decrease in the cognitive index in the combined sample of patients and controls. Our data suggest that further investigation of mechanisms that regulate the MIR137 and MIR2682 genes expression might help to understand the molecular basis of cognitive deficits in schizophrenia.


April 21, 2020  |  

A physical and genetic map of Cannabis sativa identifies extensive rearrangements at the THC/CBD acid synthase loci.

Cannabis sativa is widely cultivated for medicinal, food, industrial, and recreational use, but much remains unknown regarding its genetics, including the molecular determinants of cannabinoid content. Here, we describe a combined physical and genetic map derived from a cross between the drug-type strain Purple Kush and the hemp variety “Finola.” The map reveals that cannabinoid biosynthesis genes are generally unlinked but that aromatic prenyltransferase (AP), which produces the substrate for THCA and CBDA synthases (THCAS and CBDAS), is tightly linked to a known marker for total cannabinoid content. We further identify the gene encoding CBCA synthase (CBCAS) and characterize its catalytic activity, providing insight into how cannabinoid diversity arises in cannabis. THCAS and CBDAS (which determine the drug vs. hemp chemotype) are contained within large (>250 kb) retrotransposon-rich regions that are highly nonhomologous between drug- and hemp-type alleles and are furthermore embedded within ~40 Mb of minimally recombining repetitive DNA. The chromosome structures are similar to those in grains such as wheat, with recombination focused in gene-rich, repeat-depleted regions near chromosome ends. The physical and genetic map should facilitate further dissection of genetic and molecular mechanisms in this commercially and medically important plant. © 2019 Laverty et al.; Published by Cold Spring Harbor Laboratory Press.


April 21, 2020  |  

The role of genomic structural variation in the genetic improvement of polyploid crops

Many of our major crop species are polyploids, containing more than one genome or set of chromosomes. Polyploid crops present unique challenges, including difficulties in genome assembly, in discriminating between multiple gene and sequence copies, and in genetic mapping, hindering use of genomic data for genetics and breeding. Polyploid genomes may also be more prone to containing structural variation, such as loss of gene copies or sequences (presence–absence variation) and the presence of genes or sequences in multiple copies (copy-number variation). Although the two main types of genomic structural variation commonly identified are presence–absence variation and copy-number variation, we propose that homeologous exchanges constitute a third major form of genomic structural variation in polyploids. Homeologous exchanges involve the replacement of one genomic segment by a similar copy from another genome or ancestrally duplicated region, and are known to be extremely common in polyploids. Detecting all kinds of genomic structural variation is challenging, but recent advances such as optical mapping and long-read sequencing offer potential strategies to help identify structural variants even in complex polyploid genomes. All three major types of genomic structural variation (presence–absence, copy-number, and homeologous exchange) are now known to influence phenotypes in crop plants, with examples of flowering time, frost tolerance, and adaptive and agronomic traits. In this review, we summarize the challenges of genome analysis in polyploid crops, describe the various types of genomic structural variation and the genomics technologies and data that can be used to detect them, and collate information produced to date related to the impact of genomic structural variation on crop phenotypes. We highlight the importance of genomic structural variation for the future genetic improvement of polyploid crops.


April 21, 2020  |  

Long-read assembly of the Chinese rhesus macaque genome and identification of ape-specific structural variants.

We present a high-quality de novo genome assembly (rheMacS) of the Chinese rhesus macaque (Macaca mulatta) using long-read sequencing and multiplatform scaffolding approaches. Compared to the current Indian rhesus macaque reference genome (rheMac8), rheMacS increases sequence contiguity 75-fold, closing 21,940 of the remaining assembly gaps (60.8 Mbp). We improve gene annotation by generating more than two million full-length transcripts from ten different tissues by long-read RNA sequencing. We sequence resolve 53,916 structural variants (96% novel) and identify 17,000 ape-specific structural variants (ASSVs) based on comparison to ape genomes. Many ASSVs map within ChIP-seq predicted enhancer regions where apes and macaque show diverged enhancer activity and gene expression. We further characterize a subset that may contribute to ape- or great-ape-specific phenotypic traits, including taillessness, brain volume expansion, improved manual dexterity, and large body size. The rheMacS genome assembly serves as an ideal reference for future biomedical and evolutionary studies.


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