scholarly journals TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data

2019 ◽  
Author(s):  
Clement Goubert ◽  
Jainy Thomas ◽  
Lindsay M. Payer ◽  
Jeffrey M. Kidd ◽  
Julie Feusier ◽  
...  
Keyword(s):  
Population Genomics ◽  
Mobile Element ◽  
Whole Genome Sequencing Data ◽  
Human Populations ◽  
Whole Genome ◽  
Structural Variants ◽  
Sequencing Data ◽  
1000 Genomes ◽  
Standard Set ◽  
Whole Genome Resequencing

ABSTRACTAlu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alu are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alu and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline -- TypeTE -- which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a ‘gold standard’ set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

scholarly journals TypeTE: a tool to genotype mobile element insertions from whole genome resequencing data

2020 ◽  
Vol 48 (6) ◽  
pp. e36-e36 ◽  
Author(s):  
Clément Goubert ◽  
Jainy Thomas ◽  
Lindsay M Payer ◽  
Jeffrey M Kidd ◽  
Julie Feusier ◽  
...  
Keyword(s):  
Population Genomics ◽  
Mobile Element ◽  
Whole Genome Sequencing Data ◽  
Human Populations ◽  
Whole Genome ◽  
Structural Variants ◽  
Sequencing Data ◽  
1000 Genomes ◽  
Alu Insertions ◽  
Whole Genome Resequencing

Abstract Alu retrotransposons account for more than 10% of the human genome, and insertions of these elements create structural variants segregating in human populations. Such polymorphic Alus are powerful markers to understand population structure, and they represent variants that can greatly impact genome function, including gene expression. Accurate genotyping of Alus and other mobile elements has been challenging. Indeed, we found that Alu genotypes previously called for the 1000 Genomes Project are sometimes erroneous, which poses significant problems for phasing these insertions with other variants that comprise the haplotype. To ameliorate this issue, we introduce a new pipeline – TypeTE – which genotypes Alu insertions from whole-genome sequencing data. Starting from a list of polymorphic Alus, TypeTE identifies the hallmarks (poly-A tail and target site duplication) and orientation of Alu insertions using local re-assembly to reconstruct presence and absence alleles. Genotype likelihoods are then computed after re-mapping sequencing reads to the reconstructed alleles. Using a high-quality set of PCR-based genotyping of >200 loci, we show that TypeTE improves genotype accuracy from 83% to 92% in the 1000 Genomes dataset. TypeTE can be readily adapted to other retrotransposon families and brings a valuable toolbox addition for population genomics.

scholarly journals Prioritising positively selected variants in whole-genome sequencing data using FineMAV

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fadilla Wahyudi ◽  
Farhang Aghakhanian ◽  
Sadequr Rahman ◽  
Yik-Ying Teo ◽  
Michał Szpak ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Population Genomics ◽  
Software Tool ◽  
Human Populations ◽  
Whole Genome ◽  
Sequencing Data ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Genome Browsers

Abstract Background In population genomics, polymorphisms that are highly differentiated between geographically separated populations are often suggestive of Darwinian positive selection. Genomic scans have highlighted several such regions in African and non-African populations, but only a handful of these have functional data that clearly associates candidate variations driving the selection process. Fine-Mapping of Adaptive Variation (FineMAV) was developed to address this in a high-throughput manner using population based whole-genome sequences generated by the 1000 Genomes Project. It pinpoints positively selected genetic variants in sequencing data by prioritizing high frequency, population-specific and functional derived alleles. Results We developed a stand-alone software that implements the FineMAV statistic. To graphically visualise the FineMAV scores, it outputs the statistics as bigWig files, which is a common file format supported by many genome browsers. It is available as a command-line and graphical user interface. The software was tested by replicating the FineMAV scores obtained using 1000 Genomes Project African, European, East and South Asian populations and subsequently applied to whole-genome sequencing datasets from Singapore and China to highlight population specific variants that can be subsequently modelled. The software tool is publicly available at https://github.com/fadilla-wahyudi/finemav. Conclusions The software tool described here determines genome-wide FineMAV scores, using low or high-coverage whole-genome sequencing datasets, that can be used to prioritize a list of population specific, highly differentiated candidate variants for in vitro or in vivo functional screens. The tool displays these scores on the human genome browsers for easy visualisation, annotation and comparison between different genomic regions in worldwide human populations.

scholarly journals Population Genomics of American Mink Using Whole Genome Sequencing Data

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 258
Author(s):  
Karim Karimi ◽  
Duy Ngoc Do ◽  
Mehdi Sargolzaei ◽  
Younes Miar
Keyword(s):  
Population Genomics ◽  
Association Studies ◽  
American Mink ◽  
Population History ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Effective Population ◽  
Cross Validation Error

Characterizing the genetic structure and population history can facilitate the development of genomic breeding strategies for the American mink. In this study, we used the whole genome sequences of 100 mink from the Canadian Centre for Fur Animal Research (CCFAR) at the Dalhousie Faculty of Agriculture (Truro, NS, Canada) and Millbank Fur Farm (Rockwood, ON, Canada) to investigate their population structure, genetic diversity and linkage disequilibrium (LD) patterns. Analysis of molecular variance (AMOVA) indicated that the variation among color-types was significant (p < 0.001) and accounted for 18% of the total variation. The admixture analysis revealed that assuming three ancestral populations (K = 3) provided the lowest cross-validation error (0.49). The effective population size (Ne) at five generations ago was estimated to be 99 and 50 for CCFAR and Millbank Fur Farm, respectively. The LD patterns revealed that the average r2 reduced to <0.2 at genomic distances of >20 kb and >100 kb in CCFAR and Millbank Fur Farm suggesting that the density of 120,000 and 24,000 single nucleotide polymorphisms (SNP) would provide the adequate accuracy of genomic evaluation in these populations, respectively. These results indicated that accounting for admixture is critical for designing the SNP panels for genotype-phenotype association studies of American mink.

scholarly journals Cyrius: accurate CYP2D6 genotyping using whole genome sequencing data

2020 ◽  
Author(s):  
Xiao Chen ◽  
Fei Shen ◽  
Nina Gonzaludo ◽  
Alka Malhotra ◽  
Cande Rogert ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Sequence Similarity ◽  
Ethnically Diverse ◽  
Haplotype Frequency ◽  
Superior Performance ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Structural Variants ◽  
Sequencing Data

AbstractResponsible for the metabolism of 25% of clinically used drugs, CYP2D6 is a critical component of personalized medicine initiatives. Genotyping CYP2D6 is challenging due to sequence similarity with its pseudogene paralog CYP2D7 and a high number and variety of common structural variants (SVs). Here we describe a novel bioinformatics method, Cyrius, that accurately genotypes CYP2D6 using whole-genome sequencing (WGS) data. We show that Cyrius has superior performance (96.5% concordance with truth genotypes) compared to existing methods (84-86.8%). After implementing the improvements identified from the comparison against the truth data, Cyrius’s accuracy has since been improved to 99.3%. Using Cyrius, we built a haplotype frequency database from 2504 ethnically diverse samples and estimate that SV-containing star alleles are more frequent than previously reported. Cyrius will be an important tool to incorporate pharmacogenomics in WGS-based precision medicine initiatives.

scholarly journals Local ancestry prediction with PyLAE

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12502
Author(s):  
Nikita Moshkov ◽  
Aleksandr Smetanin ◽  
Tatiana V. Tatarinova
Keyword(s):  
Genome Sequencing ◽  
Gold Standard ◽  
Source Code ◽  
Genomic Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Local Ancestry ◽  
1000 Genomes ◽  
A Genome

Summary We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. Availability and implementation The source code and installation manual are available at https://github.com/smetam/pylae.

scholarly journals Identifying non-identical-by-descent rare variants in population-scale whole genome sequencing data

2020 ◽  
Author(s):  
Kelsey E. Johnson ◽  
Benjamin F. Voight
Keyword(s):  
Genome Sequencing ◽  
Rare Variants ◽  
Whole Genome Sequencing Data ◽  
Human Populations ◽  
Whole Genome ◽  
Sequencing Data ◽  
Shared Haplotype ◽  
Identical By Descent ◽  
Recurrent Mutations ◽  
Gene Conversions

AbstractThe site frequency spectrum in human populations is not accurately modeled by an infinite sites model, which assumes that all mutations are unique. Despite the pervasiveness of recurrent mutations, we lack computational methods to identify these events at specific sites in population sequencing data. Rare alleles that are identical-by-descent (IBD) are expected to segregate on a long, shared haplotype background that descends from a common ancestor. However, alleles introduced by recurrent mutation or by non-crossover gene conversions are identical-by-state and will have a shorter expected shared haplotype background. We hypothesized that the expected difference in shared haplotype background length can distinguish IBD and non-IBD variants in population sequencing data without pedigree information. We implemented a Bayesian hierarchical model and used Gibbs sampling to estimate the posterior probability of IBD state for rare variants, using simulations to demonstrate that our approach accurately distinguishes rare IBD and non-IBD variants. Applying our method to whole genome sequencing data from 3,621 individuals in the UK10K consortium, we found that non-IBD variants correlated with higher local mutation rates and genomic features like replication timing. Using a heuristic to categorize non-IBD variants as gene conversions or recurrent mutations, we found that potential gene conversions had expected properties such as enriched local GC content. By identifying recurrent mutations, we can better understand the spectrum of recent mutations in human populations, a source of genetic variation driving evolution and a key factor in understanding recent demographic history.

scholarly journals Mobile Element Insertions and Associated Structural Variants in Longitudinal Breast Cancer Samples

2020 ◽  
Author(s):  
Cody J. Steely ◽  
Kristi L. Russell ◽  
Julie E. Feusier ◽  
Yi Qiao ◽  
Gabor Marth ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Progression ◽  
Metastatic Breast ◽  
Mobile Element ◽  
Mobile Elements ◽  
Whole Genome Sequencing Data ◽  
Breast Cancer Patients ◽  
Structural Variants ◽  
Sequencing Data ◽  
Visual Confirmation

AbstractWhile mobile elements are largely inactive in healthy somatic tissues, increased activity has been found in cancer tissues, with significant variation among different cancer types. In addition to insertion events, mobile elements have also been found to mediate many structural variation events in the genome. Here, to better understand the timing and impact of mobile element insertions and mobile element-mediated structural variants in cancer, we examined their activity in longitudinal samples of four metastatic breast cancer patients. With whole-genome sequencing data from multiple timepoints through tumor treatment and progression, we used mobile element detection software followed by visual confirmation of the insertions. From this analysis we identified 11 mobile element insertions or mobile element-mediated structural variants, and found that the majority (nine of the eleven) of these occurred early in tumor progression. Two of the identified insertions were SVA elements, which have not been examined in previous cancer studies. Most of the variants appear to impact intergenic regions; however, we identified a mobile element-mediated translocation in MAP2K4 and a mobile element-mediated deletion in YTHDF2 that likely inactivate reported tumor suppressor genes. MAP2K4 is part of the JNK signaling pathway, influencing cell growth and proliferation. The high variant allele frequency of this translocation and the important function of MAP2K4 indicate that this mobile element-mediated translocation is likely a driver mutation. Overall, using a unique longitudinal dataset, we find that most variants are likely passenger mutations in the four patients we examined, but some variants impact tumor progression.

scholarly journals NPSV: A simulation-driven approach to genotyping structural variants in whole-genome sequencing data

GigaScience ◽  
2021 ◽  
Vol 10 (7) ◽  
Author(s):  
Michael D Linderman ◽  
Crystal Paudyal ◽  
Musab Shakeel ◽  
William Kelley ◽  
Ali Bashir ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
De Novo ◽  
Training Data ◽  
Next Generation Sequencing Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Next Generation ◽  
Structural Variants ◽  
Sequencing Data ◽  
Generation Sequencing

Abstract Background Structural variants (SVs) play a causal role in numerous diseases but are difficult to detect and accurately genotype (determine zygosity) in whole-genome next-generation sequencing data. SV genotypers that assume that the aligned sequencing data uniformly reflect the underlying SV or use existing SV call sets as training data can only partially account for variant and sample-specific biases. Results We introduce NPSV, a machine learning–based approach for genotyping previously discovered SVs that uses next-generation sequencing simulation to model the combined effects of the genomic region, sequencer, and alignment pipeline on the observed SV evidence. We evaluate NPSV alongside existing SV genotypers on multiple benchmark call sets. We show that NPSV consistently achieves or exceeds state-of-the-art genotyping accuracy across SV call sets, samples, and variant types. NPSV can specifically identify putative de novo SVs in a trio context and is robust to offset SV breakpoints. Conclusions Growing SV databases and the increasing availability of SV calls from long-read sequencing make stand-alone genotyping of previously identified SVs an increasingly important component of genome analyses. By treating potential biases as a “black box” that can be simulated, NPSV provides a framework for accurately genotyping a broad range of SVs in both targeted and genome-scale applications.

scholarly journals Polymorphic mobile element insertions contribute to gene expression and alternative splicing in human tissues

2020 ◽  
Author(s):  
Xiaolong Cao ◽  
Yeting Zhang ◽  
Lindsay M Payer ◽  
Hannah Lords ◽  
Jared P Steranka ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Genome Sequencing ◽  
Quantitative Trait ◽  
Mobile Element ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Human Tissues ◽  
Sequencing Data ◽  
Different Tissues

AbstractBackgroundMobile elements are a major source of human structural variants and some mobile elements can regulate gene expression and alternative splicing. However, the impact of polymorphic mobile element insertions (pMEIs) on gene expression and splicing in diverse human tissues has not been thoroughly studied. The multi-tissue gene expression and whole genome sequencing data generated by the Genotype-Tissue Expression (GTEx) project provide a great opportunity to systematic determine pMEIs’ role in gene expression regulation in human tissues.ResultsUsing the GTEx whole genome sequencing data, we identified 20,545 high-quality pMEIs from 639 individuals. We then identified pMEI-associated expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) in 48 tissues by joint analysis of variants including pMEIs, single-nucleotide polymorphisms, and insertions/deletions. pMEIs were predicted to be the potential causal variant for 3,522 of the 30,147 significant eQTLs, and 3,717 of the 21,529 significant sQTLs. The pMEIs associated eQTLs and sQTLs show high level of tissue-specificity, and the pMEIs were enriched in the proximity of affected genes and in regulatory elements. Using reporter assays, we confirmed that several pMEIs associated with eQTLs and sQTLs can alter gene expression levels and isoform proportions.ConclusionOverall, our study shows that pMEIs are associated with thousands of gene expression and splicing variations in different tissues, and pMEIs could have a significant role in regulating tissue-specific gene expression/splicing. Detailed mechanisms for pMEI’s role in gene regulation in different tissues will be an important direction for future human genomic studies.

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