scholarly journals EagleImp: Fast and Accurate Genome-wide Phasing and Imputation in a Single Tool

2022 ◽  
Author(s):  
Lars Wienbrandt ◽  
David Ellinghaus
Keyword(s):  
Memory Management ◽  
Imputation Accuracy ◽  
Simulated Data ◽  
Genotype Imputation ◽  
Whole Genome Sequencing Data ◽  
Common Variants ◽  
Sequencing Data ◽  
1000 Genomes ◽  
Genome Wide ◽  
Reference Genomes

Background: Reference-based phasing and genotype imputation algorithms have been developed with sublinear theoretical runtime behaviour, but runtimes are still high in practice when large genome-wide reference datasets are used. Methods: We developed EagleImp, a software with algorithmic and technical improvements and new features for accurate and accelerated phasing and imputation in a single tool. Results: We compared accuracy and runtime of EagleImp with Eagle2, PBWT and prominent imputation servers using whole-genome sequencing data from the 1000 Genomes Project, the Haplotype Reference Consortium and simulated data with more than 1 million reference genomes. EagleImp is 2 to 10 times faster (depending on the single or multiprocessor configuration selected) than Eagle2/PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical GWAS studies, EagleImp provides same or higher imputation accuracy than the Sanger Imputation Service, Michigan Imputation Server and the newly developed TOPMed Imputation Server, despite larger (not publicly available) reference panels. It has many new features, including automated chromosome splitting and memory management at runtime to avoid job aborts, fast reading and writing of large files, and various user-configurable algorithm and output options. Conclusions: Due to the technical optimisations, EagleImp can perform fast and accurate reference-based phasing and imputation for future very large reference panels with more than 1 million genomes. EagleImp is freely available for download from https://github.com/ikmb/eagleimp.

scholarly journals Whole-genome reference panel of 1,781 Northeast Asians improves imputation accuracy of rare and low-frequency variants

2019 ◽  
Author(s):  
Seong-Keun Yoo ◽  
Chang-Uk Kim ◽  
Hie Lim Kim ◽  
Sungjae Kim ◽  
Jong-Yeon Shin ◽  
...  
Keyword(s):  
Imputation Accuracy ◽  
Northeast Asia ◽  
Low Frequency ◽  
Genotype Imputation ◽  
Reference Panel ◽  
Whole Genome Sequencing Data ◽  
Reference Database ◽  
Whole Genome ◽  
Sequencing Data ◽  
Northeast Asian

AbstractGenotype imputation using the reference panel is a cost-effective strategy to fill millions of missing genotypes for the purpose of various genetic analyses. Here, we present the Northeast Asian Reference Database (NARD), including whole-genome sequencing data of 1,781 individuals from Korea, Mongolia, Japan, China, and Hong Kong. NARD provides the genetic diversities of Korean (n=850) and Mongolian (n=386) ancestries that were not present in the 1000 Genomes Project Phase 3 (1KGP3). We combined and re-phased the genotypes from NARD and 1KGP3 to construct a union set of haplotypes. This approach established a robust imputation reference panel for the Northeast Asian populations, which yields the greatest imputation accuracy of rare and low-frequency variants compared with the existing panels. Also, we illustrate that NARD can potentially improve disease variant discovery by reducing pathogenic candidates. Overall, this study provides a decent reference panel for the genetic studies in Northeast Asia.

scholarly journals NARD: whole-genome reference panel of 1779 Northeast Asians improves imputation accuracy of rare and low-frequency variants

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Seong-Keun Yoo ◽  
Chang-Uk Kim ◽  
Hie Lim Kim ◽  
Sungjae Kim ◽  
Jong-Yeon Shin ◽  
...  
Keyword(s):  
Imputation Accuracy ◽  
Low Frequency ◽  
Reference Panel ◽  
Whole Genome Sequencing Data ◽  
Reference Database ◽  
Whole Genome ◽  
Sequencing Data ◽  
Phase 3 ◽  
1000 Genomes ◽  
Northeast Asian

Abstract Here, we present the Northeast Asian Reference Database (NARD), including whole-genome sequencing data of 1779 individuals from Korea, Mongolia, Japan, China, and Hong Kong. NARD provides the genetic diversity of Korean (n = 850) and Mongolian (n = 384) ancestries that were not present in the 1000 Genomes Project Phase 3 (1KGP3). We combined and re-phased the genotypes from NARD and 1KGP3 to construct a union set of haplotypes. This approach established a robust imputation reference panel for Northeast Asians, which yields the greatest imputation accuracy of rare and low-frequency variants compared with the existing panels. NARD imputation panel is available at https://nard.macrogen.com/.

scholarly journals Population-level genome-wide STR typing in Plasmodium species reveals higher resolution population structure and genetic diversity relative to SNP typing

2021 ◽  
Author(s):  
Jiru Han ◽  
Jacob E Munro ◽  
Anthony Kocoski ◽  
Alyssa E Barry ◽  
Melanie Bahlo
Keyword(s):  
Genetic Diversity ◽  
Large Scale ◽  
Tandem Repeats ◽  
Plasmodium Species ◽  
Whole Genome Sequence ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Genome Wide ◽  
Field Samples

Short tandem repeats (STRs) are highly informative genetic markers that have been used extensively in population genetics analysis. They are an important source of genetic diversity and can also have functional impact. Despite the availability of bioinformatic methods that permit large-scale genome-wide genotyping of STRs from whole genome sequencing data, they have not previously been applied to sequencing data from large collections of malaria parasite field samples. Here, we have genotyped STRs using HipSTR in more than 3,000 Plasmodium falciparum and 174 Plasmodium vivax published whole-genome sequence data from samples collected across the globe. High levels of noise and variability in the resultant callset necessitated the development of a novel method for quality control of STR genotype calls. A set of high-quality STR loci (6,768 from P. falciparum and 3,496 from P. vivax) were used to study Plasmodium genetic diversity, population structures and genomic signatures of selection and these were compared to genome-wide single nucleotide polymorphism (SNP) genotyping data. In addition, the genome-wide information about genetic variation and other characteristics of STRs in P. falciparum and P. vivax have been made available in an interactive web-based R Shiny application PlasmoSTR (https://github.com/bahlolab/PlasmoSTR).

scholarly journals Novel functional sequences uncovered through a bovine multiassembly graph

2021 ◽  
Vol 118 (20) ◽  
pp. e2101056118
Author(s):  
Danang Crysnanto ◽  
Alexander S. Leonard ◽  
Zih-Hua Fang ◽  
Hubert Pausch
Keyword(s):  
Genetic Diversity ◽  
Reference Genome ◽  
Bos Taurus ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Reference Allele ◽  
Genomic Analyses ◽  
Reference Quality ◽  
Reference Genomes

Many genomic analyses start by aligning sequencing reads to a linear reference genome. However, linear reference genomes are imperfect, lacking millions of bases of unknown relevance and are unable to reflect the genetic diversity of populations. This makes reference-guided methods susceptible to reference-allele bias. To overcome such limitations, we build a pangenome from six reference-quality assemblies from taurine and indicine cattle as well as yak. The pangenome contains an additional 70,329,827 bases compared to the Bos taurus reference genome. Our multiassembly approach reveals 30 and 10.1 million bases private to yak and indicine cattle, respectively, and between 3.3 and 4.4 million bases unique to each taurine assembly. Utilizing transcriptomes from 56 cattle, we show that these nonreference sequences encode transcripts that hitherto remained undetected from the B. taurus reference genome. We uncover genes, primarily encoding proteins contributing to immune response and pathogen-mediated immunomodulation, differentially expressed between Mycobacterium bovis–infected and noninfected cattle that are also undetectable in the B. taurus reference genome. Using whole-genome sequencing data of cattle from five breeds, we show that reads which were previously misaligned against the Bos taurus reference genome now align accurately to the pangenome sequences. This enables us to discover 83,250 polymorphic sites that segregate within and between breeds of cattle and capture genetic differentiation across breeds. Our work makes a so-far unused source of variation amenable to genetic investigations and provides methods and a framework for establishing and exploiting a more diverse reference genome.

scholarly journals Transposable element profiles reveal cell line identity and loss of heterozygosity in Drosophila cell culture

Genetics ◽  
2021 ◽  
Author(s):  
Shunhua Han ◽  
Preston J Basting ◽  
Guilherme B Dias ◽  
Arthur Luhur ◽  
Andrew C Zelhof ◽  
...  
Keyword(s):  
Cell Culture ◽  
Cell Line ◽  
Loss Of Heterozygosity ◽  
Cell Lines ◽  
Whole Genome Sequencing Data ◽  
Sequencing Data ◽  
Drosophila Cell ◽  
Genome Wide ◽  
Cell Culture Systems ◽  
Cell Line Identity

Abstract Cell culture systems allow key insights into biological mechanisms yet suffer from irreproducible outcomes in part because of cross-contamination or mislabelling of cell lines. Cell line misidentification can be mitigated by the use of genotyping protocols, which have been developed for human cell lines but are lacking for many important model species. Here we leverage the classical observation that transposable elements (TEs) proliferate in cultured Drosophila cells to demonstrate that genome-wide TE insertion profiles can reveal the identity and provenance of Drosophila cell lines. We identify multiple cases where TE profiles clarify the origin of Drosophila cell lines (Sg4, mbn2, and OSS_E) relative to published reports, and also provide evidence that insertions from only a subset of LTR retrotransposon families are necessary to mark Drosophila cell line identity. We also develop a new bioinformatics approach to detect TE insertions and estimate intra-sample allele frequencies in legacy whole-genome sequencing data (called ngs_te_mapper2), which revealed loss of heterozygosity as a mechanism shaping the unique TE profiles that identify Drosophila cell lines. Our work contributes to the general understanding of the forces impacting metazoan genomes as they evolve in cell culture and paves the way for high-throughput protocols that use TE insertions to authenticate cell lines in Drosophila and other organisms.

scholarly journals A high-quality reference panel reveals the complexity and distribution of structural genome changes in a human population

2016 ◽  
Author(s):  
Jayne Y. Hehir-Kwa ◽  
Tobias Marschall ◽  
Wigard P. Kloosterman ◽  
Laurent C. Francioli ◽  
Jasmijn A. Baaijens ◽  
...  
Keyword(s):  
Association Studies ◽  
Whole Genome Sequencing Data ◽  
Strong Linkage Disequilibrium ◽  
Genome Wide Association Studies ◽  
Sequencing Data ◽  
Full Spectrum ◽  
Disease Phenotypes ◽  
Human Genomes ◽  
Genome Wide ◽  
Genome Variants

AbstractStructural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation.Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals. Our findings are essential for genome-wide association studies.

scholarly journals Evaluating genotype imputation pipeline for ultra-low coverage ancient genomes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ruoyun Hui ◽  
Eugenia D’Atanasio ◽  
Lara M. Cassidy ◽  
Christiana L. Scheib ◽  
Toomas Kivisild
Keyword(s):  
Ancient Dna ◽  
Imputation Accuracy ◽  
Single Step ◽  
Genotype Imputation ◽  
Common Variants ◽  
High Coverage ◽  
Input Mode ◽  
Missing Genotypes ◽  
Low Coverage ◽  
Reference Bias

Abstract Although ancient DNA data have become increasingly more important in studies about past populations, it is often not feasible or practical to obtain high coverage genomes from poorly preserved samples. While methods of accurate genotype imputation from > 1 × coverage data have recently become a routine, a large proportion of ancient samples remain unusable for downstream analyses due to their low coverage. Here, we evaluate a two-step pipeline for the imputation of common variants in ancient genomes at 0.05–1 × coverage. We use the genotype likelihood input mode in Beagle and filter for confident genotypes as the input to impute missing genotypes. This procedure, when tested on ancient genomes, outperforms a single-step imputation from genotype likelihoods, suggesting that current genotype callers do not fully account for errors in ancient sequences and additional quality controls can be beneficial. We compared the effect of various genotype likelihood calling methods, post-calling, pre-imputation and post-imputation filters, different reference panels, as well as different imputation tools. In a Neolithic Hungarian genome, we obtain ~ 90% imputation accuracy for heterozygous common variants at coverage 0.05 × and > 97% accuracy at coverage 0.5 ×. We show that imputation can mitigate, though not eliminate reference bias in ultra-low coverage ancient genomes.

scholarly journals Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Julian R. Homburger ◽  
Cynthia L. Neben ◽  
Gilad Mishne ◽  
Alicia Y. Zhou ◽  
Sekar Kathiresan ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Imputation Accuracy ◽  
European Ancestry ◽  
Whole Genome ◽  
Common Variants ◽  
Genotyping Array ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Low Coverage

Abstract Background Inherited susceptibility to common, complex diseases may be caused by rare, pathogenic variants (“monogenic”) or by the cumulative effect of numerous common variants (“polygenic”). Comprehensive genome interpretation should enable assessment for both monogenic and polygenic components of inherited risk. The traditional approach requires two distinct genetic testing technologies—high coverage sequencing of known genes to detect monogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). We assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs. Methods First, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for 3 common diseases—coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF)—calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in a cohort of 11,502 individuals of European ancestry. Results We found imputation accuracy r2 values of greater than 0.90 for all ten samples—including those of African and Ashkenazi Jewish ancestry—with lcWGS data at 0.5×. GPSs calculated using lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the 3 common diseases (r2 = 0.93–0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 individuals of European ancestry, confirming odds ratios per standard deviation increment ranging 1.28 to 1.59, consistent with previous studies. Conclusions lcWGS is an alternative technology to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.

scholarly journals Deep genotype imputation captures virtually all heritability of autoimmune vitiligo

2020 ◽  
Vol 29 (5) ◽  
pp. 859-863 ◽  
Author(s):  
Genevieve H L Roberts ◽  
Stephanie A Santorico ◽  
Richard A Spritz
Keyword(s):  
Complex Disease ◽  
Rare Variants ◽  
Association Studies ◽  
Genotype Imputation ◽  
Genome Wide Association Studies ◽  
Common Variants ◽  
Genome Wide ◽  
Autoimmune Vitiligo ◽  
Family Based ◽  
Project Data

Abstract Autoimmune vitiligo is a complex disease involving polygenic risk from at least 50 loci previously identified by genome-wide association studies. The objectives of this study were to estimate and compare vitiligo heritability in European-derived patients using both family-based and ‘deep imputation’ genotype-based approaches. We estimated family-based heritability (h2FAM) by vitiligo recurrence among a total 8034 first-degree relatives (3776 siblings, 4258 parents or offspring) of 2122 unrelated vitiligo probands. We estimated genotype-based heritability (h2SNP) by deep imputation to Haplotype Reference Consortium and the 1000 Genomes Project data in unrelated 2812 vitiligo cases and 37 079 controls genotyped genome wide, achieving high-quality imputation from markers with minor allele frequency (MAF) as low as 0.0001. Heritability estimated by both approaches was exceedingly high; h2FAM = 0.75–0.83 and h2SNP = 0.78. These estimates are statistically identical, indicating there is essentially no remaining ‘missing heritability’ for vitiligo. Overall, ~70% of h2SNP is represented by common variants (MAF > 0.01) and 30% by rare variants. These results demonstrate that essentially all vitiligo heritable risk is captured by array-based genotyping and deep imputation. These findings suggest that vitiligo may provide a particularly tractable model for investigation of complex disease genetic architecture and predictive aspects of personalized medicine.

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