scholarly journals High coverage whole genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

2021 ◽  
Author(s):  
Marta Byrska-Bishop ◽  
Uday S. Evani ◽  
Xuefang Zhao ◽  
Anna O. Basile ◽  
Haley J. Abel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
High Coverage ◽  
Entire Cohort ◽  
1000 Genomes ◽  
Low Coverage

ABSTRACTThe 1000 Genomes Project (1kGP), launched in 2008, is the largest fully open resource of whole genome sequencing (WGS) data consented for public distribution of raw sequence data without access or use restrictions. The final (phase 3) 2015 release of 1kGP included 2,504 unrelated samples from 26 populations, representing five continental regions of the world and was based on a combination of technologies including low coverage WGS (mean depth 7.4X), high coverage whole exome sequencing (mean depth 65.7X), and microarray genotyping. Here, we present a new, high coverage WGS resource encompassing the original 2,504 1kGP samples, as well as an additional 698 related samples that result in 602 complete trios in the 1kGP cohort. We sequenced this expanded 1kGP cohort of 3,202 samples to a targeted depth of 30X using Illumina NovaSeq 6000 instruments. We performed SNV/INDEL calling against the GRCh38 reference using GATK’s HaplotypeCaller, and generated a comprehensive set of SVs by integrating multiple analytic methods through a sophisticated machine learning model, upgrading the 1kGP dataset to current state-of-the-art standards. Using this strategy, we defined over 111 million SNVs, 14 million INDELs, and ∼170 thousand SVs across the entire cohort of 3,202 samples with estimated false discovery rate (FDR) of 0.3%, 1.0%, and 1.8%, respectively. By comparison to the low-coverage phase 3 callset, we observed substantial improvements in variant discovery and estimated FDR that were facilitated by high coverage re-sequencing and expansion of the cohort. Specifically, we called 7% more SNVs, 59% more INDELs, and 170% more SVs per genome than the phase 3 callset. Moreover, we leveraged the presence of families in the cohort to achieve superior haplotype phasing accuracy and we demonstrate improvements that the high coverage panel brings especially for INDEL imputation. We make all the data generated as part of this project publicly available and we envision this updated version of the 1kGP callset to become the new de facto public resource for the worldwide scientific community working on genomics and genetics.

scholarly journals High Coverage Whole Genome Sequencing of the Expanded 1000 Genomes Project Cohort Including 602 Trios

2021 ◽  
Author(s):  
Marta Byrska-Bishop ◽  
Uday S. Evani ◽  
Xuefang Zhao ◽  
Anna O. Basile ◽  
Haley J. Abel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
High Coverage ◽  
1000 Genomes

scholarly journals Optimizing Genomic Selection in Dezhou Donkey Using Low Coverage Whole Genome Sequencing

2021 ◽  
Author(s):  
Changheng Zhao ◽  
Jun Teng ◽  
Xinhao Zhang ◽  
Dan Wang ◽  
Xinyi Zhang ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genomic Selection ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Low Cost ◽  
Imputation Accuracy ◽  
Genotype Imputation ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Low Coverage

Abstract Background Low coverage whole genome sequencing is a low-cost genotyping technology. Combining with genotype imputation approaches, it is likely to become a critical component of cost-efficient genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low coverage whole genome sequence data and genomic selection based on the imputed genotype data. The specific aims were: (i) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, MAFs, and imputation pipelines; and (ii) to assess the accuracy of genomic selection under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single- vs multi-trait models. Results We found that a high imputation accuracy (> 0.95) can be achieved for sequence data with sequencing depth as low as 1x and the number of sequenced individuals equal to 400. For genomic selection, the best performance was obtained by using a marker density of 410K and a G matrix constructed using marker dosage information. Multi-trait GBLUP performed better than single-trait GBLUP. Conclusions Our study demonstrates that low coverage whole genome sequencing would be a cost-effective method for genomic selection in Dezhou Donkey.

scholarly journals Kssd: sequence dimensionality reduction by k-mer substring space sampling enables real-time large-scale datasets analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Huiguang Yi ◽  
Yanling Lin ◽  
Chengqi Lin ◽  
Wenfei Jin
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Large Scale ◽  
Real Data ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Sequence Read Archive ◽  
Large Scale Dataset ◽  
Ncbi Sequence Read Archive

AbstractHere, we develop k -mer substring space decomposition (Kssd), a sketching technique which is significantly faster and more accurate than current sketching methods. We show that it is the only method that can be used for large-scale dataset comparisons at population resolution on simulated and real data. Using Kssd, we prioritize references for all 1,019,179 bacteria whole genome sequencing (WGS) runs from NCBI Sequence Read Archive and find misidentification or contamination in 6164 of these. Additionally, we analyze WGS and exome runs of samples from the 1000 Genomes Project.

scholarly journals Genotyping by low-coverage whole-genome sequencing in intercross pedigrees from outbred founders: a cost efficient approach

2018 ◽  
Author(s):  
Yanjun Zan ◽  
Thibaut Payen ◽  
Mette Lillie ◽  
Christa F. Honaker ◽  
Paul B. Siegel ◽  
...  
Keyword(s):  
High Resolution ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Genotype Imputation ◽  
Whole Genome ◽  
Efficient Manner ◽  
Founder Line ◽  
Cost Efficient ◽  
Low Coverage

ABSTRACTBackgroundExperimental intercrosses between outbred founder populations are powerful resources for mapping loci contributing to complex traits (Quantitative Trait Loci or QTL). Here, we present an approach and accompanying software for high-resolution genotype imputation in such populations using whole-genome high coverage sequence data on founder individuals (∼30×) and low coverage sequence data on intercross individuals (∼0.4×). The method is illustrated in a large F2 pedigree between lines of chickens that have been divergently selected for 40 generations for the same trait (body weight at 8 weeks of age).ResultsDescribed is how hundreds of individuals were whole-genome sequenced in a cost- and time-efficient manner using a Tn5-based library preparation protocol optimized for this application. In total, 7.6M markers segregated in this pedigree and 10.0 to 13.7% were informative for imputing the founder line genotypes within the F0-F2 families. The genotypes imputed from low coverage sequence data were consistent with the founder line genotypes estimated using SNP and microsatellite markers both at individual imputed sites (92%) and across the genome of individual chickens (93%). The resolution of the recombination breakpoints was high with 50% being resolved within <10kb.ConclusionsA method for genotype imputation from low-coverage whole-genome sequencing in outbred intercrosses is described and evaluated. By applying it to an outbred chicken F2 cross it is illustrated that it provides high quality, high-resolution genotypes in a time and cost efficient manner.

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 Whole-genome sequencing of nine esophageal adenocarcinoma cell lines

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1336 ◽  
Author(s):  
Gianmarco Contino ◽  
Matthew D. Eldridge ◽  
Maria Secrier ◽  
Lawrence Bower ◽  
Rachael Fels Elliott ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Esophageal Adenocarcinoma ◽  
Cell Lines ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Whole Genome ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide Variants ◽  
High Coverage ◽  
Single Nucleotide

Esophageal adenocarcinoma (EAC) is highly mutated and molecularly heterogeneous. The number of cell lines available for study is limited and their genome has been only partially characterized. The availability of an accurate annotation of their mutational landscape is crucial for accurate experimental design and correct interpretation of genotype-phenotype findings. We performed high coverage, paired end whole genome sequencing on eight EAC cell lines—ESO26, ESO51, FLO-1, JH-EsoAd1, OACM5.1 C, OACP4 C, OE33, SK-GT-4—all verified against original patient material, and one esophageal high grade dysplasia cell line, CP-D. We have made available the aligned sequence data and report single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number alterations, identified by comparison with the human reference genome and known single nucleotide polymorphisms (SNPs). We compare these putative mutations to mutations found in primary tissue EAC samples, to inform the use of these cell lines as a model of EAC.

scholarly journals Large-scale whole-genome sequencing of three diverse Asian populations in Singapore

2018 ◽  
Author(s):  
Degang Wu ◽  
Jinzhuang Dou ◽  
Xiaoran Chai ◽  
Claire Bellis ◽  
Andreas Wilm ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Human Genetics ◽  
Genotype Imputation ◽  
Whole Genome ◽  
Fine Scale ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Asian Populations

AbstractAsian populations are currently underrepresented in human genetics research. Here we present whole-genome sequencing data of 4,810 Singaporeans from three diverse ethnic groups: 2,780 Chinese, 903 Malays, and 1,127 Indians. Despite a medium depth of 13.7×, we achieved essentially perfect (>99.8%) sensitivity and accuracy for detecting common variants and good sensitivity (>89%) for detecting extremely rare variants with <0.1% allele frequency. We found 89.2 million single-nucleotide polymorphisms (SNPs) and 9.1 million small insertions and deletions (INDELs), more than half of which have not been cataloged in dbSNP. In particular, we found 126 common deleterious mutations (MAF>0.01) that were absent in the existing public databases, highlighting the importance of local population reference for genetic diagnosis. We describe fine-scale genetic structure of Singapore populations and their relationship to worldwide populations from the 1000 Genomes Project. In addition to revealing noticeable amounts of admixture among three Singapore populations and a Malay-related novel ancestry component that has not been captured by the 1000 Genomes Project, our analysis also identified some fine-scale features of genetic structure consistent with two waves of prehistoric migration from south China to Southeast Asia. Finally, we demonstrate that our data can substantially improve genotype imputation not only for Singapore populations, but also for populations across Asia and Oceania. These results highlight the genetic diversity in Singapore and the potential impacts of our data as a resource to empower human genetics discovery in a broad geographic region.

scholarly journals 353 ASAS-EAAP Talk: Low-coverage whole-genome sequencing in local livestock breeds

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 81-82
Author(s):  
Joaquim Casellas ◽  
Melani Martín de Hijas-Villalba ◽  
Marta Vázquez-Gómez ◽  
Samir Id Lahoucine
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Error Rate ◽  
Allele Frequencies ◽  
Paternity Testing ◽  
Sequencing Error ◽  
Whole Genome ◽  
Genomic Evaluation ◽  
Sequencing Error Rate ◽  
Low Coverage

Abstract Current European regulations for autochthonous livestock breeds put a special emphasis on pedigree completeness, which requires laboratory paternity testing by genetic markers in most cases. This entails significant economic expenditure for breed societies and precludes other investments in breeding programs, such as genomic evaluation. Within this context, we developed paternity testing through low-coverage whole-genome data in order to reuse these data for genomic evaluation at no cost. Simulations relied on diploid genomes composed by 30 chromosomes (100 cM each) with 3,000,000 SNP per chromosome. Each population evolved during 1,000 non-overlapping generations with effective size 100, mutation rate 10–4, and recombination by Kosambi’s function. Only those populations with 1,000,000 ± 10% polymorphic SNP per chromosome in generation 1,000 were retained for further analyses, and expanded to the required number of parents and offspring. Individuals were sequenced at 0.01, 0.05, 0.1, 0.5 and 1X depth, with 100, 500, 1,000 or 10,000 base-pair reads and by assuming a random sequencing error rate per SNP between 10–2 and 10–5. Assuming known allele frequencies in the population and sequencing error rate, 0.05X depth sufficed to corroborate the true father (85,0%) and to discard other candidates (96,3%). Those percentages increased up to 99,6% and 99,9% with 0,1X depth, respectively (read length = 10,000 bp; smaller read lengths slightly improved the results because they increase the number of sequenced SNP). Results were highly sensitive to biases in allele frequencies and robust to inaccuracies regarding sequencing error rate. Low-coverage whole-genome sequencing data could be subsequently integrated into genomic BLUP equations by appropriately constructing the genomic relationship matrix. This approach increased the correlation between simulated and predicted breeding values by 1.21% (h2 = 0.25; 100 parents and 900 offspring; 0.1X depth by 10,000 bp reads). Although small, this increase opens the door to genomic evaluation in local livestock breeds.

scholarly journals Genome Sequencing of Polydrug-, Multidrug-, and Extensively Drug-Resistant Mycobacterium tuberculosis Strains from South India

2019 ◽  
Vol 8 (12) ◽  
Author(s):  
Sivakumar Shanmugam ◽  
Narender Kumar ◽  
Dina Nair ◽  
Mohan Natrajan ◽  
Srikanth Prasad Tripathy ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
South India ◽  
Sequence Data ◽  
Whole Genome ◽  
Drug Resistant ◽  
Resistance Mutations ◽  
Content Type ◽  
Extensively Drug Resistant

The genomes of 16 clinical Mycobacterium tuberculosis isolates were subjected to whole-genome sequencing to identify mutations related to resistance to one or more anti-Mycobacterium drugs. The sequence data will help in understanding the genomic characteristics of M. tuberculosis isolates and their resistance mutations prevalent in South India.

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