scholarly journals Genomic Rearrangements Considered as Quantitative Traits

2016 ◽  
Author(s):  
Martha Imprialou ◽  
André Kahles ◽  
Joshua G. Steffen ◽  
Edward J. Osborne ◽  
Xiangchao Gan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Traits ◽  
Sequence Data ◽  
Expression Study ◽  
High Coverage ◽  
Fungal Disease Resistance ◽  
Large Populations ◽  
Study Association ◽  
Low Coverage ◽  
Population Sequence

AbstractTo understand the population genetics of structural variants (SVs), and their effects on phenotypes, we developed an approach to mapping SVs, particularly transpositions, segregating in a sequenced population, and which avoids calling SVs directly. The evidence for a potential SV at a locus is indicated by variation in the counts of short-reads that map anomalously to the locus. These SV traits are treated as quantitative traits and mapped genetically, analogously to a gene expression study. Association between an SV trait at one locus and genotypes at a distant locus indicate the origin and target of a transposition. Using ultra-low-coverage (0.3x) population sequence data from 488 recombinant inbred Arabidopsis genomes, we identified 6,502 segregating SVs. Remarkably, 25% of these were transpositions. Whilst many SVs cannot be delineated precisely, PCR validated 83% of 44 predicted transposition breakpoints. We show that specific SVs may be causative for quantitative trait loci for germination, fungal disease resistance and other phenotypes. Further we show that the phenotypic heritability attributable to sequence anomalies differs from, and in the case of time to germination and bolting, exceeds that due to standard genetic variation. Gene expression within SVs is also more likely to be silenced or dysregulated. This approach is generally applicable to large populations sequenced at low-coverage, and complements the prevalent strategy of SV discovery in fewer individuals sequenced at high coverage.

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 Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

2018 ◽  
Author(s):  
Roger Ros-Freixedes ◽  
Battagin Mara ◽  
Martin Johnsson ◽  
Gregor Gorjanc ◽  
Alan J Mileham ◽  
...  
Keyword(s):  
Sequence Data ◽  
Cost Effective ◽  
Reference Allele ◽  
High Coverage ◽  
Sequencing Errors ◽  
Percentage Points ◽  
Low Coverage ◽  
The Impact ◽  
Genotype Concordance

AbstractBackgroundInherent sources of error and bias that affect the quality of the sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing there is a need to understand the impact of these errors and bias on resulting genotype calls.ResultsWe used a dataset of 26 pigs sequenced both at 2x with multiplexing and at 30x without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, a default and desired step for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points.ConclusionsWe propose a simple pipeline to correct this bias and we recommend that users of low-coverage sequencing be wary of unexpected biases produced by tools designed for high-coverage sequencing.

scholarly journals Integrating genomic resources to present full gene and promoter capture probe sets for bread wheat

2018 ◽  
Author(s):  
Laura-jayne Gardiner ◽  
Thomas Brabbs ◽  
Alina Akhunova ◽  
Katherine Jordan ◽  
Hikmet Budak ◽  
...  
Keyword(s):  
Bread Wheat ◽  
Agronomic Traits ◽  
Sequence Data ◽  
Gene Promoter ◽  
Capture Probe ◽  
Sequence Coverage ◽  
High Coverage ◽  
Large Gene ◽  
Target Capture ◽  
Large Populations

AbstractBackgroundWhole genome shotgun re-sequencing of wheat is expensive because of its large, repetitive genome. Moreover, sequence data can fail to map uniquely to the reference genome making it difficult to unambiguously assign variation. Re-sequencing using target capture enables sequencing of large numbers of individuals at high coverage to reliably identify variants associated with important agronomic traits.ResultsWe present and validate two gold standard capture probe sets for hexaploid bread wheat, a gene and a promoter capture, which are designed using recently developed genome sequence and annotation resources. The captures can be combined or used independently. We demonstrate that the capture probe sets effectively enrich the high confidence genes and promoters that were identified in the genome alongside a large proportion of the low confidence genes and promoters. Finally, we demonstrate successful sample multiplexing that allows generation of adequate sequence coverage for SNP calling while significantly reducing cost per sample for gene and promoter capture.ConclusionsWe show that a capture design employing an ‘island strategy’ can enable analysis of the large gene/promoter space of wheat with only 2×160 Mb probe sets. Furthermore, these assays extend the regions of the wheat genome that are amenable to analyses beyond its exome, providing tools for detailed characterization of these regulatory regions in large populations.

scholarly journals Genotype calling and haplotype inference from low coverage sequence data in heterozygous plant genome using HetMap

2022 ◽  
Author(s):  
Hao Gong ◽  
Bin Han
Keyword(s):  
Wild Rice ◽  
Hybrid Rice ◽  
Sequence Data ◽  
Genotype Imputation ◽  
Plant Genome ◽  
High Coverage ◽  
Software Packages ◽  
Heterozygous Plant ◽  
Low Coverage ◽  
Genotype Inference

Abstract Many software packages and pipelines had been developed to handle the sequence data of the model species. However, Genotyping from complex heterozygous plant genome needs further improvement on the previous methods. Here we present a new pipeline available at https://github.com/Ncgrhg/HetMapv1) for variant calling and missing genotype imputation from low coverage sequence data for heterozygous plant genomes. To check the performance of the HetMap on the real sequence data, HetMap was applied to both the F1 hybrid rice population which consists of 1495 samples and wild rice population with 446 samples. Four high coverage sequence hybrid rice accessions and two high coverage sequence wild rice accessions, which were also included in low coverage sequence data, are used to validate the genotype inference accuracy. The validation results showed that HetMap archived significant improvement in heterozygous genotype inference accuracy (13.65% for hybrid rice, 26.05% for wild rice) and total accuracy compared with other similar software packages. The application of the new genotype with the genome wide association study also showed improvement of association power in two wild rice phenotypes. It could archive high genotype inference accuracy with low sequence coverage with a small population size with both the natural population and constructed recombination population. HetMap provided a powerful tool for the heterozygous plant genome sequence data analysis, which may help the discover of new phenotype regions for the plant species with complex heterozygous genome.

Comparative gene expression study in WM164 melanoma cells treated with dimethylacrylshikonin.

2017 ◽  
Author(s):  
N Kretschmer ◽  
A Deutsch ◽  
B Rinner ◽  
M Scheideler ◽  
R Bauer
Keyword(s):  
Gene Expression ◽  
Melanoma Cells ◽  
Gene Expression Study ◽  
Expression Study

A gene expression study of the glutamate-nitric oxide pathway in schizophrenia

2018 ◽  
Author(s):  
Makoto Kinoshita ◽  
Florian Freudenberg ◽  
Esin Candemir ◽  
Sarah Kittel-Schneider
Keyword(s):  
Gene Expression ◽  
Nitric Oxide ◽  
Gene Expression Study ◽  
Expression Study ◽  
Nitric Oxide Pathway

scholarly journals Genotyping Strategies Using ddRAD Sequencing in Farmed Arctic Charr (Salvelinus alpinus)

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 899
Author(s):  
Fotis Pappas ◽  
Christos Palaiokostas
Keyword(s):  
Genetic Diversity ◽  
Salvelinus Alpinus ◽  
Arctic Charr ◽  
Cost Effective ◽  
Limiting Factors ◽  
Phenotypic Traits ◽  
Production Volume ◽  
High Coverage ◽  
Genomic Relationships ◽  
Low Coverage

Incorporation of genomic technologies into fish breeding programs is a modern reality, promising substantial advances regarding the accuracy of selection, monitoring the genetic diversity and pedigree record verification. Single nucleotide polymorphism (SNP) arrays are the most commonly used genomic tool, but the investments required make them unsustainable for emerging species, such as Arctic charr (Salvelinus alpinus), where production volume is low. The requirement to genotype a large number of animals for breeding practices necessitates cost effective genotyping approaches. In the current study, we used double digest restriction site-associated DNA (ddRAD) sequencing of either high or low coverage to genotype Arctic charr from the Swedish national breeding program and performed analytical procedures to assess their utility in a range of tasks. SNPs were identified and used for deciphering the genetic structure of the studied population, estimating genomic relationships and implementing an association study for growth-related traits. Missing information and underestimation of heterozygosity in the low coverage set were limiting factors in genetic diversity and genomic relationship analyses, where high coverage performed notably better. On the other hand, the high coverage dataset proved to be valuable when it comes to identifying loci that are associated with phenotypic traits of interest. In general, both genotyping strategies offer sustainable alternatives to hybridization-based genotyping platforms and show potential for applications in aquaculture selective breeding.

scholarly journals Genome diversity in Ukraine

GigaScience ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Taras K Oleksyk ◽  
Walter W Wolfsberger ◽  
Alexandra M Weber ◽  
Khrystyna Shchubelka ◽  
Olga T Oleksyk ◽  
...  
Keyword(s):  
Sequence Data ◽  
Copy Number Variations ◽  
Genomic Variation ◽  
High Coverage ◽  
Genome Data ◽  
New Information ◽  
Genome Wide ◽  
Public Data ◽  
Genome Wide Data ◽  
Multiple Samples

Abstract Background The main goal of this collaborative effort is to provide genome-wide data for the previously underrepresented population in Eastern Europe, and to provide cross-validation of the data from genome sequences and genotypes of the same individuals acquired by different technologies. We collected 97 genome-grade DNA samples from consented individuals representing major regions of Ukraine that were consented for public data release. BGISEQ-500 sequence data and genotypes by an Illumina GWAS chip were cross-validated on multiple samples and additionally referenced to 1 sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage. Results The genome data have been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, copy number variations, single-nucletide polymorphisms, and microsatellites. To our knowledge, this study provides the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for medical research in a large understudied population. Conclusions Our results indicate that the genetic diversity of the Ukrainian population is uniquely shaped by evolutionary and demographic forces and cannot be ignored in future genetic and biomedical studies. These data will contribute a wealth of new information bringing forth a wealth of novel, endemic and medically related alleles.

scholarly journals A long reads-based de-novo assembly of the genome of the Arlee homozygous line reveals chromosomal rearrangements in rainbow trout

2021 ◽  
Author(s):  
Guangtu Gao ◽  
Susana Magadan ◽  
Geoffrey C Waldbieser ◽  
Ramey C Youngblood ◽  
Paul A Wheeler ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Chromosome Number ◽  
Genome Assembly ◽  
De Novo Assembly ◽  
De Novo ◽  
Sequence Data ◽  
Structural Variations ◽  
High Coverage ◽  
Haploid Chromosome Number ◽  
Long Reads

Abstract Currently, there is still a need to improve the contiguity of the rainbow trout reference genome and to use multiple genetic backgrounds that will represent the genetic diversity of this species. The Arlee doubled haploid line was originated from a domesticated hatchery strain that was originally collected from the northern California coast. The Canu pipeline was used to generate the Arlee line genome de-novo assembly from high coverage PacBio long-reads sequence data. The assembly was further improved with Bionano optical maps and Hi-C proximity ligation sequence data to generate 32 major scaffolds corresponding to the karyotype of the Arlee line (2 N = 64). It is composed of 938 scaffolds with N50 of 39.16 Mb and a total length of 2.33 Gb, of which ∼95% was in 32 chromosome sequences with only 438 gaps between contigs and scaffolds. In rainbow trout the haploid chromosome number can vary from 29 to 32. In the Arlee karyotype the haploid chromosome number is 32 because chromosomes Omy04, 14 and 25 are divided into six acrocentric chromosomes. Additional structural variations that were identified in the Arlee genome included the major inversions on chromosomes Omy05 and Omy20 and additional 15 smaller inversions that will require further validation. This is also the first rainbow trout genome assembly that includes a scaffold with the sex-determination gene (sdY) in the chromosome Y sequence. The utility of this genome assembly is demonstrated through the improved annotation of the duplicated genome loci that harbor the IGH genes on chromosomes Omy12 and Omy13.

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