The Genome Russia project: closing the largest remaining omission on the world Genome map

Oleksyk, Tarás K.; Brukhin, Vladimir; O’Brien, Stephen J.

doi:10.1186/s13742-015-0095-0

Cited by 18 publications

(24 citation statements)

References 10 publications

(16 reference statements)

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“…Samples were successfully sequenced using BGI's DNBSEQ™ technology, and cross-validated by Illumina sequencing and genotyping. This data is offered to the scientific community to help fill the gaps in the map of the local genomic variation in Eastern Europe that has been largely omitted in the global genomic surveys [10]. Each volunteer participant in this study had an opportunity to review the informed consent, have been explained the nature of the genome data, and made a personal decision about making it public.…”

Section: The Contextmentioning

confidence: 99%

“…People of Ukraine carry many previously known and several novel genetic variants with clinical and functional importance that in many cases show allele frequencies different from neighboring populations in the rest of Europe, including Poland to the West, Romania to the South, the Baltics to the north and Russia to the northeast. While several large genome projects already exists contributing to the understanding of the global genetic variation, many of the rare and endemic alleles that have not been yet identified by the international databases such as the 1,000 Genomes project, and currently not available in standard genotyping panels for association testing for human diseases, and glaring white spots still exists on the genetic maps in local populations of Eastern Europe [10]. We fully expect that the future sampling and sequencing will continue to improve and complete the detailed picture of genomic diversity in people across the country and contribute to the further development of genetic approaches in biomedical research and applications.…”

Section: Collection Of Medically Related Variantsmentioning

confidence: 99%

“…The justifications for collecting, sequencing and analyzing populations from this part of Europe has been outlined earlier [10,63], and the new database is a step into that direction. Given its unique history, the genome diversity data from Ukraine will contribute a wealth of new information bringing forth different risk and/or protective alleles that do not exist nor associate with disease, elsewhere in the world.…”

Section: Re-use Potentialmentioning

confidence: 99%

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Genome Diversity in Ukraine

Oleksyk

Wolfsberger

Weber

et al. 2020

Preprint

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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 the public data release. DNBSEQ-G50 sequences, and genotypes by an Illumina GWAS chip were cross-validated on multiple samples, and additionally referenced to a sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage. The genome data has been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, CNVs, SNPs and microsatellites. This study is providing the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for historic and medical research in a large understudied population. While most of the common variation is shared with other European populations, this survey of population variation contributes a number of novel SNPs and structural variants that have not been reported in the gnomAD/1KG databases representing global distribution of genomic variation. These endemic variants will become a valuable resource for designing future population and clinical studies, help address questions about ancestry and admixture, and will fill a missing place in the puzzle characterizing human population diversity in Eastern Europe. Our results indicate that genetic diversity of the Ukrainian population is uniquely shaped by the evolutionary and demographic forces, and cannot be ignored in the future genetic and biomedical studies. This data will contribute a wealth of new information bringing forth different risk and/or protective alleles. The newly discovered low frequency and local variants can be added to the current genotyping arrays for genome wide association studies, clinical trials, and in genome assessment of proliferating cancer cells.

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Section: The Contextmentioning

confidence: 99%

Section: Collection Of Medically Related Variantsmentioning

confidence: 99%

Section: Re-use Potentialmentioning

confidence: 99%

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Genome Diversity in Ukraine

Oleksyk

Wolfsberger

Weber

et al. 2020

Preprint

Self Cite

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“…For example, Generation Scotland (Smith et al, 2006) includes DNA, tissues, and phenotypic information from nearly 30,000 Scots (Generation Scotland, 2016), the 100,000 Genomes Project, sequenced the genomes of 100,000 NHS patients with rare diseases aiming to understand the aetiology of their conditions from their genomic data (Caulfield et al, 2017) and the UK Biobank project, sequenced the complete genomes of over half a million individuals (Sudlow et al, 2015) with the aim of improving the prevention, diagnosis, and treatment of a wide range of diseases (Bycroft et al, 2018). Pending projects include the Genome Russia Project, which aims to fill the gap in the mapping of human populations by providing the whole-genome sequences of some 3,000 people from a variety of regions of Russia (Oleksyk et al, 2015). Biobanks are not without controversy.…”

Section: Association Studies and Biobanksmentioning

confidence: 99%

Population Genetic Considerations for Using Biobanks as International Resources in the Pandemic Era and Beyond

Carress

Lawson

Elhaik

2020

Preprint

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The past years saw the rise of genomic biobanks and mega-scale meta-analysis of genomic data that promise to reveal the genetic underpinnings of health and disease. However, the overrepresentation of Europeans in genomic studies not only limit the global understanding of disease risk and intervention efficacy, but also inhibit viable research into the genomic differences between carriers and patients. Whilst the community has agreed that more diverse samples are required, it is not enough to blindly increase diversity; the diversity must be quantified, compared, and annotated to lead to insight. Genetic annotations from separate biobanks need to be comparable, computable, operate without access to raw data due to privacy concerns. Comparability is key both for regular research and to allow international comparison in response to pandemics. Here, we evaluate the appropriateness of commonly used genomic tools used to depict population structure in a standardized and comparable manner. The end goal is to reduce the effects of confounding and learn from genuine variation in genetic effects on phenotypes across populations, which will improve the value of biobanks, locally and internationally, increase the accuracy of association analyses, and inform developmental efforts. Admixed PopulationA population of individuals with ancestors from two or more relatively distinct populations relatively recent in human history. Admixture mappingGene mapping of susceptibility alleles for genetic disease that show differential risk by ancestry, correlating the degree of ancestry near to genomic regions with greater disease risk. Bayesian clusteringAssignment of individuals to clusters based on genetic similarity without assuming predefined populations, using statistical methods that allow inferences to be drawn from the data and prior information. Expectation-Maximisation (EM) algorithmAn iterative method to find maximum likelihood estimates (MLE) of parameters in statistical models, altering between an expectation step (creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters) and a maximisation step (computes parameters maximizing the output of the expectation step). Genetic Relatedness MatrixThe GRM represents the genomic similarities among all individuals. Each cell in the matrix measures the genotypic correlation between a pair of individuals (the rows and columns). The GRM can be used with a phenotypic distance matrix to estimate heritability without estimating the phenotypic effect of individual SNPs. Hidden Markov Model (HMM)A statistical Markov model, that is a randomly changing system assumed to consist of future states that only depend on current states, whereby states are unobservable (hidden). Markov Chain Monte Carlo (MCMC)A simulation method used in Bayesian calculations, incorporating a class of algorithms that can obtain a sample of the desired distribution by observing several steps of the Markov chain, which is a sequence of a probability of events that ...

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“…The resulting data were then assembled and annotated by the local college. 1,2 This full circle connection between community and academia bootstrapped a much larger effort to examine tropical parrot genomes, produced multiple informatics tools and created the Caribbean Genome Center g . Research continues to appear derived from this example of public-academia cooperation.…”

Section: The Solutions?mentioning

confidence: 99%

Inviting the Public: The Impact on Informatics Arising From Emerging Global Health Research Paradigms

Gayle¹,

Minie

Nilsson³

2014

Biocomputing 2015

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This workshop will focus on disruptive processes impacting research arising from the increasing ability of individuals to create, curate and share data with scientists. Encompassing processes from funding research to providing samples to creating algorithms, including the public will require new approaches even as it opens up new possibilities. We will hear from a few researchers at the forefront of these disruptive processes, followed by a moderated discussion with the audience about these topics.

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The Genome Russia project: closing the largest remaining omission on the world Genome map

Cited by 18 publications

References 10 publications

Genome Diversity in Ukraine

Genome Diversity in Ukraine

Population Genetic Considerations for Using Biobanks as International Resources in the Pandemic Era and Beyond

Inviting the Public: The Impact on Informatics Arising From Emerging Global Health Research Paradigms

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