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Algorithms for Personal Genomics
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Algorithms for Personal Genomics

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (10 April 2019) | Viewed by 19316

Special Issue Editors

Dr. Gwênlyn Glusman

E-Mail Website
Guest Editor
Institute for Systems Biology, Seattle, WA 98109, USA
Interests: computational genomics; disease and wellness genetics; genome analysis; bioinformatics; big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Max Robinson

E-Mail
Guest Editor
Institute for Systems Biology, 401 Terry Avenue N, Seattle, WA 98109, USA

Special Issue Information

Dear Colleagues,

Personal genome sequences support not only the identification of close relatives, the classification of genetic background, and the assessment of genetic risks for the benefit of the individual, but can also be aggregated to detect the repetition of individuals in cohorts, detect family relationships, find matched controls for research subjects, evaluate evidence of selection, reconstruct haplotypes, and support association studies for medical research, legal, forensic, or historical purposes for the benefit of society. As genetic testing expands out of the research laboratory, into medical practice and the direct-to-consumer market, there is increasing public interest in efficient and private analysis of personal genomic variation for all of these purposes, for prioritizing and assessing rare or novel variants and combinations of variants, and for expanding our knowledge of disease risks associated with common variants.

This Special Issue aims to present novel algorithms for the analysis of personal genomes. Analysis can be at any stage of the data life cycle, from quality control to visualization, annotation, and assessment or interpretation at the individual, family, or cohort level. While not a requirement, we welcome methods for the integration of genomes with other data types such as multi-omics, Quantified Self and Electronic Health Records.

The emphasis is on algorithmic innovation. Ideally, the methods presented would involve brand new algorithms, significant changes to existing algorithms, or the repurposing of algorithms that were not previously applied in this field.

Dr. Gwênlyn Glusman
Dr. Max Robinson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

For this Special Issue we are glad to offer a 15% discount from our APC to all planned contributions. Please contact and inform [email protected] in advance for this purpose.

Keywords

  • algorithms
  • individual genomes
  • personal genomics
  • big data
  • genomic variation
  • genome analysis
  • genome interpretation
  • genetic data integration
  • haplotype reconstruction
  • cohorts
  • family relationships
  • genetic testing
  • hashing

Published Papers (4 papers)

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Research

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13 pages, 3595 KiB  
Article
Genotype Fingerprints Enable Fast and Private Comparison of Genetic Testing Results for Research and Direct-to-Consumer Applications
by Max Robinson and Gustavo Glusman
Genes 2018, 9(10), 481; https://doi.org/10.3390/genes9100481 - 04 Oct 2018
Cited by 1 | Viewed by 4797
Abstract
Genetic testing has expanded out of the research laboratory into medical practice and the direct-to-consumer market. Rapid analysis of the resulting genotype data now has a significant impact. We present a method for summarizing personal genotypes as ‘genotype fingerprints’ that meets these needs. [...] Read more.
Genetic testing has expanded out of the research laboratory into medical practice and the direct-to-consumer market. Rapid analysis of the resulting genotype data now has a significant impact. We present a method for summarizing personal genotypes as ‘genotype fingerprints’ that meets these needs. Genotype fingerprints can be derived from any single nucleotide polymorphism-based assay, and remain comparable as chip designs evolve to higher marker densities. We demonstrate that these fingerprints support distinguishing types of relationships among closely related individuals and closely related individuals from individuals from the same background population, as well as high-throughput identification of identical genotypes, individuals in known background populations, and de novo separation of subpopulations within a large cohort through extremely rapid comparisons. Although fingerprints do not preserve anonymity, they provide a useful degree of privacy by summarizing a genotype while preventing reconstruction of individual marker states. Genotype fingerprints are therefore well-suited as a format for public aggregation of genetic information to support ancestry and relatedness determination without revealing personal health risk status. Full article
(This article belongs to the Special Issue Algorithms for Personal Genomics)
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13 pages, 267 KiB  
Opinion
Beyond Genes: Re-Identifiability of Proteomic Data and Its Implications for Personalized Medicine
by Kurt Boonen, Kristien Hens, Gerben Menschaert, Geert Baggerman, Dirk Valkenborg and Gokhan Ertaylan
Genes 2019, 10(9), 682; https://doi.org/10.3390/genes10090682 - 05 Sep 2019
Cited by 19 | Viewed by 3565
Abstract
The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the [...] Read more.
The increasing availability of high throughput proteomics data provides us with opportunities as well as posing new ethical challenges regarding data privacy and re-identifiability of participants. Moreover, the fact that proteomics represents a level between the genotype and the phenotype further exacerbates the situation, introducing dilemmas related to publicly available data, anonymization, ownership of information and incidental findings. In this paper, we try to differentiate proteomics from genomics data and cover the ethical challenges related to proteomics data sharing. Finally, we give an overview of the proposed solutions and the outlook for future studies. Full article
(This article belongs to the Special Issue Algorithms for Personal Genomics)
6 pages, 835 KiB  
Conference Report
Opportunities and Challenges in Interpreting and Sharing Personal Genomes
by Irit R. Rubin and Gustavo Glusman
Genes 2019, 10(9), 643; https://doi.org/10.3390/genes10090643 - 25 Aug 2019
Cited by 4 | Viewed by 3847
Abstract
The 2019 “Personal Genomes: Accessing, Sharing and Interpretation” conference (Hinxton, UK, 11–12 April 2019) brought together geneticists, bioinformaticians, clinicians and ethicists to promote openness and ethical sharing of personal genome data while protecting the privacy of individuals. The talks at the conference focused [...] Read more.
The 2019 “Personal Genomes: Accessing, Sharing and Interpretation” conference (Hinxton, UK, 11–12 April 2019) brought together geneticists, bioinformaticians, clinicians and ethicists to promote openness and ethical sharing of personal genome data while protecting the privacy of individuals. The talks at the conference focused on two main topic areas: (1) Technologies and Applications, with emphasis on personal genomics in the context of healthcare. The issues discussed ranged from new technologies impacting and enabling the field, to the interpretation of personal genomes and their integration with other data types. There was particular emphasis and wide discussion on the use of polygenic risk scores to inform precision medicine. (2) Ethical, Legal, and Social Implications, with emphasis on genetic privacy: How to maintain it, how much privacy is possible, and how much privacy do people want? Talks covered the full range of genomic data visibility, from open access to tight control, and diverse aspects of balancing benefits and risks, data ownership, working with individuals and with populations, and promoting citizen science. Both topic areas were illustrated and informed by reports from a wide variety of ongoing projects, which highlighted the need to diversify global databases by increasing representation of understudied populations. Full article
(This article belongs to the Special Issue Algorithms for Personal Genomics)
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7 pages, 313 KiB  
Commentary
Proprietary Algorithms for Polygenic Risk: Protecting Scientific Innovation or Hiding the Lack of It?
by A. Cecile J.W. Janssens
Genes 2019, 10(6), 448; https://doi.org/10.3390/genes10060448 - 13 Jun 2019
Cited by 6 | Viewed by 6645
Abstract
Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should [...] Read more.
Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should respect customer autonomy and informed decision making and maximize opportunities for transparency. The algorithm itself is only one piece of the information that is deemed essential for understanding how prediction algorithms are developed and evaluated. Companies should be encouraged to disclose everything else, including the expected risk distribution of the algorithm when applied in the population, using a benchmark DNA dataset. A standardized presentation of information and risk distributions allows customers to compare test offers and scientists to verify whether the undisclosed algorithms could be valid. A new model of oversight in which stakeholders collaboratively keep a check on the commercial market is needed. Full article
(This article belongs to the Special Issue Algorithms for Personal Genomics)
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