Local Haplotype Classifiers enable Efficient, Flexible, and Secure Genotype Imputation and Downstream Analyses Article Swipe
Muhammad Nadeem Cheema
,
Anam Nazir
,
Jung-Ho Moon
,
Yujeong Oh
,
Ardalan Naseri
,
Degui Zhi
,
Xiaoqian Jiang
,
Miran Kim
,
Arif Ozgun Harmanci
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.12.01.626205
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.12.01.626205
The decreasing cost of genotyping technologies led to abundant availability and usage of genetic data. Although it offers many potentials for improving health and curing diseases, genetic data is highly intrusive in many aspects of individual privacy. Secure genotype analysis methods have been developed to perform numerous tasks such as genome-wide association studies, meta-analysis, kinship inference, and genotype imputation outsourcing. Here we present a new approach for using lightweight haplotype classifier models to use predicted haplotype information in a flexible privacy-preserving framework to perform genotype imputation and downstream tasks. Compared to the previous secure methods that rely main on linear models, our approach utilizes efficient models that rely on utilizing haplotypic information, which improves accuracy and increases the throughput of imputation by performing multiple imputations per model evaluation.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.12.01.626205
- https://www.biorxiv.org/content/biorxiv/early/2024/12/05/2024.12.01.626205.full.pdf
- OA Status
- green
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405056348
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405056348Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.12.01.626205Digital Object Identifier
- Title
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Local Haplotype Classifiers enable Efficient, Flexible, and Secure Genotype Imputation and Downstream AnalysesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-05Full publication date if available
- Authors
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Muhammad Nadeem Cheema, Anam Nazir, Jung-Ho Moon, Yujeong Oh, Ardalan Naseri, Degui Zhi, Xiaoqian Jiang, Miran Kim, Arif Ozgun HarmanciList of authors in order
- Landing page
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https://doi.org/10.1101/2024.12.01.626205Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/12/05/2024.12.01.626205.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/12/05/2024.12.01.626205.full.pdfDirect OA link when available
- Concepts
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Imputation (statistics), Computer science, Inference, Data mining, Genotyping, Haplotype, Genotype, Machine learning, Artificial intelligence, Missing data, Biology, Genetics, GeneTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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