Bayesian mixture model for clustering rare-variant effects in human genetic studies Article Swipe
Guhan Venkataraman
,
Yosuke Tanigawa
,
Matti Pirinen
,
Manuel A. Rivas
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.08.03.454967
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.08.03.454967
Rare-variant aggregate analysis from exome and whole genome sequencing data typically summarizes with a single statistic the signal for a gene or the unit that is being aggregated. However, when doing so, the effect profile within the unit may not be easily characterized across one or multiple phenotypes. Here, we present an approach we call Multiple Rarevariants and Phenotypes Mixture Model (MRPMM), which clusters rare variants into groups based on their effects on the multivariate phenotype and makes statistical inferences about the properties of the underlying mixture of genetic effects. Using summary statistic data from a meta-analysis of exome sequencing data of 184,698 individuals in the UK Biobank across 6 populations, we demonstrate that our mixture model can identify clusters of variants responsible for significantly disparate effects across a multivariate phenotype; we study three lipid and three renal traits separately. The method is able to estimate (1) the proportion of non-null variants, (2) whether variants with the same predicted consequence in one gene behave similarly, (3) whether variants across genes share effect profiles across the multivariate phenotype, and (4) whether different annotations differ in the magnitude of their effects. As rare-variant data and aggregation techniques become more common, this method can be used to ascribe further meaning to association results.
Related Topics
Concepts
Multivariate statistics
Biology
Computational biology
Biobank
Phenotype
Exome
Cluster analysis
Statistic
Genome-wide association study
Genetic association
Bayesian probability
Genetics
Exome sequencing
Gene
Statistics
Single-nucleotide polymorphism
Genotype
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.08.03.454967
- https://www.biorxiv.org/content/biorxiv/early/2021/08/06/2021.08.03.454967.full.pdf
- OA Status
- green
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3188604774
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3188604774Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2021.08.03.454967Digital Object Identifier
- Title
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Bayesian mixture model for clustering rare-variant effects in human genetic studiesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-08-05Full publication date if available
- Authors
-
Guhan Venkataraman, Yosuke Tanigawa, Matti Pirinen, Manuel A. RivasList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.08.03.454967Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/08/06/2021.08.03.454967.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2021/08/06/2021.08.03.454967.full.pdfDirect OA link when available
- Concepts
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Multivariate statistics, Biology, Computational biology, Biobank, Phenotype, Exome, Cluster analysis, Statistic, Genome-wide association study, Genetic association, Bayesian probability, Genetics, Exome sequencing, Gene, Statistics, Single-nucleotide polymorphism, Genotype, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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