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The solution surface of the Li-Stephens haplotype copying model
The Li-Stephens (LS) haplotype copying model forms the basis of a number of important statistical inference procedures in genetics. LS is a probabilistic generative model which supposes that a sampled chromosome is an imperfect mosaic of other chromosomes found in a population. In the frequentist se...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10410957/ https://www.ncbi.nlm.nih.gov/pubmed/37559098 http://dx.doi.org/10.1186/s13015-023-00237-z |
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author | Jin, Yifan Terhorst, Jonathan |
author_facet | Jin, Yifan Terhorst, Jonathan |
author_sort | Jin, Yifan |
collection | PubMed |
description | The Li-Stephens (LS) haplotype copying model forms the basis of a number of important statistical inference procedures in genetics. LS is a probabilistic generative model which supposes that a sampled chromosome is an imperfect mosaic of other chromosomes found in a population. In the frequentist setting which is the focus of this paper, the output of LS is a “copying path” through chromosome space. The behavior of LS depends crucially on two user-specified parameters, [Formula: see text] and [Formula: see text] , which are respectively interpreted as the rates of mutation and recombination. However, because LS is not based on a realistic model of ancestry, the precise connection between these parameters and the biological phenomena they represent is unclear. Here, we offer an alternative perspective, which considers [Formula: see text] and [Formula: see text] as tuning parameters, and seeks to understand their impact on the LS output. We derive an algorithm which, for a given dataset, efficiently partitions the [Formula: see text] plane into regions where the output of the algorithm is constant, thereby enumerating all possible solutions to the LS model in one go. We extend this approach to the “diploid LS” model commonly used for phasing. We demonstrate the usefulness of our method by studying the effects of changing [Formula: see text] and [Formula: see text] when using LS for common bioinformatic tasks. Our findings indicate that using the conventional (i.e., population-scaled) values for [Formula: see text] and [Formula: see text] produces near optimal results for imputation, but may systematically inflate switch error in the case of phasing diploid genotypes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13015-023-00237-z. |
format | Online Article Text |
id | pubmed-10410957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104109572023-08-10 The solution surface of the Li-Stephens haplotype copying model Jin, Yifan Terhorst, Jonathan Algorithms Mol Biol Research The Li-Stephens (LS) haplotype copying model forms the basis of a number of important statistical inference procedures in genetics. LS is a probabilistic generative model which supposes that a sampled chromosome is an imperfect mosaic of other chromosomes found in a population. In the frequentist setting which is the focus of this paper, the output of LS is a “copying path” through chromosome space. The behavior of LS depends crucially on two user-specified parameters, [Formula: see text] and [Formula: see text] , which are respectively interpreted as the rates of mutation and recombination. However, because LS is not based on a realistic model of ancestry, the precise connection between these parameters and the biological phenomena they represent is unclear. Here, we offer an alternative perspective, which considers [Formula: see text] and [Formula: see text] as tuning parameters, and seeks to understand their impact on the LS output. We derive an algorithm which, for a given dataset, efficiently partitions the [Formula: see text] plane into regions where the output of the algorithm is constant, thereby enumerating all possible solutions to the LS model in one go. We extend this approach to the “diploid LS” model commonly used for phasing. We demonstrate the usefulness of our method by studying the effects of changing [Formula: see text] and [Formula: see text] when using LS for common bioinformatic tasks. Our findings indicate that using the conventional (i.e., population-scaled) values for [Formula: see text] and [Formula: see text] produces near optimal results for imputation, but may systematically inflate switch error in the case of phasing diploid genotypes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13015-023-00237-z. BioMed Central 2023-08-09 /pmc/articles/PMC10410957/ /pubmed/37559098 http://dx.doi.org/10.1186/s13015-023-00237-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Jin, Yifan Terhorst, Jonathan The solution surface of the Li-Stephens haplotype copying model |
title | The solution surface of the Li-Stephens haplotype copying model |
title_full | The solution surface of the Li-Stephens haplotype copying model |
title_fullStr | The solution surface of the Li-Stephens haplotype copying model |
title_full_unstemmed | The solution surface of the Li-Stephens haplotype copying model |
title_short | The solution surface of the Li-Stephens haplotype copying model |
title_sort | solution surface of the li-stephens haplotype copying model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10410957/ https://www.ncbi.nlm.nih.gov/pubmed/37559098 http://dx.doi.org/10.1186/s13015-023-00237-z |
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