Cargando…
The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection
BACKGROUND: Sequence information generated from next generation sequencing is often computationally phased using haplotype-phasing algorithms. Utilizing experimentally derived allele or haplotype information improves this prediction, as routinely used in HLA typing. We recently established a large d...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371619/ https://www.ncbi.nlm.nih.gov/pubmed/30744658 http://dx.doi.org/10.1186/s12967-019-1791-9 |
_version_ | 1783394593351401472 |
---|---|
author | Srivastava, Kshitij Wollenberg, Kurt R. Flegel, Willy A. |
author_facet | Srivastava, Kshitij Wollenberg, Kurt R. Flegel, Willy A. |
author_sort | Srivastava, Kshitij |
collection | PubMed |
description | BACKGROUND: Sequence information generated from next generation sequencing is often computationally phased using haplotype-phasing algorithms. Utilizing experimentally derived allele or haplotype information improves this prediction, as routinely used in HLA typing. We recently established a large dataset of long ERMAP alleles, which code for protein variants in the Scianna blood group system. We propose the phylogeny of this set of 48 alleles and identify evolutionary steps to derive the observed alleles. METHODS: The nucleotide sequence of > 21 kb each was used for all physically confirmed 48 ERMAP alleles that we previously published. Full-length sequences were aligned and variant sites were extracted manually. The Bayesian coalescent algorithm implemented in BEAST v1.8.3 was used to estimate a coalescent phylogeny for these variants and the allelic ancestral states at the internal nodes of the phylogeny. RESULTS: The phylogenetic analysis allowed us to identify the evolutionary relationships among the 48 ERMAP alleles, predict 4243 potential ancestral alleles and calculate a posterior probability for each of these unobserved alleles. Some of them coincide with observed alleles that are extant in the population. CONCLUSIONS: Our proposed strategy places known alleles in a phylogenetic framework, allowing us to describe as-yet-undiscovered alleles. In this new approach, which relies heavily on the accuracy of the alleles used for the phylogenetic analysis, an expanded set of predicted alleles can be used to infer alleles when large genotype data are analyzed, as typically generated by high-throughput sequencing. The alleles identified by studies like ours may be utilized in designing of microarray technologies, imputing of genotypes and mapping of next generation sequencing data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1791-9) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6371619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63716192019-02-25 The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection Srivastava, Kshitij Wollenberg, Kurt R. Flegel, Willy A. J Transl Med Research BACKGROUND: Sequence information generated from next generation sequencing is often computationally phased using haplotype-phasing algorithms. Utilizing experimentally derived allele or haplotype information improves this prediction, as routinely used in HLA typing. We recently established a large dataset of long ERMAP alleles, which code for protein variants in the Scianna blood group system. We propose the phylogeny of this set of 48 alleles and identify evolutionary steps to derive the observed alleles. METHODS: The nucleotide sequence of > 21 kb each was used for all physically confirmed 48 ERMAP alleles that we previously published. Full-length sequences were aligned and variant sites were extracted manually. The Bayesian coalescent algorithm implemented in BEAST v1.8.3 was used to estimate a coalescent phylogeny for these variants and the allelic ancestral states at the internal nodes of the phylogeny. RESULTS: The phylogenetic analysis allowed us to identify the evolutionary relationships among the 48 ERMAP alleles, predict 4243 potential ancestral alleles and calculate a posterior probability for each of these unobserved alleles. Some of them coincide with observed alleles that are extant in the population. CONCLUSIONS: Our proposed strategy places known alleles in a phylogenetic framework, allowing us to describe as-yet-undiscovered alleles. In this new approach, which relies heavily on the accuracy of the alleles used for the phylogenetic analysis, an expanded set of predicted alleles can be used to infer alleles when large genotype data are analyzed, as typically generated by high-throughput sequencing. The alleles identified by studies like ours may be utilized in designing of microarray technologies, imputing of genotypes and mapping of next generation sequencing data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1791-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-11 /pmc/articles/PMC6371619/ /pubmed/30744658 http://dx.doi.org/10.1186/s12967-019-1791-9 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Srivastava, Kshitij Wollenberg, Kurt R. Flegel, Willy A. The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection |
title | The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection |
title_full | The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection |
title_fullStr | The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection |
title_full_unstemmed | The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection |
title_short | The phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection |
title_sort | phylogeny of 48 alleles, experimentally verified at 21 kb, and its application to clinical allele detection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371619/ https://www.ncbi.nlm.nih.gov/pubmed/30744658 http://dx.doi.org/10.1186/s12967-019-1791-9 |
work_keys_str_mv | AT srivastavakshitij thephylogenyof48allelesexperimentallyverifiedat21kbanditsapplicationtoclinicalalleledetection AT wollenbergkurtr thephylogenyof48allelesexperimentallyverifiedat21kbanditsapplicationtoclinicalalleledetection AT flegelwillya thephylogenyof48allelesexperimentallyverifiedat21kbanditsapplicationtoclinicalalleledetection AT srivastavakshitij phylogenyof48allelesexperimentallyverifiedat21kbanditsapplicationtoclinicalalleledetection AT wollenbergkurtr phylogenyof48allelesexperimentallyverifiedat21kbanditsapplicationtoclinicalalleledetection AT flegelwillya phylogenyof48allelesexperimentallyverifiedat21kbanditsapplicationtoclinicalalleledetection |