Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation

Research output: Contribution to journalJournal articleResearchpeer-review

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Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation. / Marcos, Sofia; Parejo, Melanie; Estonba, Andone; Alberdi, Antton.

In: Advanced Genetics, Vol. 3, No. 3, 2100065, 2022.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Marcos, S, Parejo, M, Estonba, A & Alberdi, A 2022, 'Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation', Advanced Genetics, vol. 3, no. 3, 2100065. https://doi.org/10.1002/ggn2.202100065

APA

Marcos, S., Parejo, M., Estonba, A., & Alberdi, A. (2022). Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation. Advanced Genetics, 3(3), [2100065]. https://doi.org/10.1002/ggn2.202100065

Vancouver

Marcos S, Parejo M, Estonba A, Alberdi A. Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation. Advanced Genetics. 2022;3(3). 2100065. https://doi.org/10.1002/ggn2.202100065

Author

Marcos, Sofia ; Parejo, Melanie ; Estonba, Andone ; Alberdi, Antton. / Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation. In: Advanced Genetics. 2022 ; Vol. 3, No. 3.

Bibtex

@article{f5f712c00f584036856f8fcc62949b5b,
title = "Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation",
abstract = "Metagenomic datasets of host-associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses. However, genotype imputation can be employed to reconstruct host genotypes if a reference panel is available. Here, the performance of a two-step strategy is tested to impute genotypes from four types of reference panels built using different strategies to low-depth host genome data (≈2× coverage) recovered from intestinal samples of two chicken genetic lines. First, imputation accuracy is evaluated in 12 samples for which both low- and high-depth sequencing data are available, obtaining high imputation accuracies for all tested panels (>0.90). Second, the impact of reference panel choice in population genetics statistics on 100 chickens is assessed, all four panels yielding comparable results. In light of the observations, the feasibility and application of the applied imputation strategy are discussed for different species with regard to the host DNA proportion, genomic diversity, and availability of a reference panel. This method enables leveraging insofar discarded host DNA to get insights into the genetic structure of host populations, and in doing so, facilitates the implementation of hologenomic approaches that jointly analyze host and microbial genomic data.",
author = "Sofia Marcos and Melanie Parejo and Andone Estonba and Antton Alberdi",
note = "{\textcopyright} 2022 The Authors. Advanced Genetics published by Wiley Periodicals LLC.",
year = "2022",
doi = "10.1002/ggn2.202100065",
language = "English",
volume = "3",
journal = "Advanced Genetics",
issn = "2641-6573",
publisher = "Wiley",
number = "3",

}

RIS

TY - JOUR

T1 - Recovering High-Quality Host Genomes from Gut Metagenomic Data through Genotype Imputation

AU - Marcos, Sofia

AU - Parejo, Melanie

AU - Estonba, Andone

AU - Alberdi, Antton

N1 - © 2022 The Authors. Advanced Genetics published by Wiley Periodicals LLC.

PY - 2022

Y1 - 2022

N2 - Metagenomic datasets of host-associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses. However, genotype imputation can be employed to reconstruct host genotypes if a reference panel is available. Here, the performance of a two-step strategy is tested to impute genotypes from four types of reference panels built using different strategies to low-depth host genome data (≈2× coverage) recovered from intestinal samples of two chicken genetic lines. First, imputation accuracy is evaluated in 12 samples for which both low- and high-depth sequencing data are available, obtaining high imputation accuracies for all tested panels (>0.90). Second, the impact of reference panel choice in population genetics statistics on 100 chickens is assessed, all four panels yielding comparable results. In light of the observations, the feasibility and application of the applied imputation strategy are discussed for different species with regard to the host DNA proportion, genomic diversity, and availability of a reference panel. This method enables leveraging insofar discarded host DNA to get insights into the genetic structure of host populations, and in doing so, facilitates the implementation of hologenomic approaches that jointly analyze host and microbial genomic data.

AB - Metagenomic datasets of host-associated microbial communities often contain host DNA that is usually discarded because the amount of data is too low for accurate host genetic analyses. However, genotype imputation can be employed to reconstruct host genotypes if a reference panel is available. Here, the performance of a two-step strategy is tested to impute genotypes from four types of reference panels built using different strategies to low-depth host genome data (≈2× coverage) recovered from intestinal samples of two chicken genetic lines. First, imputation accuracy is evaluated in 12 samples for which both low- and high-depth sequencing data are available, obtaining high imputation accuracies for all tested panels (>0.90). Second, the impact of reference panel choice in population genetics statistics on 100 chickens is assessed, all four panels yielding comparable results. In light of the observations, the feasibility and application of the applied imputation strategy are discussed for different species with regard to the host DNA proportion, genomic diversity, and availability of a reference panel. This method enables leveraging insofar discarded host DNA to get insights into the genetic structure of host populations, and in doing so, facilitates the implementation of hologenomic approaches that jointly analyze host and microbial genomic data.

U2 - 10.1002/ggn2.202100065

DO - 10.1002/ggn2.202100065

M3 - Journal article

C2 - 36620197

VL - 3

JO - Advanced Genetics

JF - Advanced Genetics

SN - 2641-6573

IS - 3

M1 - 2100065

ER -

ID: 334019139