Evidence map›Paper›PMID 39283896›Full record

ArticlePLoS computational biology2024

Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data.

Silja Tammi, Satu Koskela, Blood Service Biobank, Kati Hyvärinen, Jukka Partanen, Jarmo Ritari

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Silja TammiFinnish Red Cross Blood Service, Research and Development, Helsinki, Finland.ORCID https://orcid.org/0000-0003-3696-0612
Satu KoskelaFinnish Red Cross Blood Service, Research and Development, Helsinki, Finland.
Blood Service Biobank
Kati HyvärinenFinnish Red Cross Blood Service, Research and Development, Helsinki, Finland.
Jukka PartanenFinnish Red Cross Blood Service, Research and Development, Helsinki, Finland.ORCID https://orcid.org/0000-0001-6681-4734
Jarmo RitariFinnish Red Cross Blood Service, Research and Development, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In addition to the classical HLA genes, the major histocompatibility complex (MHC) harbors a high number of other polymorphic genes with less established roles in disease associations and transplantation matching. To facilitate studies of the non-classical and non-HLA genes in large patient and biobank cohorts, we trained imputation models for MICA, MICB, HLA-E, HLA-F and HLA-G alleles on genome SNP array data. We show, using both population-specific and multi-population 1000 Genomes references, that the alleles of these genes can be accurately imputed for screening and research purposes. The best imputation model for MICA, MICB, HLA-E, -F and -G achieved a mean accuracy of 99.3% (min, max: 98.6, 99.9). Furthermore, validation of the 1000 Genomes exome short-read sequencing-based allele calling against a clinical-grade reference data showed an average accuracy of 99.8%, testifying for the quality of the 1000 Genomes data as an imputation reference. We also fitted the models for Infinium Global Screening Array (GSA, Illumina, Inc.) and Axiom Precision Medicine Research Array (PMRA, Thermo Fisher Scientific Inc.) SNP content, with mean accuracies of 99.1% (97.2, 100) and 98.9% (97.4, 100), respectively.

Indexed as

AllelesHistocompatibility Antigens Class IPolymorphism, Single NucleotideComputational BiologyGenome, HumanHLA-E AntigensHumansHistocompatibility Antigens Class IHLA-E AntigensHLA-F antigensMHC class I-related chain AMICB antigen

Identifiers

PMID39283896
PMCPMC11426482

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.