Evidence map›Paper›PMID 38643062›Full record

ArticleHuman molecular genetics2024

Impact of the inaccessible genome on genotype imputation and genome-wide association studies.

Eva König, Jonathan Stewart Mitchell, Michele Filosi, Christian Fuchsberger

Abstract read
In one paragraph

Article in Human molecular genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

4 authors.

Eva KönigInstitute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Via Volta 21, Bolzano 39100, Italy.ORCID 0000-0002-2079-3525
Jonathan Stewart MitchellInstitute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Via Volta 21, Bolzano 39100, Italy.
Michele FilosiInstitute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Via Volta 21, Bolzano 39100, Italy.
Christian FuchsbergerInstitute for Biomedicine (affiliated to the University of Lübeck), Eurac Research, Via Volta 21, Bolzano 39100, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genotype imputation is widely used in genome-wide association studies (GWAS). However, both the genotyping chips and imputation reference panels are dependent on next-generation sequencing (NGS). Due to the nature of NGS, some regions of the genome are inaccessible to sequencing. To date, there has been no complete evaluation of these regions and their impact on the identification of associations in GWAS remains unclear. In this study, we systematically assess the extent to which variants in inaccessible regions are underrepresented on genotyping chips and imputation reference panels, in GWAS results and in variant databases. We also determine the proportion of genes located in inaccessible regions and compare the results across variant masks defined by the 1000 Genomes Project and the TOPMed program. Overall, fewer variants were observed in inaccessible regions in all categories analyzed. Depending on the mask used and normalized for region size, only 4%-17% of the genotyped variants are located in inaccessible regions and 52 to 581 genes were almost completely inaccessible. From the Cooperative Health Research in South Tyrol (CHRIS) study, we present a case study of an association located in an inaccessible region that is driven by genotyped variants and cannot be reproduced by imputation in GRCh37. We conclude that genotyping, NGS, genotype imputation and downstream analyses such as GWAS and fine mapping are systematically biased in inaccessible regions, due to missed variants and spurious associations. To help researchers assess gene and variant accessibility, we provide an online application (https://gab.gm.eurac.edu).

Indexed as

Genome, HumanGenome-Wide Association StudyGenotypeHigh-Throughput Nucleotide SequencingPolymorphism, Single NucleotideHumansaccessibilitygenotyping chipsGWASNGSweb tool

Identifiers

PMID38643062
PMCPMC11227617

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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.