Evidence map›Paper›PMID 36790822›Full record

ArticleMolecular biology and evolution2023

HaploBlocks: Efficient Detection of Positive Selection in Large Population Genomic Datasets.

Benedikt Kirsch-Gerweck, Leonard Bohnenkämper, Michel T Henrichs, Jarno N Alanko, Hideo Bannai, Bastien Cazaux, Pierre Peterlongo, Joachim Burger, Jens Stoye, Yoan Diekmann

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2023. 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

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. SLAB: A Sweep Line Algorithm in PBWT for Finding Haplotype Block Cores.bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
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

10 authors.

Benedikt Kirsch-GerweckPalaeogenetics Group, Institute of Organismic and Molecular Evolution (iomE), Johannes Gutenberg University, 55128 Mainz, Germany.
Leonard BohnenkämperFaculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
Michel T HenrichsFaculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.
Jarno N AlankoDepartment of Computer Science, University of Helsinki, P.O 68, Pietari Kalmin katu 5, 00014 Helsinki, Finland.
Hideo BannaiM&D Data Science Center, Tokyo Medical and Dental University (TMDU), 2-3-10 Kanda-Surugadai, Chiyoda-ku, Tokyo 101-0062, Japan.
Bastien CazauxCNRS, Centrale Lille, UMR 9189, Univ. Lille, CRIStAL, F-59000 Lille, France.
Pierre PeterlongoGenScale, Inria/Irisa Campus de Beaulieu, 35042 Rennes Cedex, France.ORCID 0000-0003-0776-6407
Joachim BurgerPalaeogenetics Group, Institute of Organismic and Molecular Evolution (iomE), Johannes Gutenberg University, 55128 Mainz, Germany.
Jens StoyeFaculty of Technology and Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany.ORCID 0000-0002-4656-7155
Yoan DiekmannPalaeogenetics Group, Institute of Organismic and Molecular Evolution (iomE), Johannes Gutenberg University, 55128 Mainz, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic regions under positive selection harbor variation linked for example to adaptation. Most tools for detecting positively selected variants have computational resource requirements rendering them impractical on population genomic datasets with hundreds of thousands of individuals or more. We have developed and implemented an efficient haplotype-based approach able to scan large datasets and accurately detect positive selection. We achieve this by combining a pattern matching approach based on the positional Burrows-Wheeler transform with model-based inference which only requires the evaluation of closed-form expressions. We evaluate our approach with simulations, and find it to be both sensitive and specific. The computational resource requirements quantified using UK Biobank data indicate that our implementation is scalable to population genomic datasets with millions of individuals. Our approach may serve as an algorithmic blueprint for the era of "big data" genomics: a combinatorial core coupled with statistical inference in closed form.

Indexed as

Genetics, PopulationMetagenomicsGenomeGenomicsHaplotypesbig datagenome scannatural selectionpopulation genetics

Identifiers

PMID36790822
PMCPMC9985328

What OpenQuestion holds

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Registered trials

None linked

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.