Evidence map›Paper›PMID 26722118›Full record

ArticleBioinformatics (Oxford, England)2016

Utilizing the Jaccard index to reveal population stratification in sequencing data: a simulation study and an application to the 1000 Genomes Project.

Dmitry Prokopenko, Julian Hecker, Edwin K Silverman, Marcello Pagano, Markus M Nöthen, Christian Dina, Christoph Lange, Heide Loehlein Fier

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed, 4 pooled it
–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

40 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Identification of 16 novel Alzheimer's disease loci using multi-ancestry meta-analyses.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Pooled it
  3. The European respiratory journal · 2022
    Pooled it
  4. Pooled it
  5. Intracellular protein GBF1 displays significant associations with amyloid pathology in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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  8. Science advances · 2026
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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

8 authors.

Dmitry ProkopenkoInstitute of Genomic Mathematics, University of Bonn, Bonn, Germany.
Julian HeckerInstitute of Genomic Mathematics, University of Bonn, Bonn, Germany.
Edwin K SilvermanChanning Division of Network Medicine, Brigham and Women's Hospital.
Marcello PaganoDepartment of Biostatistics, Harvard School of Public Health, Boston, USA.
Markus M NöthenInstitute of Human Genetics, University of Bonn, Bonn, Germany.
Christian DinaInstitut National de la Santé et de la Recherche Médicale (INSERM) Unité Mixte de Recherche (UMR) 1087, l'institut du thorax, Nantes, France, Centre National de la Recherche Scientifique (CNRS) UMR 6291, l'institut du thorax, Nantes, France, Université de Nantes, l'institut du thorax, Nantes, France and Centre Hospitalier Universitaire (CHU) de Nantes, l'institut du thorax, Service de Cardiologie, Nantes, France.
Christoph LangeChanning Division of Network Medicine, Brigham and Women's Hospital, Department of Biostatistics, Harvard School of Public Health, Boston, USA.
Heide Loehlein FierInstitute of Genomic Mathematics, University of Bonn, Bonn, Germany, Department of Biostatistics, Harvard School of Public Health, Boston, USA.

Funding

Metabolomic signatures of empysema and COPD progression in the COPDGene cohortR01HL089897 · NHLBI · NATIONAL JEWISH HEALTH · PI CRAPO, JAMES D · 2012 to 2016
$31.1M
(2 of 2) Genetic Epidemiology of COPDR01HL089856 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2012 to 2016
$18.9M
Novel Statistical Approaches to Mental Health Phenotype Analysis in GWA StudiesR01MH081862 · NIMH · HARVARD SCHOOL OF PUBLIC HEALTH · PI LANGE, CHRISTOPH · 2009 to 2014
$2.0M
A New Approach to Mental Health Phenotypes in Family Genomewide AssociationR01MH087590 · NIMH · HARVARD SCHOOL OF PUBLIC HEALTH · PI LANGE, CHRISTOPH · 2010 to 2013
$1.6M
NHLBI NIH HHS R01 HL089856NHLBI NIH HHS R01 HL089897NIMH NIH HHS R01 MH081862NIMH NIH HHS R01 MH087590
6 · The paper itself

Abstract

motivationPopulation stratification is one of the major sources of confounding in genetic association studies, potentially causing false-positive and false-negative results. Here, we present a novel approach for the identification of population substructure in high-density genotyping data/next generation sequencing data. The approach exploits the co-appearances of rare genetic variants in individuals. The method can be applied to all available genetic loci and is computationally fast. Using sequencing data from the 1000 Genomes Project, the features of the approach are illustrated and compared to existing methodology (i.e. EIGENSTRAT). We examine the effects of different cutoffs for the minor allele frequency on the performance of the approach. We find that our approach works particularly well for genetic loci with very small minor allele frequencies. The results suggest that the inclusion of rare-variant data/sequencing data in our approach provides a much higher resolution picture of population substructure than it can be obtained with existing methodology. Furthermore, in simulation studies, we find scenarios where our method was able to control the type 1 error more precisely and showed higher power. AVAILABILITY AND IMPLEMENTATION: CONTACT: dmitry.prokopenko@uni-bonn.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

GenomeAnimalsComputer SimulationGene FrequencyGenetic Association StudiesGenetic VariationGenotypeHigh-Throughput Nucleotide SequencingHumans

Identifiers

PMID26722118
PMCPMC5860507

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.