Evidence map›Paper›PMID 25898925›Full record

ArticleEuropean journal of human genetics : EJHG2016

Leveraging ancestry to improve causal variant identification in exome sequencing for monogenic disorders.

Robert Brown, Hane Lee, Ascia Eskin, Gleb Kichaev, Kirk E Lohmueller, Bruno Reversade, Stanley F Nelson, Bogdan Pasaniuc

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European journal of human genetics : EJHG, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Robert BrownBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Hane LeeDepartment of Pathology and Laboratory Medicine, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Ascia EskinDepartment of Human Genetics, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Gleb KichaevBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Kirk E LohmuellerBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.
Bruno ReversadeInstitute of Medical Biology, Human Genetics and Embryology Laboratory, A*STAR, Singapore, Singapore.ORCID http://orcid.org/0000-0002-4070-7997
Stanley F NelsonDepartment of Pathology and Laboratory Medicine, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Bogdan PasaniucBioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA.

Funding

STATISTICAL METHODS FOR GENE MAPPINGR01GM053275 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANGE, KENNETH L · 1995 to 2019
$9.3M
Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
UCLA Muscular Dystrophy Core CenterP30AR057230 · NIAMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SPENCER, MELISSA JAN · 2009 to 2018
$6.2M
Metrics and methods for cross-population fine mappingR03CA162200 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PASANIUC, BOGDAN · 2012 to 2013
$149k
NCI NIH HHS R03 CA162200NCI NIH HHS R03-CA162200NHGRI NIH HHS T32 HG002536NHGRI NIH HHS T32-HG002536NIAMS NIH HHS P30 AR057230NIAMS NIH HHS P30AR057230NIGMS NIH HHS R01 GM053275NIGMS NIH HHS R01-GM053275
6 · The paper itself

Abstract

Recent breakthroughs in exome-sequencing technology have made possible the identification of many causal variants of monogenic disorders. Although extremely powerful when closely related individuals (eg, child and parents) are simultaneously sequenced, sequencing of a single case is often unsuccessful due to the large number of variants that need to be followed up for functional validation. Many approaches filter out common variants above a given frequency threshold (eg, 1%), and then prioritize the remaining variants according to their functional, structural and conservation properties. Here we present methods that leverage the genetic structure across different populations to improve filtering performance while accounting for the finite sample size of the reference panels. We show that leveraging genetic structure reduces the number of variants that need to be followed up by 16% in simulations and by up to 38% in empirical data of 20 exomes from individuals with monogenic disorders for which the causal variants are known.

Indexed as

AlgorithmsExomeModels, StatisticalPolymorphism, Single NucleotideComputational BiologyComputer SimulationFemaleGenetic Diseases, InbornGenetic VariationGenome, HumanHigh-Throughput Nucleotide SequencingHumansInheritance PatternsMalePedigreeRacial Groups

Identifiers

PMID25898925
PMCPMC4795218

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.