Evidence map›Paper›PMID 29677560›Full record

ArticleMutation research2018

Effective discovery of rare variants by pooled target capture sequencing: A comparative analysis with individually indexed target capture sequencing.

Seungjin Ryu, Jeehae Han, Trina M Norden-Krichmar, Nicholas J Schork, Yousin Suh

Open access · greenAbstract readComparative StudyValidation Study
In one paragraph

Article in Mutation research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
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

7 citing papers in PubMed, 28 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Seungjin RyuDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Jeehae HanDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
Trina M Norden-KrichmarThe Scripps Research Institute, La Jolla, CA 92037, USA.
Nicholas J SchorkThe Scripps Research Institute, La Jolla, CA 92037, USA; J. Craig Venter Institute, La Jolla, CA, 92037, USA.
Yousin SuhDepartment of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA; Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, 10461, USA; Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, NY, 10461, USA. Electronic address: yousin.suh@einstein.yu.edu.
Albert Einstein College of Medicine · USScripps Research Institute · US

Funding

THE IMPACT OF CELLULAR DEFENSE ON THE ROLE OF Ku80 IN GENOME MAINTENANCE AND LONGP01AG017242 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR SAN ANT · PI VIJG, JAN · 1999 to 2023
$46.1M
Translational Research CoreP30DK020541 · NIDDK · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI STREAMSON C CHUA · 2015 to 2026
$27.7M
Validation and characterization of the identified variants associated with human longevity in mouse modelsU19AG056278 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI JAN VIJG · 2017 to 2026
$24.5M
STATISTICS AND DATA MANAGEMENT COREP01AG027734 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI BARZILAI, NIR J · 2007 to 2012
$9.3M
Combinatorial Actions of Genetic Variants and Gender Bias of Alzherimer's DiseaseR01AG057706 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI GAGE, FRED H, GLASS, CHRISTOPHER K · 2017 to 2021
$8.1M
Systems Modeling of Alzheimers Disease in C. elegansRF1AG057341 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI MURPHY, COLEEN TARA, SUH, YOUSIN · 2017 to 2017
$3.7M
From in vivo to in vitro heterochronic parabiosis to identify geronic factorsR01AG057433 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI RANDO, THOMAS A., SUH, YOUSIN · 2017 to 2020
$2.1M
Association Analysis- SNPs in Longevity Assurance GenesR01AG024391 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI SUH, YOUSIN · 2004 to 2008
$1.6M
NIA NIH HHS P01 AG017242NIA NIH HHS P01 AG027734NIA NIH HHS R01 AG024391NIA NIH HHS R01 AG057433NIA NIH HHS R01 AG057706NIA NIH HHS RF1 AG057341NIA NIH HHS U19 AG056278NIDDK NIH HHS P30 DK020541
6 · The paper itself

Abstract

Identification of all genetic variants associated with complex traits is one of the most important goals in modern human genetics. Genome-wide association studies (GWAS) have been successfully applied to identify common variants, which thus far explain only small portion of heritability. Interests in rare variants have been increasingly growing as an answer for this missing heritability. While next-generation sequencing allows detection of rare variants, its cost is still prohibitively high to sequence a large number of human DNA samples required for rare variant association studies. In this study, we evaluated the sensitivity and specificity of sequencing for pooled DNA samples of multiple individuals (Pool-seq) as a cost-effective and robust approach for rare variant discovery. We comparatively analyzed Pool-seq vs. individual-seq of indexed target capture of up to 960 genes in ∼1000 individuals, followed by independent genotyping validation studies. We found that Pool-seq was as effective and accurate as individual-seq in detecting rare variants and accurately estimating their minor allele frequencies (MAFs). Our results suggest that Pool-seq can be used as an efficient and cost-effective method for discovery of rare variants for population-based sequencing studies in individual laboratories.

Indexed as

Genetic VariationFemaleGenome-Wide Association StudyHigh-Throughput Nucleotide SequencingHumansMaleGenetic variantIndividually indexed target capture sequencingPooled target capture sequencingRare variant

Identifiers

PMID29677560
PMCPMC5962423
OpenAlexW2795030612

What OpenQuestion holds

Textmetadata
LicenceTDM
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.