Evidence map›Paper›PMID 32457519›Full record

ArticleEuropean journal of human genetics : EJHG2020

Medium-coverage DNA sequencing in the design of the genetic association study.

Chao Xu, Ruiyuan Zhang, Hui Shen, Hong-Wen Deng

Abstract read
In one paragraph

Article in European journal of human genetics : EJHG, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Genome-wide selective sweep analysis in high-altitude Changthangi goats reveals candidate genes for pashmina fiber production.Mammalian genome : official journal of the International Mammalian Genome Society · 2025
    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

4 authors.

Chao XuCenter for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA.
Ruiyuan ZhangCenter for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA.
Hui ShenCenter for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA.ORCID http://orcid.org/0000-0003-0335-6064
Hong-Wen DengCenter for Bioinformatics and Genomics, Department of Biostatistics and Data Science, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA. hdeng2@tulane.edu.ORCID http://orcid.org/0000-0002-0387-8818

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Ning Liu · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI HONG-WEN DENG · 2017 to 2026
$24.3M
Epigenomewide DNA Methylation Study for Osteoporosis RiskR01AR059781 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2012 to 2016
$3.1M
Identification of Metabolomic Profiles for Sarcopenia Traits in Older Whites and BlacksR01AG061917 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI SHEN, HUI, ZHAO, QI · 2019 to 2023
$3.0M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
Integration of brain imaging with genomic and epigenomic dataR01MH104680 · NIMH · TULANE UNIVERSITY OF LOUISIANA · PI CALHOUN, VINCE D, DENG, HONG-WEN · 2014 to 2017
$2.1M
NIAMS NIH HHS R01 AR059781NIAMS NIH HHS R01 AR069055NIA NIH HHS R01 AG061917NIA NIH HHS U19 AG055373NIGMS NIH HHS P20 GM109036NIMH NIH HHS R01 MH104680
6 · The paper itself

Abstract

DNA sequencing is a widely used tool in genetic association study. Sequencing cost remains a major concern in sequencing-based study, although the application of next generation sequencing has dramatically decreased the sequencing cost and increased the efficiency. The choice of sequencing depth and the sequencing sample size will largely determine the final study investment and performance. Many studies have been conducted to find a cost-effective design of sequencing depth that can achieve certain sequencing accuracy using minimal sequencing cost. The strategies previously studied can be classified into two groups: (1) single-stage to sequence all the samples using either high (>~30×) or low (<~10×) sequencing depth; and (2) two-stage to sequence an affordable number of individuals at a high-coverage followed by a large sample of low-coverage sequencing. However, limited studies examined the performance of the medium-coverage (10-30×) sequencing depth for a genetic association study, where the optimum sequencing depth may exist. In this study, using a published simulation framework, we comprehensively compared the medium-coverage sequencing (MCS) to the single- and two-stage high/low-coverage sequencing in terms of the power and type I error of the variant discovery and association testing. We found, given certain sequencing effort, MCS yielded a comparable discovery power and better type I error control compared with the best (highest power) scenarios using other high- and low-coverage single-stage or two-stage designs. However, MCS was not as competent as other designs with respect to the association power, especially for the rare variants and when the sequencing investment was limited.

Indexed as

Genome-Wide Association StudyHumansPolymorphism, GeneticSensitivity and SpecificitySequence Analysis, DNA

Identifiers

PMID32457519
PMCPMC7608440

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.