Evidence map›Paper›PMID 38362534›Full record

ArticleHarvard data science review2020

Learning Lessons on Reproducibility and Replicability in Large Scale Genome-Wide Association Studies.

Xihong Lin

Abstract read
In one paragraph

Article in Harvard data science review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. PROVGEN: A Privacy-Preserving Approach for Outcome Validation in Genomic Research.Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Open Science Practices in Psychiatric Genetics: A Primer.Biological psychiatry global open science · 2024
    Review
  9. 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

1 author.

Xihong LinDepartment of Biostatistics and Department of Statistics, Harvard University.

Funding

Translating Molecular and Clinical Data to Population Lung Cancer Risk AssessmentU19CA203654 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI Christopher I. Amos, Rayjean J. Hung · 2017 to 2026
$23.7M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Powering whole genome sequence-based genetic discovery for common human diseases- Extended 2021-2022.U01HG009088 · NHGRI · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIN, XIHONG, NEALE, BENJAMIN MICHAEL · 2016 to 2021
$5.1M
NCI NIH HHS R35 CA197449NCI NIH HHS U19 CA203654NHGRI NIH HHS U01 HG009088
6 · The paper itself

Abstract

Reproducibility and replicability play a pivotal role in science. The article reflects on reproducibility and replicability as they figure in large scale genome-wide association studies. Overall, we emphasize the importance of enhancing data reproducibility, analysis reproducibility, and result replicability. We make recommendations pertaining to the development of study designs that address 1) batch effects and selection bias, 2) the incorporation of discrete discovery and replication phases, and 3) the procurement of a large sample size. We emphasize the importance of systematic and transparent data generation, processing, and quality control pipelines, as well as a rigorous field-specific standardized analysis protocol, We offer guidance with respect to collaborative frameworks, open access analysis tools, and software, and the use of supporting mandates, infrastructure, and repositories for data and resource sharing. Finally, we identify the role of incentives and culture in fueling the production of reproducible and replicable research through partnerships of researchers, funding agencies, and journals.

Indexed as

Analysis reproducibilityAnalysis standardizationBatch effectsCollaborative frameworkCommunity culture buildingData repositoriesData reproducibilityData standardization and harmonizationMulti-phase designOpen sciencePartnershipResult replicabilitySelection biasStatistical inferenceStudy designTransparency

Identifiers

PMID38362534
PMCPMC10869125

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.