Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Multitrait GWAS to connect disease variants and biological mechanisms

PLoS Genetics(2021)10.1371/journal.pgen.1009713Source: DataRank Database

Multitrait GWAS to connect disease variants and biological mechanisms is a research paper published in PLoS Genetics (2021). On theSindex it has a DataRank of 0.550. It has been cited 38 times.

N/A

0.550DataRank · unranked

Scored on demand from live citation data

Open Access38 citations

Download PDF

Cite:

Linked data & code

refsnp

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 8.34)OA: goldTopics (22)Linked Datasets (25)DataCite Reuse (25)IDs (PubMed, PMC, OpenAlex)Annotations (1)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.550

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Authors (17)

Vincent Laville ORCID,Zachary R. McCaw ORCID,Zihuai He ORCID,Vincent Guillemot ORCID,Carla Lasry

Live enrichment

PubMed:34460823 PMC:PMC8437297 OpenAlex:https://openalex.org/W3196798176

ANR

Grant: ANR-16-CONV-0005

National Institutes of Health

Grant: R03DE025665

Lundbeck Foundation

Grant: R335-2019-2339

FWCI

8.34

Citation Percentile

1.0%

Citation Trend

2021

2022

2023

2024

2025

goldLicense: cc-by

journalhttps://doi.org/10.1371/journal.pgen.1009713

publisherhttps://doi.org/10.1371/journal.pgen.1009713

publisherhttps://dx.plos.org/10.1371/journal.pgen.1009713

repositoryhttps://pubmed.ncbi.nlm.nih.gov/34460823

https://pure.au.dk/portal/en/publications/12d4353b-5f65-49f1-836e-2067c8506790

Fields of Study

Genetic Associations and EpidemiologyBioinformatics and Genomic NetworksHIV Research and Treatment

MeSH Terms

HumansPhenotypeCluster AnalysisComputational BiologyGenetic Predisposition to DiseasePolymorphism, Single NucleotideQuantitative Trait LociGenome-Wide Association Study

Keywords

Genome-wide association studyBiologyGenetic associationComputational biologyGenetic architecturePhenotypeDECIPHERGeneticsGeneSingle-nucleotide polymorphismGenotype

Sustainable Development Goals

SDG 0: Good health and well-being

Additional file 1 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26308951.v1figshare

Additional file 1 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26308951figshare

Additional file 2 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26309069.v1figshare

Additional file 2 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26309069figshare

Additional file 12 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707928figshare

Additional file 12 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707928.v1figshare

Additional file 4 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26749232figshare

Additional file 3 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26749229figshare

Additional file 3 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26749229.v1figshare

Additional file 4 of Meta-analysis of six dairy cattle breeds reveals biologically relevant candidate genes for mastitis resistance

10.6084/m9.figshare.26749232.v1figshare

Additional file 7 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707913figshare

Additional file 8 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707916figshare

Additional file 10 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707922.v1figshare

Additional file 11 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707925.v1figshare

Additional file 11 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707925figshare

Additional file 7 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707913.v1figshare

Additional file 8 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707916.v1figshare

Additional file 10 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707922figshare

Additional file 9 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707919.v1figshare

Additional file 9 of Multi-trait GWAS for diverse ancestries: mapping the knowledge gap

10.6084/m9.figshare.26707919figshare

Multitrait GWAS to connect disease variants and biological mechanisms

FAIR Checklist

DataRank Breakdown

Authors (17)

Funding (3 grants)

Impact

Open Access

Topics

Linked Datasets (25)

Annotations (Europe PMC)