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A flexible modeling and inference framework for estimating variant effect sizes from GWAS summary statistics

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.04.18.488696Source: DataRank Database

A flexible modeling and inference framework for estimating variant effect sizes from GWAS summary statistics is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 42 times.

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Open Access42 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: greenTopics (20)IDs (PubMed, OpenAlex)

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Authors (4)

Nicholas A. Sinnott-Armstrong ORCID,Themistocles L. Assimes ORCID,Jonathan K. Pritchard ORCID,Jeffrey P. Spence ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W4224287934

National Institutes of Health

Grant: 5U01HG012069-05

Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory code

National Institutes of Health

Grant: 5T32HG000044-23

Institutional Training Grant in Genome Science

National Institutes of Health

Grant: 5R01HG011432-03

New methods for constructing and evaluating polygenic scores

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/04/19/2022.04.18.488696.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.04.18.488696

repositoryhttps://doi.org/10.1101/2022.04.18.488696

Fields of Study

Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and Animals0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

InferenceGenome-wide association studyComputer scienceLinkage disequilibriumTraitStatisticsMachine learningData miningArtificial intelligenceEconometricsMathematicsBiologySingle-nucleotide polymorphismGenetics