Evidence map›Paper›PMID 36928108›Full record

ArticleNucleic acids research2023

Rye: genetic ancestry inference at biobank scale.

Andrew B Conley, Lavanya Rishishwar, Maria Ahmad, Shivam Sharma, Emily T Norris, I King Jordan, Leonardo Mariño-Ramírez

Open access · goldAbstract read
In one paragraph

Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
9.4field-weighted citation impact, top 2% 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Admixture mapping identifies complex trait associations with local ancestry in themedRxiv : the preprint server for health sciences · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. GWAS for Periodontitis Phenotypes Using Multi-Ancestry All of Us Research Platform.medRxiv : the preprint server for health sciences · 2025
    Article
  18. Article
  19. Article
  20. 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

7 authors at 2 institutions in 2 countries.

Andrew B ConleyNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
Lavanya RishishwarNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-2055-9392
Maria AhmadNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
Shivam SharmaNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
Emily T NorrisNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.
I King JordanIHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, USA.ORCID 0000-0003-4996-2203
Leonardo Mariño-RamírezNational Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA.ORCID 0000-0002-5716-8512
National Institutes of Health · USGeorgia Institute of Technology · US

Funding

Genomics and Biobank Research for Health EquityZIAMD000018 · NIMHD · NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES · PI MARIÑO-RAMÍREZ, LEONARDO · 2020 to 2025
$2.7M
Epidemiology and Genomics of Health DisparitiesZIAMD000016 · NIMHD · NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES · PI MARINO-RAMIREZ, LEONARDO · 2019 to 2021
$1.6M
NIMHD NIH HHS 1ZIAMD000016
6 · The paper itself

Abstract

Biobank projects are generating genomic data for many thousands of individuals. Computational methods are needed to handle these massive data sets, including genetic ancestry (GA) inference tools. Current methods for GA inference do not scale to biobank-size genomic datasets. We present Rye-a new algorithm for GA inference at biobank scale. We compared the accuracy and runtime performance of Rye to the widely used RFMix, ADMIXTURE and iAdmix programs and applied it to a dataset of 488221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis of genomic variant samples from ancestral reference populations and query individuals. The algorithm's accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares regression. Rye produces highly accurate GA estimates for three-way admixed populations-African, European and Native American-compared to RFMix and ADMIXTURE (${R}^2 = \ 0.998 - 1.00$), and shows 50× runtime improvement compared to ADMIXTURE on the UK Biobank dataset. Rye analysis of UK Biobank samples demonstrates how it can be used to infer GA at both continental and subcontinental levels. We discuss user consideration and options for the use of Rye; the program and its documentation are distributed on the GitHub repository: https://github.com/healthdisparities/rye.

Indexed as

Genetics, PopulationSecaleAlgorithmsBiological Specimen BanksGenomicsHumansPolymorphism, Single Nucleotide

Identifiers

PMID36928108
PMCPMC10164567
OpenAlexW4327676743

What OpenQuestion holds

Textmetadata
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.