Evidence map›Paper›PMID 39975339›Full record

ArticlebioRxiv : the preprint server for biology2025

Old vs. New Local Ancestry Inference in HCHS/SOL: A Comparative Study.

Xueying Chen, Hao Wang, Iris Broce, Anders Dale, Bing Yu, Laura Y Zhou, Xihao Li, Maria Argos, Martha L Daviglus, Jianwen Cai and 2 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Xueying ChenDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Hao WangDepartment of Radiology, University of California San Diego, La Jolla, CA, USA.
Iris BroceDepartment of Neurosciences, University of California, San Diego, San Diego, California, USA.
Anders DaleDepartment of Radiology, University of California San Diego, La Jolla, CA, USA.
Bing YuDepartment of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Laura Y ZhouDepartment of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
Xihao LiDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID 0000-0001-8151-0106
Maria ArgosDepartment of Environmental Health, School of Public Health, Boston University, Boston, MA, USA.
Martha L DaviglusInstitute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA.
Jianwen CaiDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Nora FranceschiniDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Tamar SoferDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.ORCID 0000-0001-8520-8860

Funding

Pilot Program CoreP30ES027792 · NIEHS · UNIVERSITY OF CHICAGO · PI Gokhan M. Mutlu · 2017 to 2026
$13.6M
Multi-omics study of ancestry enriched associations in Hispanics/LatinosR01HG013163 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Nora Franceschini, Tamar Sofer · 2024 to 2026
$2.1M
Multi-omics study of ancestry enriched associations in Hispanics/LatinosR56HG013163 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI FRANCESCHINI, NORA, SOFER, TAMAR · 2023 to 2023
$350k
HISPANIC COMMUNITY HEALTH STUDY-268065233N01HC065233 · HC · COORDINATING CENTER · PI CHAMBLESS, LLOYD E · 2006 to 2006
–
NHGRI NIH HHS R01 HG013163NHGRI NIH HHS R56 HG013163NHLBI NIH HHS N01 HC065233NHLBI NIH HHS N01 HC065234NHLBI NIH HHS N01 HC065235NHLBI NIH HHS N01 HC065236NHLBI NIH HHS N01 HC065237NIEHS NIH HHS P30 ES027792
6 · The paper itself

Abstract

Hispanic/Latino populations are admixed, with genetic contributions from multiple ancestral populations. Studies of genetic association in these admixed populations often use methods such as admixture mapping, which relies on inferred counts of "local" ancestry, i.e., of the source ancestral population at a locus. Local ancestries are inferred using external reference panels that represent ancestral populations, making the choice of inference method and reference panel critical. This study used a dataset of Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) to evaluate the "old" local ancestry inference performed using the state-of-the-art inference method, RFMix, alongside "new" inferences performed using Fast Local Ancestry Estimation (FLARE), which also used an updated reference panel. We compared their performance in terms of global and local ancestry correlations, as well as admixture mapping-based associations. Overall, the old RFMix and new FLARE inferences were highly similar for both global and local ancestries, with FLARE-inferred datasets yielding admixture mapping results consistent with those computed from RFMix. However, in some genomic regions the old and new local ancestries have relatively lower correlations (Pearson R < 0.9). Most of these genomic regions (86.42%) were mapped to either ENCODE blacklist regions, or to gene clusters, compared to 7.67% of randomly-matched regions with high correlations (Pearson R > 0.97) between old and new local ancestries.

Identifiers

PMID39975339
PMCPMC11838596

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.