Evidence map›Paper›PMID 38039339›Full record

ArticlePLoS genetics2023

FiMAP: A fast identity-by-descent mapping test for biobank-scale cohorts.

Han Chen, Ardalan Naseri, Degui Zhi

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.8field-weighted citation impact, top 6% 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

14 citing papers in PubMed, 13 citations in OpenAlex.

  1. HiFiMAP: High-resolution fast identity-by-descent mapping test.medRxiv : the preprint server for health sciences · 2026
    Article
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  8. Review
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  10. Fast simulation of identity-by-descent segments.Bulletin of mathematical biology · 2025
    Article
  11. Fast simulation of identity-by-descent segments.bioRxiv : the preprint server for biology · 2025
    Article
  12. Article
  13. Estimating evolutionary and demographic parameters via ARG-derived IBD.bioRxiv : the preprint server for biology · 2024
    Article
  14. 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

3 authors at 1 institution in 1 country.

Han ChenHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.ORCID 0000-0002-9510-4923
Ardalan NaseriCenter for Artificial Intelligence and Genome Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.
Degui ZhiCenter for Artificial Intelligence and Genome Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America.ORCID 0000-0001-7754-1890
The University of Texas Health Science Center at Houston · US

Funding

Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant)U01AG070112 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI FORNAGE, MYRIAM, JI, SHUIWANG · 2021 to 2025
$7.2M
Scalable methods for identity by descentR01HG010086 · NHGRI · YALE UNIVERSITY · PI Shaojie Zhang, Degui Zhi · 2018 to 2026
$4.9M
Deep-Learning-Derived Endophenotypes from Retina ImagesR01EY032768 · NEI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, RUI, ZHI, DEGUI · 2022 to 2024
$1.7M
Statistical and Computational Methods for Large-Scale Sequencing StudiesR00HL130593 · NHLBI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, HAN · 2017 to 2019
$746k
NEI NIH HHS R01 EY032768NHGRI NIH HHS R01 HG010086NHLBI NIH HHS R00 HL130593NIA NIH HHS U01 AG070112
6 · The paper itself

Abstract

Although genome-wide association studies (GWAS) have identified tens of thousands of genetic loci, the genetic architecture is still not fully understood for many complex traits. Most GWAS and sequencing association studies have focused on single nucleotide polymorphisms or copy number variations, including common and rare genetic variants. However, phased haplotype information is often ignored in GWAS or variant set tests for rare variants. Here we leverage the identity-by-descent (IBD) segments inferred from a random projection-based IBD detection algorithm in the mapping of genetic associations with complex traits, to develop a computationally efficient statistical test for IBD mapping in biobank-scale cohorts. We used sparse linear algebra and random matrix algorithms to speed up the computation, and a genome-wide IBD mapping scan of more than 400,000 samples finished within a few hours. Simulation studies showed that our new method had well-controlled type I error rates under the null hypothesis of no genetic association in large biobank-scale cohorts, and outperformed traditional GWAS single-variant tests when the causal variants were untyped and rare, or in the presence of haplotype effects. We also applied our method to IBD mapping of six anthropometric traits using the UK Biobank data and identified a total of 3,442 associations, 2,131 (62%) of which remained significant after conditioning on suggestive tag variants in the ± 3 centimorgan flanking regions from GWAS.

Indexed as

Biological Specimen BanksGenome-Wide Association StudyDNA Copy Number VariationsHaplotypesHumansPhenotypePolymorphism, Single Nucleotide

Identifiers

PMID38039339
PMCPMC10718418
OpenAlexW4389234165

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.