Evidence map›Paper›PMID 42180323›Full record

ArticlemedRxiv : the preprint server for health sciences2026

HiFiMAP: High-resolution fast identity-by-descent mapping test.

Bohong Guo, Ardalan Naseri, Ziqian Xie, Chloé Sarnowski, Degui Zhi, Han Chen

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2026. 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

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

6 authors.

Bohong GuoDepartment of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.ORCID 0000-0003-4595-912X
Ardalan NaseriDepartment of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.
Ziqian XieDepartment of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.
Chloé SarnowskiHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.ORCID 0000-0002-6090-7099
Degui ZhiDepartment of Bioinformatics and Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas 77030, USA.ORCID 0000-0001-7754-1890
Han ChenRory Meyers College of Nursing, New York University, New York, New York 10010, USA.ORCID 0000-0002-9510-4923

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
Efficient IBD mapping for Alzheimer's Disease and related brain imaging phenotypesR01AG081398 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Han Chen, Degui Zhi · 2024 to 2026
$2.1M
NIA NIH HHS R01 AG081398NIA NIH HHS U01 AG070112
6 · The paper itself

Abstract

Although traditional genome-wide association studies (GWAS) have identified numerous loci, they often ignore phased haplotype information. Identity-by-descent (IBD) mapping captures these extended haplotypic effects by modeling shared ancestral segments. However, standard statistical mapping of these segments scales poorly with biobank-sized cohorts and short IBD segments that capture older evolutionary events. To overcome this computational bottleneck, existing scalable IBD mapping frameworks aggregate shared segments into fixed sliding windows. While computationally efficient, this window-based approach generates association signals at a low resolution that often span hundreds of kilobases. To address this issue, here we present a novel High-resolution Fast IBD Mapping test (HiFiMAP) that takes snapshots of IBD segments at the single nucleotide polymorphism (SNP) level resolution. Simulation studies confirm that HiFiMAP maintains well-controlled type I error rates and exhibits superior statistical power for detecting rare variants and haplotype effects using short IBD segments. In a UK Biobank (UKB) benchmark (N=407,681), HiFiMAP mapped 640,899 SNPs at 1.92 CPU seconds per test, massively outperforming existing window-based methods (95.2 CPU seconds per test for 3,403 windows). Furthermore, applied to high-dimensional brain imaging phenotypes (N~36,000), HiFiMAP identified five novel associations previously undetected by standard GWAS approaches, including key central nervous system regulators like

Indexed as

Brain imaging phenotypesHaplotype effectsHigh-resolution association mappingIdentity-by-descent segmentsScalable statistical association test

Identifiers

PMID42180323
PMCPMC13193028

What OpenQuestion holds

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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.