ArticlemedRxiv : the preprint server for health sciences2026
HiFiMAP: High-resolution fast identity-by-descent mapping test.
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.
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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.
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Authors and funding
6 authors.
Funding
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
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