Year: 2026
Type: preprint
Abstract: Conventional genome-wide association studies (GWAS), which analyze individual single nucleotide polymorphisms (SNPs), have been instrumental in dissecting the genetic basis of quantitative traits. While short-range haplotype variants have recently demonstrated superior statistical power by capturing local linkage disequilibrium, they are often constrained by predefined, fixed-length blocks. Advances in long-read sequencing and computational phasing are making chromosome-scale haplotypes increasingly accessible, opening new opportunities for association studies. However, frameworks that effectively exploit this long-range genetic information remain limited. Here, we introduce CHAP-GWAS, a novel, dynamically adaptive algorithm designed to identify trait-associated haplotype variants from chromosome-scale phased data. Unlike existing methods, CHAP-GWAS utilizes a phenotype-driven approach that dynamically defines and extends haplotype blocks from "seed" loci, allowing for the precise capture of complex genetic effects. Using comprehensive simulations and real-world datasets from three diverse plant species ( Arabidopsis , rice, and maize), we demonstrate that CHAP-GWAS successfully identifies quantitative trait loci overlooked by conventional single-SNP and static haplotype-based approaches. By leveraging chromosome-length haplotypes, our framework significantly enhances statistical power and addresses a critical component of missing heritability. CHAP-GWAS offers a robust, scalable, and synergistic approach to genetic mapping, with the potential to advance precision agriculture and medicine through more comprehensive and accurate genetic discovery.
Source: Research Square
Authors: Zhenyu Jia, Shibo Wang, Qiong Jia, Yanru Cui, Han Qu, Ruidong Li, Lei Yu, Xuesong Wang, Chin-Sheng Teng, Si Liu, Chenwu Xu, Yuan-Ming Zhang, Sarah Wang, Shuhan Yin, Loren Ho, Meiyue Wang, Weiming Chen, Weide Zhong, Jianguo Zhu, Danelle Seymour, Shou-Wei Ding, Shizhong Xu, Wenxiu Ma, Yang Xu
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7201911634