Leveraging Human Pangenome for Improved Somatic Variant Detection

Somatic variant detection is technically challenging due to low variant allele fractions, the confounding presence of germline variation, and reference bias. Linear references such as GRCh38 miss sample-specific variation, causing misalignments and incorrect variant calls. Although telomere-to-telomere donor-specific assemblies (DSAs) accurately represent individual genomes, their application is limited by cost and technical barriers. Alternatively, the graph-based human pangenome provides a scalable framework to improve read alignment and perform genome inference. Here, we benchmarked somatic variant detection using GRCh38, graph-based pangenomes, and pangenome-inferred DSAs with a HapMap mixture dataset and the COLO829 melanoma cell line. Pangenome-guided alignment improves read mapping and somatic variant calling accuracy. Furthermore, personalized pangenomes partially reconstruct donor-specific genomic content, improving accuracy, reducing germline contamination, and enabling detection of events in loci absent or poorly represented in GRCh38. These findings demonstrate that graph-based and personalized pangenomes are effective strategies for enhancing somatic variant detection compared with GRCh38.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-01-04
DOI
https://doi.org/10.64898/2026.01.04.697580
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveraging Human Pangenome for Improved Somatic Variant Detection

Heather A. Lawson, Xiaoyu Zhuo, B. Johnson, Juan F Macias-Velasco et al.
bioRxiv (Cold Spring Harbor Laboratory)
Genomics and Rare Diseases
article

Leveraging Human Pangenome for Improved Somatic Variant Detection

Heather A. Lawson, Xiaoyu Zhuo, B. Johnson, Juan F Macias-Velasco, Edward A. Belter, Sheng Chih Jin, Derek Albracht, Daofeng Li, Mary Rhodes, Chad Tomlinson, T. Wang, Elvisa Mehinovic, Nahyun Kong, Zilan Xin, R J Fulton, Theron Palmer, Benpeng Miao, John E. Garza, Shihua Dong, Tina Lindsay, Zitian Tang, Andrew Ruttenberg, Hui Shen, Wenjin Zhang, Zefan Li, Qichen Fu, SMaHT Network Assembly/Pangenome Working Group, H. Josh Jang, Yuchen Cheng, Jiawei Shen
article en

Abstract

Somatic variant detection is technically challenging due to low variant allele fractions, the confounding presence of germline variation, and reference bias. Linear references such as GRCh38 miss sample-specific variation, causing misalignments and incorrect variant calls. Although telomere-to-telomere donor-specific assemblies (DSAs) accurately represent individual genomes, their application is limited by cost and technical barriers. Alternatively, the graph-based human pangenome provides a scalable framework to improve read alignment and perform genome inference. Here, we benchmarked somatic variant detection using GRCh38, graph-based pangenomes, and pangenome-inferred DSAs with a HapMap mixture dataset and the COLO829 melanoma cell line. Pangenome-guided alignment improves read mapping and somatic variant calling accuracy. Furthermore, personalized pangenomes partially reconstruct donor-specific genomic content, improving accuracy, reducing germline contamination, and enabling detection of events in loci absent or poorly represented in GRCh38. These findings demonstrate that graph-based and personalized pangenomes are effective strategies for enhancing somatic variant detection compared with GRCh38.

bioRxiv (Cold Spring Harbor Laboratory)
James S. McDonnell Foundation (US), Van Andel Institute (US), Washington University in St. Louis (US)
Openalex Percentile: Top 97%
Genomics and Rare Diseases
DOIOpen Access PDFOpenAlex
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