Enhancing Variant Calling in Whole-exome Sequencing Data Using Population-matched Reference Genomes

Whole-exome sequencing (WES) data are frequently used for cancer diagnosis and genome-wide association studies (GWAS), based on high-coverage read mapping, informative variant calling, and high-quality reference genomes. The center position of the currently used genome assembly, GRCh38, is now challenged by two newly published telomere-to-telomere (T2T) genomes, T2T-CHM13 and T2T-YAO, and it becomes urgent to have a comparative study to test population specificity using the three reference genomes based on real case WES data. Here, we report our analysis along this line for 19 tumor samples collected from Chinese patients. The primary comparison of the exon regions among the three references reveals that the sequences in up to ∼ 1% of target regions in T2T-YAO are widely diversified from GRCh38 and may lead to off-target in sequence capture. However, T2T-YAO still outperforms GRCh38 by obtaining 7.41% of more mapped reads. Due to more reliable read-mapping and closer phylogenetic relationship with the samples than GRCh38, T2T-YAO reduces half of variant calls of clinical significance which are mostly benign, while maintaining sensitivity in identifying pathogenic variants. T2T-YAO also outperforms T2T-CHM13 in reducing calls of Chinese-specific variants. Our findings highlight the critical need for employing population-specific reference genomes in genomic analysis to ensure accurate variant analysis and the significant benefits of tailoring these approaches to the unique genetic background of each ethnic group.

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Publication Details

Journal
Genomics Proteomics & Bioinformatics
Published
2024-10-01
DOI
https://doi.org/10.1093/gpbjnl/qzae070
Citations
2
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.57

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article

Enhancing Variant Calling in Whole-exome Sequencing Data Using Population-matched Reference Genomes

Zhancheng Gao, Wenjuan Wang, Xiangju Chen, L.M. Li et al.
2 citations
Genomics Proteomics & Bioinformatics
Cancer Genomics and Diagnostics
0.57
article

Enhancing Variant Calling in Whole-exome Sequencing Data Using Population-matched Reference Genomes

Zhancheng Gao, Wenjuan Wang, Xiangju Chen, L.M. Li, Zhenglin Du, Yukun He, Jun Yu, Shuming Guo, Yanming Zhang, Zhuo Huang, Yanan Chu, Yu Kang
article en
2 citations

Abstract

Whole-exome sequencing (WES) data are frequently used for cancer diagnosis and genome-wide association studies (GWAS), based on high-coverage read mapping, informative variant calling, and high-quality reference genomes. The center position of the currently used genome assembly, GRCh38, is now challenged by two newly published telomere-to-telomere (T2T) genomes, T2T-CHM13 and T2T-YAO, and it becomes urgent to have a comparative study to test population specificity using the three reference genomes based on real case WES data. Here, we report our analysis along this line for 19 tumor samples collected from Chinese patients. The primary comparison of the exon regions among the three references reveals that the sequences in up to ∼ 1% of target regions in T2T-YAO are widely diversified from GRCh38 and may lead to off-target in sequence capture. However, T2T-YAO still outperforms GRCh38 by obtaining 7.41% of more mapped reads. Due to more reliable read-mapping and closer phylogenetic relationship with the samples than GRCh38, T2T-YAO reduces half of variant calls of clinical significance which are mostly benign, while maintaining sensitivity in identifying pathogenic variants. T2T-YAO also outperforms T2T-CHM13 in reducing calls of Chinese-specific variants. Our findings highlight the critical need for employing population-specific reference genomes in genomic analysis to ensure accurate variant analysis and the significant benefits of tailoring these approaches to the unique genetic background of each ethnic group.

Genomics Proteomics & BioinformaticsVol. 22(5)
Chinese Academy of Sciences (CN), Peking University (CN), Beijing Institute of Genomics (CN), Xian Central Hospital (CN), Peking University People's Hospital (CN), Genomics (United Kingdom) (GB), Shanghai Center For Bioinformation Technology (CN), University of Chinese Academy of Sciences (CN)
National Science Foundation, National Natural Science Foundation of China, Peking University, Peking University People's Hospital
Openalex Percentile: Top 31%
Cancer Genomics and Diagnostics
0.57
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