Genomics Proteomics & BioinformaticsGenomicsPeer-reviewed journal article
Enhancing Variant Calling in Whole Exome Sequencing (WES) Data Using Population-Matched Reference Genomes
Shuming GuoiD, Zhuo HuangiD, Yanming ZhangiD, Yukun HeiD, Xiangju CheniD, Wenjuan WangiD, L.M. LiiD, Yu KangiD, Zhancheng GaoiD, Jun YuiD, Zhenglin DuiD, Yanan ChuiD
Abstract
Whole exon sequencing (WES) data are frequently used for cancer diagnosis and genome-wide association studies (GWAS), hinging upon 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 publicized telomere-to-telomere or 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. We here 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% target regions in YAO are widely diversified from GRCh38 and may lead to off-target in sequence capture. However, YAO still outperforms GRCh38 genomes by obtaining 7.41% more mapped reads. Due to more reliable read-mapping and closer phylogenetic relationship with the samples than GRCh38, YAO reduces half of variant calls of clinical significance which are mostly benign while keeping sensitivity in identifying pathogenic variants. YAO also outperforms 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 backgrounds of each ethnic group.
- organism: human
- assay: whole-exome sequencing
- research focus: variant calling
- research focus: benchmarking
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 2
- FWCI
- 0.53
- Field percentile
- top 37%
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