Wang Nicholas K, Wiltsie Nicholas, Winata Helena K, Fitz-Gibbon Sorel, Gonzalez Alfredo E, Zeltser Nicole, Agrawal Raag, Oh Jieun, Arbet Jaron, Patel Yash, Yamaguchi Takafumi N, Boutros Paul C
Department of Human Genetics, University of California, Los Angeles.
Jonsson Comprehensive Cancer Center, University of California, Los Angeles.
bioRxiv. 2024 Nov 3:2024.10.31.621401. doi: 10.1101/2024.10.31.621401.
Reference genomes are foundational to modern genomics. Our growing understanding of genome structure leads to continual improvements in reference genomes and new genome "builds" with incompatible coordinate systems. We quantified the impact of genome build on germline and somatic variant calling by analyzing tumour-normal whole-genome pairs against the two most widely used human genome builds. The average individual had a build-discordance of 3.8% for germline SNPs, 8.6% for germline SVs, 25.9% for somatic SNVs and 49.6% for somatic SVs. Build-discordant variants are not simply false-positives: 47% were verified by targeted resequencing. Build-discordant variants were associated with specific genomic and technical features in variant- and algorithm-specific patterns. We leveraged these patterns to create StableLift, an algorithm that predicts cross-build stability with AUROCs of 0.934 ± 0.029. These results call for significant caution in cross-build analyses and for use of StableLift as a computationally efficient solution to mitigate inter-build artifacts.
参考基因组是现代基因组学的基础。我们对基因组结构的不断深入理解促使参考基因组持续改进，并产生了具有不兼容坐标系统的新基因组“版本”。我们通过针对两种使用最广泛的人类基因组版本分析肿瘤-正常全基因组对，量化了基因组版本对种系和体细胞变异检测的影响。平均个体的种系单核苷酸多态性（SNP）的版本不一致率为3.8%，种系结构变异（SV）为8.6%，体细胞单核苷酸变异（SNV）为25.9%，体细胞SV为49.6%。版本不一致的变异并非简单的假阳性：47%通过靶向重测序得到验证。版本不一致的变异与特定的基因组和技术特征呈现变异及算法特异性模式相关。我们利用这些模式创建了StableLift算法，该算法预测跨版本稳定性的曲线下面积（AUROC）为0.934±0.029。这些结果警示在跨版本分析中要格外谨慎，并建议使用StableLift作为一种计算效率高的解决方案来减轻版本间的人为因素影响。
