Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance

Author:

Ren Luyao,Duan Xiaoke,Dong Lianhua,Zhang Rui,Yang Jingcheng,Gao Yuechen,Peng Rongxue,Hou Wanwan,Liu Yaqing,Li Jingjing,Yu Ying,Zhang Naixin,Shang Jun,Liang Fan,Wang Depeng,Chen Hui,Sun Lele,Hao Lingtong,Scherer Andreas,Nordlund Jessica,Xiao Wenming,Xu Joshua,Tong Weida,Hu Xin,Jia Peng,Ye Kai,Li Jinming,Jin Li,Hong Huixiao,Wang Jing,Fan Shaohua,Fang Xiang,Zheng YuantingORCID,Shi Leming,

Abstract

Abstract Background Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. Results We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. Conclusions The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

Funder

Shanghai Sailing Program

National Natural Science Foundation of China

National Mega Project on Major Infectious Disease Prevention

State Key Laboratory of Genetic Engineering

111 Project

Publisher

Springer Science and Business Media LLC

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