Dataset Information

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

ABSTRACT:

Background

Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance.

Results

In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels.

Conclusion

These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

SUBMITTER: Jones W

PROVIDER: S-EPMC8051128 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

Jones Wendell W Gong Binsheng B Novoradovskaya Natalia N Li Dan D Kusko Rebecca R Richmond Todd A TA Johann Donald J DJ Bisgin Halil H Sahraeian Sayed Mohammad Ebrahim SME Bushel Pierre R PR Pirooznia Mehdi M Wilkins Katherine K Chierici Marco M Bao Wenjun W Basehore Lee Scott LS Lucas Anne Bergstrom AB Burgess Daniel D Butler Daniel J DJ Cawley Simon S Chang Chia-Jung CJ Chen Guangchun G Chen Tao T Chen Yun-Ching YC Craig Daniel J DJ Del Pozo Angela A Foox Jonathan J Francescatto Margherita M Fu Yutao Y Furlanello Cesare C Giorda Kristina K Grist Kira P KP Guan Meijian M Hao Yingyi Y Happe Scott S Hariani Gunjan G Haseley Nathan N Jasper Jeff J Jurman Giuseppe G Kreil David Philip DP Łabaj Paweł P Lai Kevin K Li Jianying J Li Quan-Zhen QZ Li Yulong Y Li Zhiguang Z Liu Zhichao Z López Mario Solís MS Miclaus Kelci K Miller Raymond R Mittal Vinay K VK Mohiyuddin Marghoob M Pabón-Peña Carlos C Parsons Barbara L BL Qiu Fujun F Scherer Andreas A Shi Tieliu T Stiegelmeyer Suzy S Suo Chen C Tom Nikola N Wang Dong D Wen Zhining Z Wu Leihong L Xiao Wenzhong W Xu Chang C Yu Ying Y Zhang Jiyang J Zhang Yifan Y Zhang Zhihong Z Zheng Yuanting Y Mason Christopher E CE Willey James C JC Tong Weida W Shi Leming L Xu Joshua J

Genome biology 20210416 1

<h4>Background</h4>Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their ...[more]

PMID: 33863366

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Project description:BackgroundImputation has become a standard approach in genome-wide association studies (GWAS) to infer in silico untyped markers. Although feasibility for common variants imputation is well established, we aimed to assess rare and ultra-rare variants' imputation in an admixed Caribbean Hispanic population (CH).MethodsWe evaluated imputation accuracy in CH (N = 1,000), focusing on rare (0.1% ≤ minor allele frequency (MAF) ≤ 1%) and ultra-rare (MAF < 0.1%) variants. We used two reference panels, the Haplotype Reference Consortium (HRC; N = 27,165) and 1000 Genome Project (1000G phase 3; N = 2,504) and multiple phasing (SHAPEIT, Eagle2) and imputation algorithms (IMPUTE2, MACH-Admix). To assess imputation quality, we reported: (a) high-quality variant counts according to imputation tools' internal indexes (e.g., IMPUTE2 "Info" ≥ 80%). (b) Wilcoxon Signed-Rank Test comparing imputation quality for genotyped variants that were masked and imputed; (c) Cohen's kappa coefficient to test agreement between imputed and whole-exome sequencing (WES) variants; (d) imputation of G206A mutation in the PSEN1 (ultra-rare in the general population an more frequent in CH) followed by confirmation genotyping. We also tested ancestry proportion (European, African and Native American) against WES-imputation mismatches in a Poisson regression fashion.ResultsSHAPEIT2 retrieved higher percentage of imputed high-quality variants than Eagle2 (rare: 51.02% vs. 48.60%; ultra-rare 0.66% vs. 0.65%, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 employing HRC outperformed 1000G (64.50% vs. 59.17%; 1.69% vs. 0.75% for high-quality rare and ultra-rare variants, respectively, Wilcoxon p-value < 0.001). SHAPEIT-IMPUTE2 outperformed MaCH-Admix. Compared to 1000G, HRC-imputation retrieved a higher number of high-quality rare and ultra-rare variants, despite showing lower agreement between imputed and WES variants (e.g., rare: 98.86% for HRC vs. 99.02% for 1000G). High Kappa (K = 0.99) was observed for both reference panels. Twelve G206A mutation carriers were imputed and all validated by confirmation genotyping. African ancestry was associated with higher imputation errors for uncommon and rare variants (p-value < 1e-05).ConclusionReference panels with larger numbers of haplotypes can improve imputation quality for rare and ultra-rare variants in admixed populations such as CH. Ethnic composition is an important predictor of imputation accuracy, with higher African ancestry associated with poorer imputation accuracy.

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Dataset Information

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

Background

Results

Conclusion

Publications

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

Similar Datasets

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