Evaluating the molecular diagnostic yield of joint genotyping-based approach for detecting rare germline pathogenic and putative loss-of-function variants

Abstract

Purpose: Cohort-based germline variant characterization is the standard approach for pathogenic variant discovery in clinical and research samples. However, the impact of cohort size on the molecular diagnostic yield of joint genotyping is largely unknown.

Methods: Head-to-head comparison of the molecular diagnostic yield of joint genotyping in two cohorts of 239 cancer patients in the absence and then in the presence of 100 additional germline exomes.

Results: In 239 testicular cancer patients, 4 (7.4%, 95% confidence interval [CI]: 2.1-17.9) of 54 pathogenic variants in the cancer predisposition and American College of Medical Genetics and Genomics (ACMG) genes were missed by one or both computational runs of joint genotyping. Similarly, 8 (12.1%, 95% CI: 5.4-22.5) of 66 pathogenic variants in these genes were undetected by joint genotyping in another independent cohort of 239 breast cancer patients. An exome-wide analysis of putative loss-of-function (pLOF) variants in the testicular cancer cohort showed that 162 (8.2%, 95% CI: 7.1-9.6) pLOF variants were only detected in one analysis run but not the other, while 433 (22.0%, 95% CI: 20.2-23.9%) pLOF variants were filtered out by both analyses despite having sufficient sequencing coverage.

Conclusion: Our analysis of the standard germline variant detection method highlighted a substantial impact of concurrently analyzing additional genomic data sets on the ability to detect clinically relevant germline pathogenic variants.

Figure 1:. Overview of the study design.

A head-to-head comparison was conducted to evaluate the molecular diagnostic yield of the Genome Analysis Toolkit Joint Genotyping (GATK-JG) based germline variant detection in two independent cohorts of 239 cancer patients in the presence and absence of an additional germline sample set of 100 germline exomes. (BAM: Binary Alignment Map, VQSR: Variant Quality Score Recalibration, ACMG: American College of Medical Genetics and Genomics, OMIM: Online Mendelian Inheritance in Men, pLOF: putative loss-of-function)

Figure 2:. Exome-wide analysis of germline variant discovery in the presence and absence of additional genomics datasets.

A and B; Confusion matrices of the final quality classification status of the germline variants detected in the testicular and breast cancer cohorts, respectively, between the first and second computational runs. C and D; Manhattan plots of the p-values for the germline variants, filtered by GATK-JG in both computational runs, to be absent by chance in a randomly selected 239 individuals from the European ancestry. A total of 184,827 variants had a p-value <1.76e-07 (depicted in 2C by the horizontal dotted red line) in the testicular cancer cohort and 116,078 variants had a p-value <2.04e-07 (depicted in 2D by the horizontal dotted red line) in the breast cancer cohort, suggesting a non-random underdetection effect of the GATK-JG for common variants across coding regions.

Figure 3:. Detection of rare germline pathogenic in cancer patients using GATK-JG.

A; A confusion matrix of the quality class assignment of the pathogenic germline variants detected in 239 testicular cancer patients in the cancer-predisposition and ACMG gene sets (n=151) in the presence and absence of the additional cancer-free cohort. B; A total of 50 (92.6%) pathogenic variants were consistently detected by GATK-JG in the testicular cancer cohort (n=239) while 4 (7.4%) clinically actionable pathogenic variants were detected by GATK-JG in only one or none of the computational runs despite being present in the raw genomic data file (C-F), highlighting a substantial limitation of the current standard germline variant detection method. G-H; Conducting similar analyses on an independent cohort of 239 breast cancer patients showed that of 66 pathogenic variants in the raw variant callset, only 58 (87.9%, 95%CI:77.5–94.6) pathogenic variants were considered “high-quality” by GATK-JG while 8 (12.1%, 95%CI:5.4–22.5) variants went undetected by one or both computational runs. I-L; Representative example of pathogenic cancer-risk variants that went undetected by one or both of GATK-JG runs.

Figure 4:. Detection of rare germline pLOF variants in cancer patients using GATK-GJ.

A; Evaluating rare germline truncating variants in clinically relevant genes (n=5197), detected by GATK-JG in the testicular cancer cohort (n=239) in the presence and absence of the 100 additional germline WES samples, showed a substantial discrepancy of the final germline callsets between the two computational runs. B & C; Two representative examples of pLOF variants that were filtered out by GATK-JG in both analysis runs (due to low GATK-generated Quality Tranches) but existed in the raw genomic data (Binary Alignment Map [BAM] file) of testicular cancer patients. The observed 14bp deletion in MPO (c.1555_1568del) is a known pathogenic variant that has been reported previously by clinical laboratories in several patients with myeloperoxidase deficiency (OMIM: 254600), an autosomal recessive condition associated with a higher risk of disseminated candidiasis. Similarly, LIPT1:c.369del is a known likely pathogenic variant that has been seen in patients with Lipoyltransferase 1 deficiency, another autosomal recessive condition associated with delayed psychomotor development, cerebellar atrophy, bradycardia, and liver dysfunction. D; Performing an exome-wide analysis of germline pLOF variants in an independently sequenced 239 breast cancer patients showed similarly substantial cohort size-driven variability in the ability to detect these potentially relevant germline alterations. E & F; Two representative examples of pLOF variants that were filtered out by GATK-JG in both computational runs but existed in the raw germline genomic data of breast cancer patients.

Figure 5:. Performance of GATK-JG in detecting pathogenic and pLOF variants in 12 clinically oriented phenotype-specific multi-gene panels.

In the testicular cancer cohort (n=239), more pLOF variants were considered “high quality” in the presence of additional samples for GATK-JG (A). However, GATK-JG detected more pLOF variants in the analyzed MGPs in the breast cancer cohort (n=239) when the germline exomes of this cohort were analyzed in the absence of any other genomic dataset (B). Overall, these findings demonstrated significant variability of GATK-JG ability to detect pLOF variants in clinically relevant genes.