Analyzing and Reanalyzing the Genome: Findings from the MedSeq Project

Abstract

Although genome sequencing is increasingly available in clinical and research settings, many questions remain about the interpretation of sequencing data. In the MedSeq Project, we explored how much effort is required to evaluate and report on more than 4,500 genes reportedly associated with monogenic conditions, as well as pharmacogenomic (PGx) markers, blood antigen serotyping, and polygenic risk scores in 100 individuals (50 with cardiomyopathy and 50 healthy) randomized to the sequencing arm. We defined the quality thresholds for determining the need for Sanger confirmation. Finally, we examined the effort needed and new findings revealed by reanalyzing each genome (6-23 months after initial analysis; mean 13 months). Monogenic disease risk and carrier status were reported in 21% and 94% of participants, respectively. Only two participants had no monogenic disease risk or carrier status identified. For the PGx results (18 genotypes in six genes for five drugs), the identified diplotypes prompted recommendation for non-standard dosing of at least one of the analyzed drugs in 95% of participants. For blood antigen studies, we found that 31% of participants had a rare blood antigen genotype. In the cardiomyopathy cohort, an explanation for disease was identified in 48% of individuals. Over the course of the study, 14 variants were reclassified and, upon reanalysis, 18 new variants met criteria for reporting. These findings highlight the quantity of medically relevant findings from a broad analysis of genomic sequencing data as well as the need for periodic reinterpretation and reanalysis of data for both diagnostic indications and secondary findings.

Leukodystrophies and Genetic Leukoencephalopathies in Children Specified by Exome Sequencing in an Expanded Gene Panel

“…Studies have also proven that periodic reanalysis of the data in individuals not diagnosed on initial testing could improve the diagnostic yield. 31,32 However, in the study by Lynch et al, 8 resequencing the negative patients with whole exome did not improve the diagnostic yield. It is also emphasized that a negative result should always prompt the physician to pursue alternate diagnostic approaches if the phenotype is convincing.…”

Frequency of ClinVar Pathogenic Variants in Chronic Kidney Disease Patients Surveyed for Return of Research Results at a Cleveland Public Hospital

“…While the patients consented to participate in this pilot study knowing that results would not be returned to them or their physicians, these data inform the complexity of returning genetic results generated in a research setting to diverse participants. Consistent with the MedSeq Project [17], which performed whole genome sequencing on cardiomyopathy patients and healthy participants, approximately 20% of the present study population would be receiving results for one to two pathogenic ClinVar two-star variants. All 130 participants would receive research results if ClinVar-designated pathogenic or likely pathogenic variants of all levels of evidence are to be returned.…”

“…Also, variant classification and interpretation can change over time as more data are collected. ClinVar is not a static database [37], and information relayed in a static report issued to a participant could quickly become outdated [17]. Here, we observed that 45% of one-star pathogenic variants from an earlier version of ClinVar were upgraded to two-star pathogenic or likely pathogenic in a 2019 version of ClinVar.…”

“…Returning clinically-generated results either to patients or physicians for even a handful of genetic variants requires substantial resources and infrastructure not yet widely available [17]. Reports of “medically actionable” variants observed in early whole-exome sequencing studies of reference sample sets or disease consortia have suggested that, depending on the kind of variant to be reported (e.g., pathogenic variant, pharmacogenomic variant), most if not all participants would receive results [18–20].…”

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“…This contrasts with the 1-4 hours we typically require for an ES/GS research analysis to identify candidate variants and manually inspect the transcriptome for abnormalities. In one recent report, the time required was up to 6-8 hours for genome analysis (14). The following are several case examples of this approach in making diagnoses that also show the limitations of commonly used diagnostic tests.…”