Best practices for the analytical validation of clinical whole-genome sequencing intended for the diagnosis of germline disease

Abstract

Whole-genome sequencing (WGS) has shown promise in becoming a first-tier diagnostic test for patients with rare genetic disorders; however, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading healthcare and research organizations in the US and Canada, was formed to expand access to high-quality clinical WGS by publishing best practices. Here, we present consensus recommendations on clinical WGS analytical validation for the diagnosis of individuals with suspected germline disease with a focus on test development, upfront considerations for test design, test validation practices, and metrics to monitor test performance. This work also provides insight into the current state of WGS testing at each member institution, including the utilization of reference and other standards across sites. Importantly, members of this initiative strongly believe that clinical WGS is an appropriate first-tier test for patients with rare genetic disorders, and at minimum is ready to replace chromosomal microarray analysis and whole-exome sequencing. The recommendations presented here should reduce the burden on laboratories introducing WGS into clinical practice, and support safe and effective WGS testing for diagnosis of germline disease.

Keywords: Genetic testing; Laboratory techniques and procedures; Next-generation sequencing.

Fig. 1. Clinical whole-genome sequencing workflow.

The workflow for clinical WGS involves three major analysis steps spanning wet laboratory and informatics processes: primary (blue) analysis refers to the technical production of DNA sequence data from biological samples through the process of converting raw sequencing instrument signals into nucleotides and sequence reads; secondary (green) analysis refers to the identification of DNA variants through read alignment and variant calling; and tertiary (yellow) analysis refers to variant annotation, filtering and prioritization, classification, interpretation, and reporting. Health record information and phenotype can be mined and converted to Human Phenotype Ontology (HPO) terms to aid variant interpretation. Primary analysis involves the sample, and library preparation and sequencing with base calling followed by extensive quality control (QC). During this stage, genotyping with an orthogonal method (SNP-array or targeted assay) is performed for QC purposes. Secondary analysis involves mapping, read alignment, and variant calling. Different classes of variation (SNVs, SV, CNVs, mitochondrial, and repeat expansions) will use different algorithms that can be run in parallel. Aside from QC of alignment and variant calling, the orthogonal genotyping can be used to ensure no sample mix-up has occurred throughout the workflow. Tertiary analysis begins with the annotation of variants followed by filtering, prioritization, and variant classification depending on the phenotype and clinical indication for testing. Classification of variants according to ACMG guidelines may be automated, but the final interpretation involves human intervention and will ultimately be driven by the case phenotype. Variants are reported based on relevance to the primary indication for testing and secondary, or incidental findings not associated with the reason for testing following any necessary confirmation method. Confirmation may be performed with an orthogonal wet laboratory method or in silico examination of the data based on how the test was validated. Clinical correlation (pink) is performed by the ordering physician, which may involve iterative feedback and collaboration with the laboratory (dotted arrows). Throughout the process, collection of aggregate data will be necessary to generate internal allele frequencies and for sharing of interpreted data with repositories.

Fig. 2. Key steps in the analytical validation of a clinical WGS test.

Key steps in the analytical validation of clinical WGS include test development optimization, test validation, and quality management. Each step involves activities that lead to defined outcomes.