Computational and Experimental Analysis of Genetic Variants.

Genomics has grown exponentially over the last decade. Common variants are associated with physiological changes through statistical strategies such as Genome-Wide Association Studies (GWAS) and quantitative trail loci (QTL). Rare variants are associated with diseases through extensive filtering tools, including population genomics and trio-based sequencing (parents and probands). However, the genomic associations require follow-up analyses to narrow causal variants, identify genes that are influenced, and to determine the physiological changes. Large quantities of data exist that can be used to connect variants to gene changes, cell types, protein pathways, clinical phenotypes, and animal models that establish physiological genomics. This data combined with bioinformatics including evolutionary analysis, structural insights, and gene regulation can yield testable hypotheses for mechanisms of genomic variants. Molecular biology, biochemistry, cell culture, CRISPR editing, and animal models can test the hypotheses to give molecular variant mechanisms. Variant characterizations can be a significant component of educating future professionals at the undergraduate, graduate, or medical training programs through teaching the basic concepts and terminology of genetics while learning independent research hypothesis design. This article goes through the computational and experimental analysis strategies of variant characterization and provides examples of these tools applied in publications. © 2022 American Physiological Society. Compr Physiol 12:3303-3336, 2022.

Rapid whole-genome sequencing identifies a homozygous novel variant, His540Arg, in HSD17B4 resulting in D-bifunctional protein deficiency disorder diagnosis.

Rapid whole-genome sequencing (rWGS) allows for a diagnosis to be made quickly and impact medical management, particularly in critically ill children. Variants identified by this approach are often not identified using other testing methodologies, such as carrier screening or gene sequencing panels, targeted panels, or chromosomal microarrays. However, rWGS can identify variants of uncertain significance (VUSs), which challenges clinicians in the rapid return of information to families. Here we present a case of the metabolic condition D-bifunctional protein deficiency in a neonate with epilepsy and hypotonia born to consanguineous parents. Sequencing revealed a homozygous VUS in HSD17B4, c.1619A > G (p.His540Arg). Preliminary results were delivered within 3 d of sample receipt. Previous parental carrier screening included the HSD17B4 gene but was reported as negative. The molecular finding directed the clinical team to assess phenotypic overlap and investigate next steps in terms of confirmation of the findings and potential medical management of the patient. Clinical metabolic testing of fatty acids confirmed the diagnosis. Computational analysis of HSD17B4 His540Arg showed the change to likely impact dimerization based on structural insights, with the histidine conserved and selected throughout all 223 species assessed for this amino acid. This variant clusters around several pathogenic and likely pathogenic variants in HSD17B4 This case demonstrates the utility of rWGS, the potential for receiving uncertain results, and the downstream implications for confirmation or rejection of a molecular diagnosis by the clinical team.