The field of genomics and precision medicine is rapidly evolving, bringing forth innovative diagnostic and treatment approaches. The cutting-edge techniques and technologies discussed in the following papers provide insight into how whole-exome sequencing (WES), whole-genome sequencing (WGS), and deep computational analysis are reshaping healthcare. Our spotlight is on recent customer publications featuring the study of a unique heritable form of rickets in a domestic longhair cat, the comparison between prenatal exome versus genome sequencing for diagnosis, and the use of pharmacovariome scanning coupled with machine learning for clinical pharmacogenomics.
Feline precision medicine using whole-exome sequencing identifies a novel frameshift mutation for vitamin D-dependent rickets type 2
A 14-week-old female domestic longhair kitten presented with shifting lameness and disproportionately smaller size compared with a co-housed littermate.
Hematology and serum biochemical testing were conducted to investigate causes for delayed growth, and radiographs of the appendicular skeleton were obtained.
The afflicted kitten had marked hypocalcemia, mild hypophosphatemia and substantial elevations in alkaline phosphatase activity, as well as pathognomonic radiographic findings consistent with rickets. Skeletal changes and hypocalcemia prompted testing of concentrations of parathyroid hormone (PTH) and vitamin D metabolites. Endocrine testing demonstrated significant increases in serum concentrations of PTH and 1,25-dihydroxycholecalciferol (calcitriol), supporting a diagnosis of vitamin D-dependent rickets type 2. Provision of analgesia, supraphysiologic doses of calcitriol and calcium carbonate supplementation achieved normalization of the serum calcium concentration and restoration of normal growth, although some skeletal abnormalities persisted. Once skeletally mature, ongoing calcitriol supplementation was not required. Whole-exome sequencing (WES) was conducted to identify the underlying DNA variant. A cytosine deletion at cat chromosome position B4:76777621 in VDR (ENSFCAT00000029466:c.106delC) was identified and predicted to cause a stop codon in exon 2 (p.Arg36Glufs*18), disrupting >90% of the receptor. The variant was unique and homozygous in this patient and absent in the sibling and approximately 400 other cats for which whole-genome and whole-exome data were available.
Conclusions and relevance
A unique, heritable form of rickets was diagnosed in a domestic longhair cat. WES identified a novel frameshift mutation affecting the gene coding for the vitamin D3 receptor, determining the likely causal genetic variant. Precision medicine techniques, including whole-exome and whole-genome sequencing, can be a standard of care in cats to identify disease etiologies, and to target therapeutics and personalize treatment.
Habacher G, Malik R, Lait PJ, et al. Feline precision medicine using whole-exome sequencing identifies a novel frameshift mutation for vitamin D-dependent rickets type 2. Journal of Feline Medicine and Surgery. 2023;25(6). doi:10.1177/1098612X231165630
Comprehensive prenatal diagnostics: Exome versus genome sequencing
This study aimed to assess the diagnostic yield of prenatal genetic testing using trio whole exome sequencing (WES) and trio whole genome sequencing (WGS) in pregnancies with fetal anomalies by comparing the results with conventional chromosomal microarray (CMA) analysis.
A total of 40 pregnancies with fetal anomalies or increased nuchal translucency (NT ≥ 5 mm) were included between the 12th and 21st week of gestation. Trio WES/WGS and CMA were performed in all cases.
The trio WES/WGS analysis increased the diagnostic yield by 25% in cases with negative CMA results. Furthermore, all six chromosomal aberrations identified by CMA were independently detected by WES/WGS analysis. In total, 16 out of 40 cases obtained a genetic sequence variant, copy number variant, or aneuploidy explaining the phenotype, resulting in an overall WES/WGS diagnostic yield of 40%. WES analysis provided a more reliable identification of mosaic sequence variants than WGS because of its higher sequencing depth.
Prenatal WES/WGS proved to be powerful diagnostic tools for fetal anomalies, surpassing the diagnostic yield of CMA. They have the potential to serve as standalone methods for prenatal diagnosis. The study highlighted the limitations of WGS in accurately detecting mosaic variants, which is particularly relevant when analyzing chorionic villus samples.
Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics
This pilot study aims to identify and functionally assess pharmacovariants in whole exome sequencing data. While detection of known variants has benefited from pharmacogenomic-dedicated bioinformatics tools before, in this paper we have tested novel deep computational analysis in addition to artificial intelligence as possible approaches for functional analysis of unknown markers within less studied drug-related genes.
Pharmacovariants from 1800 drug-related genes from 100 WES data files underwent (a) deep computational analysis by eight bioinformatic algorithms (overall containing 23 tools) and (b) random forest (RF) classifier as the machine learning (ML) approach separately. ML model efficiency was calculated by internal and external cross-validation during recursive feature elimination. Protein modelling was also performed for predicted highly damaging variants with lower frequencies. Genotype–phenotype correlations were implemented for top selected variants in terms of highest possibility of being damaging.
Five deleterious pharmacovariants in the RYR1, POLG, ANXA11, CCNH, and CDH23 genes identified in step (a) and subsequent analysis displayed high impact on drug-related phenotypes. Also, the utilization of recursive feature elimination achieved a subset of 175 malfunction pharmacovariants in 135 drug-related genes that were used by the RF model with fivefold internal cross-validation, resulting in an area under the curve of 0.9736842 with an average accuracy of 0.9818 (95% CI: 0.89, 0.99) on predicting whether a carrying individuals will develop adverse drug reactions or not. However, the external cross-validation of the same model indicated a possible false positive result when dealing with a low number of observations, as only 60 important variants in 49 genes were displayed, giving an AUC of 0.5384848 with an average accuracy of 0.9512 (95% CI: 0.83, 0.99).
While there are some technologies for functionally assess not-interpreted pharmacovariants, there is still an essential need for the development of tools, methods, and algorithms which are able to provide a functional prediction for every single pharmacovariant in both large-scale datasets and small cohorts. Our approaches may bring new insights for choosing the right computational assessment algorithms out of high throughput DNA sequencing data from small cohorts to be used for personalized drug therapy implementation.
Tafazoli, A., Mikros, J., Khaghani, F. et al. Pharmacovariome scanning using whole pharmacogene resequencing coupled with deep computational analysis and machine learning for clinical pharmacogenomics. Hum Genomics 17, 62 (2023). https://doi.org/10.1186/s40246-023-00508-1
The highlighted publications underscore the profound impact of genomics technologies, such as VarSeq software from Golden Helix, in medical practice. With its robust, intuitive, and versatile capabilities, VarSeq has proven to be instrumental in the research featured, enabling both whole-exome and whole-genome sequencing as well as complex computational analyses. As Golden Helix continues to innovate and refine its solutions, such as VarSeq, it contributes significantly to advancements in diagnostics, treatment personalization, and disease prevention. The future of genomics and precision medicine promises to be more vibrant and efficient with powerful tools like VarSeq shaping the path forward.