Genomic research continues to uncover new insights into disease risk, treatment response, and the complex genetic factors underlying human health. This August, we’re excited to highlight three recent publications from our customers that showcase the breadth of questions genomic analysis can help address – from understanding the relationship between type 2 diabetes and vitamin D levels, to improving the analytical standardization of liquid biopsy testing for breast cancer, to uncovering a rare combination of genetic variants in a child with multiple cancers.
Together, these studies showcase how rigorous genomic analysis can support both discovery-driven research and the development of reliable molecular workflows with potential clinical impact.
Type 2 Diabetes Causally Reduces Circulating Vitamin D Levels: A Multi-Ancestry Mendelian Randomization Study
Background: Vitamin D deficiency affects an estimated 30–50% of the global population and may be influenced by limited sun exposure, diet, genetics, and ancestry. Although low vitamin D levels have been associated with cardiometabolic diseases such as type 2 diabetes, research has not established a clear causal relationship, and clinical trials have found that supplementation does not prevent type 2 diabetes. Genetic studies and Mendelian randomization analyses are being used to further investigate whether vitamin D levels directly influence cardiometabolic disease risk.
Objective: The study investigated whether genetically predicted vitamin D levels are associated with endocrine and cardiometabolic health risks across European, South Asian, and African populations using genome-wide genetic data and polygenic scores.
Subjects and Methods: The study analyzed genetic, vitamin D, and health data from over 475,000 individuals across European, South Asian, and African populations. Researchers used quality-controlled genetic data to create ancestry-specific polygenic scores for vitamin D and type 2 diabetes, then used statistical and Mendelian randomization analyses to investigate whether genetically predicted vitamin D levels have causal relationships with type 2 diabetes, coronary artery disease, stroke, and other cardiometabolic risk factors.
Results: The results showed that genetically higher vitamin D levels were strongly associated with higher measured 25(OH)D levels, but generally did not significantly reduce the risk of type 2 diabetes or coronary artery disease. In contrast, a higher genetic risk for type 2 diabetes was consistently associated with lower vitamin D levels across European, South Asian, and African populations, and Mendelian randomization supported a potential causal effect of increased T2D risk leading to lower vitamin D levels.
Conclusions: The study found that genetically higher vitamin D levels or supplementation did not protect against type 2 diabetes or cardiovascular disease, while individuals with a higher genetic risk for T2D were more likely to have vitamin D deficiency, suggesting that improving vitamin D status may still play a role in overall metabolic and cardiovascular health.
How SVS Was Used: “We also used cumulative genetic instrumental variable methods (PGS) to obtain estimates of the causal association between circulating vitamin D levels and T2D and determined the direction of causality by performing a bidirectional MR study [31,40]. The associations between the exposure (T2D) and the outcome (25(OH)D) levels, and vice versa, are estimated from different cohorts, mainly UKBB (EU, SA, and AF) and AIDHS/SDS. The combined estimates were calculated using the conventional MR method [58,61]. In sensitivity analyses, we used the two-stage least squares (2SLSs) method to validate the causal effect and the strength of the association since the allelic score methods were used for the MR [61,62]. In stage 1, the exposure of interest is regressed on the polygenic score (controlling for covariates of age, gender, BMI, and ancestry) to obtain predicted values of the exposure. Stage 2 estimates the causal effect by regressing the predicted values of the exposure obtained from the first stage [63] and F values > 10 were considered to confirm the causal effect. All analyses were performed using PLINK 2.0 [64], SVS version 8.9.1 (Golden Helix, Bozeman, MT, USA), and SPSS version 31 (IBM, New York City, NY, USA), and R (version 4.3.3).”
Citation: Rout, M., Blackett, P., & Sanghera, D. K. (2026). Type 2 Diabetes Causally Reduces Circulating Vitamin D Levels: A Multi-Ancestry Mendelian Randomization Study. Nutrients, 18(12), 1944. https://doi.org/10.3390/nu18121944
Standardized Analytical Verification of ctDNA ESR1 Mutation Testing in Metastatic HR+/HER2- Breast Cancer: A European Multicentre Study Using dPCR and NGS-Based Liquid Biopsy
Background: Activating ESR1 mutations are a common driver of endocrine resistance in HR+/HER2- metastatic breast cancer, making sensitive and standardized liquid biopsy testing with dPCR or NGS essential for identifying mutations and guiding treatment.
Objective: This European multicenter study evaluated dPCR- and NGS-based liquid biopsy workflows across six academic laboratories to establish analytical performance and support reliable, standardized ESR1 testing in routine clinical practice.
Subjects and Methods: The study involved six European academic laboratories that independently verified dPCR and NGS-based liquid biopsy workflows for ESR1 mutation detection, using standardized reference materials and assessing key analytical parameters such as sensitivity, specificity, limit of detection, and limit of blank.
Results: Across the six laboratories, both dPCR and NGS demonstrated high sensitivity and specificity for ESR1 mutation detection, with most assays reliably detecting mutations at approximately 0.05–0.1% VAF and showing minimal background signal in negative controls.
Conclusions: The study demonstrates that reliable ESR1 mutation testing can be implemented across European laboratories using either dPCR or NGS, provided assays undergo local verification and key factors such as DNA input, sensitivity, background signal, and preanalytical handling are carefully controlled.
How GenomeBrowse Was Used: “Sequencing data were analyzed using platform-specific bioinformatics pipelines. Fundación Jiménez Díaz University Hospital and Veneto Institute of Oncology/University of Padova used the Plasma-SeqSensei IVD Software (v1.3.1) for alignment, variant calling, and reporting. Università degli Studi di Napoli Federico II used the Ion Torrent Genexus Software (v6.8.4.0), complemented by visual inspection of BAM files using GenomeBrowse (Golden Helix). Istituto Europeo di Oncologia used the AVENIO Oncology Analysis Software.”
Citation: Rojo, F., Guarneri, V., Fusco, N. et al. Standardized Analytical Verification of ctDNA ESR1 Mutation Testing in Metastatic HR+/HER2− Breast Cancer: A European Multicentre Study Using dPCR and NGS-Based Liquid Biopsy. Mol Diagn Ther (2026). https://doi.org/10.1007/s40291-026-00850-9
Concurrent Germline RB1 & Mosaic TP53 in a Child with Multiple Childhood Cancers
Background: Pathogenic variants in RB1 and TP53 are associated with inherited cancer predisposition syndromes that significantly increase the risk of childhood cancers, particularly retinoblastoma and osteosarcoma. While these genetic conditions are well established individually, the combination of pathogenic variants in both RB1 and TP53 is extremely rare, with no previously reported cases, and may potentially lead to a more aggressive cancer course.
Objective: The objective of this study was to investigate a patient who developed three distinct childhood cancers (retinoblastoma, osteosarcoma, and myelodysplastic syndrome) to better understand the underlying genetic factors contributing to this rare combination of malignancies.
Subjects and Methods: The study used whole-genome and RNA sequencing of tumor tissue, along with genomic analyses, to identify somatic mutations, structural changes, copy-number alterations, loss of heterozygosity, and gene fusions.
Results: The patient was diagnosed with bilateral retinoblastoma as an infant, followed by osteosarcoma at age 3 and MDS/AML at age 5, ultimately dying at age 7. Genetic testing identified a germline RB1 pathogenic variant and a low-level somatic TP53 mosaic variant, with both variants found in the osteosarcoma and evidence that they contributed to the development of her multiple cancers.
Conclusions: This case demonstrates an exceptionally rare combination of germline RB1 and somatic TP53 mosaicism, which likely contributed to the patient developing multiple childhood cancers at unusually young ages. The findings highlight the importance of screening for mosaic genetic variants when clinical features strongly suggest a cancer predisposition syndrome, even when standard germline testing is negative, and suggest that genetic predisposition may also increase vulnerability to treatment-related cancers.
How VarSeq Was Used: “Tumor WGS was performed at a sequencing depth of > 50×. Sequenced reads were mapped to the human reference genome (hg38/GRCh38) and somatic variant calling performed using GATK (best practice guidelines) and Strelka2. Genomic variants were inspected using VarSeq 2.5.0 (Golden Helix) and IGV (Integrative Genomics Viewer). Variant analysis included single nucleotide variants (SNVs) and small insertions/deletions (indels) filtered for a VAF ≥ 10%. Somatic SVs, including copy number variants (CNVs) and LOH, were identified from WGS data using Manta, Smoove, and Tiddit. In addition, SVs were also assessed from the CytoScan HD array (ThermoFisher). RNA extracted from tissue was also analyzed by total RNA sequencing (Illumnina) to identify gene fusions using a combination of three fusion callers, FusionMap (Ge et al. 2011), Star-Fusion (Haas et al. 2019), and Arriba (Uhrig et al. 2021).”
Citation: Nielsen, O. H., U. K. Stoltze, P. A. Gregersen, et al. 2026. “ Concurrent Germline RB1 & Mosaic TP53 in a Child With Multiple Childhood Cancers.” American Journal of Medical Genetics Part A 1–5. https://doi.org/10.1002/ajmg.a.70256.
