Design and implementation of a custom next generation sequencing panel for selected vitamin D associated genes.

BACKGROUND: Biologically active vitamin D has an important regulatory role within the genome. It binds the vitamin D receptor (VDR) in order to control the expression of a wide range of genes as well as interacting with the epigenome to modify chromatin and methylation status. Vitamin D deficiency is associated with several human diseases including end-stage renal disease. METHODS: This article describes the design and testing of a custom, targeted next generation sequencing (NGS) panel for selected vitamin D associated genes. Sequencing runs were used to determine the effectiveness of the panel for variant calling, to compare efficiency and data across different sequencers, and to perform representative, proof of principle association analyses. These analyses were underpowered for significance testing. Amplicons were designed in two pools (163 and 166 fragments respectively) and used to sequence two cohorts of renal transplant recipients on the Ion Personal Genome Machine (PGM)™ and Ion S5™ XL desktop sequencers. RESULTS: Coverage was provided for 43.8 kilobases across seven vitamin D associated genes (CYP24A1, CUBN, VDR, GC, NADSYN1, CYP27B1, CYP2R1) as well as 38 prioritised SNPs. Sequencing runs provided sufficient sequencing quality, data output and validated the effective library preparation and panel design. CONCLUSIONS: This novel, custom-designed, validated panel provides a fast, cost effective, and specific approach for the analysis of vitamin D associated genes in a wide range of patient cohorts. This article does not report results from a controlled health-care intervention.

A HuGE Review and Meta-Analyses of Genetic Associations in New Onset Diabetes after Kidney Transplantation.

PURPOSE: New onset diabetes after transplantation (NODAT) is a serious complication following solid organ transplantation. There is a genetic contribution to NODAT and we have conducted comprehensive meta-analysis of available genetic data in kidney transplant populations. METHODS: Relevant articles investigating the association between genetic markers and NODAT were identified by searching PubMed, Web of Science and Google Scholar. SNPs described in a minimum of three studies were included for analysis using a random effects model. The association between identified variants and NODAT was calculated at the per-study level to generate overall significance values and effect sizes. RESULTS: Searching the literature returned 4,147 citations. Within the 36 eligible articles identified, 18 genetic variants from 12 genes were considered for analysis. Of these, three were significantly associated with NODAT by meta-analysis at the 5% level of significance; CDKAL1 rs10946398 p = 0.006 OR = 1.43, 95% CI = 1.11-1.85 (n = 696 individuals), KCNQ1 rs2237892 p = 0.007 OR = 1.43, 95% CI = 1.10-1.86 (n = 1,270 individuals), and TCF7L2 rs7903146 p = 0.01 OR = 1.41, 95% CI = 1.07-1.85 (n = 2,967 individuals). CONCLUSION: Evaluating cumulative evidence for SNPs associated with NODAT in kidney transplant recipients has revealed three SNPs associated with NODAT. An adequately powered, dense genome-wide association study will provide more information using a carefully defined NODAT phenotype.