A New Panel-Based Next-Generation Sequencing Method for ADME Genes Reveals Novel Associations of Common and Rare Variants With Expression in a Human Liver Cohort - Klein, Kathrin; Tremmel, Roman; Winter, Stefan; Fehr, Sarah; Battke, Florian; Scheurenbrand, Tim; Schaeffeler, Elke; Biskup, Saskia; Schwab, Matthias; Zanger, Ulrich M. (Frontiers in Genetics, 2019)
We developed a panel-based NGS pipeline for comprehensive analysis of 340 genes involved in absorption, distribution, metabolism and excretion (ADME) of drugs, other xenobiotics, and endogenous substances. The 340 genes comprised phase I and II enzymes, drug transporters and regulator/modifier genes within their entire coding regions, adjacent intron regions and 5’ and 3'UTR regions, resulting in a total panel size of 1,382 kbp. We applied the ADME NGS panel to sequence genomic DNA from 150 Caucasian liver donors with comprehensive phenotype data. This revealed an average read-depth of 343 (range 27 - 811), while 99% of the 340 genes were covered on average at least 100–fold. Direct comparison of variant calling with 363 available genotypes determined independently by other methods revealed an overall accuracy of > 99%. Of 15,727 SNV and small INDEL variants, 12,022 had a MAF below 2%, including 8,921 singletons. In total we found 7,167 novel variants. Functional predictions were computed for coding variants (n=4,017) by three algorithms (Polyphen 2, Provean, and SIFT), resulting in 1,466 variants (36.5%) concordantly predicted to be damaging, while 1,019 variants (25.4%) were predicted to be tolerable. In agreement with other studies we found that less common variants were enriched for deleterious variants. Cis-eQTL analysis of common variants (MAF ≥ 2%) revealed significant associations for 90 variants in 31 genes after Bonferroni correction, most of which were located in non-coding regions. For less common variants (MAF < 2%), we applied the SKAT-O test and identified significant associations to gene expression for ADH1C and GSTO1. Moreover, our data allow comparison of functional predictions with actual phenotypic data to prioritize variants for further analysis.