Pharmacogenomics analysis using whole exome sequencing v1

Abstract

For whole-exome sequencing, genomic DNA was extracted from peripheral blood cells and amplified with a 175-250 bp DNA fragment. The protein coding region of human genomic DNA was collected using an ion-exchange enzyme Exome Panel (Thermo Fisher Scientific, Waltham, Mass.). To load the DNA sample into the semiconductor chip, the library configuration was done by using the Ion Ampliseq Exome Library Kit Plus containing 57,742,646 bp (1.85% of the human genomic region) as described in the manufacturer's instructions (Thermo Fisher Scientific, Waltham, MA). The library was diluted to ~ 10 pM. We then combined the barcode library into sets of two barcodes. The exon-riched DNA libraries were sequenced using the ion Proton platform according to the manufacturer's instructions. (Thermo Fisher Scientific, Waltham, Mass.). The average depth of exome sequencing ranging from 80 to 120X was obtained and considered to be of sufficient depth to examine the exon for mutations. Raw reads were initially mapped to the human reference genome build (GRCh37) using the Torrent Mapping Alignment Program (TMAP). Genome Analysis Toolkit 2.8-1 and HaplotypeCaller [1] were used to call single base mutation types (SNV) and short insert / delete (INDEL), respectively. All variants were annotated by the Sorting Intolerant from Tolerant (SIFT) [2] algorithm using the SIFT database distributed by the J. Craig Venter Institute (http://sift.jcvi.org/). The SIFT human database that supports GRCh37 Ensembl release 63 (the latest version) was downloaded. After SIFT score annotation, we calculated gene deleteriousness score for each genes. The gene deleteriousness score (G) of a gene was defined as the geometric mean of the SIFT scores for all SIFT score annotated nonsynonymous coding variants of the gene to evaluate the overall impact of multiple deleterious variants on the gene [3]. Drug deleteriousness score (D) was calculated by the geometric mean of the G scores for all drug related pharmacokinetics/pharmacodynamics genes based on the knowlegde of DrugBank database (accessed September 4, 2015 at http://www.drugbank.ca/). References [1] McKenna AH, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research. 2010 Jul 19;gr.107524.110. [2] Kumar P, Henikoff S, Ng PC. Predicting the effects of coding nonsynonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4:1073–81. [3] Lee KH, Baik SY, Lee SY, Park CH, Park PJ, Kim JH. Genome Sequence sequence Variability variability Predicts predicts Drug Precautions and Withdrawals from the Market. PLOoS ONE. 2016 30;11(9):e0162135.