Whole genome sequencing in drug discovery research: a one fits all solution?
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1 Whole genome sequencing in drug discovery research: a one fits all solution? Marc Sultan, September 24th, 2015 Biomarker Development, Translational Medicine, Novartis On behalf of the BMD WGS pilot team: Robert Bruccoleri, Stine Buechmann-Moller, Nicole Cheung, Anita Fernandez, Nicole Hartmann, Yunsheng He, Xiaoyu Jiang, Li Lei, Bolan Linghu, Thomas Morgan, Nirmala Nanguneri, Thomas Schlitt, Kevin Sloan, Jill Somers, Marc Sultan, Frank Staedtler, Joseph Szustakowski, Marie Waldvogel, Daniela Wieser, Fan Yang, Xiaojun Zhao
2 Outline o Introduction o WGS Pilot study results o Summary & Challenges 2 MipTec Marc Sultan September 24 th 2015 WGS Pilot Business Use Only
3 Introduction 3 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
4 Why Whole Genome Sequencing? Illustration by Pete Ellis/ 4 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
5 WGS Principle 5 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
6 Finding The Differences 6 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
7 HiSeq X ten 160 Genomes system 3 days On market since January 2014 Designed & marketed for population-scale sequencing projects 7 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only Provided by Illumina
8 Patterned Flowcell Nanowell substrate Billions of ordered wells Defined feature size Optimal cluster spacing Exclusion amplification Delivers single template per well Simultaneous seeding and amplification 8 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
9 WGS, WES, OmniExome chip WES ($1000) Omnichip($500) X X X X X X X X X X X exon1 exon2 exon3 WGS ($ ) 9 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
10 WGS pilot goals: assessing the utility of WGS Can WGS replace other profiling platforms? Can small and large structural variants be accurately called? Can variants be accurately called in HLA and ADME genes? 10 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
11 WGS Pilot Questions to be answered 113 DNA samples from 6 sub-projects 30x genome-wide coverage with ~1 billion short DNA reads per sample NIST GiB Clinical Study Academic collaboration Study Questions N Basic proficiency Comparison to highly curated variant calls Comparison to candidate genotyping Comparison to SNP chips (Omni Bead Chip) Comparison to exome sequencing Can we detect large structural variant in a particular gene? Confirmation of a particular deletion? Clinical Study Comparison to ADME chip data (DMET plus chip) 13 ADME reference Evaluation of challenging ADME genes 8 HLA reference panel Evaluate the quality of HLA calls from X10 data? MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
12 WGS Pilot Workflow Sample logistics WGS Variant calling (12-16 weeks) analysis Candidate genes/ Pathways (2~3 weeks) Rare disease pedigree (1-2 months) Exploratory genome analysis (> 2 Broad Institute 12 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
13 Statistics Key statistics for raw data # samples 113 Raw data size from Broad Paired-end read length Ave. total reads per sample 23 TB % of reads after filtering 60% Ave. coverage per sample Coverage on Agilent exome targeted region 151 base 1.06 Billion 32x 38x 99.5% > 10x 82% > 30x 13 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
14 WGS Pilot Results 14 MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
15 NIST Genome in a Bottle 99.9% concordance with GiB in high confidence regions WGS data quality of Broad is higher than Macrogen 0.1% error rate in variant calls as estimated by Mendelian inheritance errors 15 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
16 Comparison of WGS with OmniExome5 Chip 99.7% concordance for common genotypes ~97% variants called found Omni chip are found in WGS Variant calls from WGS Variant calls by Omni chip ~12,000,000 ~3,000,000 ~96, IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
17 Comparison of WGS with Exome-seq (WES) 99.6% concordance for common genotypes ~94% variants found in WES are found in WGS Variant calls by WGS Variant calls by WES ~13,000,000 ~2,000,000 ~140, IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
18 WGS vers DMET Chip Drug Metabolizing Enzymes and Tansporters 99.5% concordance for ~12,000 (959*13) common genotypes WGS was be in some cases more accurate or specific (probe design) Main Advantage of DMET platform: software for star allele prediction and phenotype prediction (high complexity) Main Advantage WGS: additional sites easily accessible 18 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
19 Example Mismatch DMET and WGS Probe AM_10799 designed to detect A or T allele DMET.genotype A/A No star allele definition for A/G genotype WGS.genotype A/G I386V AM_10799 (CYP1A2): Designed to detect *4 allele: A/T (I386F) 19 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
20 HLA alleles called with WGS data WGS can be used to genotype classical HLA genes with accuracy >95% compared to results generated from conventional methods. Accuracy of typing can be influenced by type of software used. Caveat: sample size in this evaluation is small, considering large number of polymorphisms in HLA gene HLA gene Fraction of correct allele call OpiTYPE Omixon Athlates Accuracy (%) Fraction of correct allele call Accuracy (%) Fraction of correct allele call Accuracy (%) HLA-A 20/20 100% 19/20 95% 20/20 100% HLA-B 20/20 100% 20/20 100% 19/20 95% HLA-C 20/20 100% 18/20 90% 19/20 95% HLA-DQA1 NA NA 20/20 100% NA NA HLA-DQB1 NA NA 20/20 100% 19/20 95% HLA-DPB1 NA NA 20/20 100% NA NA HLA-DRB1 NA NA 20/20 100% 19/20 90% Opitype: open source; Omixon: currently have license; Athlates: evaluation license 20 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
21 Confirmation of known large structural variants ARMS2 3 UTR deletion in clinical study deletion of CYP2D6 gene in 3 ADME reference samples bp deletion in KRT77 gene in collaboration study 240-bp tandem duplication in KIAA1109 gene in collaboration study 21 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
22 Duplication and deletion of CYP2D6 2X 2X 2X >2X >2X 1X 1X 1X CYP2D6 deletion Samples1-3 have 2 copies (depth:86x, 56x, 47x), samples 4,5 have > 2 copies(depth:67x, 82x), samples 6-8 have one copy(depth:32x, 29x, 28x) 22 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
23 Summary & Challenges 23 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
24 Summary of results Can WGS replace other profiling platforms? WES, Omni, targeted genotyping: yes DMET chips: WGS need better software support Can small and large structural variants be accurately called? Small variants: yes Large variants: challenging but promising Can variants be accurately called in HLA and ADME genes? HLA: yes ADME: yes for small variants, challenging for large structural variants 24 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
25 Summary WGS is technically successful High cross-platform concordance of WGS data (>99%) Discover more variants than other technologies (coding/non-coding variants, structural variants) Analysis algorithms are rapidly improving WGS is valuable for generating and testing new hypothese in clinical studies standarized experimental procedure that enables retrospective analyses (dictionary approach) Key to interpret big WGS data is to filter and integrate on diverse sources 25 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
26 WGS opportunities in clinical studies Familial genetic studies High-priority or competitive programs requiring quick interrogation of genetic data in response to new discoveries High-priority studies with a priori genetic hypotheses - candidate genes and pathways Strategic disease indications where heritability is moderate-to-high (Asthma, COPD) as part of Pan-Omic strategy 26 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
27 Interpretation of results too much data? Interpreting numerous mutations in small samples is challenging > 4 million variants per patient? A small subset of candidate disease causal mutations Pinpointing a small subset of disease causal variants is a non-trivial task. Large number of variants per individual make association tests impossible for «typical» sample sizes limited scope for «hypothesis free» approaches Huge data: an efficient strategy is required to store, organize, and query the data. 27 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only MipTec Marc Sultan September, 10th 2015 WGS Pilot Business Use Only
28 Challenges Data volume: large amounts of data are generated (WGS pilot: 23TB raw data plus data generated during analysis) Long term storage costs File transfer times are considerable Analysis not yet standard, best practices are rapidly changing Data generation and analysis takes longer Ethics/legal concerns: incidental findings, consent, cloud based storage? 28 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
29 WGS pilot team Robert Bruccoleri Stine Buechmann-Moller Nicole Cheung Anita Fernandez Nicole Hartmann Yunsheng He Xiaoyu Jiang Li Lei Bolan Linghu Thomas Morgan Nirmala Nanguneri Thomas Schlitt Kevin Sloan Jill Somers Marc Sultan Frank Staedtler Joseph Szustakowski Marie Waldvogel Daniela Wieser Fan Yang Xiaojun Zhao 29 IDD Marc Sultan June 10th 2015 WGS Pilot Business Use Only
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