Human Genotyping Facility (HuGE-F) Rotterdam Study, GenR, BIOBANKING Parelsnoer, BBMRI, many more NEXT GEN SEQUENCING Bench marking with top institutes of the world HIGH THROUGPUT ARRAYS High throughput genotyping techniques GENOTYPING Collaborations in large consortia TRANSCRIPTOMICS Functional studies in mouse models and cell lines Linda Broer EPIGENETICS l.broer@erasmusmc.nl MICROBIOMICS Department of Internal Medicine GWAS, imputation, methylation analysis, Human Genetics Facility (HuGe-F) BIOINFORMATICS exome and transcriptome analysis www.glimdna.org Outline Outline � Lab organization � Lab organization � Sample management � Sample management � Genotyping � Genotyping � Data analysis � Data analysis � Novel developments � Novel developments 1
Laboratory organization Outline � Wet lab: working on biological samples � Lab organization � Pre-PCR area � Sample management � Post-PCR area � Genotyping � Technicians, PhD students, PostDocs � Data analysis � Novel developments � Dry lab: working on data-analysis � (Bio)Informaticians, PhD students, PostDocs Performing a genetic association study v Sample preparation Blood/tissue Success of your study collection depends largely on DNA quality and proper DNA-isolation storage and handling Quality control Sample processing control Storage 2
DNA isolation DNA isolation from blood � Many kits available for DNA isolation � Magnetic particle-based method (Promega, others) Blood/tissue � Easy to automate collection � Choice depends on: � Low hands-on time � Quantity & molecular weight of DNA � Salting-out DNA-isolation � Required purity � No automation � Time & expense � Lot of hands-on time Quality control Sample processing control Storage DNA quality control DNA quality High molecular weight DNA, little � DNA quality measurement smearing DNA with inpurity Blood/tissue � Testing degradation of DNA on Lower molecular collection weight DNA agarose gel with degradation DNA-isolation � Purity (OD 260/280 > 1.7) Quality control � Pico green measurement Sample processing control RNA contamination Storage 3
Sample processing control Sample swap detection � Gender determination to find sample � Gender determination: a way to find swaps of samples during: Blood/tissue swaps � Collection phase collection � DNA isolation � Different blanc positions per plate � Plating out (reformatting) DNA-isolation � GWAS � Swaps can only be detected in male-female studies Quality control � Unsuspected twinning � Call rate Sample processing � Only part of the swaps can be found control � Heterozygosity outliers � Same gender swaps not detected Storage % of sample swaps (determined by gender check) Storage of DNA 10 � Work-solution: 4 o C Blood/tissue 9 collection 8 � Long-term storage: -20 o C 7 DNA-isolation 6 5 Quality control 4 3 Sample processing 2 control 1 Storage 0 study 1 study 2 study 3 study 4 study 5 study 6 study 7 4
Outline � Lab organization � Sample management � Genotyping � Data analysis � Novel developments Sequencing Many techniques Population genetics: technology driven Population genetics: technology driven � � Time required for genotyping 1 SNP in 7.000 DNA samples from “the Rotterdam Study”: Time required for genotyping 1 SNP in 7.000 DNA samples from “the Rotterdam Study”: Association study with 1 DNA variant � � 1996 6 months: RFLP, Epp tubes 1996 6 months � � 1999 3 months: RFLP, 96-well plates 1999 3 months � � 2001 1 week: SBE, 384-well plates 2001 1 week � � 2003 1 day: Taqman (manual) 2003 1 day Association study with all common � � 2004 6 hrs: Taqman (automated) 2004 6 hrs DNA variants in one gene � � 2005 3 hrs: Taqman, Deerac, “Fast” PCR 2005 3 hrs � 2008 6 sec Genome-wide association study � 2008 6 sec: Illumina 1000K array, 1000 DNAs/week 2010 0.00001 sec 2010 0.00001 sec Illumia Hiseq, next-gen sequencing Sequencing: causal alleles? � 2015 0.000001 sec � 2015 0.000001 sec Illumina X10 5
Which genotyping technique to use? Array-technology for genotyping SNPs � Created for genotyping many SNPs (> 0.3 million) � Two major companies: Illumina & Affymetrix (ThermoFisher) � Illumina: tagSNP optimized � Affymetrix: population-specific arrays � Primarily used for Genome-wide testing � GWAS � But also for: pharmacogenetics, clinical research, linkage analysis One SNP May Serve as Proxy for many others What is a Genome-Wide Association Study? � Method for interrogating all common variations across human genome SNP1 SNP2 SNP3 SNP4 SNP5 SNP6 SNP7 SNP8 ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ � Based on classic association study design CAGATCGCTGGATGAATCGCATCTGTAAGCAT � GWAS is based on “Linkage Disequilibrium”: CGGATTGCTGCATGGATCGCATCTGTAAGCAC Variation inherited in groups, or blocks, so not all (millions) of variants have to be tested CAGATCGCTGGATGAATCGCATCTGTAAGCAT CAGATCGCTGGATGAATCCCATCAGTACGCAT CGGATTGCTGCATGGATCCCATCAGTACGCAT CGGATTGCTGCATGGATCCCATCAGTACGCAC 6
One SNP May Serve as Proxy for many others One SNP May Serve as Proxy for many others Block 1 Block 2 Block 1 Block 2 SNP3 SNP4 SNP5 SNP6 SNP3 SNP5 SNP6 SNP1 SNP2 SNP7 SNP8 SNP7 SNP8 ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ CAGATCGCTGGATGAATCGCATCTGTAAGCAT CAGATCGCTGGATGAATCGCATCTGTAAGCAT CGGATTGCTGCATGGATCGCATCTGTAAGCAC CGGATTGCTGCATGGATCGCATCTGTAAGCAC CAGATCGCTGGATGAATCGCATCTGTAAGCAT CAGATCGCTGGATGAATCGCATCTGTAAGCAT CAGATCGCTGGATGAATCCCATCAGTACGCAT CAGATCGCTGGATGAATCCCATCAGTACGCAT CGGATTGCTGCATGGATCCCATCAGTACGCAT CGGATTGCTGCATGGATCCCATCAGTACGCAT CGGATTGCTGCATGGATCCCATCAGTACGCAC CGGATTGCTGCATGGATCCCATCAGTACGCAC One SNP May Serve as Proxy for many others Imputations Block 1 Block 2 SNP3 SNP5 SNP8 ↓ ↓ ↓ CAGATCGCTGGATGAATCGCATCTGTAAGCAT CGGATTGCTGCATGGATCGCATCTGTAAGCAC CAGATCGCTGGATGAATCGCATCTGTAAGCAT CAGATCGCTGGATGAATCCCATCAGTACGCAT CGGATTGCTGCATGGATCCCATCAGTACGCAT CGGATTGCTGCATGGATCCCATCAGTACGCAC 7
Imputation quality for different arrays Bead design Address Probe 23 b 50 b � Each silica bead is 3 µm in diameter � 23 bp address: unique sequence for each bead-type � Address is used to identify the beads on the array � 50 bp allele-specific probe Procedure (day 1) Procedure (day 2) � DNA normalization and whole genome amplification � Hybridization on array, single base extension � SBE: 1 base added to the probe SNP Fragmented gDNA Whole genome amplification 200 ng DNA bead Address Probe DNA pellet after bead T-DNP amplification Address Probe Labelled ddNTP 8
Procedure (day 3) Procedure (day 3) DNA collection on array Every dot represents a SNP Colors: Red & green: homozygous Yellow: heterozygous Running genotyping arrays: problems encountered Outline Bad quality: degradation, Reagens failure Corrupted, missing contaminated � Lab organization � Sample management DNA-Amplification DMAP file DNA � Genotyping Hybridization on array Arrays are � Data analysis ARRAY scanned � Novel developments Signal visualization Bad quality arrays Robot problems Scanner failure 9
Analysis of array data GenomeStudio Genotype � Generate intensity data for 2 alleles clusters � Assign genotypes based on clustering Information on samples � (Almost) no manual review of data � too many SNPs Genotype � Low MAF SNPs are most difficult to call per sample � Different pipeline depending on manufacturer of array A SNP cluster plot Same SNP different view 10
Quality of genotypes GenTrain Score: overall quality score Quality of genotypes AB T mean: Location of heterozygote clusters 11
Extreme location of heterozygote cluster How are genotype clusters determined? � Manifest file � probe information � Cluster file � location of AA, AB and BB clusters � Commercial arrays � provided by Illumina � All cleaning of clusters performed for you � Custom arrays � need to create yourself � A lot of manual checking of clusters based on QC values Quality of samples Export options from GenomeStudio � Very flexible! Percentage of genotyped � Every table can be subset to desired columns and exported variants � Final report files can contain any number of columns � Plink plug-in available � Major QC and analysis software 10 th percentile of distribution of GenCall scores 12
Axiom Analysis Suite Axiom Analysis Suite � Parallelization possible! � If your computer has enough capacity � Not possible to change QC settings after run has started � You need to restart the run if you want to change anything Summary results Some interesting statistics 13
Sample Table Export options from Axiom Analysis Suite � Exports are less flexible compared to Illumina � Plink export available � VCF file export available � One nice feature � genotype call to functional allele transformation � Pharmacogenetics Pharmacogenetic calls Determine how well drugs are metabolized 14
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