MOLMED SNP COURSE 2018 General Introduction André G Uitterlinden Genetic Laboratory Department of Internal Medicine Department of Epidemiology & Biostatistics Department of Clinical Chemistry our website…!! www.glimdna.org
ROTTERDAM – OLDEBARNEVELDSTRAAT - MULTATULI Viewed from the moon we are all equal Portret gemaakt door Mathieu Ficheroux, 1974
We differ from each other… DNA variation causes differences in: Development Appearance Behaviour Ageing Diseases
Why are DNA polymorphisms important ? Disease DTC fun Evolution Forensics This is what happens when there are NO POLYMORPHISMS Slide stolen from Prof Axel Themmen
“The Human Genome Project” What will DNA tell about this stain in a dress Bill Clinton Tony Blair Craig Venter Francis Collins * 26 Juni 2000: Press conference Bill Clinton & Tony Blair: "working draft“, 95% gesequenced * 14 april 2003: finished: 99% gesequenced. >>Cheaper and Faster!! Costs: $ 2.7 miljard (instead of $ 3 billion estimated costs) Timing: 1990 - 2003 (instead of 2005)
AGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAG COMPLEX GENETICS: HUMAN DNA IS HIGHLY VARIABLE GACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTG TGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCT GCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTT CGATTGCCGCTAGCTAGAACAAAATAGCG G TATTTTGGGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATT DNA Variants are: TGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTT “SNP=Single Nucleotide Polymorphism” CTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGG A GTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCG *Frequent in the Genome (based on 500k WGS/WES ) GGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTC TAGCTGCTGACGTGC C AGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGT - Many : >150 million variable loci in human genome (~3%) TAGCGTATGCTAGCTAGTGATCGATGCTA G TAGCTAGCTA G CTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATC CTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGA -Types: “SNPs” , in/del, CNV, VNTR CCATTGG A CTAGGGGATTGACCAGTA G GCTGCG A TTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAG CTGCGATGCTGGACTG A ACGCCCCTCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGAGGAGTCTGACTG -Databases: dbSNP, HapMap, 1KG, “local” NGS efforts,.. TGGACTAGGGGATTGACCAGTAGGCTGCGATTCG G ATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATG GATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTAC ATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCT A GCTGATCGATCAT AACCG TATA AGGGCTAGCTAGCTGATCGATCGATGCTAGC T AGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTA *Frequent in the Population: “IN/DEL=Insertion Deletion” GAACAAAATAGCGGTATTTTGGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATG > 5 % = common polymorphism CTAGTGAT C GATGCTAGTAAGGAGTCTGACTGACCATTG G ACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGA AAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCC 1 – 5 % = less common variant GCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGA T GCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTA TCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAG “CNV=Copy Number Variation” < 1 % = rare variant/mutation TGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGC GGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGG ACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGAT TACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGAC CGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTA GTCGATCGATCGA T CGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAG C TAGC TAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAG “VNTR=Variable Nunber of Tandem Repeats” TATTTTGGGCTAGCTAGCTGATCGATCAT C GATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATC GTGGGGGGTTAAATG CACACACACACACACACACACACACACACACACA GATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGG GCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCT GATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGT G GTGGGGGGTTAAATGCG CGCTAGCTAGAACAAAATAGCGGTATTT T GGAGGAGTCTGACTGACCATTGGACTAGG G GATTGACCAGTAGGCTGCGATTCGGATG ATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGG A TGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGC GACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAG GCTAGCTAGTGATCGATGCTAGT A GCTAGCTAGCTGATCGA
The influence of “technology-push” Time needed for analysing 1 SNP in 7.000 DNA samples 1996 6 months : RFLP, Epp tubes 1999 3 months: RFLP, 96-well plates 2001 1 week: SBE, 384-well plates 2003 1 day: Taqman (manual) 2004 6 hrs : Taqman, Caliper pipetting robot 2005 3 hrs: Taqman, Deerac, “Fast” PCR 2007 6 sec: Illumina 550K array, 600 DNAs/week 2010 < 0.0006 sec: Illumina HiSeq2000 NGS Sequencers
Arrays are better than Sequencing Arrays are preferred in large-scale application (compared to sequencing) 30-100x (!) cheaper Only relevant DNA variants Customizable Very high throughput Easy data analysis and automation DTC companies prefer arrays Less ethical issues 700,000 DNA variants on the GSA array: GWAS, Clinical, pharmacogenetics, HLA, forensic, mitochondrial, ancestry, blood groups, etc. 28 euro for GSA array
EU GSA consortium Coordinating center HuGe-F Erasmus MC By end 2018 there will be many SNP array datasets.. Existing: academic data 1 million samples (global) UK Biobank 0.5 mio samples (UK) Millions Veterans Program (MVP) 1 million samples (USA) FinGen 0.5 mio samples (Finland) 23andme >2 mio samples (USA centric) Avera, Kaiser Permanente 0.6 mio samples (USA) New: Europe 1 004 992 Netherlands 168 992 GSA sales 2016/2017/2018 >20 million samples (USA centric) Canada/USA 28 209 EU-GSA 1.1 million samples (global) Australia 37 219 Asia 21 952 ~25 million samples with SNP array data…… South America 1 150 TOTAL 1 093 522 Africa 0
Erasmus MC Genomics Core Facility: SERVICES EXAMPLES SERVICE PRICES* Rotterdam Study, GenR, BIOBANKING/DNA isolation DNA isolation 6 euro Parelsnoer, BBMRI, GEFOS, many more WES (50x) 350 euro 2 nd and 3 rd GENERATION SEQUENCING Bench marking with top institutes of the world HIGH THROUGPUT ARRAYS GSA Array (800k) 28 euro GENOTYPING Collaborations in large consortia TRANSCRIPTOMICS RNA Seq 300 euro Core facility for BBMRI EPIGENETICS Functional studies in cell EPIC array (850k) 245 euro lines MICROBIOME -16S 24 euro -Metagenomics 200 euro GWAS, imputation, methylation analysis, BIOINFORMATICS exome and transcriptome, Support >500 euro microbiome analysis *Prices are for standard service; inquire for other options (July 2018) WWW.GLIMDNA.ORG
Why do we study DNA variation ? *Biology: - Mechanism : understand cause of disease - Treatment : finding new potential drug targets *Prediction: - (Early) diagnostics with a stable marker : understand how DNA variation contributes to variation in: - Risk of disease (vulnarability): “personalized medicine” - “Response-to-treatment” (medication, diet): “pharmacogenetics”
AGING RESEARCH
Some words: SNPs, alleles, genotypes and haplotypes SNP= Single Nucleotide Polymorphism Chromosomes: strand Allele A A C G from Father + + from Mother C G A T Genotype Haplotype Allele A A C G C G A T
“Simple” versus “Complex” Disease “Simple”/Monogenic Disease “Complex” Disease • severe phenotype • mild phenotype • early onset • late onset • rare • common • Mendelian inheritance • complex inheritance • e.g.: cystic fibrosis, • e.g.: diabetes, asthma, osteogenesis imperfecta osteoporosis, etc. Mutations Polymorphisms Cause: + polymorphisms + mutations
Twin Studies Demonstrate “Heritability” Heritable diseases and traits: Diabetes Rheumatoid arthritis Breast cancer Lung cancer Osteoarthrosis BMI Menopause Weight Height Menarche Infidelity cholesterol Entrepreneurship Uric acid Paget’s Disease Infectious disease susceptibility Depression Ankylosing spondylitis Eye colour Myocardial Infarction Osteoporosis Skin colour Longevity Stroke Eye diseases Baldness Telomere length Smoking behaviour Etc. Etc.
The Genetic Architecture of Diseases/Traits : study designs to identify “risk” alleles common, complex rare, monogenic Whole Exome Sequencing (WES) few “big” effects of common alleles (ApoE, CFH) big Effect Size Next-Generation Sequencing Genome-Wide (WES/WGS of reference sets) Association small + Study (GWAS) Arrays/Imputation rare common Frequency Genetic Variant
Genome-Wide Association Study (GWAS) DATA ANALYSIS (e.g., PLINK): DNA collection : e.g. 1000 cases vs. 1000 controls Each dot is one SNP in, e.g, 2000 subjects AA AB BB Illumina Affymetrix AA → SNP 1 14 18 X 1 2 3 4 5 6 7 8 10 12 BB→ SNP 2 AA Chromosomes AB→ SNP 3 . . BB . . Select SNPs Combine GWAS . . AB AB→ SNP 550,000 Replication - Effects per SNP are usually small - We are looking at common variants Meta-Analysis of all data
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