General Introduction Andr G Uitterlinden Genetic Laboratory - - PowerPoint PPT Presentation

MOLMED SNP COURSE 2018

André G Uitterlinden Genetic Laboratory Department of Internal Medicine

Department of Epidemiology & Biostatistics Department of Clinical Chemistry

General Introduction

• ur website…!!

www.glimdna.org

ROTTERDAM – OLDEBARNEVELDSTRAAT - MULTATULI

Portret gemaakt door Mathieu Ficheroux, 1974

Viewed from the moon we are all equal

We differ from each other…

DNA variation causes differences in:

• Development
• Appearance
• Behaviour
• Ageing
• Diseases

This is what happens when there are NO POLYMORPHISMS

Why are DNA polymorphisms important ?

Evolution Forensics Disease

Slide stolen from Prof Axel Themmen

DTC fun

“The Human Genome Project”

* 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)

Bill Clinton Tony Blair Craig Venter Francis Collins

What will DNA tell about this stain in a dress

AGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAG GACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTG TGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCT GCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTT CGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATT TGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTT CTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCG GGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTC TAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGT TAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATC CTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGA CCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAG CTGCGATGCTGGACTGAACGCCCCTCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGAGGAGTCTGACTG TGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATG GATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTAC ATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCAT AACCGTATAAGGGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTA GAACAAAATAGCGGTATTTTGGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATG CTAGTGATCGATGCTAGTAAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGA AAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCC GCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTA TCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAG TGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGG ACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGAC CGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTA GTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGC TAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAG TATTTTGGGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATC GTGGGGGGTTAAATGCACACACACACACACACACACACACACACACACAGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGG GCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCT GATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCG CGCTAGCTAGAACAAAATAGCGGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATG ATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGC GACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAG GCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGA

“SNP=Single Nucleotide Polymorphism”

DNA Variants are: *Frequent in the Genome (based on 500k WGS/WES)

• Many: >150 million variable loci in human genome (~3%)
• Types: “SNPs” , in/del, CNV, VNTR
• Databases: dbSNP, HapMap, 1KG, “local” NGS efforts,..

*Frequent in the Population:

> 5 % = common polymorphism 1 – 5 % = less common variant < 1 % = rare variant/mutation

COMPLEX GENETICS: HUMAN DNA IS HIGHLY VARIABLE

“IN/DEL=Insertion Deletion” “CNV=Copy Number Variation” “VNTR=Variable Nunber of Tandem Repeats”

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

The influence of “technology-push”

28 euro for GSA array

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.

1 093 522

Europe 1 004 992 Netherlands 168 992 Canada/USA 28 209 Australia 37 219 Asia 21 952 South America 1 150 Africa 0

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: GSA sales 2016/2017/2018 >20 million samples (USA centric) EU-GSA 1.1 million samples (global)

TOTAL

~25 million samples with SNP array data……

Erasmus MC Genomics Core Facility: SERVICES

BIOBANKING/DNA isolation GENOTYPING 2nd and 3rd GENERATION SEQUENCING BIOINFORMATICS TRANSCRIPTOMICS EPIGENETICS HIGH THROUGPUT ARRAYS MICROBIOME

Rotterdam Study, GenR, Parelsnoer, BBMRI, GEFOS, many more Bench marking with top institutes of the world Collaborations in large consortia Core facility for BBMRI GWAS, imputation, methylation analysis, exome and transcriptome, microbiome analysis Functional studies in cell lines DNA isolation 6 euro WES (50x) 350 euro GSA Array (800k) 28 euro RNA Seq 300 euro EPIC array (850k) 245 euro

• 16S

24 euro

• Metagenomics

200 euro Support >500 euro SERVICE PRICES* EXAMPLES *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

A C G T A G C A A C G T A G C A SNP= Single Nucleotide Polymorphism Genotype Allele Haplotype Allele

+ +

strand Chromosomes: from Father from Mother

“Simple” versus “Complex” Disease

“Simple”/Monogenic Disease

• severe phenotype
• early onset
• rare
• Mendelian inheritance
• e.g.: cystic fibrosis,
• steogenesis imperfecta

“Complex” Disease

• mild phenotype
• late onset
• common
• complex inheritance
• e.g.: diabetes, asthma,
• steoporosis, etc.

Mutations

+ polymorphisms

Polymorphisms

+ mutations

Cause:

Diabetes Breast cancer Osteoarthrosis Menopause Height Infidelity Entrepreneurship Paget’s Disease Depression Eye colour Osteoporosis Longevity Eye diseases Telomere length Etc.

Twin Studies Demonstrate “Heritability”

Heritable diseases and traits:

Rheumatoid arthritis Lung cancer BMI Weight Menarche cholesterol Uric acid Infectious disease susceptibility Ankylosing spondylitis Myocardial Infarction Skin colour Stroke Baldness Smoking behaviour Etc.

Effect Size Frequency Genetic Variant

rare, monogenic common, complex rare common small big

The Genetic Architecture of Diseases/Traits :

study designs to identify “risk” alleles

Genome-Wide Association Study (GWAS)

few “big” effects of common alleles (ApoE, CFH)

Whole Exome Sequencing (WES)

Next-Generation Sequencing

(WES/WGS of reference sets) +

Arrays/Imputation

AA→ BB→ AB→ . . . AB→ SNP1 SNP2 SNP3 . . . SNP550,000 1 2 3 4 5 6 7 8 14 18 X

Chromosomes

10 12 AA AB BB AA BB AB

DATA ANALYSIS (e.g., PLINK):

Replication Illumina Affymetrix

Genome-Wide Association Study (GWAS)

Select SNPs

DNA collection: e.g. 1000 cases vs. 1000 controls

Each dot is one SNP in, e.g, 2000 subjects

Meta-Analysis of all data Combine GWAS

• Effects per SNP are usually small
• We are looking at common variants

HERC2/OCA2 gene

12 kb on Chr. 15q11

Rotterdam Study: Kayser et al, Am J Hum Genet, 2008

A “Dubai”plot: GWAS of human iris colour Chromosome / position P - value (-log 10)

P < 1.10-206 n = 5974

A “Holland”plot: GWAS for BMD in the Rotterdam Study

N=5,000

5 x 10-8

• Rotterdam Study
• ERF Study
• Twins UK
• deCODE Genetics
• Framingham Study

LUMBAR SPINE BMD

Rivadeneira et al., Nat Genet., 2009

A real Manhattan plot: “height” in the GIANT consortium

5 x 10-8

Lango, Estrada, Rivadeneira et al., Nature, 2010

• 180,000 subjects
• 180 loci identified
• 10-15% variance explained

Willer et al., Nature Genetics, jan 2009: 145 authors

GWAS issues: *GWAS hits are just a start to find causal variant(s) *Much follow-up research, …to be done by you (in collab.) *GWAS creates new genome annotation/function/biology (e.g., miRNA, lncRNA) *Small effect size does NOT mean small biological relevance

290 s statistically i ind ndep epen enden ent s signa nals; s; ~20% explained g

gen enetic v vari riance

New ! w !! : : UK Biobank/Rep eproGen en A Age-at at-Men enopause M e Met eta-an analy lysis

(as per 10/09/18; John Perry, personal communication; unpublished)

• 202,000 women
• 61,000 women from 33 GWAS (1KG imputed, 11 million variants)
• 34,500 women from BCAC (HRC imputed. 15 million variants)
• 106,163 women from UK Biobank. (HRC imputed. 12 million variants)

Per 12 May 2018:

• 3,379 publications
• 61,620 unique SNP-trait

associations. (www.ebi.ac.uk/GWAS )

GWAS ….drinking from the firehose

With GWAS for common variants we have: *Genotyped only 1 mio (0.3%) nucleotides in the human genome *By imputation to reference data: ~50 mio nucleotides *Selected for “Universal/Cosmopolitan” variants *Explained 5-70% of genetic variance per disease *Analysed not all phenotypes…

Human Genomic Life Course Epidemiology

Rotterdam Study Age (yr)

Bone Mineral Density

Bone growth Peak BMD Bone Loss 50 75 25 100

EPOS CALEUR AGGO

Osteoporosis: Low BMD, fractures men women

DNA/RNA collections for OMICS studies Maternal genotype Paternal genotype Environmental factors “Chronological” vs. “Biological” Ageing

bone phenoptyes

B-Proof intervention

Birth Death

Bone as an Example...

Off-spring

GenR

! OPTIMAL EPIDEMIOLOGICAL DESIGN:

A single-centre, longitudinal population-based cohort study of normal elderly Dutch, started in 1990, with 25 years of follow-up ! LARGE: Total = 20,000 men and women of age ≥ 45 yrs ! VERY DEEP PHENOTYPING: 5 Follow-up measurements with ~1,500 per subject each time : height, bmi, brain MRI, DXA, cholesterol level, blood pressure, glucose, etc. etc. etc. ! ETHNICALLY HOMOGENEOUS: 99% White Caucasian ! EXTENSIVE GENOMICS DATA AVAILABLE: GWAS, RNA expression (array+ NGS), DNA methylation (450K), Whole Exome Sequencing, 16S microbiome, telomeres, mitochondrial DNA, metabolomics,..

“ERGO: Erasmus Rotterdam Gezondheid Ouderen” “The Rotterdam Study”

Overview of sample numbers with “omics” datasets across the 3 Rotterdam Study (RS) cohorts with the number and type of measurement for each omic method

Genomics data type Total Datapoints/sample RS I* RS II* RS III* Number Type GWAS SNP data 11,502 40,000,000 SNPs 6291 2157 3054 Exome array 3,183 250,000 SNPs 3183 – – Whole exome sequencing (WES) 3,778 693,000 Variants 3778 – – Whole genome sequencing (WGS) 96 3,000,000 Variants 96 – – Genome wide expression (array) 881 25,000 Genes – – 881 Genome wide expression (RNA Seq) 829 18,000,000 Reads – 500 329 Genome wide DNA methylation 1,600 450,000 CpG’s 100 500 1000 Telomere length (PCR) 1,800 1 – 1800 – – Mitochondrial DNA (PCR) 500 1 – 500 – – Microbiome 16S rRNA (faeces) 2,000 500 OTU’s – – 2000 Metabolomics (NMR/UPLC MS) 1,826 4000 Metabolites 1826 – – Metabolomics (NMR “Nightingale”) 5,381 228 Metabolites 2880 663 1838 Serum protein profile** 9,820 35 Proteins 3812 2542 3466

Total ‘omic’ data points in RS: 43,196 × 62,422,765 = 2,696,413,756,940 (2.7 x 1010 )

SNP single nucleotide polymorphism, CpG a two-nucleotide position (C next to G on the same strand) of which the C can be methylated; OTU operational taxonomic unit *RS1, First cohort of the Rotterdam Study; RS2, Second cohort of the Rotterdam Study; RS3, Third cohort of the Rotterdam Study **Total estradiol, total testosterone, sex hormone-binding globulin, dehydroepiandrosterone, dehydroepiandrosterone sulfate, androstenedione, 17- hydroxyprogesterone, cortisol, corticosterone, 11-desoxycortisol, vitamin D, thyroid stimulating hormone, free T4, interleukins, C-reactive protein, Insulin-like growth factor 1, insulin, iron, ferritin, transferrin, fibrinogen, homocysteine, folic acid, riboflavine, pyridoxine, SAM/SAH ratio, cobalamine, Lp-PLA2, Fas/Fas-L, abeta42/40

(Samples x Datapoints)

*to convince yourself+colleagues+society that the observation is true and generalizable *Methodology -in one centre- is different and/or flawed:

• transformed cell lines are genomically unstable
• wrong/mixed cell lines (HeLa!)
• bad antibodies
• complicated and/or different method
• human error, fraud

*Effect sizes are small (GWAS, omics data) *The modelsystem is not representative for humans, e.g.:

• worm/insect/mouse biology is not similar to human biology
• only one (inbred mouse) strain is used (n=1 human, …a strange one!)
• only one iPS cell line is used (n=1 human)
• a small and/or strange human sample is used (cases only; an isolated

population; etc.)

Replication is needed (a few reasons):

⇒ Replication in an independent sample/lab

(nót doing the experiment 6 times!)

=>”Tri-angulation”

(Davey-Smith, Munafo, Nature 2018)

SOLUTION:

• Provide replication, and publish in one and the same paper, with

colleagues

• Acknowledge contributions (e.g., with many authors)

Grades of Evidence and the Reproducibility Crisis

Level Method Science disciplines

• Large scale collaborative prospective

meta-analysis of individual level data in consortia

• Meta-analysis of published data
• >2 large studies (n > 1000 each)
• 1-3 smaller studies
• 1 small study (n<500), NO replication
• Expert Opinion…

Very Good Not so Good

• Complex Genetics
• Physics
• Astronomy
• Sociology
• Psychology
• Medicine

Cell Biology

• The biomedical community publishes 2,5 mio papers per year
• Only 40% papers describe results that can be replicated (the “reproducibility crisis”)

Collaboration doesn’t come easy…..

>> Donald Trump’s view on EUROPE…. ?

(From: Yanko Tsvetkov, alphadesigner.com)

Wall !! Wall !! Wall !! Wall !! Wall !! Wall !! Wall !!

A “Culture” Change in doing Research:

GLOBAL AL CO COLLABORA RATIONS I NS IN N CO COMPLEX EX G GENET ENETICS CS

Exam ampl ple: t e: the “ “GIANT” cons nsor

• rtium

um:

>2,000, 000,000 000 participan pants…

SUNLIGHT consortium GSA- consortium

Clinical Expression:

Risk Factors:

Fracture Risk

Bone Strength Impact Force Fall Risk

DNA mutations and polymorphisms

BMD Quality Geometry

Osteoporotic fracture is a “complex” phenotype: Environmental factors: diet, exercise, sun exposure, ...

Hip fx Wrist fx Vertebral fx etc.

Age, Sex, Age-at-Menopause, Height, OA, etc.

The next challenge: Environmental Factors The field needs: standardization, harmonization, replication HOLLAND BELGIUM

> 1100 mg/day < 500 mg/day Dietary Calcium intake Geographical distance: <100km

Foto: Barbara Obermayer-Pietsch Foto: Stuart Ralston

– 1,103 conditionally independent SNPs from 515 loci (301 novel) – 2x the previous study! – 20% of the trait variance explained – 1.5x increase!

Slide by John Morris; Morris J, Kemp J et al. Nature Genetics (acepted)

UK Biobank: Largest BMD* GWAS so far…

in 426,824 White-British participants

*estimated BMD from heel QUS

population frequency

• f BMD

value

Monogenic Mutations with large effects Polymorphisms with subtle effects Rare variants Rare variants Common variants Monogenic Mutations with large effects

BMD value

LRP5 SOST ClCN7 TCIRG1 CATK OSTM1 RANKL RANK COLIA1 COLIA2 CRTAP LEPRE LRP5 CYP17 ESR1 PLS3

Low High

LINKAGE IN PEDIGREES+ EXOME SEQUENCING

GWAS in GEFOS + GENOMOS consortia

ANALYTICAL APPROACHES: EXOME + GENOMESEQUENCING EXOME + GENOME SEQUENCING LINKAGE IN PEDIGREES + EXOME SEQUENCING

Genetic “architecture” of human phenotypes: the example of BMD

ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23

EN1 LGR4 PLS3

ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23 ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23 ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23 ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23 ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23 ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23 ANXA11 LIN7C RSPH10B TNFAIP8L3 ARHGAP1 LRP3 RTDR1 TNFRSF11B BBOX1 LRP4 RUNX2 TNFSF11 BCR LRP5 SERPINE2 TOE1 CDC5L LSM12 SETD4 TOP2B CDK5 LYRM5 SFTPD TSGA10IP CLIP4 MAP3K11 SHFM1 TSPYL6 COL11A1 MAP3K12 SIRT3 TSR1 CTNNB1 MBL2 SLC25A13 TTC21B CYLD MEF2C SLC45A1 UNKL DAB2IP MEOX1 SNX20 USHBP1 DCDC1 MEPE SOX4 WDFY1 DLX5 MKKS SOX6 WDR43 DLX6 MPP2 SOX9 WDR86 DYDC1 MPP3 SP1 WDR88 ERC1 MYO9B SP7 WFIKKN1 ESR1 NAB1 SPIRE1 WNT1 FOXC2 PAX6 SPP1 WNT10B FOXF1 PIGC SPTBN1 WNT16 GPR141 PKD2L1 STARD3NL WNT3 GPR177 PLAC9 STK38L WNT4 GRB10 PTPRN2 SUPT3H WNT4 HDAC5 QRFP SUV420H1 WNT5B IBSP RAB18 TIPARP WNT9B IGFBP6 RADIL TLR5 XKR9 INSIG2 RBMS3 TMEM16J ZBTB40 ITGA2B RIC8B TMEM175 ZCCHC2 JAG1 RPE65 TMEM87B ZDHHC23

• 513 loci
• 20% variance explained

2011 2017/2018

Trait/Disease N Nr hits Expl Variance N Nr hits Expl Variance Height 135.000 210 14% 253.288 697 29.0% BMD 20.000 20 2-3% 426.824 515 20.0% BMI 126.000 18 ~1.5% 339.224 97 2.7% MI 6.000 9 2.8% 185.000 44 13.0% Lipids (LDL, HDL, Tg) 20.000 11 8% 617.303 562 12.3% Blood Pressure (SBP) 35.000 8 0.5% 1.006.863 901 5.7% Breast Cancer 8.000 8 5.4% 169.092* 313 20.0% Age at Menopause 10.000 4 2.7% 202.000* 290 20.0%

Progress in GWAS

Future Efforts in GWAS……….

• Unanswered Questions….:
• Causative SNP ? Causative gene ? Biological mechanism ?
• Limited explained variance per trait/disease : …“dark matter”

* The Hunt for Genetic “Dark Matter” …..:

• Other types of genetic variation :
• Rare variants (<5%, <1%, etc.)
• Copy Number Variations (CNVs)
• Interaction:
• Gene-Gene and Gene-Environment
• Technological Developments…..:
• High Throughput Sequencing
• Cheap arrays with rich content (GSA, PMRA)

Progress in GWAS: BIGGER SAMPLE SIZES BY COLLABORATION

• A culture change of doing scientific research
• Genetic architecture of complex diseases
• New biology of complex diseases
• Mendelian Randomization to identify causality
• Polygenic Risk Scores (PRS)
• Cheap robust SNP array technology

What has GWAS brought us?

GOALL! Genotyping On ALL patients at Erasmus MC

Subscription to GENETIC REPORT

Commercial Partners:

Illumina, BC Platforms

Pilot Projects:

• Eye disease
• Cardiovascular Disease
• Pharmacogenetics
• Breast Cancer
• Type 2 Diabetes/Obesity
• ……

DNA Array Processing: Erasmus MC Genomics Core Facility Patient’s Home Erasmus MC as trusted Partner

Erasmus MC Partners:

*Interpretation/Counseling: Internal Medicine : Complex Diseases Clinical Genetics : Mendelian Diseases Clinical Chemistry : Pharmacogenetics *Patient inflow/Reporting: Clinical departments (per disease) Costs: < 30 euro per patient Content: 700.000 selected variants for:

• Pharmacogenetics
• Mendelian Disease Variants
• HLA types
• Clinical (actionable) Variants
• Polygenic Risk Scores Complex Disease
• Ancestry
• etc.

Information and Consent

Regular Updates with Risk Profile Information

*ALL patients undergo DNA array genotyping *Patient DNA Array results are available BEFORE clinician sees the patient