Introduction to Complex Genetics: Concepts and Tools Andr G - - PowerPoint PPT Presentation

MolMed Course “Genetics for Dummies” Rotterdam, 1 November, 2017

Introduction to Complex Genetics: Concepts and Tools

André G Uitterlinden

Genetic Laboratory Department of Internal Medicine

Department of Epidemiology Department of Clinical Chemistry

www.glimdna.org

Professor Trifonius Zonnebloem Professor Cuthbert Calculus Professeur Tryphon Tournesol

Our website…

ROTTERDAM – OLDEBARNEVELDSTRAAT - MULTATULI

Portret gemaakt door Mathieu Ficheroux, 1974

Viewed from the moon we are all equal

DNA Differences cause Phenotype Differences

AGING RESEARCH

Genetics of Ageing……

1953 1990

James Watson : 1928- Francis Crick : 1916-2004

exon

mRNA Protein Gene structure DNA base pair sequence

A A C C G C A T A A G G T T G G C G T A T T C C . . . . . . . .

From DNA to RNA to Protein....

“Genetics”

“Genomics”

“Proteomics”

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”

“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

AGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATT AGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGAC GTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGAT CGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTA GTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGACTGACCATTGGAC TAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGC GATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGAGTCT GACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGA CGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTG CGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTA GTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGT GGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGACTGACCATTGGACTAGGGGATT GACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGA CTGAACGCCCCTCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGAGGAGTCTGACTGACCATTGGACTA GGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGA TGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTAC CTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATC GATCATCGATAACCGTATAAGGGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGC GATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGA TCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTG CGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCC CGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGT CGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGC TAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAAC AAAATAGCGGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGAC GATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCT GACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTA GCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATC GATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGCTA GCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGG GGGTTAAATGCACACACACACACACACACACACACACACACACAGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGT GCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAG CTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTT AAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGG CTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCC CCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCA GTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGA

“SNP=Single Nucleotide Polymorphism”

DNA Variants are: *Frequent in the Genome (50k WGS/250k WES):

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

*Frequent in the Population:

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

HUMAN DNA IS HIGHLY VARIABLE

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

AGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATT AGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGAC GTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGAT CGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTA GTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGACTGACCATTGGAC TAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGC GATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGAGTCT GACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGA CGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTG CGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTA GTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGT GGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGAGTCTGACTGACCATTGGACTAGGGGATT GACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGG ACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGAGGAGTCTGACTGACCATTGGACT AGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGC GATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTA CCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGAT CGATCATCGATAACCGTATAAGGGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATG CGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCG ATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCT GCGATTCGGATGCGGATTGACGATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCC CCGGGCTTCTTTATTAGCTGCTGACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAG TCGATCGATCGATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAG CTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAA CAAAATAGCGGTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGAC GATTAAAAAGGATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCT GACGTGCCAGATGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTA GCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATC GATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGCTA GCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCTAGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGG GGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCGGTATTTTGGGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATC GATCGTAGCGTAGCGTATGCTAGCTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGATCATCGATCGTAGCTAGCTAGCTAGCT AGCTGATCGATCGATGCTAGCTAGCTAGCTAGTCATCTGTGGTGGGGGGTTAAATGCGATTGCCGCTAGCTAGAACAAAATAGCG GTATTTTGGAGGAGTCTGACTGACCATTGGACTAGGGGATTGACCAGTAGGCTGCGATTCGGATGCGGATTGACGATTAAAAAGG ATTACGATTAGCTGTGACGTGCAGGATGCTGCGATGCTGGACTGAACGCCCCCCGGGCTTCTTTATTAGCTGCTGACGTGCCAGA TGCTGACGTGCAGTGCGGCTGACGGTGCTTACCTGGATCGGATGCTACCAGTCGATCGATCGATCGTAGCGTAGCGTATGCTAG CTAGTGATCGATGCTAGTAGCTAGCTAGCTGATCGA

“SNP” T-C “HAPLOTYPE”

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

Single Nucleotide Polymorphisms (SNPs) are common and have subtle effects ..AACCGCATAAGG.. ..TTGGCGTATTCC.. ..AACCGTATAAGG.. ..TTGGCATATTCC..

Codon 222 Codon 222

Alanine Valine Ala Val DNA: C677T protein: Ala222Val Population frequency: 65% 35% c u

enzyme activity Hcy level Disease risk

“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

Mutations

(< 1%)

Polymorphisms

( 1%)

Cause:

DNA polymorphisms as markers

Pro:

• DNA (essentially) does not change with age (=stable) and so

allows risk analysis at very early stage (=prognostic)

• DNA is (essentially) identical in all tissues (=accessible)
• Allows risk analysis for many characteristics (=comprehensive)

Contra:

• Individual effects of most common variants are small
• Not all contributing variants (common or rare) are known

This is what happens when there are NO POLYMORPHISMS

Why is the study of polymorphisms important ?

• Human History
• Evolution
• Forensics
• Social

Sciences

• Disease
• Ageing
• Normal

variation

Slide stolen from Prof Axel Themmen

Quantitating the Genetic Contribution to Complex Traits using Twins

Monozygotic Dizygotic 100% 50% Genes Shared

H2 bone phenotypes

• BMD

50-80%

• Turnover

40-70%

• Geometry 70-85%
• QUS

~80%

• Fracture:
• Hip fx

3-70%

• Wrist fx

54%

Resemblance MZ

Twin 1 Twin 2 r2 = 0.7

DZ

Twin 1 Twin 2 r2 = 0.3

• Height

80-90%

• Menopause 60%
• BMI

60-70%

H2 bone-related phenotypes

H2 ~ 2x (r2

MZ – r2 DZ)

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.

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.

Environmental influences can differ between populations ! HOLLAND BELGIUM

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

Foto: Barbara Obermayer-Pietsch Foto: Stuart Ralston

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 Sequencers

The influence of “technology-push”

INCREASED LEVELS OF GENETIC RESOLUTION IN GENOME ANALYSIS BY HIGHER COVERAGE OF NUCLEOTIDES ANALYSED BY NEWER DNA ANALYSIS TECHNOLOGIES

(TOP ARE OLDER TECHNIQUES, BOTTOM ARE THE LATEST)

Technique: Genome Coverage: 0 % 0.1 % 0.5 % 1 % 95 %

TaqMan SNP Array SNP Array + Imputation Whole Exome Sequence (WES) Whole Genome Sequence (WGS)

TIME Next Generation Sequencing

TTIME TVISIBILITY and/or ACTIVITY NGS, SEQUENCING ARRAYS

Progres (in DNA research) is technology driven:

Human Genomics-Facility (HuGe-F), Genetic Lab, Ee 575

Samples/month

~ 1,000 - 2,000 ~ 2,500 – 20,000 ~ 4,600 ~ 600 ~ 1,000 ~ 500

Service

Biobanking:

• DNA isolation (5 ml blood)

Arrays (a few examples):

• SNP GWAS Array (e.g., GSA)
• DNA Methylation Array (450K)

NGS:

• DNA Full Exome (WES; MedExome)

(60X; Nimblegen/Agilent/Illumina)

• RNA Seq Transcriptome

(30 mio reads) * Microbiome (16S)

~ Cost/sample *

€ 10 € 32 - 350 € 217 € 350 € 317 € 40

*Prices October 2017; subject to change by project volume and date; costs are all-in and include running and QC, but excl VAT

WWW.GLIMDNA.ORG

Price of SNP arrays has gone down…

>> much more GWAS data in DNA collections >> but content has also been enriched with “goodies”

100 200 300 400 500 600 700 800 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

32 euro for GSA array (consortium price, including running)

Human Genomics Facility (HuGeF), ErasmusMC www.glimdna.org

Euro’s Time

EU-GSA project time line at the Human Genomics Facility, Erasmus MC

• Okt 2015: introduction of GSA concept by Illumina at ASHG
• Nov 2015 – feb 2016: Illumina invites genotyping centres with their offer

(order minimum 150k samples!!…)

• Dec 2015 – March 2016: HuGeF approaches people through its network
• March 2016: Collaboration with Bonn and Paris to place single order
• April 2016: HuGeF places first order of 150k (initial approval RvB)
• Jan 2016 – July 2016: contact more people to reach 500k samples
• Sept 2016: Final order of 500k samples (final approval Exec Board)
• Dec 2016: Lab is optimized for running high throughput and data

production can start

• Sept 2017: Many more orders >185 signed contracts (134

studies/cohorts)

The hunt for more samples began……

Samples processed at HuGeF for GSA: 9th January – September 4th 2017 142,205 samples:

~20.000/month

Some large GSA datasets:

• LifeLines UMCG

40k samples

• Qskin Australia

20k samples

• Estonia Biobank

35K samples

• MoBa cohort Norway

27k samples

• AROS Denmark

70k samples

• …..

Under negotiation for array typing:

• Sardinia

70K samples

• Biobank 1 Scandinavia

400K samples

• Scandinavian projects

20K and 70K samples

• Biobank 2 Scandinavia

1000k samples

• …..

Illumina EU-GSA consortium

130 groups; 685k samples (September 2017)

(coordinated by HuGeF, Genetic lab, Erasmus MC)

By mid 2018 there will be many GWAS datasets..

Existing: academic data 1 million samples (global) UK Biobank 0.5 mio samples (UK) Millions Veterans Program 1 million samples (USA) 23andme >1 mio samples (USA centricl) Kaiser Permanente 0.2 mio samples (USA) New: GSA sales 2016/2017 >6 million samples (USA centric) EU-GSA 0.7 million samples (global) Under negotiation: National biobanks (Scandinavia) 1.5 mio samples (EU)

TOTAL ~12 million samples with GWAS……

Gene Regulatory sequences

But not only for more GWAS….a shift from attention to regulatory variants to clinical/coding variants ?

Efforts with a focus on genes/coding variants:

• WES/WGS
• exome chip
• new arrays with

enhanced cinical content (e.g., GSA, PMRA)

New SNP arrays have a very “ rich” content: the goodies

Effect Size Frequency Genetic Variant

rare, monogenic common, complex

Next-Generation High-Throughput Sequencing

rare common small big

Genetic Architecture of Diseases/Traits :

Study designs to identify “risk” alleles

Linkage Analysis in pedigrees Genome-Wide Association Study (GWAS)

(very) few “big” effects (ApoE, CFH)

Exome Sequencing

“Top-down” / hypothesis free

* Genome Wide Linkage Analysis

• Pedigrees
• Sib-pairs
• Human, mouse

* Genome Wide Association (GWA) Analysis

• 100K – 2000K SNP analysis in cases/controls

* Genome Wide Sequencing

• full exome and full genome

“Bottom-up” / up-front hypothesis

* Association Analyses of Candidate Gene Polymorphisms (based on biology)

* Candidate Sequencing

• Selected regions (e.g., exons, gene regions)

Effectiveness Type of Approach

• +/-

+

Resolution

5-20 million bp 5k-50k bp 1 bp

+

1 bp

+/-

1 bp

Common risk alleles Rare risk alleles

+/- +/-

• +

+/-

Grades of Evidence

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
• Not all 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 !!

A “Culture” Change in doing Research:

GLOBAL OBAL CO COLLAB ABOR ORAT ATIONS NS IN N CO COMPL PLEX EX GEN ENETICS ETICS

Example: the “GIANT” consortium:

>1,000,000 participants…

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

NHGRI GWA Catalog www.genome.gov/GWAStudies www.ebi.ac.uk/fgpt/gwas/ Published Genome-Wide Associations through 12/2012 Published GWA at p≤5X10-8 for 17 trait categories

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 2-30% of genetic variance per disease (some exceptions) *analysed not all phenotypes…

As of 11/19/13, the catalog includes 1751 publications and 11,912 SNPs.

Per 19 sept 2017:

• 3,128 publications
• 51,054 unique SNP-trait

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

GWAS ….drinking from the firehose

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

Choice of Biobanks: Bone as an Example...

ERGO/Rotterdam Study

Age (yr)

BMD

Bone growth Peak BMD Bone Loss 50 75 25 100

EPOS GenR CALEUR AGGO

Osteoporosis: Low BMD, fractures men women

DNA collections bone endpoints

Maternal genotype Paternal genotype Environmental factors Ageing

! 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 = 15,000 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,

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

The Rotterdam Study comprises three cohorts (RS-I,-II,-III), examined every three to four years

GWAS Exome Methylation GWAS Methylation GWAS RNA Methylation

current health status medical history physical activity. current drug use (ATC-classification) use of medical facilities dietary habits smoking habits socio-economic status

Questionnaire Visits at the research centre

Anthropometry, limited physical examination, venous blood sample, glucose tolerance test

Cardiovascular diseases

• ultrasound assessment of cardiac dimensions,

diameter of the abdominal aorta, carotid arterial wall thickness and plaques thickness

• computerized ECG

Neurological diseases

• cognitive function
• indicators for Parkinson's disease
• MRI (3T)

Locomotor diseases

• Dual-Energy X-ray Absorptiometry (DEXA)
• X-rays of hands, thoraco-lumbar spine, hips, knees

Opthalmologic diseases

• extensive ophthalmologic examination

New phenotypes: Sleep patterns Skin Aging Gait Pain Sensitivity Stool , nose(Meta-genomics)

Biomaterials available

• base line + several follow-up !
• Blood :

genomic DNA (“Native” DNA is advantage over “cell line” DNA ) DNA methylation RNA

• DNA and RNA is from mixed blood cells !
• Serum + Plasma

>Proteins: measuring ~50 “classic” serum markers in all cohorts

• Urine
• Sputum (cortisol only)
• In progress: MICROBIOME
• Stool, Nose swap >> meta-genomics/micro-biome
• Skin
• Eye

Bio-banking in Genomic Epidemiology: the Rotterdam Study

The “CHARGE” consortium

“Cohorts for Heart and Aging Research in Genomic Epidemiology”

Approved by participating cohort studies jan 31, 2008, Boston, USA Cohort Acronym Contact (RSC) N Genotyping platform

Age, Gene/Environment, Susceptibility-Reykjavik AGES Tamara Harris Vilmundur Gudnason 5.000 Illumina 317K Atherosclerosis Risk in Communities ARIC Eric Boerwinkle 15.000 Affymetrix 6.0 Cardiovascular Health Study CHS Bruce Psaty 5.000 Illumina 317K Framingham Heart Study FHS Chris O’Donell 10.000 Affymetrix 550K Rotterdam Study RS Albert Hofman André Uitterlinden 12.000 Illumina 550K, 610K TOTAL N GWA 47.000

• Join GWA data for meta-analysis
• Many “Phenotype Working Groups (PWG)” (>45)
• Analyses/Publications arranged at PWG level; RSC oversees

• JAMA. 2013;309(18):1912-1920

2 cohorts, 10,938 individuals

GWAS for Helicobacter pylori serological status host susceptibility

Clinical relevance: Helicobacter pylori is a major cause of gastritis and gastroduodenal ulcer disease and can cause cancer =TLR1 =FcGR2A

EU EU-FP7 FP7 pr proj

• jec

ect: t: GEFOS EFOS (2008-2012)

Number of subjects: GENOMOS: >150,000

• f which GWAS: 40,000

www.gefos.org

= G GENOMOS study dy popul ulat ation

• n

= i idem m + GW GWAS = i idem, m, under er negot

• tiati

ation

• n / i

in devel elopm

• pment

ent

population frequency

• f BMD

value

Monogenic Mutations with large effects Polymorphisms with subtle effects Rare Rare Common 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 + GEFOS + GENOMOS

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

Genetic “architecture” of human 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

• Expl. variance :

5% >> 20%?

EN1 LGR4 PLS3

GWAS identifies “drugable targets” in bone metabolism

10-23 10-15 10-12 10-13 10-12 10-10 10-7 10-3 10-3 10-2 1. RANKL 13q14 2. OPG 8q24 3. GPR177 1p31 4. MEF2C 5q14 5. FLJ42280(?) 7q21 6. ESR1 6q25

• x.

SOST 17q21

• x.

FDPS 1q22 x. PTH 11p15 x. CATK 1q21 P value GEFOS GWAS*

Rank/Gene/location*

Yes, denosumab Yes, denosumab No No No Yes; HRT, SERMS Yes, anti-SOSTAb Yes; bisphosphonates Yes; teriparatide Yes; odanacatib

Existing Drug Target?

Estrada et al, unpublished *Ranking based on GEFOS 1 data; Rivadeneira et al. NatGenet 2009

>> “Selecting genetically supported targets could double success rate in clinical development” Nelson et al, Nat Genet vol 47, 8, 856-602, august 2015

Pharmaco-Genetics and –Genomics in Bone Research > Relevant when there is substantial inter-individual variation in response of expensive drugs, and/or serious side effects

• Bisphosphonates:

Not much inter-individual variation in response, but side-effects: ~ osteonecrosis (small GWAS: CYP2C8 variant) ~ atypical fractures

• PTH:

Expensive and inter-individual variation in response…

Pharmaco-Genetics and –Genomics

* Candidate gene analyses: TPMT ~ azathioprine (FDA approved; dose) UGT1A1 ~ irinotecan (FDA approved; dose) HLA-B*5701 ~ abacavir FDA approved; ADR)

• Drug

n cases lowest p-value genes discovered

• GWAS Drug response:
• Warfarin/acenocoumarol

181-1451 6.10-13 – 2.10-123 VKORC1, CYP2C9, CYP4F2, CYP2C18

• Interferon-alpha

293-1137 9.10-9 – 1.10-25 IL28B

• Clopidogrel

429 4.10-11 CYP2C19

• Methotrexate

434 1.7.10-10 SLCO1B1 * GWAS Adverse Drug Reactions (ADR): Simvastatin ~ myopathy 85/90 4.10-9 SLCO1B1 Flucloxacillin ~ liver injury 51/282 8.7.10-33 HLA-B*5701

• Bisphosphon. ~ ostenecrosis 25/65

1.10-6 CYP2C8

Daly et al., Nat Rev Genet 2010, 241-246

Next Gen Sequencing will:

• Assess (almost) ALL nucleotides in the human genome
• Find more variants:
• rare variants
• functional variants
• other types of variants
• Enrich existing GWAS datasets by imputation
• Link Mendelian genetics with Complex genetics
• Change the focus to the complete genome of an

individual, rather than on one gene

Effect Size Frequency Genetic Variant

rare, monogenic common, complex

Next-Generation High-Throughput Sequencing

rare common small big

Genetic Architecture of Diseases/Traits :

Study designs to identify “risk” alleles

Linkage Analysis in pedigrees Genome-Wide Association Study (GWAS)

(very) few “big” effects (ApoE, CFH)

Exome Sequencing

DNA Sequence Data in the Rotterdam Study

A historical perspective : 1995 - 2014

Analysis: Genome coverage: Samples Genotyped:

• Candidate polymorphisms
• n = 300 bp

1995-2005 12,000

• SNP arrays 550K/610K

0.1% n = 3,000,000 bp 2006 12,000

• Exome Sequencing (WES)

1% n = 38,000,000 bp 2014 3,000

• Full Genome sequencing (WGS) ~100%

n = 3,300,000,000 bp ?

Human Complex Genetics

The Next Frontiers:

>>> NEXT GEN SEQUENCING:

• EXOME SEQUENCING
• FULL GENOME SEQUENCING
• RNA SEQUENCING
• MICROBIOME SEQUENCING

>> Full genome sequencing of individual cells…..(cancer, brain, immunology,..) >> Full genome sequencing of several cells from a subject >> Coupling of genomic levels: DNA > methylation> RNA > protein >> Microbiome Sequencing (100x more bacterial cells than human cells per subject): > Flora of Intestine, Skin, Nose, Eye, Mouth, etc.

DISCOVERY meta-analysis: 7,257 whole blood samples (6 cohorts)

Differential expression with age:

Linear model adjusted for sex, smoking, fasting status, blood cell counts, RNA quality (RIN), batch effects, family structure EGCUT, FHS (gen1), InCHIANTI, KORA, RS, and SHIP

REPLICATION meta-analysis: 8,009 whole blood samples (7 cohorts)

Differential expression with age:

Linear model adjusted for sex, smoking, fasting status, blood cell counts, RNA quality (RIN), batch effects, family structure BSGS, DILGOM, FEHRMANN, FHS (gen3), GTP, HVH, and NIDDK/PHOENIX

GENERALIZATION: 4,644 samples (8 other tissues/cell types)

Differential expression with age in other tissues:

Brain (cerebellum + frontal cortex) (n=394), CD4+ cells (n=515), CD8+ cells (n=299) CD14+ cells / monocytes (n=567), LCLs (n=869), lymphocytes (n=1,244), and PBMCs (n=362) EGCUT, GARP, GENOA, BOSTON COHORT, MESA, NABEC-UKBEC, and SAFS

In total, 19,910 samples analyzed

CHARGE consortium Working Group RNA expression array data : TWAS by Age

Peters M,…van Meurs JB. Nat. Communic., Sept 2015

MICROBIOME: 362 (out of 900) OTU’s in 800 (out of 1700) faecal samples from the Rotterdam Study based on 16S

“Gut” Microbiome 16S Sequencing of 62 ERGO (RS-3 samples)

(Gut? >> faeces are actually analysed…) Radjabzadeh, Kraaij (unpublished)

A new source of physiological variability: the human “microbiome”

• Bacteria, yeast, virus, unicellular
• Everywhere in and on our body (gut,

nose, skin, ear, eye, urine, mouth, aerosols,..)

• Easy to type by 16S NGS (€40)
• changes with age, diet, etc.

“Translational Genomics”

Progress of translating DNA Research into the Hospital….. We are here….