“10,001 Dalmatians” research programme:

Discovery of genetic variants that control

human quantitative traits and predispose to diseases

Igor Rudan, Mladen Boban, Tatijana Zemunik, Gordan Lauc, Zoran Đogaš, Stipan Janković, Ivica Grković, Ana Marušić,

Janoš Terzić, Rosanda Mulić, Vjekoslav Krželj, Lina Zgaga, Zrinka Biloglav, Ivana Kolčić, Marina Pehlić,

Grgo Gunjača, Danijela Budimir, Ozren Polašek

2001. – human genome sequence

was published

Main expectation (general public, investors, researchers, pharma and biotech industries):

Linking genes with diseases and development of new treatments and “personalized medicine” – the race towards this goal begun (each group with its own approach)

Main idea:

1)Find “markers” in the genome and “tag” the whole genome as densely as possible;

2)Find consistent associations between some of those markers and disease phenotypes

3)Find genes in proximity of implicated markers – they are “disease genes”

CASES(“affected”)

CONTROLS(“unaffected”)

STRMARKER A

STR MARKER B,C…

DISEASE GENE (MUTATION)

DISEASE GENE (WILD TYPE)

Short tandem repeats (STR) –

e.g. (TA)x4 or (CTG)x7

– hundreds of STRs across

the genome

- STR marker maps were not dense, but they were still very useful to “pick” genes that caused monogenic (Mendelian) diseases

Problems with genome-wide linkage analyses using genome-wide STR maps:

1)STR markers and diseases were not always 100% linked because of incomplete penetrance of causing mutations or genetic heterogeneity of the disease: low study power

2)STR markers and disease genes were not always 100% linked because of recombination (crossing over) between them: low study power

CASES(“affected”)

CONTROLS(“unaffected”)

STRMARKER A

STR MARKER B,C…

DISEASE GENE (MUTATION)

DISEASE GENE (WILD TYPE)

Problems with genome-wide linkage analyses using genome-wide STR maps:

3) Even when a marker closest to disease gene was found with nearly 100% certainty, it still took years to find all candidate genes in regions up to 10 megabases (or more) and sequence them all to find exact causal mutation

4) Good ideas:

-Choose to study phenotypes that are precisely measurable and in good correlation with genotypes-Use populations with large linkage disequilibrium

Strategy (1): Our group proposed to rely on isolated populations (for increased LD) and pedigree-based approach (adds information) in 1999

Nat Genet 1999; 23: 397-404

Strategy (2): Our group proposed a highly polygenic model for complex traits and diseases in 2003

Genetics 2003; 163: 1011-1021

Trends Genet 2003; 19: 97-106

HIGHLY POLYGENIC GENETIC BASIS (FEW RARE VARIANTS WITHLARGE EFFECTS AND MANY COMMON WITH SMALL EFFECTS)

“-OMICS” LEVEL (PROTEOMICS, LIPIDOMICS,GLYCOMICS, METABOLOMICS)

QUANTITATIVE TRAIT LEVEL (e.g. CHOLESTEROL, BLOOD PRESSURE)

Our understanding of complex traits and diseases:

COMPLEX DISEASE PHENOTYPE

ENVIRONMENT

ENVIRONMENT

ENVIRONMENT

GWAS: MOST POWER & FUNCTIONAL RELEVANCE

Strategy (3): Our group proposed to measure large number of QTs (closer to genes - power, more chance, later - networks)

Quantitative traits: More than 100 selected initiallyANTHROPOMETRIC MEASURES PHYSIOLOGICAL MEASURES ELECTROCARDIOGRAM

Body height Systolic blood pressure (1&2) ECG (30 sec, digital)*

Body weight Diastolic blood pressure (1&2) P duration

Bicondylar brachial width Impedance - body resistency PR interval

Abdomen circumference Impedance - body reactancy QRS duration

Hip circumference Ankle-brachial BP indeks QT interval

Brachial circumference Spirometry - FVC QTc interval

Biceps skinfold Spirometry - FEV1 P axis

Triceps skinfold Spirometry - PEF QRS axis

Subscapular skinfold Spirometry - FEF25 T axis

Suprailiac skinfold Spirometry - FEF50

Abdomen skinfold Peak flow

Head circumference Bone mineral density

COGNITIVE & SLEEP TRAITS EYE MEASURES LIFESTYLE

Eysenck Personality Inventory Retinal art:ven diameter ratio Family disease history

Digit-symbol test Retinal art leng:diam ratio Birth weight

Mill-Hill vocabulary Retinal art branching angle Medical/surgical history

Standard Progress. Matrices Retinal arteriolar tortuosity Menstruation, menarche, HRT

Controlled Oral Word Assoc. Retinal arterjunction expon Rose Angina questionnaire*

Weschler Memory Scale Intraoccular pressure, OD, OS Claudication questionnaire*

Munich Chronotype Question. Fundus photography Respiratory questionnaire

GHQ-30 Autorefractor-measurements Physical activity

Intra-ocular length-measur. Smoking

Alcohol

Diet

Socioeconomic status

Quantitative traits: More than 100 selected initiallyBIOCHEMICAL MEASURES LIPIDOMICS MARKERS OF INFLAMMATION

Creatinine A large number (several FibrinogenUric acid hundred) of circulating von Willebrand's factorTotal cholesterol lipid metabolytes, e.g. D-dimersTriglycerides 132 phospholipids, CRP

HDL 70 sphingolipids, fatty acids, tPA inhibitor

LDL apolipoproteins, etc.

CalciumPhosphorousAlbuminHbA1c

Glucose

GLYCOMICS URINE TRAITS GENOTYPING

16 main groups of N-glycans, A larger number of traits Cohort 1: STR typing

4 additional groups based on quantitated in urine samples Cohort 2: 800 STR

number of antennas, and that are biomedically 317.000 SNP

3 derived variables relevant Cohort 3: 370.000 SNP + CNV

Cohort 4: 370.000 SNP + CNV

2000-2002: The British Council 2001-2004: The Wellcome Trust 2002-2003: Medical Research Council UK (1/3) 2002-2006: Ministry of Science and

Technology, Croatia 2003-2005: The Royal Society, UK 2003-2004: National Institutes of Health, USA 2003-2005: Medical Research Council UK (2/3) 2006-2009: EU fp6 EUROSPAN 2005-2010: Medical Research Council UK (3/3) 2007-2012: Ministry of S & T, Croatia (The Croatian Biobank)

Grants awarded 2000-2007 (£ 4.0 M)

Strategy (4): Finding money to start a large cohort

“Susak-10”:served to choose the

most appropriate population(2001-2002)

COHORT 1.(1001 examinee)

2003: The choice of further populations was based on demography data and population genetic studies

2003: The populations were extremely differentiated (based on analysis of 26 STR markers below); LD studies conducted using 8 STR markers on Xq13-12

COHORT 2.(1024 examinees)

“Vis”:genotyped with (i) 800 STRs and (ii) Illumina 317 k

(2003-2005)

COHORT 3.(969 examinees)

“Korcula”:genotyped with

Illumina 370 k CNV(2006-2007)

COHORT 4.(1001 examinees)

“Split”:outbred population

genotyped with Illumina 370 k CNV

(2008-2009)

Year 2005: BAD YEAR

We used 800 STR marker scan and analysed the data using genome-wide linkage analysis.

What did we find?

ABSOLUTELY NOTHING.

Other approaches (e.g. candidate genes and case-control studies)?

NO REPLICATIONS FOR ANY OF THE THOUSANDS OF REPORTED ASSOCIATIONS (…OK, MAYBE 4-5 MAX.)

The HapMap project

Tried to define “blocks” of genome between “recombination hotspots” and tag each one of them with one of more than 10 million predicted SNPs: new GWAS based on SNPs

Year 2006: TECHNOLOGICAL BREAKTHROUGH!

Affymetrix Inc. and Illumina Inc.:

Dense genome-wide scans using hundreds of thousands of SNP markers (from HapMap project – “tagging SNPs)

Year 2007: THE “BRAVE NEW WORLD” STUDY

(WTCCC, Nature, June 07, 2007)

2006.-2007. First analyses of data using SNP

2008: uric acid & gout

2009: lipid levels & coronary heart disease

2010: fasting glucose & diabetes type 2 Nat Genet 2010

2010: FVC, FEC & chr. lung disease Nat Genet 2010

2010: creatinine & chr. kidney disease Nat Genet 2010

(2011: blood pressure & stroke) JAMA 2011 ?

(2011: CFH & age-related mac. degeneration) Lancet ?

Nat Genet 2009; 41: 47-55Nat Genet 2008; 40: 437-442

Results of GWAS of QTs with “disease risk” studied

2009: smoking initiation and intensity Nat Genet 2010

2009: clotting factors VII, VIII & vWF Circulation 2010

(2010: sleep duration and latency) Nat Genet 2010 ?

(2010: human height, weight, WHR) 3 x Nat Genet 2010 ?

(2010: global lipids) Nature 2010 ?

(2010: cognitive traits) 2 x Nat Genet 2010 ?

(2010: ECG, urine, CRP, HbA1c, ABPI, P, cortisol…)

PLoS Genet 2009; 5: e1000539PLoS Genet 2009; 5: e1000504

Results of GWAS of QTs without disease risk links

Strategy (5): Next moves (plan for 2010-2012)

1. GWAS of -OMICS (“1 level down from QT”) & functional follow-up & systems biology / pathways

2. Development of novel methods for analysis of the effect of CNV and rare variants on human QTs

3. Expand the number of phenotypes measured in plasma in at least 3,000 examinees (e.g. ILs, etc.)

4. Whole-genome sequence for 1,000 examinees & the new round of consortia participation

Forthcoming (2010): GWAS of 132 circulating phospholipids (PLoS Genetics)

PLoS Genet 2009; 163: 1011-1021

Results of GWAS of LIPIDOMICS traits

Further interest of our group: GWAS of glycomics, proteomics, other metabolomics and functional follow-up

Progress in GLYCOMICS: dependent of measurement

Nature 2009; 457: 617-620

High-performance liquid chromatography (HPLC):- Glycoproteins immobilized

Rudd PM et al. (Natl. Inst. Bioprocessing Res. Train.):

refined chromatography approaches for analysis of glycosylation

- Glycans released- Fluorescent labels attached- Labelled sugars run on a normal phase HPLC column- Resulting peaks correlated to a pre-run dextran ladder

“GlycoBioGen”:A consortium led by

collaboration of Scottish, Croatian & Irish

institutions

CROATIAN CENTRE FORGLOBAL HEALTH

• Separation of plasma N-glycans in 16 chromatographic peaks using HPLC method (GP1-GP16): area under peak measured as a QT

Quantitation of glycans in human plasma:

• Unusual biological variability at population level• Significant effects of age, gender, environmental

factors• Highly varying heritabilities• Striking correlations with other biochemical QTs

J Proteome Res 2009; 8: 694-701

• FUT8: associated with GP1 in 1,000 subjects (p=5.09 x 10-8 - 7.07 x 10-8)

Results of GWAS study (Vis island, Croatia):

Strategy (5): Next moves (plan for 2010-2012)

1. GWAS of -OMICS (“1 level down from QT”) & functional follow-up & systems biology / pathways

2. Development of novel methods for analysis of the effect of CNV and rare variants on human QTs

3. Expand the number of phenotypes measured in plasma in at least 3,000 examinees (e.g. ILs, etc.)

4. Whole-genome sequence for 1,000 examinees & the new round of consortia participation

• CNVs (copy number variants):

• Nature (April 2010) – WTCCC – didn’t find any associations with disease at all;

• Rare variants:

• “Moving frames” method (by Eleftheria Zeggini at Sanger, Hinxton, Cambridge): MAGIC, DIAGRAM & SPIROMETA

• “Exome sequencing” (4-10x)

• “Deep whole-genome sequencing” (48x)

“Missing heritability”:

Strategy (5): Next moves (plan for 2010-2012)

1. GWAS of -OMICS (“1 level down from QT”) & functional follow-up & systems biology / pathways

2. Development of novel methods for analysis of the effect of CNV and rare variants on human QTs

3. Expand the number of phenotypes measured in plasma in at least 3,000 examinees (e.g. ILs, etc.)

4. Whole-genome sequence for 1,000 examinees & the new round of consortia participation

• Gordan: N-glycans

• Zoran: CRD series

• Tatijana i Vesela: T4, TSH

• Mladen: markers of oxidative stress?

• Janoš: proteomics?

• Rosanda: anti-HBV antigens?

• Ana: interleukins, CD4?

“Expand phenotypes”:

Strategy (5): Next moves (plan for 2010-2012)

1. GWAS of -OMICS (“1 level down from QT”) & functional follow-up & systems biology / pathways

2. Development of novel methods for analysis of the effect of CNV and rare variants on human QTs

3. Expand the number of phenotypes measured in plasma in at least 3,000 examinees (e.g. ILs, etc.)

4. Whole-genome sequence for 1,000 examinees & the new round of consortia participation

• Wellcome Trust Sanger Institute, Hinxton, Cambridge: agreement that 400 / 2500 first examinees with WGS will be Croatians (Korcula)

• Why? – genealogies (expanding the number through “imputation”) and dense phenotyping (hundreds of QTs)

• Project will start: end of 2011

• Value for us: GBP 4 million at present time; should get us into the “next wave” of consortia work; needs Vesna Boraska etc.

“Whole-genome sequence era”: