“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”) STR MARKER 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”) STR MARKER 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:

Strategy (2): Our group proposed a highly polygenic model for complex traits and diseases in 2003 Genetics 2003; 163: Trends Genet 2003; 19:

HIGHLY POLYGENIC GENETIC BASIS (FEW RARE VARIANTS WITH LARGE 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 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 initially

 : The British Council  : The Wellcome Trust  : Medical Research Council UK (1/3)  : Ministry of Science and Technology, Croatia  : The Royal Society, UK  : National Institutes of Health, USA  : Medical Research Council UK (2/3)  : EU fp6 EUROSPAN  : Medical Research Council UK (3/3)  : Ministry of S & T, Croatia (The Croatian Biobank) Grants awarded (£ 4.0 M) Strategy (4): Finding money to start a large cohort

“Susak-10”: served to choose the most appropriate population ( ) 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 ( )

COHORT 3. (969 examinees) “Korcula”: genotyped with Illumina 370 k CNV ( )

COHORT 4. (1001 examinees) “Split”: outbred population genotyped with Illumina 370 k CNV ( )

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)

First analyses of data using SNP

2008: uric acid & gout 2009: lipid levels & coronary heart disease 2010: fasting glucose & diabetes type 2 Nat Genet : FVC, FEC & chr. lung disease Nat Genet : 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: Results of GWAS of QTs with “disease risk” studied

2009: smoking initiation and intensity Nat Genet : 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: e PLoS Genet 2009; 5: e Results of GWAS of QTs without disease risk links

Strategy (5): Next moves (plan for ) 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: 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: 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 FOR GLOBAL 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:

FUT8: associated with GP1 in 1,000 subjects (p=5.09 x x ) Results of GWAS study (Vis island, Croatia):

Strategy (5): Next moves (plan for ) 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 ) 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 ) 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”: