Sardinia as a model popula/on
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1 Sardinia as a model popula/on Francesco Cucca francesco.cucca@irgb.cnr.it Institute for Genetic and Biomedical Research (IRGB), Italian National Research Council (CNR). Medical Genetics, University of Sassari. SardiNIA/progeNIA Università di Sassari
2 Why genetic studies in Sardinia?
3 Neighbour-joining tree of different European populations using the genetic distances computed from the distribution of the Y chromosome SNPs haplotypes CROATIA UKRAINE SARDINIA HUNGARY POLAND CATALONIA BASQUE COUNTRY GEORGIA ANDALUSIA CORSICA NORTH-CENTRAL ITALY ALBANIA GREECE LEBANON TURKEY SICILY CALABRIA
4 MOROCCO Y chromosome variation in Europe SAAMI UDMURT MARI CZECH AND SLOVAKIAN DUTCH UKRAINIAN FRENCH POLISH CROATIAN HUNGARIAN GEORGIAN CENTRAL-NORTHERN ITALIAN MACEDONIAN ALBANIAN SPANISH BASQUES CALABRIAN TURKISH SYRIAN ANDALUSIAN GREEK LEBANESE
5 H.n. 22 DE A1b1b2b E1a E1b1b1a1 E1b1b1b2 E1b1b1b1 F3 G2a2b 33 (AB)T 200 CT G2a3 I1a3a2 I2c I2a2a I2a1b I2a1a I2a1a I2a1a I2a1a J1c J2b J2a T L Q1a3c R2a1 R1a1a1 R1b1c R1b1a2 SNPs %
6 By courtesy of G. Manca
7 By courtesy of G. Manca
8 The Sardinian sequencing project
9 The Sardinian DNA sequencing project Why: 1)To correct for the initial circular ascertainment bias due to the indirect mapping approach 2) To incorporate into one experimental step both the initial detection and the so called fine mapping stages
10 Why in Sardinia? Size of the population at the foundation and during its history large enough to maintain high interindividual variability Lack of substantial population sub-structure, due to lack of large scale genetic flow from outside populations. Excellent depiction of genetic variation in Europe. Unique distribution of alleles at multiple loci due to founder effects
11 Milestones in 2 years: More samples, more SNPs Confirmed sites in dbsnp 132 Novel sites
12 FREQUENCY DISTRIBUTION OF THE DETECTED VARIANTS #Variants (Millions) MAF 0-0.5% 0.5-1% 1-5% 5-10% 10-50%
13 Satura/on, log scale FracKon of variants Data freeze 505 ID Data freeze 1123ID
14 Imputa/on quality on Sardinians Reference panel (sample size) 1000G Europeans (381) Sardinians (347) Sardinians (831) Sardinians (1488) Imputa/on accuracy (r 2 ) MAF 1-3% MAF 3-5% MAF >5%
15 Milestones in 2 years: More samples, less errors Heterozygous mismatch rate between sequence and Metabochip
16 Disegno Sperimentale Low Pass (~4x) sequencing, last data freeze: N=2,120 circa 3000 DNA Low- coverage Experimental design Sardinian Reference panel Array Data in all samples N. ~6,500 SardiNIA cohort N. ~10,000 MS, T1D, controls IMPUTATION Associa/on tes/ng 1000 Genomes
17 How to apply this information?
18 QuanKtaKve trait QualitaKve trait
19 Key ingredients 1) Deep analysis of the genetic information, capitalizing on the unique genetic features of the population 2) Careful selection of phenotypes (diseases and traits) in statistically well powered sample sets
20 Quan/ta/ve trait analysis
21 ProgeNIA Arzana Elini Ilbono Lanusei 21
22 Ra/onale Focus on geographically clustered towns to permit extensive phenotyping with enough power to find associations with traits (mainly quantitative). The structure of the project facilitates repeated visits (longitudinal studies), allows precise measures of heritability and simplifies imputation strategies.
23 The SardiNIA/ProgeNIA project ü It began in 2001, funded by the USA NIH/NIA ~7,000 volunteers, grouped in ~700 families. recruited from a cluster of 4 towns in the Ogliastra region Mainly focused on quantitative traits Longitudinal study repeated (and new) measurements every ~3 years
24 Enrollment, data collection Medical examination: Anthropometric measurements Height Weight Hip Waist BMI Cardiovascular Profile Blood pressure mesuared arms; lower legs; ankle Brachial Pressure Index Electrocardiogram Holter Monitor (24h) Echocardiogram
25 Enrollment, data collection Cardiovascular Profile Pulse Wave Velocity (PWV) SphygmoCor CP Central Blood Pressure Assessment and Management System Carotid Ultrasound
26 Enrollment, data collection Assessment of frailty Performance-based Assessments of Lower extremity Function walking speed in a four meter test Muscle strength testing by hand Grip strength A lower leg PQCT scan
27 ü Anthropometric Measurements Height, Weight, Hip, Waist, BMI ü Blood Chemistry and hematological parameters LDL, HDL, TG, Insulin, RBC, MCH, MCV, HbF, HbA2, Hb etc. ü Cardiovascular Traits quanktakve traits ü Bone mineral density and physical tests HR, SBP, DBP, PP, PWV, IMT, etc ü Personality Facets Neuroticism, Extraversion, Openess, Agreeableness, Coscientiousness, etc. ü Cytokines and pro-inflammatory molecules MCP-1, hscrp, IL-6, V-CAM, AGE etc. ü New traits under analysis: immuno-phenotypes (24)
28
29 A significant finding: LDL loci Immuno+MetaboChip Immuno+MetaboChip + sequence Also By GWAS, PCSK9 Also By GWAS, APOE Also By GWAS, LDLR Also By GWAS, SORT1 - log(p- value) Also By GWAS, APOB
30 A locus detected only by sequencing Immuno+MetaboChip Immuno+MetaboChip + sequence Only By Sequencing, HBB - log(p- value)
31 Hemoglobin- β Q39X variant Responsible for β0-thalassemia in homozygotes Protects against malaria in heterozygotes Rare in Europe, outside Sardinia Higher frequency in Sardinia (MAF ~5%) due to balancing seleckon effect against malaria Lowers LDL in heterozygotes (Maioli et al, 1989)
32 Q39X signal on is hard to detect using other approaches Affymetrix genotypes GWAS and HapMap, 1000G ImputaKon: No Signal Metabochip genotypes: Looks like a false posi/ve Singleton in 1000G and NHLBI ESP: Hard to impute Immuno+Metabochip + Sardinia panel: associa/on 4x10-18 with many surrounding SNPs Affymetrix + Sardinia Panel: associakon 2x10-8 with some surrounding SNPs True p- value aber genotyping ~10-23
33 The Immune system project analysis based on frac/ona/on of Immune cell subtypes by FACS: Current status report
34 Why assessing immune traits and disease?
35 Important features of the immune system have been discovered but there is still a large gap in understanding how the immune system functions and malfunction This lack of knowledge affects current therapies for many autoimmune diseases
36 Gaps in knowledge: Thus far the information about how the immune system is regulated derives largely from work in rodents, which cannot always be translated to humans. Genome-wide association scans (GWAS) have led to advances in the understanding of a number of complex immune-related diseases, but this information is still very partial and difficult to interpret mechanistically. Only some variants associated with quantitative variation of broadly classified cell populations and molecules have been identified, whereas very little is known about the genetic control of the levels of particular subsets of cells.
37 What have we measured so far? We measured 95 cells types, and considered absolute cell counts, but also percentage respect to parental and grandparental lineages. A total of 272 quantitative traits were available for analyses, broadly divided in the following classes: 87 General leukocyte sub-populations (B and T cells, Natural Killer, Monocytes and so on) 35 T-regulatory cells 19 Dendritic cells 131 T cells maturation stages
38
39 Level of all traits show high heritability Broad H2 Mean (Min-Max) Narrow H2 Mean (Min-Max) Treg 0.68 ( ) 0.45 ( ) DC 0.59 ( ) 0.37 ( ) MT 0.57 ( ) 0.35 ( ) TBNK 0.57 ( ) 0.36 ( ) ALL 0.62 ( ) 0.40 ( )
40 Heritability 40 16/03/2012 CNR
41 For each immune trait Disegno Sperimentale GWAS Affy+HapMap 1,403 individuals/~2.2 M markers GWAS I-Chip+M-Chip+sequence 1,628 individuals/~8.2 M markers Selection of independent lead SNPs Check overlaps with common autoimmune diseases (r2 >0.5) GWAS Catalogues Our own MS association data (~3,000 cases/~4,000 controls) List of lead SNPs-trait pair Create candidate gene list genes +/- 25Kb from lead SNP genes of csnps in LD with lead SNP genes affected by eqtls in LD with the lead SNP genes +/-100Kb involved in immune-related diseases (OMIM) Uniss CNR
42 GWAS - 53 associated loci Affy+HapMap Immunochip+Metabochip Sequencing 42 16/03/2012 CNR
43
44 Are the same DNA variants associated with these quantitative traits also associated with immune-related diseases?
45 Disease (GW significant) FACS GW significant Loci Traits (Indep) Celiac Disease 2 3 (1) Chronic kidney disease 1 3 (1) Chronic lymphocytic leukemia 1 2 (1) Hodgkin's lymphoma 1 8 (2) Multiple sclerosis 1 1 (1) Parkinson's disease 1 2 (1) Progressive supranuclear palsy 1 6 (2) Rheumatoid arthritis 2 7 (3) Systemic sclerosis 1 4 (2) Type 1 diabetes 2 9 (2) Ulcerative colitis 1 2 (1) Independent loci associated with diseases 5 Sample size: ~ 1600 individuals
46 @IL2RA gene association with CD25 cells Dendrou et al. studied the top T1D SNP rs (r2=0.77 with our top SNP) Uniss CNR
47 Associa/on in the SH2B3 gene region on chromosome 12q24 with the level of CD3+ posi/ve cells and other related traits Uniss CNR
48 To extend and better assess the association with immune related diseases: the Sardinian MS autoimmunity project Uniss CNR
49 Very low (< 30) Uniss Low (30-80) High (> 80) CNR Multiple Sclerosis prevalence (x 100,000) in various world-wide countries
50 Multiple Sclerosis prevalence in Europe and in some Mediterranean countries High risk Moderate risk Low risk 47 Pugliatti et al (EBC), Eur J Neurol Uniss CNR
51 T1D incidence (at age 0-14yrs x 100,000) in various world-wide countries High (> 20) Moderate (10-19) Low (5-9) Very low (< 4) Uniss CNR
52 T1D incidence in Europe and In some Mediterranean countries (at age 0-14yrs)* High risk Moderate risk Low risk *Adapted from EURODIAB Uniss CNR
53 THE AUTOIMMUNITY PROJECT ~3,500 healthy controls, recruited from all over the Island from the main transfusion blood centers Cases from mulkfactorial traits collected from hospitals and University clinics from all over the Island. Specifically: ~ 3,000 MulKple Sclerosis cases ~ 2,000 Type 1 Diabetes cases
54
55 CBLB gene: results using imputa/on from Sardinian sequencing
56 Immune cell phenotypes preliminary results Quantitative traits, represented by the levels of most of the immune cell populations, exhibit substantial heritability In a preliminary GWAS analysis we found a large number of genetic associations with these quantitative traits Some of these variants coincide with previously and newly detected MS and other autoimmune diseases associations, providing mechanistic clues for them
57 Conclusions: Excellent coverage of variation present in Europe The founder population augments the signal from gene variants rare elsewhere Found variants/genes are also important in other populations.
58 The SardiNIA/ProgeNIA Team Mariano Dei Sandra Lai Antonella Mulas Edoardo Fiorillo Valeria Orrù Magdalena Zoledziweska Maria Grazia Piras Monia Lobina Francesca Virdis Michele Marongiu Marco Masala Manuela Uda Serena Sanna Laura Crisponi Silvia Naitza Maria Grazia Pilia Liana Ferreli Francesco Loi Nicola Curreli Elen Balalci Stefano Loi Alessandro Deliatala Marco Orru Monica Lai Anna Cau Paola Loi Susanna Murino Lina Brundu Founded by Giuseppe Pilia and David Schlessinger with the constant support of Antonio Cao
59 Data and sample acquisition (patients) Marisa Marrosu Giulio Rosati Maurizio Melis Stephen Sawcer Jan Hillert Data and Sample Acquisition (controls) Maristella Piztalis Magdalena Zoledziweska Alessia Loi Sonia Sanna Roberta Piras Nazario Olla Mario Loviccu Fausto Poddie Data Base GianMauro Cuccuru Gianluigi Zanetti Luca Lianas Ilenia Zara
60 Statistical Team (Cagliari/Pula): Serena Sanna Carlo Sidore Maristella Steri Gabriella Sole Eleonora Porcu Giorgio Pistis Fabrice Danjou Maria Valentini Ilenia Zara Mauro Pala Sequencing team (Pula): Chris Jones Frederic Reinier Riccardo Berutti Rossano Atzeni Lidia Leoni Gianluigi Zanetti Marco Orrù Andrea Angius Fabio Busonero Roberto Cusano Francesca Urru Marco Marcelli Manuela Oppo Rosella Pilu Immunogenetics lab (Pula): Maristella Pitzalis Magdalena Zoledziewska Francesca Deidda
61 Laura Crisponi Silvia Naitza Mara Marongiu Manuela Uda Paolo Moi Giusi Marini Angela Loi Francesca Crobu Loredana Porcu Susanna Porcu Wieslawa Mentzen Francesca Manchinu Maria Franca Marongiu Francesca Anedda Sonia Sanna Alessia Loi Michael Whalen Mauro Congia Matteo Floris Stefania Olla Silvana Urru Uniss CNR
62 Acknowledgements: David Schlessinger Goncalo Abecasis Giuseppe Pilia John Todd Paolo Zanella Uniss CNR
63 To the memory of Prof.Antonio Cao Father of modern pediatrics and geneics in Sardinia
64
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