Nagy áteresztő képességű SNP mérések hasznosítása Dr. Szalai Csaba
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SNP-k • SNP = single nucleotide polymorphism = kb. pontmutáció • Általában biallélikusak, általában funkcionálisan semlegesek • Több mint 5 millió SNP van, melynek a gyakorisága >10%, 11 millió melynek >1%, Jelenleg a dbSNP adatbázisban 18 milliót találhatunk. 3
Teljes genom asszociációs vizsgálatok • Összehasonlítják az SNP-k eloszlását betegekben és egészségesekben a teljes genomban • Probléma: nagyon nehéz értékelni ilyen óriási adathalmazt (pl. 500.000x1.000 = 500 millió adat csak az SNP-kből egy 1000 fős populációnál): szakképzett bioinformatikusok kellenek 4
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Illumina
iScan System
The Human610-Quad BeadChip provides high-density genomic coverage of tag SNPs and markers designed to detect both known and novel CNV regions. It features more than 550,000 evenly spaced tag SNPs derived from HapMap data. Approximately 60,000 additional markers, developed in collaboration with deCODE Genetics, specifically target regions known or likely to contain CNVs, including segmental duplications and regions in the unSNPable genome. In combination with our Infinium HD BeadChips, an iScan System can report up to 225 million genotypes in a single day, offering the fastest path to discovery.
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Affymetrix
The new Affymetrix® Genome-Wide Human SNP Array 6.0 features more than 1.8 million markers for genetic variation, including more than 906,600 single nucleotide polymorphisms (SNPs) and more than 946,000 probes for the detection of copy number variation. The SNP Array 6.0 enables high-performance, high-powered and low-cost genotyping. 7
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GenomeLab SNPstream Genotyping System, Beckman SE Genetikai Sejt- és Immunbiológi ai Intézet: Core facility labor Throughput 4,608 to 800,000 genotypes in 24 hours (12 plex PCR) 18,432 to 3,200,000 genotypes in 24 hours (48 plex) 11
Különböző nagy áteresztőképességű (high throughput) rendszerek összehasonlítása • SNP chip: • Teljes genom asszociációs vizsgálatok: pl. 1,8 millió marker/array (Affymetrix)
SNP-k multiplex PCR-rel: pl. Single base extension (Beckman Coulter SNPstream system): Jelölt gén asszociációs vizsgálatok Jelölt régió asszociációs vizsgálatok (positional cloning)
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Jelölt régió asszociációs vizsgálat Kapcsoltsági analízis után a maximális LOD-ot tartalmazó két marker között sűrűbb markerekkel (pl. SNP-k) asszociációs vizsgálat. Nem kell ismerni a pathomechanizmust. Sok mintát igényel: sok rekombináció kell.
Pl. SE Core facility 13
Tag
SNP primer
5’
SNP site
3’
5’ PCR target
Primer Extention
Labeled terminating NTP 5’
3’ Denature & Hybridize Substrate (spot on plate)
SNP Primer
Tag complement for Hybridization capture 14
SNPstream 12 plex plate egy lyuka Imaging Channel 1 (Allele X) G
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Imaging Channel 2 (Allele Y)
G
C
C
G
G
C
C
G
G
C
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C
GC
CC
CC
CC
GC CC
GG CC
C
C
C
GG GC
GC
GG
GG
GG
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48-plex Software
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12, illetve 48plex, illetve a PCR-ekhez, illetve a single base extension-höz szükséges on line primer set tervező.
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Példák a rendszer lehetséges alkalmazására
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Példa egy poligénes betegség teljes genom szűrésének eredményére (LOD score analysis)
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Asztma genomszűrések eredményei
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Az SNP adatok és a klinikai paraméterek Bayes hálós statisztikai elemzése
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Screening of susceptibility genes of asthma on chromosome 11 and 14 Petra Sz. Kiszel* 1, Ágnes F. Semsei 1, Ildikó Ungvári 1, Adrienne Nagy 3, Márta Széll 4, Béla Melegh 5, Péter Kisfali 5, Péter Antal 6, Gábor Hullám 6, András Falus 1, Csaba SzalaiSemmelweis 2,3 1 Department of Genetics, Cell- and Immunobiology, University, Budapest, Hungary 2 Section of Immunogenomics, Hungarian Academy of Sciences, Budapest, Hungary 3 Heim Pál Pediatric Hospital, Budapest, Hungary 4 Dermatological Research Group of the Hungarian Academy of Sciences and the University of Szeged, Szeged, Hungary 5 Department of Medical Genetics and Child Development, University of Pécs, Pécs, Hungary 6 Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
Introduction
During the last decades bronchial asthma has become the most common disease of childhood. It is a chronic inflammatory disease influenced by a combination of poorly understood genetic and environmental factors. Hundreds of genome-wide linkage analysis and association studies have identified several chromosomal regions harbouring asthma susceptibility genes like chromosome 5q31, 11q12, 14q22, 17q21. More than 120 candidate genes for asthma have been described. However, not all of them have been confirmed in independent studies. The aim of the present study was to identify new potential genes in disease-associated regions, such as 11q12.2-q13.1 and 14q22.1-q22.3
Materials and methods 765 non-asthmatics and 435 asthmatics subjects were recruited and genotyped for 145 SNPs (single nucleotide polymorphisms) in chromosome region 11q12.2-q13.1 and 14q22.1-22.3. These SNPs were determined by multiplex polymerase chain reactions with single base primer extension assays (GenomeLab SNPstream, Beckman Coulter). The haplotype patterns and linkage disequilibrium between SNPs were computed by Haploview 4.1 software. The data were analysed with Bayesian multilevel analysis, conventional logistic regression and χ2 test.
Bayesian network Bayesian networks provide a more complex framework than logistic regression and χ2 test and allow arbitrary relations between all variables. The Bayesian network is a probabilistic model that consits of two parts: a dependency structure and local probability models. The dependency structure specifies how the variables are related to each other by drawing directed edges between the variables. Usually, a variable only depends on a few other variables, called the parents. The second part of this model, the local probability models, specifies how the variables depend on their parents.
Chromosome 11q12
Results
Figure 1.
Figure 2. The haplotype patterns and linkage disequilibrium of 7.5 Mb long region in chromosome 11 can be seen in asthmatic (Figure 1.) and non-asthmatic subjects (Figure 2). Further haplotype analysis is in progress by Bayesian methods.
Chromosome 14q22.1-q22.3
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Figure 4. The haplotype patterns and linkage disequilibrium of 3.5 Mb long region in chromosome 14 can be seen in asthmatic (Figure 3.) and non-asthmatic subjects (Figure 4). Further haplotype analysis is in progress by Bayesian methods.
Candidate SNPs
Chr
Function
Gene symbol
Gene name
rs10144326 rs2075598 rs10498475 rs17831682 rs607639 rs7127662 rs525574 rs3794042
chr14 chr14 chr14 chr14 chr11 chr11 chr11 chr11
synonymous intron 3’ near gene 3’ UTR downstream downstream intron intron
DLGAP5/DLG7 LGALS3 LGALS3 PTGDR
Discs large (Drozi) homolog-associated protein 5
MS4A6A
Member of MS4A family
TMEM132A
Hsp70 protein5 binding protein1
rs7928208
chr11
intron
PRPF19
PRPF19/PSO4 pre-mRNA processing factor 19 homolog
Galectin-3 Prostaglandin D2 receptor
Bayes DAG-MCMC (probability) 0.9475 0.9980 0.8706 0.7956 0.5508 0.5422 0.9997 0.9974
χ2-test (p-value) 0.0519 0.1702 0.7769 0.0008 0.2584 0.1852 0.5043 0.2541
Logistic regression (OR (95%CI)) 0.8363 (0.5902-1.1849) 0.5892 (0.2731-1.2711) 0.8891 (0.6245-1.2658) 1.9979 (1.7474-2.2843) 0.8883 (0.5642-1.3985) 0.8210 (0.6646-1.0141) 1.0171 (0.7926-1.3052) 0.9834 (0.8272-1.1691)
1.0
0.0100
1.8530 (1.6147-2.1266)
Table1. 9 SNPs in 6 genes were shown to be associated with asthma by Bayesian multilevel analysis (DAG-MCMC: directed acyclic graphs-Markov Chain Monte Carlo). Those SNPs and genes whose roles in asthma susceptibilty were confirmed by χ2-test and logistic regression had blue background.
Discussion
Some of these results confirm several previous studies. Previously, the PTGDR and LGALS3 genes have also been found to be associated with asthma. DLG7 is in linkage disequilibrium with LGALS3. Another two genes TMEM132A and PRPF19 are strongly linked to an asthma susceptibility gene, G-protein coupled receptor (gpr44). New potential candidate gene could be MS4A6A. MS4A6A might have similar function as the FcepsilonRI beta subunit, one of the most studied asthma susceptibility genes, because they have similar structure and both belong to the membrane spanning 4 domains protein family. Further studies are in progress to reveal the functional role of these new candidate genes.
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Jelölt gén asszociációs vizsgálat: OBEKON Obezitás genetikai hátterének vizsgálata
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A teljes genom genotipizálásának gyakorlati alkalmazásai ‘Personal genomics’
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Köszönöm a figyelmet!
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