Nagy áteresztő képességű SNP mérések hasznosítása. Dr. Szalai Csaba

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1 Nagy áteresztő képességű SNP mérések hasznosítása Dr. Szalai Csaba 1

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3 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

semlegesek Több mint 5 millió SNP van, melynek a gyakorisága

4 Teljes genom asszociációs vizsgálatok Összehasonlítják az SNP-k eloszlását betegekben és egészségesekben a teljes genomban Probléma: nagyon nehéz értékelni ilyen óriási adathalmazt (pl x1.000 = 500 millió adat csak az SNP-kből egy 1000 fős populációnál): szakképzett bioinformatikusok kellenek 4

értékelni ilyen óriási adathalmazt (pl. 500.000x1.

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6 Illumina 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. iscan System 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. 6

Approximately 60,000 additional markers, developed in collaboration with decode Genetics, specifically target regions known or likely to contain CNVs,

7 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

polymorphisms (SNPs) and more than 946,000 probes for the detection of copy number

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11 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

labor Throughput 4,608 to 800,000 genotypes in 24 hours

12 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) 12

1,8 millió marker/array (Affymetrix) SNP-k multiplex PCR-rel: pl.

13 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

14 Tag 5 SNP primer SNP site 3 5 PCR target Primer Extention Labeled terminating NTP 3 5 Substrate (spot on plate) Denature & Hybridize SNP Primer Tag complement for Hybridization capture 14

15 SNPstream 12 plex plate egy lyuka Imaging Channel 1 (Allele X) Imaging Channel 2 (Allele Y) G G C C G G C C C C G G G G G C C C GC CC GG CC CC GC GC GC GG GG GG CC CC GG 15

16 48-plex Software 16

17 12, illetve 48plex, illetve a PCR-ekhez, illetve a single base extension-höz szükséges on line primer set tervező. 17

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20 Példák a rendszer lehetséges alkalmazására 20

21 Példa egy poligénes betegség teljes genom szűrésének eredményére (LOD score analysis) 21

22 Asztma genomszűrések eredményei 22

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24 Az SNP adatok és a klinikai paraméterek Bayes hálós statisztikai elemzése 24

25 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, 1 Department of Genetics, Cell- and Immunobiology, Csaba Szalai Semmelweis 2,3 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 14q 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 χ 2 test. Bayesian network Bayesian networks provide a more complex framework than logistic regression and χ 2 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. Results Chromosome 11q12 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 Figure 3. 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 rs rs rs rs rs rs rs rs rs Bayes DAG-MCMC χ 2 -test Logistic regression Chr Function Gene symbol Gene name (probability) (p-value) (OR (95%CI)) chr14 synonymous DLGAP5/DLG7 Discs large (Drozi) homolog-associated protein ( ) chr14 intron LGALS ( ) Galectin-3 chr14 3 near gene LGALS ( ) chr14 3 UTR PTGDR Prostaglandin D2 receptor ( ) chr11 downstream ( ) MS4A6A Member of MS4A family chr11 downstream ( ) chr11 intron ( ) TMEM132A Hsp70 protein5 binding protein1 chr11 intron ( ) chr11 intron PRPF19 PRPF19/PSO4 pre-mrna processing factor 19 homolog ( ) 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. 25

26 Jelölt gén asszociációs vizsgálat: OBEKON Obezitás genetikai hátterének vizsgálata 26

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28 A teljes genom genotipizálásának gyakorlati alkalmazásai Personal genomics 28

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34 Köszönöm a figyelmet! 34

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