Digital SNP Analysis of Cancers - 基因體學講義genomed.dlearn.kmu.edu.tw/DigitalSNPCancer.pdf · Digital PCR transforms the exponential, analog signals obtained from conventional

Digital SNP Analysis of Cancers

Hsueh-Wei Chang1 張學偉

Email: [email protected]://genomed.dlearn.kmu.edu.tw

1. Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University

SNP = Single Nucleotide Polymorphism

(read in SNiP)

SNPs are an abundant form of common genome variation, distinguished from rare variations by a requirement for the least abundant allele to have a frequency of 1% or more.

4 states of a SNP are possible (A,T,C,G)

TCATCTGTGGGAAGGGAGTCCCTGGCTAATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTGATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTAATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTGATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCC

diffe

rent

alle

les

TCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTCATACTGCCCCCACCACAGCTCCTCATCTGTGGGAAGGGAGTCCCTGGCTTATACTGCCCCCACCACAGCTCC

diffe

rent

alle

les

But the diallelic type is the most common SNP (e.g. T->C)

SNPs will be evenly distributed across the human genome

~ 25,000 non-synonymous cSNPs

~ 50,000 synonymous cSNPs

~ 25,000 regulatory region SNPs

~ 50,000 intragenic non-coding SNPs

~ 50,000 distributed intergenic SNPs

SNP Analysis

Linkage & Association Analysis

PharmacogenomicsDiagnosis

Personalized Medicine

Genetic Instability

Genetic instability &Microsatellite markers

Figure 9.8

Primers

Microsatellite markerParents

heterozygous

Parentsdelete

Normal Delete

Primer

Microsatellite marker

Parentsdelete Primers

destroy

Normal Delete


Lose of heterozygosity(LOH)

Microsatellite marker needs the long template (~200 to 350 bp)

Intact DNA

Degraded DNA No PCR[degrade partly]

false-positive for LOH

Normal Cancer Mixed

Oral Oncology 36 (2000) 484-490

DNA released from non-neoplastic cells, canmask allelic imbalance (AI) because it is difficult to quantify the allelic ratio usingmicrosatellite markers.

Jump from Analogue


SNP marker

to Digital

Principle of Digital SNP Analysis

Why called it “ Digital ”

Microsatellite markers occur less frequent than SNP

Molecular Beacons : Structure

Sanjay Tyagi and Fred Kramer (1996) Nature Biotechnology 14: 303-308

fluorophore quencher

SNP-specific Molecular Beacon

Ch SNPs Forward Primer Reverse Primer Molecular Beacon-green (flouorescein) Molecular Beacon-red (Hex)1p 8118 CAGGGCAAGACGCTGTGGT AACAGAATGTGCTTCCCTCCC CACGCTGCCCAGCGCACGGCCGTG CACGCTGCCCAGTGCACGGCCGTG7q 273 AGGGCTAGAGTATGAGAAGTCC GTAATTTAGGTGAGCTATCCAGAG CACGGTTTTTTTTCCCTATAACGTG CACGGTTTTTTTTTCCTATAACGTG8p 1085 CACTGAATGCTCTGCCATGA AACCTGTCCTTGTGGGTGAT CACGATGAGCCACAAGCAGCACGTG CACGATGAGCCGCAAGCAGCCGTG12p 852 TGATCTGCTTCTCCCACGA TGGAGTCCCAGACATTGCA CACGATCACGTCCGTGGCCTTCCGTG CACGATCACGTCTGTGGCCTTCCGTG15q 1861 ACAGCCATTTATTATGTTTACTTGG AGAATAATTGTGATAAGAATTCCCC CACGAGCCAACACGGAGGTGACGTG CACGAGCCAACATGGAGGTGACGTG17p p53-e4 AAGACCCAGGTCCAGATGA GGTGTAGGAGCTGCTGGTG CACGGCTCCCCCCGTGGCCCGTG CACGGCTCCCCGCGTGGCCCGTG18q 1468 AGCGAGCATCAGAATCACCT CGGGACAAGCAGCATCT CACGTGGGGCTTACAAATTAGTATCGTG CACGTGGGGCTTACGAATTAGTATCGTG

Ch: chromosomal arm; SNPs: single nucleotide polymorphisms

Primers and probes used for digital SNP analysis

DNA diluted to 384-well plate

DNA templates become all-or-nonein each well of 384-well plate

PCR / hybrid with MB

Digital PCR pattern all-or-none

PNAS February 27, 2001 vol. 98 no. 5 p.2640–2645

1 2 3 4 5 6 7 8 9 10 11 12 Average Sample

10.9 13.0 0.9 14.6 14.8 22.3 1.3 6.7 11.1 5.6

1.2 1.0 18.5 1.3 8.4 0.3 1.6 10.0 0.4 0.7 16.2 11.6 49.2 10.1 11.7 0.8 10.2 0.5 1.6 1.2 1.5 0.9

1.7 9.5 14.6 12.8 11.29.5 11.2 0.9 0.9 14.8 13.7

6.2 0.4 0.7 1.4 1.1 1.2 18.7

10.2 1.0 1.8 1.7 0.8 11.4 1.1 0.7 0.6 0.8

1.1 11.1 0.8 1.2 10.5 12.6 1.1 1.1 11.4 13.2 1.6

4.5 15.2 11.0 9.3 11.2 0.69.6 2.7 30.9 9.90.9 10.8

11.9 11.9 0.7 10.5 12.4 13.7 10.8 13.2 11.8 8.7 14.0 0.4 11.0 0.9 10.9 6.1 0.8 10.4 9.5 19.3 1.1 15.0 11.2 8.0

1.0 10.2 14.0 9.8 15.2 13.2 9.9 11.1 12.6 9.1 8.8 11.8 10.5 13.7 17.5 11.8 Allel G

1.0 1.2 1.3 1.3 1.1 1.1 0.4 1.3 0.9 0.8 1.0 Allel A 0 H2O

6.9

9.2

Normal

Adenoma

Normal Tumor

G:R = 32:34allelic balance

G:R = 62:16allelic imbalance

Assess genetic abnormality by counting alleles

40%

50%

60%

70%

80%

90%

100%

0 50 100 150 200

Allele Count

Alle

le R

atio

Allelic Imbalance

Allelic Balance

Tumor DNA

Normal DNA

Sequencial probability ratio test (SPRT)determines allelic imbalance

The paternal or maternal alleles within a DNA sample are individually counted, thus allowing a quantitative measure of allelic imbalance in the presence of normal DNA.

The advantages of Digital SNP analysis

in assessing allelic imbalance

1. As compared with microsatellite markers the PCR products derivedfrom the two SNP alleles at every locus are of the same size and therefore their analysis is not biased by the preferential DNA degradation of larger alleles.

2. The Digital SNP approach, which amplifies single allele templates in the PCR reaction, can precisely determine the number of allelesexamined in each experiment.

Accordingly, SNP genotyping is “digital” involving the detection or absence of a specific allele rather than “analogue” as is microsatellite genotyping that measures the length of microsatellites.

3. A statistical method, SPRT, can be employed to conclude whether allelic imbalance is present in the background DNA.

Indeed, it has been shown that allelic imbalance can be demonstrated inmuch higher percentage in colorectal carcinomas using Digital SNP analysis than the traditional methods using microsatellite markers.

[Science. 244: 207-11, 1989. & Nat Biotechnol. 19: 78-81., 2001.]

Digital PCR transforms the exponential, analog signals obtained from conventional PCR to linear, digital signals, allowing statistical analysisof the PCR product.

Digital SNP analysis is able to discriminate different alleles (e.g., wild type vs. mutant or paternal vs. maternal alleles).

Digital PCR has been applied in detection of allelic imbalance in clinical specimens

providing a promising molecular diagnostic tool for cancer detection.

Summary for Digital SNP analysis

Digital SNP analysis for Ovarian Cancer

J National Cancer Institute 94 (22), 1697-1703. (2002)

Allelic Imbalance (AI)

• represents losses or gains of defined chromosomal regions

• is useful in elucidating the molecular basis of cancer as well as cancer detection.

Two problems for Traditional Methods1. plasma DNA is a mixture of neoplastic and non-neoplastic

DNA

2. plasma DNA is often degraded to a variable extent

Pla

sma

DN

A (n

g/m

l)

0

500

1000

1500

2000

2500

600065007000

n = 380% + n = 26

7.7% +

n = 7229.2% +

n = 2433.3% +

n = 9047.8% +

Normal

Non-tu

mor dis

ease

Benign

solid

tumor

Leuk

emia

Malign

ant s

olid t

umor

Plasma DNA concentrations in different physiological situations

Diagnosis Patients (n) n > 50 ng/ml % > 50 ng/ml

Healthy normal 44 0 0%Non-neoplastic Dx 164 33 20% Anemia 8 2 25%

AIDS 10 4 40%

Musculoskeletal Dx 10 3 30%

Diabetes Mellitus 7 1 14%

Cardiovascular Dx 13 2 15%

Asthma and pulmonary Dx 5 1 20%

Essential hypertension 6 1 17%

Infectious Dx 19 5 26%

Autoimmune Dx 10 4 40%

Status post transplant 23 3 13%

Liver Dx 9 1 11%

Trauma 8 0 0%

Neurological disorder 12 2 17%

Gynecological Dx 10 1 10%

Drug abuse 8 2 25%

Others 8 1 13%

Neoplastic Dx 122 64 52% Ovarian tumor 54 30 56%

Endometrium/cervix CA 10 5 50%

Head & Neck CA 11 8 73%

Sarcoma 4 1 25%

Breast CA 8 4 50%

Lung CA 11 6 55%

Gastrointenstinal CA 11 5 45%

Brain tumor 5 2 40%

Others 6 3 50%

CA: carcinoma; Dx: disease

Plasma DNA concentration in healthy normal individuals and patients.

Ch SNPs Forward Primer Reverse Primer Molecular Beacon-green (flouorescein) Molecular Beacon-red (Hex)1p 8118 CAGGGCAAGACGCTGTGGT AACAGAATGTGCTTCCCTCCC CACGCTGCCCAGCGCACGGCCGTG CACGCTGCCCAGTGCACGGCCGTG7q 273 AGGGCTAGAGTATGAGAAGTCC GTAATTTAGGTGAGCTATCCAGAG CACGGTTTTTTTTCCCTATAACGTG CACGGTTTTTTTTTCCTATAACGTG8p 1085 CACTGAATGCTCTGCCATGA AACCTGTCCTTGTGGGTGAT CACGATGAGCCACAAGCAGCACGTG CACGATGAGCCGCAAGCAGCCGTG12p 852 TGATCTGCTTCTCCCACGA TGGAGTCCCAGACATTGCA CACGATCACGTCCGTGGCCTTCCGTG CACGATCACGTCTGTGGCCTTCCGTG15q 1861 ACAGCCATTTATTATGTTTACTTGG AGAATAATTGTGATAAGAATTCCCC CACGAGCCAACACGGAGGTGACGTG CACGAGCCAACATGGAGGTGACGTG17p p53-e4 AAGACCCAGGTCCAGATGA GGTGTAGGAGCTGCTGGTG CACGGCTCCCCCCGTGGCCCGTG CACGGCTCCCCGCGTGGCCCGTG18q 1468 AGCGAGCATCAGAATCACCT CGGGACAAGCAGCATCT CACGTGGGGCTTACAAATTAGTATCGTG CACGTGGGGCTTACGAATTAGTATCGTG

Ch: chromosomal arm; SNPs: single nucleotide polymorphisms

Primers and probes used for digital SNP analysis

40%

50%

60%

70%

80%

90%

100%

0 50 100 150 200

Allele Count

Alle

le R

atio

Allelic Imbalance

Allelic Balance

Plasma DNA

Lymphocyte DNA

SPRT analysis

Allelic status in plasma DNA of ovarian cancer patients

Allelic balanceAllelic imbalance Homozygous (non-informative)

Undetermined

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3

1p 8118

5q 1756

7p 273

8p 1085

12q 852

15q 1861

17p p53

18p 331

18

266 65

199 38 3

1309

264 15 8 17

186 23 42 61 92

210 26 6 10 94

746 36 28

131 8

110

0.3

N/A

N/A

0.5

0.3

0.4

0.3

0.4

0.3

0.3

0.5

0.3

0.4

N/A

N/A

0.7

0.7

0.4

N/A

0.5

0.3

0.7

0.3

0.3

0.3

0.3

0.9

X

167 X 18 25 X

580 11 19 15 46 28

115 8 X

1260 76 36 X

220

875 X 9 30

270 16

3185

28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4

1p 8118

5q 1756

7p 273

8p 1085

12q 852

15q 1861

17p p53

18p 331

2

146 38 3 67

149 5

338

491

103 92

147 65

285 20

538 39 25 40 14 78 35

895

2481 18

504

1190

0.4

0.4

0.4

N/A

0.5

0.7

0.4

0.6

0.4

0.6

0.6

0.7

0.7

0.7

0.4

0.9

0.3

0.5

0.3

0.3

0.4

0.6

0.7

0.9

0.4

0.7

0.7

8 84 X 29

600 75 15

250

377 66

979 13

979

209 19

231

643

319

788 X X

580

365 66 38

2397

304

Fraction of tumor DNA

CA125 (U/ml)

Stage

Chrom

osome

SN

P

Plasma DNA (ng/ml)

Plasma DNA (ng/ml)

Fraction of tumor DNA

CA125 (U/ml)

Patient No.

Patient No.

Stage

Chrom

osome

SN

P

An ROC curve (Receiver-operating characteristic) is a graphic representation of the sensitivity plotted against the false-positive rate (i.e., 1 minus specificity).

The area under the ROC curve is a measure of the overall ability of a diagnostic test with multiple cutoffs to distinguish between diseased and control individuals.

Conclusion

These findings suggest that measurement ofplasma DNA levels may not be sensitive or specific for cancer diagnosis but detection of allelic imbalance in plasma DNA using the Digital SNP analysis holds a great promise for the early detection of cancer.

mesothelial cells & inflammatory cells

ovarian serous carcinoma cell

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

T13

T14

T15

T16

T17

T18

T19

T20

Tumor

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

4 0 %

6 0 %

8 0 %

1 0 0 %

0 5 0 1 0 0 1 5 0 2 0 0

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

40%

60%

80%

100%

0 50 100 150 200

4 0 %

6 0 %

8 0 %

1 0 0 %

0 5 0 1 0 0 1 5 0 2 0 0

40%

60%

80%

100%

0 50 100 150 200

N1

N2

N3

N4

N5

N6

N7

N8

N9

N10

N11

N12

N13

N14

N15

N16

N17

N18

N19

N20

Normal

8118 486 273 2952 331 852 1085 8118 486 273 2952 331 852 1085

T1 OVCA 45 1388 N1 NFT 49 10

T2 OVCA 55 127 N2 NFT 48 168

T3 OVCA 71 240 N3 NFT 58 2

T4 OVCA 73 606 N4 NFT 73 8

T5 OVCA 44 31 N5 NFT 55 22

T6 OVCA 60 405 N6 NFT 41 2

T7 OVCA 53 42 N7 NFT 62 15

T8 OVCA 36 1430 N8 NFT 44 9

T9 OVCA 45 550 N9 NFT 62 16

T10 Colon CA 69 2711 N10 NFT 41 25

T11 OVCA 48 180 N11 NFT 75 19

T12 OVCA 51 58 N12 NFT 38 293

T13 Pancrease CA 53 37 N13 NFT 53 7

T14 OVCA 53 399 N14 NFT 44 4

T15 Colon CA 46 289 N15 NFT 34 1141

T16 OVCA 53 880 N16 Ovarian cyst 45 10

T17 OVCA 74 5774 N17 Ovarian cyst 65 6

T18 OVCA 59 40 N18 NFT 63 208



SNP markersCase No. Diagnosis Age DNA conc. Age DNA conc.

SNP markers

OVCA: ovarian carcinoma; CA: carcinoma; NFT: negative for tumor; Conc: concentration; NI: not informative;

Case No. Diagnosis

Tumor Normal

Allelic balanceAllelic imbalance Homozygous (non-informative)

Undetermined

negative-cytology

positive allelic imbalance in ascitic fluid who was known to have stage III ovarian carcinoma at the time of sample collection

Am J Pathol 2002, 160:1223–1228

S B T (Serous borderline tumor)

1p5q8p

18q 22q

Xp

1p5q8p

18q 22q

Xp

1p5q8p

18q 22q

Xp

Conventional serous carcinoma

Invasive MPSC

Noninvasive MPSC

Paraffin tissue DNA

Allelic balanceAllelic imbalanceHomozygous (non-informative)

1p5q8p

18q 22q

Xp

Primer dimer

PCR product

Urine DNA for PCR (Work in taiwan)

Conclusion I

Digital SNP analysis can count the allelic imbalance in samples from plasma, ascites, paraffin, urine …etc.

in the presence of background (normal) DNA.

Digital SNP analysis for Bladder Cancer

FP-TDI stands for template directed dye terminator incorporation assay with detection by fluorescence polarization

FP–TDI Assay

Validate the Taiwanese SNP of interests

by

Body fluid-based Digital SNP analysis

Adapted from Genome Research

9:492-498, 1999

Adapted from Genome Research

9:492-498, 1999

Fluororescence Polarization

C/C

C/TT/T

Control

Good SNP candidate

Bad SNP candidate

Not SNP

Not SNP

Typicalresults

SNP Analysis

Linkage association Study

PharmacogenomicsDiagnosis

Personalized Medicine

Genetic Instability

Gene 234 (1999) 177–186

LD (Linkage disequilibrium)

Strategies for association analysis

Genotype Analysis Using Human Hair Shaft

Cancer Epidemiology, Biomarkers & Prevention 11: 925–929, 2002

Hsueh-Wei Chang, Ching-Yu Yen, Shyun-Yui Liu, Gad Singer, and Ie-Ming Shih*

Apolipoprotein E4: an allele associated with many diseases

Ann Med.32(2): 118-127, 2000 . Smith, Jonathan D

TTTCCC

1 9 17 25 33 412 10 18 26 34 423 11 19 27 35 434 12 20 28 36 445 13 21 29 37 456 14 22 30 38 467 15 23 31 39 478 16 24 32 40 48

APO-E 112 APO-E 158

C825T sample panel

SNP Genotyping

APO-E158

CC

TC

TT

CCTGCAGAAGCGCCTGGC

CCTGCAGAAGTGCCTGGC

CCTGCAGAAG GCCTGGC TC

CC TC TT

T allele

C allele

72bp

39bp + 33bp

PCR-RFLP

Populations n

E2/E2 E2/E3 E3/E3 E2/E4 E3/E4 E4/E4 ε2 ε3 ε4

Taiwanese 48 2.1 14.6 68.8 0.0 12.5 2.1 9.4 82.3 8.3ns

Chinese22 141 1.4 12.1 70.9 0.0 14.9 0.7 7.4 84.4 8.2ns

European19 590 0.8 9.3 57.3 1.9 27.3 3.4 6.4 75.6 18.0*

ns = not significant; * p < 0.03

APO-E

Allele (%)Genotype (%)

Genotype and allele frequency of APO-E

Populations n

TT TC CC

Taiwanese 48 23.0ns 41.0 36.0

Chinese12 960 22.4ns 50.6 27.0

European12 277 10.0* 43.7 46.2

ns = not significant; *p < 0.001

Genotype (%)

C825T

Genotyping of C825T

Conclusion II

Digital SNP analysis can apply for High throughput genotyping

SNP Genotype & Haplotype

2003-01-21: Conceptual Web Site up2003-5-27 : International HapMap Meeting at CSHL2003-06-25: Allele frequencies submitted to dbSNP2003-07-01: Quality control SNPs2003-11-01: First major public data release!

Expression of P-glycoprotein and Genotype (MDR1 3435)

IncreasedC C

NormalC T

ReducedT T

P-gp Expression P-gp Genotype

Stage I Stage II Stage III

Cancer Progression

Digital SNP Data of Cancers(AI for certain chromosomes)

TRENDS in Biotechnology Vol.19 No.12 December 2001

personalized medicine

Documents

Digital SNP Analysis of Cancers - 基因體學講義genomed.dlearn.kmu.edu.tw/DigitalSNPCancer.pdf · Digital PCR transforms the exponential, analog signals obtained from conventional