1 application of doe methodology to snp assay development geetha rajavelu gretchen kiser 2002...
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1
Application of DOE Methodology to SNP Assay Development
Geetha RajaveluGretchen Kiser
2002 Quality & Productivity Research ConferenceTempe, AZ
June 5-7, 2002
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Presentation Outline
• Background Info
• Overview of SNP Assay
• Identifying Variables for Screening Experiment
• Screening Experiment Results
• Optimization Work
• Overall Performance Improvements
• Concluding Remarks
3
Background Info
• In Q3 2000, CodeLink slides
were affected by unexplained
background.
• All aspects of slide manufacturing
and assay processes were
examined for root causes.
• An effort was launched to
thoroughly characterize all
processes (with no apriori
assumptions).
4
SNP Assay Modules
Target Amplification
Target Pooling & Fragmentation
SBE Thermal Cycling SBE Slide Washing, SA-Alexa Labeling & Post-labeling Slide Washing
Drying
Scanning
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S = major alleles = minor allele
PCR clean-up &
Fragmentation
Add reaction mixture to Bioarray
Allele-Specific Extension
Washing &Secondary Labeling
SNP
PCR
DNA genomicprimersprimers
SNP
SNP
Whole target DNA
SNP
Fragmented target
SS
SNP
S s S
Target hybridizes to probes on Bioarray
S s S
Extension reaction on Bioarray
Mediated by a DNA polymerase and labeled nucleotides
Allele on Bioarrayready to scan
SNP Assay Overview
6
Screening Experiment Info
Objective:
– To evaluate the contribution of any one variable, and the interactive contribution of any two variables of the SBE Thermal Cycling module, to the assay performance, as measured by the Signal/Noise ratio, the Call Rate and Accuracy, the Mean Pad IOD, and the Mean Blank Pad IOD.
Preliminary Info:– Factor ranges based on single-variable experiments
– Nuisance Factors: Dispense Slide Batches, Thermal Cyclers
Design Details:– 212-6 fractional factorial design
– Involved 65 runs, 2 slides/run
– Took around 9 days to complete experiment
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Identifying Variables for the Allele-Specific Extension Reaction
Thermal Cycler Number of Cycles (A)
Extension Time (B)
Denaturation Time (C)
Extension Temperature (D)
Denaturation Temperature (E)
Reaction Mixture Target Concentration (F)
Enzyme Concentration (G)
tNTP Concentration (H)
Salt1 Concentration (J)
Salt2 Concentration (K)
Buffer Concentration (L)
Buffer pH (M)
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Selecting Significant Factor Effects
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Screening Experiment Analysis Results
Response Significant Factor Effects Adj R-Squared Curvature?D, G, K, C, E, DHB, H, AK, DG, HKF, H, G, DEDJ, DH, AK, J, DAF, K, EJA, D, G, B, E, K, AG, DGC, CGA, B, D, G, H, AD, DGE, KJ, DJA, H, E, G, BJ, JKAF, AK, DFA, HG, AF, BJ, DF, AE, AKJK, CF, AL, JM, CE
Call Rate 78% Yes
Call Accuracy 23% No
S/N 80% Yes
Mean IOD 85% Yes
Mean Blank Pad IOD 75% No
Stdev Blank Pad IOD 57% Yes
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Interaction Effects
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Interaction Effects
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Interaction Effects
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Parameters shaded in green represent subset chosen for
inclusion in the optimization experiment.
Screening out Variables for Optimization Phase
Mean IOD Mean Blnk IOD Signal/Noise Call Rate Call Accuracytotal
A # of Cycles 3 3 3 0.5 9.5B Extn Time 3 1.5 2 1 7.5C Dent Time 1 2 3
D Extn Temp 3 0.5 3 3 2 11.5E Dent Temp 2 2 2 2 8F Target Conc 0.5 3 3.5G Enzyme Conc 3 2 3 3 3 14H tNTP Conc 3 3 1 3 10J Salt1 Conc 1 1.5 2 4.5K Salt2 Conc 2 1.5 2 3 1 9.5L Buffer Conc 0M Buffer pH 0
Assay Parameter(Weight Values: hi=3, med=2, lo=1; (2 factor=50%))
Influence of Different Assay Parameters on Various Outputs
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Response Surface Experiment InfoObjective:
– To determine factor settings which optimize the assay performance, as measured by the Signal/Noise ratio, the Call Rate and Accuracy, the Mean Pad IOD, and the Mean Blank Pad IOD.
Variables:
-- # of Cycles (A) -- Enzyme Conc (E)
-- Extension Time (B) -- tNTP Conc (F)
-- Extension Temp (C) -- Salt2 Conc (G)
-- Denaturation Temp (D)
Design Details:– Screening design augmented with axial runs using face-centered cube & D-Optimality
criterion (24 runs total)
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Response Surface Modeling Results
Response Model Terms Adj R-Squared
C, E, G, C2
CF, D, F, AG, BFG, CE, AB, BGE, FC, AG, GBC, CE, EFC, E
A, C2, B
G, DF, D, AE, BE, AC, A2, DE, F
C, E, F, A, B, G
DE, D, C2, G
2,
AB, A2, AE, AG, CG
F, A
E, CG, G2, AG, F
2, D, B, CF
AC, C2, BF, EF, BG, D
2
F, A
E, CG, AG, B, G2
AD, BG, EF, AC, FG, B2, D
2, CD, F
2
Call Rate 73%
Call Accuracy 17%
S/N 81%
Mean IOD 84%
Mean Blank Pad IOD 63%
Stdev Blank Pad IOD 46%
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Numerical Optimization Criteria
Lower Upper Lower Upper
Name Goal Limit Limit Weight Weight Importance# of Cycles is in range 2 14 1 1 5Extn Time is in range 5 15 1 1 5Extn Temp is in range 50 70 1 1 5Dentrn Temp is in range 80 90 1 1 5Enzyme Conc is in range 3.1 11.1 1 1 5tNTP Conc is in range 0.25 2.25 1 1 5Salt2 Conc is in range 1 3 1 1 5Call Rate maximize 89.9999989 99.999999 0.1 1 5Call Accuracy maximize 0 100 0.1 1 3S/N maximize 6 17.6702 0.1 1 4Ave Pad IOD maximize 200000 622987 0.1 1 4Ave Blnk Pad IOD minimize 535 8000 1 0.1 5
Constraints
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Numerical Optimization Results
No.# of Cycles
Extn Time
Extn Temp
Dentrn Temp
Enzyme Conc
tNTP Conc
Salt2 Conc
Call Rate
Call Accuracy S/N
Ave Pad IOD
Ave Blnk Pad IOD Desirability
1 6.85 14.45 56.42 82.61 11.10 2.25 3.00 102.226 93.4249 15.7688 672581 1007.04 0.980484722 6.83 13.19 56.94 83.00 11.10 2.25 3.00 102.023 93.7322 14.741 628146 959.96 0.980454923 8.39 11.54 56.65 81.97 11.10 2.24 3.00 101.829 93.8326 14.8812 638299 976.299 0.980217384 8.99 11.77 55.76 81.43 11.10 2.25 3.00 101.708 93.5732 15.398 661269 1008.08 0.980013995 8.27 13.77 54.85 83.39 11.09 2.25 2.97 100.962 93.0696 15.7982 725328 1025.48 0.979982366 7.17 14.21 57.69 81.12 11.04 2.25 3.00 102.776 93.833 15.8991 644298 1037.99 0.979894157 8.45 12.84 56.34 84.53 10.64 2.25 3.00 100.778 93.5463 14.4109 652428 982.111 0.979344998 9.06 14.81 55.68 81.55 11.10 2.20 2.95 101.812 93.1557 17.5359 769929 1142.76 0.978984429 9.48 10.72 55.46 80.89 11.10 2.23 3.00 101.576 93.644 14.9877 637577 1030.23 0.97890117
10 6.07 14.94 59.06 85.43 11.10 2.09 2.93 101.307 94.1715 14.3735 623063 1074.38 0.9769832411 7.21 14.90 57.98 83.76 11.10 2.25 2.39 100.731 94.2739 17.6699 955383 1247.23 0.9768885612 8.48 9.36 56.46 83.34 11.10 2.25 2.60 99.9952 94.3395 14.5201 761095 1098.61 0.97661037
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Optimal Region
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Optimal Region
20
Optimal Region
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SNP Assay Modules – Characterized & Optimized
Target Amplification
Target Pooling & Fragmentation(23 factorial; face-centered design in 3 factors)
SBE Thermal Cycling(2 12-6 fractional factorial; face-centered design in 7 factors)
SBE Slide Washing, SA-Alexa Labeling & Post-labeling Slide Washing
(27-2 fractional factorial; face-centered design in 5 factors)
Drying
Scanning
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Overall Performance Improvements
Group 1DOE specified master mixDOE specified thermocycle programLow salt TNT mixLow SA-Alexa 532 dilutionOptimized Staining/Washing conditions
Group 2DOE specified master mixDOE specified thermocycle programHigh salt TNT mixHigh SA-Alexa 532 dilutionOptimized Staining/Washing conditions
Current Protocol Group 3Current master mixCurrent thermocycle programCurrent TNT mixCurrent SA-Alexa 532 dilutionCurrent Staining/Washing conditions
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CR By Condition
96.0
96.5
97.0
97.5
98.0
98.5
99.0
99.5
100.0
100.5
1 2 3
Condition
With Control
Dunnett's
0.05
All Pairs
Tukey-Kramer
0.05
Higher Call Rate with DOE-derived Conditions
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Higher Call Accuracy with DOE-derived Conditions
ACC By Condition
97.0
97.5
98.0
98.5
99.0
99.5
100.0
100.5
1 2 3
Condition
With Control
Dunnett's
0.05
All Pairs
Tukey-Kramer
0.05
25
IOD By Condition
100000
200000
300000
400000
500000
600000
700000
1 2 3
Condi tion
With Control
Dunnett' s
0.05
All Pairs
Tukey-Kramer
0.05
3X Improvement in Probe Intensity
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S/N By Condition
5
10
15
20
25
30
1 2 3
Condi tion
With Control
Dunnett' s
0.05
All Pairs
Tukey-Kramer
0.05
3X Improvement in Signal/Noise
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Lower Background with DOE-derived Conditions
Blk. IOD By Condition
800
900
1000
1100
1200
1300
1400
1500
1600
1 2 3
Condition
With Control
Dunnett's
0.05
All Pairs
Tukey-Kramer
0.05
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Group 1 Group 2 Current Protocol Group 3
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Concluding Remarks
• All experiments were planned and executed using Project
Management approach (risks/assumptions, scheduling, etc.)
• Sequential approach to experimentation is critical to
studying complex processes.
• Able to beat all odds!!
The probability of successfully completing an experiment is
inversely proportional to the number of runs, i.e.,1/65.
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Acknowledgements
• SNP Assay Development Team
• Project Management Team