Genomics Workshop

1
Genomics Workshop Demography of Aging Centers
Biomarker Network Meeting in Conjunction with
the Annual Meeting of the PAA April 14,
900 AM to 330 PM Hyatt Regency, Dallas, Texas
Sponsored by USC/UCLA Center of Biodemography
and Population Health Organized by Teresa
Seeman, Steven Cole, Eileen Crimmins
2
Tactical aspects of study administration and
sample capture/storage
Biological overview of genetics functional
genomics
Strategic aspects of study design and data
analysis
Lunch
Technical aspects of study design and data
analysis
Perspectives on the State of the Field
Application clinic
3
Tactical aspects of study administration and
sample capture/storage

• DNA
• New sample capture
• Methods e.g., Oragene, leukocytes
• Consent administrative issues
• Retrospective analyses
• Sources blood spots, cheek swabs, etc
• Consent administrative issues
• Epigenetics
• DNA methylation
• Histone acetylation chromatin dynamics
• Tissue specificity (vs DNA)
• Tactical issues Reports from the Field
• I wish Id known then
• RNA
• Identifying appropriate target tissues
• Whole blood, PBMC, saliva, hair, path specim.
• Sample capture/storage
• Consent administrative issues

4
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5
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6
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7
Tactical aspects of study administration and
sample capture/storage

• DNA
• New sample capture
• Methods e.g., Oragene, leukocytes
• Consent administrative issues
• Retrospective analyses
• Sources blood spots, cheek swabs, etc
• Consent administrative issues
• Epigenetics
• DNA methylation
• Histone acetylation chromatin dynamics
• Tissue specificity (vs DNA)
• Tactical issues Reports from the Field
• I wish Id known then
• RNA
• Identifying appropriate target tissues
• Whole blood, PBMC, saliva, hair, path specim.
• Sample capture/storage
• Consent administrative issues

8
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9
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10
Tactical aspects of study administration and
sample capture/storage

• DNA
• New sample capture
• Methods e.g., Oragene, leukocytes
• Consent administrative issues
• Retrospective analyses
• Sources blood spots, cheek swabs, etc
• Consent administrative issues
• Epigenetics
• DNA methylation
• Histone acetylation chromatin dynamics
• Tissue specificity (vs DNA)
• Tactical issues Reports from the Field
• I wish Id known then
• RNA
• Identifying appropriate target tissues
• Whole blood, PBMC, saliva, hair, path specim.
• Sample capture/storage
• Consent administrative issues

11
DNA
IL6
Gene
12
DNA
IL6
Gene
13
RNA
DNA
IL6
Gene
14
Health
RNA
DNA
IL6
Gene
15
Tactical aspects of study administration and
sample capture/storage

• DNA
• New sample capture
• Methods e.g., Oragene, leukocytes
• Consent administrative issues
• Retrospective analyses
• Sources blood spots, cheek swabs, etc
• Consent administrative issues
• Epigenetics
• DNA methylation
• Histone acetylation chromatin dynamics
• Tissue specificity (vs DNA)
• Tactical issues Reports from the Field
• I wish Id known then
• RNA
• Identifying appropriate target tissues
• Whole blood, PBMC, saliva, hair, path specim.
• Sample capture/storage
• Consent administrative issues

16
Biological overview of genetics functional
genomics

• Theoretical framework Genes, Environments,
  transcription, and health
• Genetic influences (missing h, penetrance
  R-square, etc.)
• Functional genomics
• Transcription factors
• Epigenetics
• Gene-Environment interactions
• Regulatory polymorphism
• Coding polymorphism
• System dynamics
• Feedback, network pleiotropy
• Recursive developmental trajectories

17
DNA
IL6
Gene
18
Biological overview of genetics functional
genomics

• Theoretical framework Genes, Environments,
  transcription, and health
• Genetic influences (missing h, penetrance
  R-square, etc.)
• Functional genomics
• Transcription factors
• Epigenetics
• Gene-Environment interactions
• Regulatory polymorphism
• Coding polymorphism
• System dynamics
• Feedback, network pleiotropy
• Recursive developmental trajectories

19
DNA
IL6
Gene
20
DNA
IL6
Gene
21
RNA
DNA
IL6
Gene
22
Health
RNA
DNA
IL6
Gene
23
Health
RNA
DNA
IL6
Gene
24
Social Environment
Health
RNA
DNA
IL6
Gene
25
Social Environment
Health
RNA
DNA
IL6
Gene
26
Social Environment
Health
RNA
DNA
IL6
Gene
27
Social Environment
Health
RNA
DNA
IL6
Gene
28
IL6 gene transcription
TCT TGCGATGCTA AAG
IL6
29
IL6 gene transcription
NE
TCT TGCGATGCTA AAG
IL6
30
IL6 gene transcription
NE
PKA
TCT TGCGATGCTA AAG
IL6
31
IL6 gene transcription
NE
PKA
P
GATA1
TCT TGCGATGCTA AAG
IL6
32
IL6 gene transcription
NE
PKA
TCT TGCGATGCTA AAG
IL6
33
IL6 gene transcription
NE
PKA
TCT TGCGATGCTA AAG
IL6
34
Socio-environmental regulation of IL6
p .008
35
Biological overview of genetics functional
genomics

• Theoretical framework Genes, Environments,
  transcription, and health
• Genetic influences (missing h, penetrance
  R-square, etc.)
• Functional genomics
• Transcription factors
• Epigenetics
• Gene-Environment interactions
• Regulatory polymorphism
• Coding polymorphism
• System dynamics
• Feedback, network pleiotropy
• Recursive developmental trajectories

36
DNA
IL6
Gene
37
DNA
IL6
Gene
38
Health
RNA
DNA
IL6
Gene
39
Health
RNA
DNA
IL6
Gene
40
DNA
IL6
Gene
41
Biological overview of genetics functional
genomics

• Theoretical framework Genes, Environments,
  transcription, and health
• Genetic influences (missing h, penetrance
  R-square, etc.)
• Functional genomics
• Transcription factors
• Epigenetics
• Gene-Environment interactions
• Regulatory polymorphism
• Coding polymorphism
• System dynamics
• Feedback, network pleiotropy
• Recursive developmental trajectories

42
Social Environment
Health
RNA
DNA
IL6
Gene
43
Social Environment
Health
RNA
DNA
G/C
IL6
Gene
44
Social Environment
Health
RNA
DNA
G/C
IL6
Gene
45
Social Environment
DNA
G/C
IL6
Gene
46
Gene x Environment Interaction In silico
IL6
TCT TGCGATGCTA AAG
47
Gene x Environment Interaction In silico
VGATA1_01 .943
IL6
TCT TGCGATGCTA AAG
48
Gene x Environment Interaction In silico
VGATA1_01 .943
IL6
TCT TGCGATGCTA AAG
C
49
Gene x Environment Interaction In silico

50
Gene x Environment Interaction In silico
In vitro
IL6 promoter WT -174C
Transcriptional activity (fold-change)
Norepinephrine (mM) 0 10 - 0
10
51
Gene x Environment Interaction In silico
In vitro
IL6 promoter WT -174C
Difference p lt .0001
Transcriptional activity (fold-change)
Norepinephrine (mM) 0 10 - 0
10
52
Gene x Environment Interaction
IL6 -174 GG IL6 -174 CC/GC
p .008
53
Gene x Environment Interaction
IL6 -174 GG IL6 -174 CC/GC
p .439
p .008
54
Biological overview of genetics functional
genomics

• Theoretical framework Genes, Environments,
  transcription, and health
• Genetic influences (missing h, penetrance
  R-square, etc.)
• Functional genomics
• Transcription factors
• Epigenetics
• Gene-Environment interactions
• Regulatory polymorphism
• Coding polymorphism
• System dynamics
• Feedback, network pleiotropy
• Recursive developmental trajectories

55
Social Environment
Health
RNA
DNA
IL6
Gene
56
Social Environment
Health
RNA
DNA
IL6
G/C
Gene
57
Social Environment
Health2
RNA2
DNA
IL6
G/C
Gene
58
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60
Biological overview of genetics functional
genomics

• Theoretical framework Genes, Environments,
  transcription, and health
• Genetic influences (missing h, penetrance
  R-square, etc.)
• Functional genomics
• Transcription factors
• Epigenetics
• Gene-Environment interactions
• Regulatory polymorphism
• Coding polymorphism
• System dynamics
• Feedback, network pleiotropy
• Recursive developmental trajectories

61
Social Environment
Health
RNA
DNA
IL6
Gene
62
Behavior
Social Environment
RNA
DNA
IL6
Gene
63
Gene-Environment Correlation
Behavior
Social Environment
RNA
DNA
IL6
Gene
64
Gene-Environment Correlation
Behavior
Social Environment
RNA
DNA
IL6
Gene
65
Gene-Environment Correlation
Behavior
Social Environment
RNA
DNA
IL6
Gene
66
Gene-Environment Correlation
Behavior
Social Environment
RNA
DNA
IL6
Gene
67
Gene-Environment Correlation
Behavior
Social Environment
Recursive Molecular Remodeling
RNA
DNA
IL6
Gene
68
Recursive developmental remodeling
Body1
Cole (2009) Current Directions in Psychological
Science
69
Recursive developmental remodeling
Environment1
Body1
Cole (2009) Current Directions in Psychological
Science
70
Recursive developmental remodeling
Behavior1
Environment1
Body1
Cole (2009) Current Directions in Psychological
Science
71
Recursive developmental remodeling
Behavior1
Environment1
Body1
RNA1
Cole (2009) Current Directions in Psychological
Science
72
Recursive developmental remodeling
Time 2
Body2
Cole (2009) Current Directions in Psychological
Science
73
Recursive developmental remodeling
Time 2
Environment2
Body2
Cole (2009) Current Directions in Psychological
Science
74
Recursive developmental remodeling
Cole (2009) Current Directions in Psychological
Science
75
Recursive developmental remodeling
Cole (2009) Current Directions in Psychological
Science
76
Recursive developmental remodeling
RNA intra-organismic adaptation
Cole (2009) Current Directions in Psychological
Science
77
Biological overview of genetics functional
genomics

• Theoretical framework Genes, Environments,
  transcription, and health
• Genetic influences (missing h, penetrance
  R-square, etc.)
• Functional genomics
• Transcription factors
• Epigenetics
• Gene-Environment interactions
• Regulatory polymorphism
• Coding polymorphism
• System dynamics
• Feedback, network pleiotropy
• Recursive developmental trajectories

78
Strategic aspects of study design and data
analysis

• Basic substantive objectives study designs
• Gene discovery (e.g., genetic epidemiology)
• Environmental regulation of health (via
  transcription)
• Gene-Environment interaction

79
DNA
IL6
Gene
80
Health
DNA
IL6
Gene
81
Strategic aspects of study design and data
analysis

• Basic substantive objectives study designs
• Gene discovery (e.g., genetic epidemiology)
• Environmental regulation of health (via
  transcription)
• Gene-Environment interaction

82
Health
DNA
IL6
Gene
83
Health
RNA
DNA
IL6
Gene
84
Strategic aspects of study design and data
analysis

• Basic substantive objectives study designs
• Gene discovery (e.g., genetic epidemiology)
• Environmental regulation of health (via
  transcription)
• Gene-Environment interaction

85
Health
RNA
DNA
IL6
Gene
86
Health
RNA
DNA
G/C
IL6
G/C
Gene
87
Strategic aspects of study design and data
analysis

• Basic substantive objectives study designs
• Gene discovery (e.g., genetic epidemiology)
• Environmental regulation of health (via
  transcription)
• Gene-Environment interaction

Antagonistic pleiotropy
88
Antagonistic pleiotropy
Older Adult Adolescent
p .007
p .032
3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0
CRP mg/L / Adversity SD
IL6 -174 CC GC GG CC GC GG
89
Antagonistic pleiotropy
Older Adult Adolescent
p .007
p .032
3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0
CRP mg/L / Adversity SD
IL6 -174 CC GC GG CC GC GG
90
Antagonistic pleiotropy
Older Adult Adolescent
p .007
p .032
3.0 2.0 1.0 0.0 -1.0 -2.0 -3.0
CRP mg/L / Adversity SD
IL6 -174 CC GC GG CC GC GG
Evolution deletes disadvantage, particularly to
the young
91
Outcome
GG GC CC
92
Fishers regression
Outcome
GG GC CC
y a b(G) e
93
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) e
94
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) c(Env) d(G x Env) e
95
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) e ? c(Env) d(G x Env) e
96
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) e ? c(Env) d(G x Env) e
? power
97
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) e ? c(Env) d(G x Env) e
? power ? parameter estimate
bias
98
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) e ? c(Env) d(G x Env) e
? power ? parameter estimate
bias Marginal 0
99
Strategic aspects of study design and data
analysis

• Basic substantive objectives study designs
• Gene discovery (e.g., genetic epidemiology)
• Environmental regulation of health (via
  transcription)
• Gene-Environment interaction

Antagonistic pleiotropy
Valid statistical models are one major reason
that substantive interests (environments) matter.
100
Strategic aspects of study design and data
analysis

• Basic substantive objectives study designs
• Gene discovery (e.g., genetic epidemiology)
• Environmental regulation of health (via
  transcription)
• Gene-Environment interaction

Antagonistic pleiotropy
Valid statistical models are one major reason
that substantive interests (environments)
matter. OK, then, lets have lunch.
101
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• The bioinformatic middle road
• Environmental regulation of health (via
  transcription)
• Candidate transcript studies
• Genome-wide approaches
• Gene-Environment interaction
• Statistical issues
• Revisiting the bioinformatic middle road

102
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

103
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104
Gene x Environment Interaction
IL6
TCT TGCGATGCTA AAG
105
IL6
TCT TGCGATGCTA AAG
C
106
Gene x Environment Interaction In silico

VGATA1_01 .943
IL6
TCT TGCGATGCTA AAG
C
107
Gene x Environment Interaction In silico

108
Gene x Environment Interaction In silico
In vitro
IL6 promoter WT -174C
Difference p lt .0001
Transcriptional activity (fold-change)
Norepinephrine (mM) 0 10 - 0
10
109
Gene x Environment Interaction
IL6 -174 GG IL6 -174 CC/GC
p .439
p .008
110
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

111
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112
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113
Well ID1 ID2 RFU1 RFU2 Ct1 Ct2 Call A0
1 053 053 1094.39 956.90 42.53 41.36 Heterozy
gote A02 065 065 -43.33 1519.25 60.00 40.39
Allele2 A03 075 075 1126.77
890.96 42.82 42.02 Heterozygote A04 079 079
2095.09 25.36 42.84 60.00 Allele1 A05 087 0
87 2187.80 18.09 41.27 60.00 Allele1
114
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

115
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116
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117
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118
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

119
Fishers regression
Outcome
GG GC CC
120
Fishers regression
Outcome
GG GC CC
121
Fishers regression
Outcome
GG GC CC
122
Fishers regression
Outcome
GG GC CC
123
Fishers regression
Outcome
GG GC CC
y a b(G)
124
Fishers regression
Outcome
GG GC CC
y a b(G) y a b(GG) c(GC) d(CC)
125
Fishers regression
Outcome
GG GC CC
y a b(G) y a b(GG) c(GC) d(CC)
126
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

127
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128
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129
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130
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131
Fishers regression
Outcome
GG GC CC
y a b(G rs1800795)
132
Fishers regression
Outcome
GG GC CC
y a b(G rs1800795) y a b(G rs1800795)
c(T rs20937) .
133
Fishers regression
Outcome
GG GC CC
y a b(G rs1800795) y a b(Haplotype
containing rs1800795)
134
Fishers regression
Outcome
GG GC CC
y a b(G rs1800795) y a b(Haplotype
containing rs1800795) y a b(ATTCGTAC)
135
Fishers regression
Outcome
GG GC CC
HapMap Tag SNP
y a b(G rs1800795) y a b(Haplotype
containing rs1800795) y a b(ATTCGTAC)
136
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

137
Linkage-driven indirect association gradients
138
Linkage-driven indirect association gradients
139
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

140
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141
Culture/behavior/exposure Environment
142
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143
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144
Ancestry classification via mitochondrial
haplogroups (also Y haplogroups for paternal
lineage)
145
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

146
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147
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148
CRP
CVD
149
CRP
CVD
CRP
150
CRP
CVD
CRP
151
CRP
CVD
CRP
IL-6
152
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• - Candidate identification
• - Targeted genotyping
• a. PCR
• b. High-throughput approaches
• - Statistical models
• a. Fishers basic regression model
• b. Multivariate mapping / association /
  recombination
• i. Recombination
• ii. Haplotype blocks
• c. Confounding
• i. Linkage disequilibrium haplotype analyses
• ii. Ethnic stratification
• Phenotypic ascertainment
• Genetic ancestry
• iii. Mendelian randomization

153
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies

154
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies

155
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• - Marker selection for blind search tag SNPs
• - Massively parallel genotyping
• a. Array-based strategies
• Deep resequencing
• - Statistical models
• a. Main effect models
• Interaction models
• Managing Type I error
• - Bonferronni FDR
• - Internal cross-validation
• - External replication

156
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157
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• - Marker selection for blind search tag SNPs
• - Massively parallel genotyping
• a. Array-based strategies
• Deep resequencing
• - Statistical models
• a. Main effect models
• Interaction models
• Managing Type I error
• - Bonferronni FDR
• - Internal cross-validation
• - External replication

158
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159
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160
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161
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• - Marker selection for blind search tag SNPs
• - Massively parallel genotyping
• a. Array-based strategies
• Deep resequencing
• - Statistical models
• a. Main effect models
• Interaction models
• Managing Type I error
• - Bonferronni FDR
• - Internal cross-validation
• - External replication

162
Fishers regression
Outcome
GG GC CC
y a b(G) y a b(GG) c(GC) d(CC)
163
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) y a b(GG) c(GC) d(CC)
164
Fishers regression
Environment A
Environment B
Outcome
Outcome
GG GC CC
GG GC CC
y a b(G) c(Env) d(G x Env) y a
b(GG) c(GC) d(CC) e(Env) f(Env x GG)
g(Env x GC) h(Env x CC)
165
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• - Marker selection for blind search tag SNPs
• - Massively parallel genotyping
• a. Array-based strategies
• Deep resequencing
• - Statistical models
• a. Main effect models
• Interaction models
• Managing Type I error
• - Bonferronni FDR
• - Internal cross-validation
• - External replication

166
Type 1 / false positive error
167
Type 1 / false positive error Confirmatory
hypothesis testing (candidate genes) 1
hypothesis 1 t-test 1 p-value no problem p
lt .05 p lt .05
168
Type 1 / false positive error Confirmatory
hypothesis testing (candidate genes) 1
hypothesis 1 t-test 1 p-value no problem p
lt .05 p lt .05 Gene mapping (exploratory
association testing) Gene expression 22,000
p-values 1,100 false positives (p lt
.05) p(false discovery gt 0)
.999999999999999999999999
169
Type 1 / false positive error Confirmatory
hypothesis testing (candidate genes) 1
hypothesis 1 t-test 1 p-value no problem p
lt .05 p lt .05 Gene mapping (exploratory
association testing) Gene expression 22,000
p-values 1,100 false positives (p lt
.05) p(false discovery gt 0)
.999999999999999999999999 Gene polymorphism
10,000,000 p-values 500,000 false positives (p
lt .05) p(false discovery gt 0)
.999999999999999999999999
170
What to do?
171
What to do? 1. Increase stringency
(intra-study) Bonferroni correct ( p
.05/22,000 .00000227 ) Choice huge samples or
massive Type 2 false negative error
172
What to do? 1. Increase stringency
(intra-study) Bonferroni correct ( p
.05/22,000 .00000227 ) Choice huge samples or
massive Type 2 false negative
error Model/simulate error Randomization test or
FDR modeling less conservative
bias Unimpressive yield p .00000300 if
youre lucky. Still too conservative, and
biased ( omitted true effects in error term )
173
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174
What to do? 1. Increase stringency
(intra-study) Bonferroni correct ( p
.05/22,000 .00000227 ) Choice huge samples or
massive Type 2 false negative
error Model/simulate error Randomization test or
FDR modeling less conservative
bias Unimpressive yield p .00000300 if
youre lucky. Still too conservative, and
biased ( omitted true effects in error term )
175
What to do? 1. Increase stringency
(intra-study) Bonferroni correct ( p
.05/22,000 .00000227 ) Choice huge samples or
massive Type 2 false negative
error Model/simulate error Randomization test or
FDR modeling less conservative
bias Unimpressive yield p .00000300 if
youre lucky. Still too conservative, and
biased ( omitted true effects in error term )
Use a better sampling design
176
Population prevalence design
177
Population prevalence design
Outcome-stratified design
178
What to do? 1. Increase stringency
(intra-study) Bonferroni correct ( p
.05/22,000 .00000227 ) Choice huge samples or
massive Type 2 false negative
error Model/simulate error Randomization test or
FDR modeling less conservative
bias Unimpressive yield p .00000300 if
youre lucky. Still too conservative, and
biased ( omitted true effects in error term )
Use a better sampling design
179

• What to do?
• 1. Increase stringency (intra-study)
• Bonferroni correct ( p .05/22,000 .00000227 )
• Choice huge samples or massive Type 2 false
  negative error
• Model/simulate error
• Randomization test or FDR modeling less
  conservative bias
• Unimpressive yield p .00000300 if youre
  lucky.
• Still too conservative, and biased ( omitted true
  effects in error term )
• Use a better sampling design
• Replicate (inter-study or intra-study
  cross-validation)
• .05 x .05 x .05 .000125 x 22,000 2.75
  false positives ( vs. 1,100 )

180
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181

• What to do?
• 1. Increase stringency (intra-study)
• Bonferroni correct ( p .05/22,000 .00000227 )
• Choice huge samples or massive Type 2 false
  negative error
• Model/simulate error
• Randomization test or FDR modeling less
  conservative bias
• Unimpressive yield p .00000300 if youre
  lucky.
• Still too conservative, and biased ( omitted true
  effects in error term )
• Use a better sampling design
• Replicate (inter-study or intra-study
  crossvalidation)
• .05 x .05 x .05 .000125 x 22,000 2.75
  false positives ( vs. 1,100 )

182
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• - Marker selection for blind search tag SNPs
• - Massively parallel genotyping
• a. Array-based strategies
• Deep resequencing
• - Statistical models
• a. Main effect models
• Interaction models
• Managing Type I error
• - Bonferronni FDR
• - Internal cross-validation
• - External replication

183
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies

184
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• The bioinformatic middle road biological
  hypotheses buy power

185
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• The bioinformatic middle road biological
  hypotheses buy power
• - Candidate set selection
• a. Regulatory polymorphism
• b. Coding polymorphism
• - Statistical considerations
• a. Power
• b. Differential enrichment

186
In silico prediction of Gene x Environment
Interaction
IL6
TCT TGCGATGCTA AAG
187
In silico prediction of Gene x Environment
Interaction In silico
188
In silico prediction of Gene x Environment
Interaction In silico
In
vitro
IL6 promoter WT -174C
Difference p lt .0001
Transcriptional activity (fold-change)
Norepinephrine (mM) 0 10 - 0
10
189
In silico prediction of Gene x Environment
Interaction In vivo
IL6 -174 GG IL6 -174 CC/GC
p .439
p .008
190
1205 GRE-modifying SNPs
191
Gene set enrichment analysis
192
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• The bioinformatic middle road biological
  hypotheses buy power
• - Candidate set selection
• a. Regulatory polymorphism
• b. Coding polymorphism
• - Statistical considerations
• a. Power
• b. Differential enrichment

193
Population prevalence design
Outcome-stratified design
194
Population prevalence design
Outcome-stratified design
GEscan
GEscan
195
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• The bioinformatic middle road biological
  hypotheses buy power
• - Candidate set selection
• a. Regulatory polymorphism
• b. Coding polymorphism
• - Statistical considerations
• a. Power
• b. Differential enrichment

196
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197
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• The bioinformatic middle road biological
  hypotheses buy power
• - Candidate set selection
• a. Regulatory polymorphism
• b. Coding polymorphism
• - Statistical considerations
• a. Power
• b. Differential enrichment

198
Technical aspects of study design and data
analysis

• Study designs, assay technologies, and
  statistical methods
• Gene discovery (e.g., genetic epidemiology)
• Candidate gene studies
• Genome-wide association studies
• The bioinformatic middle road biological
  hypotheses buy power

199
Technical take-home points

• Strengths weaknesses of alternative approaches
• Candidate gene studies focus on 1 candidate
• Advantages
• - Scientifically tractable incremental
  cross-validatable
• - Maximal statistical power (focused hypothesis)
• Disadvantages
• - Can only discover what we already know
  (i.e., biased)
• Genome-wide association studies focus on all
  candidates
• Advantages
• - Unbiased de novo discovery
• Disadvantages
• - Minimal statistical power, particularly for
  interactions
• The bioinformatic middle road focus on a small
  set of causally plausible candidates (unbiased
  search of regulatory and coding