Part II: Methods and Approaches 1: Assessing Disease Associations and Interactions Chapter 7 Tables


Pages in this Report
  1. Index
  2. Preface
  3. Chapter 1
  4. Chapter 4
  5. Chapter 7
  6. Chapter 9
  7. Chapter 10
  8. Chapter 11
  9. Chapter 22
  10. Chapter 23
  11. Chapter 24
  12. Chapter 28
  13. Chapter 29
Related Pages

Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease

 

Facing the Challenge of Complex Genotypes and Gene-environment Interaction: the basic epidemiologic units in case-control and case-only designs

Lorenzo D. Botto and Muin J. Khoury

G a
E a
Cases
Controls
Odds Ratio
Contrast
Main Information
Table 7-1Layout for a case-control study assessing the effect of a genotype (G) and an environmental factor (E)
+
+
a
b
ah/bg
A
A vs. D
 Joint genotype and environmental factor vs. none
+
–
c
d
ch/dg
B
B vs. D
 Genotype alone vs. none
–
+
e
f
eh/fg
C
C vs. D
 Environmental factor alone vs. none
–
–
g
h
1
D
Common reference
Other Measures Odds Ratio Main Information
aG, genotype; E, environmental factor
Case only odds ratio
ag/ce
 Departure from multiplicative model of interaction
Control only odds ratio
bh/df
 Independence of factors in population
Multiplicative interaction
A/(B*C)
 Deviation from multiplicative model of interaction
Additive interaction
A-(B+C-1)
 Deviation from additive model of interaction
Stratified 1-a
ad/bc
 Association with environmental factor among people with genotype
Stratified 1-b
eh/fg
 Association with environmental factor among people without genotype
Stratified 2-a
af/be
 Association with genotype among people exposed to environmental factor
Stratified 1-b
ch/dg
 Association with genotype among people not exposed to environmental factor

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1) The primary data are displayed clearly and completely.
2) The primary measures of association-relative risk estimates for each factor alone and for the joint exposure-are readily generated. Because they use the same reference group, these estimates can be compared.
3) Attributable fractions can be computed separately for each exposure alone and for the joint exposure.
4) Relative risk estimates can be used to assess the relation between the joint exposure and the individual exposures. For example, the departure from additive or multiplicative models of interactions can be readily derived from the table.
5) Risk estimates stratified by either exposure can also be calculated if needed.
6) For case-control studies, the case-only and the control-only odds ratios can be easily computed. For adequately chosen control groups, the control-only odds ratio estimates exposure dependencies in the underlying population.
Table 7-2Advantages of the 2X4 table in the study of gene-environment interactions

 

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Factor V a OC a Cases Controls Odds Ratio 95% CI AF-Exp (%) a AF-Pop (%) a Exposure Frequency in Controls (%)
Table 7-3Analysis of oral contraceptive use, presence of Factor V Leiden mutation, and risk for venous thromboembolism
+
+
25
2
ORge
34.7
7.83-310.0
97.1
15.7
1.2
+
–
10
4
ORg
6.9
1.83-31.80
85.6
5.5
2.4
–
+
84
63
ORe
3.7
2.18-6.32
73.0
39.6
37.3
–
–
36
100
Ref
Ref
59.2
Total
155
169
a Factor V: +, Presence of Factor V Leiden mutation (heterozygotes and homozygotes)
– , absence of Factor V Leiden mutation
OC: +, current use of oral contraceptives
-, no current use of oral contraceptives
AF-Exp (%): Attributable Fraction (percent) among exposed cases
AF- Pop (%): Attributable Fraction (percent) among all cases in the population
Expected OR-GE
Departure from expected
Note: the departure of the observed from the expected effect of the joint exposure depends on the definition of no interaction, as shown below for simple additive and multiplicative definitions.
Additive
(3.7 + 6.9) – 1 =
9.6
34.7 – 9.6=
25.07
Multiplicative
(3.7 * 6.9) =
25.7
34.7/25.7=
1.4

 

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Table 7-4 Comparing the stratified and case-only with the 2×4 approacha
Comparison with stratified analysis
FactorV a
Factor V Present
Factor V Absent
Ratio of odds ratios
Cases
Controls
Cases
Controls
Oral contraceptive use
+
25
2
84
63
–
10
4
36
100
Odds Ratio (95%CI)
5.0 (0.8-31.8)
3.7 (2.2-6.1)
1.4
Case-only and control-only odds ratios
Case-only odds ratio
(25*36)/(10*84) =
1.1
Control-only odds ratio
(2*100)/(4*63) =
0.8
1.4
a The data are from Table 3.
Source: Note that ratios of odds ratios are identical to departure from multiplicative model (Table 3)

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Can be used to
Considerations
Table 7-5Advantages and disadvantages of using case-only studies to screen for complex genotypes in disease etiology
—Screen for genotypes with highest potential impact on disease
Provides upper limit of attributable fraction associated with genotype
—Screen for supramultiplicative interactions between multiple loci Tools include, for example, log-linear analyses and other methods of clustering
—Provide clues to etiologic heterogeneity and determinants of phenotypes and severity Shows variations in genotype frequencies and interactions by different disease phenotypes, severity, or age at onset
Can improve
—Speed of study Particularly useful in conditions for which well-designed disease registries are available
—Precision of estimates Eliminates controls and associated variance
—Validity of findings Assumes no population stratification
—Efficiency of subsequent studies Contributes to efficient case-control design by highlighting notable case- and population subsets, factors with highest potential impact, and potential sample size issues
But have disadvantages
—Validity sensitive to assumptions Results are exquisitely sensitive to independence of factors in population
—Limited information Provides few data on marginal effects, relative and absolute risks, and non-multiplicative interactions

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Page last reviewed: January 1, 2004 (archived document)
Content source: Office of Science (OS), Office of Genomics and Precision Public Health