BSHI 2002 Glasgow, Scotland STATISTICAL ANALYSIS OF HLA AND DISEASE ASSOCIATIONS M. Tevfik DORAK Department of Epidemiology University of Alabama at Birmingham

BSHI 2002Glasgow, Scotland

STATISTICAL ANALYSIS OF STATISTICAL ANALYSIS OF HLA AND DISEASE HLA AND DISEASE

ASSOCIATIONSASSOCIATIONS

M. Tevfik DORAKM. Tevfik DORAKDepartment of EpidemiologyDepartment of Epidemiology

University of Alabama at BirminghamUniversity of Alabama at BirminghamU.S.A.U.S.A.(2002)(2002)

http://www.dorak.info

http://www.dorak.info/


This workshop will cover categorical data analysis for case-control design and some concepts in

population genetics

AIMSAIMS

Familiarization with common statistical tests useful in HLA and disease association studies

Clarification of several statistical concepts

Discussion of common mistakes

Interpretation of results


Why would you do an association Why would you do an association study?study?

Disease gene mapping and positional cloning

Molecular profiling(to predict susceptibility, outcome, response, prognosis)

Basic science (to learn about disease development and

subsequently to design diagnostic tests or new treatment)


Meaning of an associationMeaning of an association

Population stratification (confounding by ethnicity) or other spurious associations

Linkage disequilibrium (confounding by locus)

Direct involvement in the disease process


Cross-validation of resultsCross-validation of results

Replication (population level and/or family-based)

Functional studies

Split the sample into two random groups(if nothing else can be done!)


Failure to replicateFailure to replicate

False positive in the original study

False negative in the second one

Population specificity

Population stratification


Considerations at the beginningConsiderations at the beginning

Will you have enough power?

Who are the controls? Unrelated or family-based?A subgroup vs another one (males vs females)?

Prospective sequential sampling or retrospective convenience samples for cases?

Remember you will be testing whether the cases and controls are from the same population. The answer shouldn’t be obvious at the beginning.


An example of power calculationAn example of power calculation

Proportion Difference Power / Sample Size Calculation

Significance Level (alpha): .05 (Usually 0.05)

Power (% chance of detecting): .80 (Usually 80)

First Group Population Proportion: .40 (Between 0.0 and 1.0)

Second Group Population Proportion: .60 (Between 0.0 and 1.0)

Relative Sample Sizes Required: 2.0 (For equal samples, use 1.0)

Sample Size Required: Group 1: 80 Group 2: 160

(Sample sizes become 115 : 231 for P = 0.01)


An example of power calculationAn example of power calculation

Proportion Difference Power / Sample Size Calculation

Significance Level (alpha): .01 (Usually 0.05)

Power (% chance of detecting): .80 (Usually 80)

First Group Population Proportion: .05 (Between 0.0 and 1.0)

Second Group Population Proportion: .10 (Between 0.0 and 1.0)

Relative Sample Sizes Required: 2.0 (For equal samples, use 1.0)

Sample Size Required: Group 1: 538 Group 2: 1077

http://statpages.org/proppowr.html


Beware of the following flaws and fallacies of Beware of the following flaws and fallacies of epidemiologic studiesepidemiologic studies

confounders (known or unknown)

selection biasresponse bias

misclassification biasvariable observer

Hawthorne effect (changes caused by the observer in the observed values)

diagnostic accuracy biasregression to the mean

significance Turkeynerd of nonsignificance

cohort effectecologic fallacy

Berkson bias (selection bias in hospital-based studies)

SEE: http://www.dorak.info/epi/bc.html

http://dorakmt.tripod.com/epi/bc.html


Categorical Data AnalysisCategorical Data Analysis* 2x2 Table Analysis for Association

Chi-squared (Pearson, Yates)FisherG-testMcNemar's test: TDT, HRR(Logistic Regression)

* Odds Ratio - Relative RiskDifference between OR and RRWoolf-Haldane ModificationComparison of two ORsAdjusted OR

* Linkage DisequilibriumComparison of two LDs

* RxC (multicontingency) Table AnalysisChi-squaredG-testExact Tests (needed for HWE)Trend Test (frequently overlooked)

See http://www.dorak.info/hla/stat.html


The SAS System The SAS System FREQ Procedure Output – I FREQ Procedure Output – I

Statistic DF Value Prob

Chi-Square 1 7.9047 0.0049Likelihood Ratio Chi-Square 1 8.0067 0.0047Continuity Adj. Chi-Square 1 7.3064 0.0069Mantel-Haenszel Chi-Square 1 7.8840 0.0050Phi Coefficient -0.1439Contingency Coefficient 0.1424Cramer's V -0.1439

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

Fisher's Exact Test

Cell (1,1) Frequency (F) 45Left-sided Pr <= F 0.0033Right-sided Pr >= F 0.9983Table Probability (P) 0.0016Two-sided Pr <= P 0.0066


The SAS System The SAS System FREQ Procedure Output – II FREQ Procedure Output – II

Estimates of the Common Relative Risk (Row1/Row2)

Type of Study Method Value 95% Confidence Limits Case-Control Mantel-Haenszel 0.5359 0.3461 0.8299(Odds Ratio) Logit 0.5359 0.3461 0.8299Cohort Mantel-Haenszel 0.6595 0.4892 0.8891(Col1 Risk) Logit 0.6595 0.4892 0.8891Cohort Mantel-Haenszel 1.2306 1.0666 1.4198(Col2 Risk) Logit 1.2306 1.0666 1.4198


□○■

BC

AC BB

□ ○

●BB

BC AB

□ ○

●

□ ○

■BC AB

AB CD AC BD

“ transmitted allele“ “case”

“ Non-transmitted allele” “control”

Parent-Case Trios in TDTParent-Case Trios in TDT/HRR/HRR


- AN EXAMPLE OF TDT -- AN EXAMPLE OF TDT -

TRANSMISSION DISEQUILIBRIUM OF HLA-B62 TO THE TRANSMISSION DISEQUILIBRIUM OF HLA-B62 TO THE PATIENTS WITH CHILDHOOD AMLPATIENTS WITH CHILDHOOD AML

(Dorak et al, BSHI 2002)(Dorak et al, BSHI 2002)

Out of 13 parents heterozygote for B62, 12 transmitted B62 to the affected child and 1 did not

McNemar’s test results:P = 0.0055 (with continuity correction)odds ratio = 12.0, 95% CI = 1.8 to 513

Nontransmitted Allele

B62 Other

Transmitted Allele

B62 x 12

Other 1 y


Multiple comparisonsMultiple comparisons

Not needed if the study is not hypothesis driven (i.e., a fishing experiment)

Not needed if the study is hypothesis driven ('Possible relevance of the HLA system' is not a valid

hypothesis in this context. Those studies belong to the fishing experiments group)

Therefore, it is not clear when it is needed in HLA association studies. Most frequently, it is an

excuse for a busy reviewer to avoid a comprehensive review

Best solution is to avoid facing this problem -ideally by replication and/or functional data to support the statistical association before it is

dismissed as a spurious result of multiple comparisons


Common Mistakes in Statistical Evaluation Common Mistakes in Statistical Evaluation of Association Study Results - Iof Association Study Results - I

Confusion between corrections (Yates/Williams for continuity VS Bonferroni)

Confusion between RR and OR (they are not the same)

Confusion between expected and observed values in cells of a contingency table

Small sample size issue Don’t confuse a negative result with lack of power

(‘No significant difference between the two groups and they were pooled’ VS ‘the difference did not reach significance due to small

sample size’ are different interpretations of the same phenomenon, i.e., lack of power)

Using Chi-squared test for small sample size (why not use Fisher all the time?)

Using Chi-squared test for HWE (use exact test or G-test)


Common Mistakes in Statistical Evaluation Common Mistakes in Statistical Evaluation of Association Study Results - IIof Association Study Results - II

One-tailed and two-tailed P values (always use two-tailed)

Trend test for a multicontingency table? (if appropriate, more powerful)

Multiple comparison issue

Failure to give the strength of the association (OR, RR, RH)

Use of the word ‘proof’. Does statistics prove anything?(A ‘P value’ provides a sense of the strength of the evidence for or

against the null hypothesis of no association)

Reliance on large sample effect to achieve significance

Showing P values as 0.000 (this means P < 0.001)

Confusion between association and linkage


Association and Causality?Association and Causality?

However strong an association does not necessarily mean However strong an association does not necessarily mean causation. Several criteria have been proposed to assess the causation. Several criteria have been proposed to assess the role of an associated marker in causation. Some of those are role of an associated marker in causation. Some of those are as follows:as follows:

1. Biological plausibility1. Biological plausibility2. Strength of association (this is 2. Strength of association (this is notnot measured by the measured by the PP value)value)3. Dose response (are heterozygotes intermediate between 3. Dose response (are heterozygotes intermediate between the two homozygotes, or is homozygosity showing a stronger the two homozygotes, or is homozygosity showing a stronger association than just having the marker?)association than just having the marker?)4. Time sequence (this is inherent in the germ-line nature of 4. Time sequence (this is inherent in the germ-line nature of HLA genes)HLA genes)5. Consistency (next slide lists reasons for inconsistency in 5. Consistency (next slide lists reasons for inconsistency in HLA association studies)HLA association studies)6. Specificity of the association to the disease studied 6. Specificity of the association to the disease studied


Why Are the Inconsistencies? (I)Why Are the Inconsistencies? (I)

1. Mistakes in genotyping (lack of HWE in controls is 1. Mistakes in genotyping (lack of HWE in controls is usually an indication of problems with typing rather than usually an indication of problems with typing rather than selection, admixture, nonrandom mating or other reasons of selection, admixture, nonrandom mating or other reasons of departure from HWE)departure from HWE) 2. Poor control selection (would your controls be in the 2. Poor control selection (would your controls be in the case group if they had the disease, and would the cases be case group if they had the disease, and would the cases be in your control group if they were free of the disease?)in your control group if they were free of the disease?) 3. Design problems including the statistical power issue 3. Design problems including the statistical power issue (negative results due to lack of statistical power should be (negative results due to lack of statistical power should be distinguished from truly negative results observed despite distinguished from truly negative results observed despite having sufficient power)having sufficient power) 4. Publication bias (are there many more studies with 4. Publication bias (are there many more studies with negative results but we have never heard about them?)negative results but we have never heard about them?) 5. Disease misclassification or misclassification bias5. Disease misclassification or misclassification bias


Why Are the Inconsistencies? (II)Why Are the Inconsistencies? (II)

6. Excessive type I errors (are the positive results due to 6. Excessive type I errors (are the positive results due to using using P P < 0.05 as the statistical significance?)< 0.05 as the statistical significance?) 7. Posthoc and subgroup analysis (are positive results due 7. Posthoc and subgroup analysis (are positive results due to fishing (data dredging)?)to fishing (data dredging)?) 8. Unjustified multiple comparisons and subsequent type II 8. Unjustified multiple comparisons and subsequent type II errorerror 9. Failure to consider the mode of inheritance in a genetic 9. Failure to consider the mode of inheritance in a genetic diseasedisease 10. Failure to account for the LD structure of the gene 10. Failure to account for the LD structure of the gene (only haplotype-tagging markers will show the association, (only haplotype-tagging markers will show the association, other markers within the same gene may fail to show an other markers within the same gene may fail to show an association and generate background noise)association and generate background noise) 11. Likelihood that the gene studied account for a small 11. Likelihood that the gene studied account for a small proportion of the variability in risk proportion of the variability in risk


Further InformationFurther Information

Select ‘Biostatistics' or ‘Epidemiology’ at

http://www.dorak.info

or write to me at

dorakmt :at: lycos.com[please do not add to your address book as it will change periodically]

http://www.dorak.info/


I am grateful to the BSHI Organizing Committee for giving me the opportunity to run this

workshop at BSHI 2002 in Glasgow.

I particularly thank Nancy Henderson and Ian Galbraith also for their hospitality.

BSHI AGMBSHI AGM

5:15 pm5:15 pm

All members should attendAll members should attend

Documents

BSHI 2002 Glasgow, Scotland STATISTICAL ANALYSIS OF HLA AND DISEASE ASSOCIATIONS M. Tevfik DORAK Department of Epidemiology University of Alabama at Birmingham