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Topic #7 Single-Locus Association Studies: Case-Control StudiesPowerPoint Presentation

Topic #7 Single-Locus Association Studies: Case-Control Studies

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### Topic #7Single-Locus Association Studies:Case-Control Studies

University of Wisconsin

Genetic Analysis Workshop

June 2011

Outline

- Case-Control Study:
- Two-allele, single locus model
- Alternative Tests for Association

- Quantitative Outcomes:
- Two-allele, single locus model
- Alternative Tests

- Multiple Testing (Topic #8):

Tests (Models) of Association

- Genotype: Distribution of 3 genotypes differs in the two groups (unstructured alternative)
Standard c2 on 2df

- Recessive: Relative frequency of A1A1 differs in two groups
c2 on 1df

- Dominant: Relative frequency of A2A2 differs in two groups
c2 on 1df

Case-Control Example: Genotype Test

Test #1: Compare genotype frequency in cases and controls

Test: c2(2df) = 27.7; p < 10-5

Case-Control Example: Recessive Test

Test #2: Rate of E4/E4 in cases and controls

Test: c2 (1df) = 5.46; p =.019

Case-Control Example: Dominant Test

Test #3: Compares rate of +/+ in Cases and Controls

Test: c2(1df) = 27.3; p < 10-6

Simple Association: More Tests

- Trend (Cochran-Armitage): Regress proportion of cases on # of risk alleles (here E4)
- Allele Test: Count alleles rather than individuals (assumes HWE)
- Case-only design: Test whether cases are in HWE
- Logistic Model

Allele Test

Test #5: Allele Frequency Comparison

Test: c2(1df) = 26.8; p < 10-6

Sasieni, P. D. (1997). From genotypes to genes: Doubling the sample size. Biometrics, 53(4), 1253-1261.

Case-Only Design: Test for HWE

Test #6: Departure from HWE

Test: c2(1df) = 0.4; p = .52

(Little power for multiplicative model)

And there are more: A 7th (and later 8th!) test

- Log-additive/logistic:

Advantage of Logistic Framework

- Can easily accommodate covariates
- Can accommodate alternative models (e.g., dominance or recessive models) with dummy variables
- Test of H0: bi = 0 is very nearly same as allele test

Genetic Determinants of Human Ageing and Longevity Project

- Aim:
- Identify genetic variants associated with extreme longevity

- Basic Design:
- 1200 cases (1905) and 800 controls (MADT)
- Candidate-gene approach: 168 genes

- Genotyping:
- 1536 SNPs using Illumina’s Golden Gate Array

Summary of plink Data Cleaning of GCLC_Clean

- Start: 1200 Cases, 800 Controls, 13 SNPs
- Eliminate:
- 293 (103 cases/90 controls) individuals with > 10% missing
- 1 SNP eliminated because > 10% missing
- 1 SNP fail HWE at p < .001
- 1 SNP eliminated due to low MAF

- Final sample: 997 cases, 710 controls and 13 SNPs in GCLC

plink Implementation of Association Tests

- Basic association test (allelic):
plink --file gclc_clean --assoc

(generates plink.assoc)

plink Association Output (plink.assoc)

CHR SNP BP A1 F_A F_U A2 CHISQ P OR

6 rs7742367 53469235 G 0.169 0.1472 A 2.906 0.08826 1.178

6 rs670548 53474948 G 0.3507 0.39 A 5.504 0.01898 0.8447

6 rs661603 53478066 G 0.4626 0.4085 A 9.752 0.001791 1.246

6 rs16883912 53481730 A 0.1093 0.09437 G 1.988 0.1585 1.177

6 rs572496 53485578 A 0.5005 0.4458 G 9.952 0.001607 1.246

6 rs617066 53491877 A 0.3296 0.2648 G 16.52 4.82e-005 1.365

6 rs2100375 53493434 A 0.3539 0.3077 G 7.936 0.004846 1.232

6 rs531557 53497954 T 0.4769 0.433 A 6.421 0.01128 1.194

6 rs16883966 53505685 G 0.05308 0.0346 A 6.506 0.01075 1.564

6 rs4712035 53509062 C 0.1745 0.1711 G 0.06685 0.796 1.024

6 rs2397147 53509546 G 0.432 0.388 A 6.608 0.01015 1.2

6 rs534957 53514310 G 0.3258 0.338 C 0.5596 0.4544 0.9464

6 rs675908 53521259 G 0.3246 0.3383 A 0.7009 0.4025 0.94

Highlighted nominally significant at p < .05

plink Implementation of Association Tests

- Basic association test (allelic):
plink --file gclc_clean --assoc

(generates plink.assoc)

- Genetic model based tests (genotype, trend, domin, recess):
plink --file gclc_clean --model

(generates plink.model)

Association ‘Model’ Tests for 13 GCLC SNPs

Highlighted

In Red, nominally significant at p < .05,

In Blue, significant after Bonferroni correction p < .004 (i.e., 05/13)

Low Frequency SNPs

- Within the 13 GCLC SNPs, rs16883966had MAF < .05 (.049 in Danish 1905 and .037 in MADT)
- For this SNP unable to compute test statistic for Genotype, Dominant, & Recessive models because of low cell frequencies (Exp < .05)

plink Implementation of Association Tests

- Basic association test (allelic):
plink --file gclc_clean --assoc

(generates plink.assoc)

- Genetic model based tests (genotype, trend, domin, recess):
plink --file gclc_clean --model

(generates plink.model)

- Fisher exact test (the 8th!):
plink --file gclc_clean --fisher

(generates plink.fisher)

- Logistic:
plink --file gclc_clean --logistic

(generates plink.logistic)

Reparameterized Single-locus Model

Genotypic Values

A2A2

A1A1

A1A2

u11

u12

u22

-a

d

a

d is dominance parameter; when d = 0, locus is additive

Additive Genetic Variance

Note: d contributes to additive variance whenever q is not equal to .5

Dominance Genetic Variance

Note: There is dominance variance only when d is not 0

Complete Additivity

Slope of regression line =a

Additive genetic variance = regression variance

1

0

2

Partial Dominance

Slope of regression line = a

Dominance = Residual Variance

Additive genetic variance = regression variance

1

0

2

Complete Dominance

Dominance = Residual Variance

Slope of regression line = a

Additive genetic variance = regression variance

1

0

2

Some Conclusions

- Dominance effects contribute to additive genetic variance
- Even with complete Mendelian dominance, additive variance typically exceeds dominance variance (exception would be overdominance)

Power Calculation in Quanto for Quantitative Trait

- In a study of 1000 unrelated individuals, what is our power to detect a single locus effect?
- Strength of genetic effect (R2g)
- Risk allele frequency?

Quanto G Power Calculation

- Outcome/Design:
- Continuous Independent Individuals
- Hypothesis:
- Gene Only
- Gene:
- Allele Frequency .10 to .90 by .20
- Additive model
- Outcome Model:
- R2g = .001 to .019 by .002
- Power:
- Sample Size = 1000 to 1000 by 0
- Type I error rate = .05, two-sided
- Calculate:

Computed Power for N=1000(Minor Allele = Risk Allele)

% Variance Accounted For

Association with a Quantitative Phenotype

- Genotype: 10 SNP markers in the COMT gene, including rs4680
- Sample: 7235 participants in MCTFR longitudinal research
- Phenotype: General externalizing composite (having an overall mean of ~ 0.0, SD ~ .36)
plink --bfilecomt --phen ext.dat --mpheno 2 --missing-phenotype -99.0

--assoc –qt-means

Output: plink.qassoc

CHR SNP BP NMISS BETA SE R2 T P

22 rs4646312 18328337 7233 -0.003598 0.006141 4.747e-005 -0.5859 0.558

22 rs165656 18328863 7232 -0.01252 0.005983 0.0006056 -2.093 0.03637

22 rs165722 18329013 7235 -0.01346 0.005974 0.0007017 -2.254 0.02424

22 rs2239393 18330428 7233 -0.003556 0.006125 4.662e-005 -0.5806 0.5615

22 500437 18330763 7232 -0.004062 0.006127 6.079e-005 -0.663 0.5074

22 rs4680 18331271 7234 -0.01358 0.005973 0.0007139 -2.273 0.02305

22 rs4646316 18332132 7235 -0.002434 0.007201 1.58e-005 -0.3381 0.7353

22 rs165774 18332561 7235 0.009351 0.006543 0.0002823 1.429 0.153

22 rs174699 18334458 7235 -0.0124 0.01288 0.0001281 -0.9626 0.3358

22 rs165599 18336781 7233 -0.004997 0.006435 8.337e-005 -0.7765 0.4375

Highlighted: Nominally significant at p < .05

Output: plink.qassoc.means (rs4680)

Simple Association: Conclusions

- Power depends on which test is used
- In the absence of a strong hypothesis, most use tests that assume heterozygote risk is intermediate (trend, logistic, allelic)
- While the trend test is generally preferred, logistic (~allelic) has advantages in generalizability

- We now need to worry about multiple testing!

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