Sequential Kernel Association Tests for the Combined Effect of Rare and Common Variants Journal club...
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Transcript of Sequential Kernel Association Tests for the Combined Effect of Rare and Common Variants Journal club...
Sequential Kernel Association Tests for the Combined Effect of Rare and Common Variants
Journal club (Nov/13)SH Lee
Introduction
• Sequence data– Rare and unidentified variants
• Groupwise association tests– Omnibus tests– Burden test, CMC test, SKAT test• Up-weighting for rare, • down-weighting for common• Rare/common variants tested separately
Introduction• This study develops a joint test of rare/common– Combining burden/SKAT test for rare/common
• Can be applied to – whole exome sequencing + GWAS – Deep resequencing of GWAS loci
• Basically can analyse all variants including rare, low-frequency and common variants
• Simulation (type 1 error, power)• Real data, CD and Autism
Materials and Methods
Definition of rare/common• <0.01 rare• 0.01-0.05 low frequency• >0.05 common
Or• <1/sqrt(2*n) rare • >1/sqrt(2*n) common– n = 500, rare MAF < 0.031– n = 10000, rare MAF < 0.007
Materials and Methods
• Testing for the overall effect of rare and common variants– Rare for Burden test– Common for SKAT test
Weighted-sum statisticsFishers method of combining the p values
Weighted-sum statistics
• Within a region (e.g. a gene) having m variants– g(*) is a linear or logistic link function – Alpha is for covariates– X is n x m matrix– Beta is regression coefficient and random variable
Weighted sum score test(Variance component score test)
Taking the first derivative of log-likelihood respect with the variance τ
P-value from κχ2ν
κ is scale parameter, v is degree of freedom
Weighted sum score test(Variance component score test)
Wu et al (2010) AJHG 86: 929; Liu et al (2008) BMC Bioinformatics 8: 292;
Lin (1997) Biometrika 84: 309; White (1982) Econometrica 50: 1
Weighted sum score test(Variance component score test)
• ρ : the correlation between regression coefficients • If perfectly correlated (ρ = 1), they will be all the same after
weighting, and one should collapse the variants first before running regression, i.e., the burden test
• If the regression coefficients are unrelated to each other, one should use SKAT
Lee et al. (2012) AJHG 91: 224
Burden-C, SKAT-C
• Combined test statistic for rare and common– Weighting beta(p,1,25) for rare, – beta(p,0.5,0.5) for common
• Partitioning rare and common variants
Other methods
• Burden-A, SKAT-A– Adaptive combining rare/common– Searching φ for the minimum p-value
• Burden-F, SKAT-F– Fisher’s combination method
Simulation
• Sequence data on 10,000 haplotypes on 1 Mb region
• Calibrated model for the European pop• Random sample of a region of 5 or 25 kb and
simulated data with 1000-5000 individuals • Proportion of cases in the sample is 0.5
Disease model
Methods
Type I error
• The proposed methods agrees with the expectation
Power (separation cut-off)
• Using burden-C test• Power with different separation cut-offs• 1/sqrt(2n) will be used further
Power (proposed methods)
• Power for 8 different tests• The proposed combination tests outperform
Power
• Rare/common causal variants (model 1, 2, 3, 6)– The combination methods perform better
Power
• Common causal variants (model 5)– The combination methods perform better
• Rare causal variants (model 4)– The combination methods perform similarly
Power (proposed methods)
•The proposed combination methods outperform CMC for all 6 disease models•The proposed combination methods outperform the original SKAT for all 6 disease models
Power
•For model 1-4 which include only risk variants• SKAT better than Burden when prop. risk variants is small (10%)• Burden better than SKAT when prop. risk variants is large (30%)
Power
• Model 1-3 which include both rare/common• SKAT-F better than burden-F regardless of prop. risk variants
• Model 5 which include only common risk variants• SKAT better than burden regardless of prop. risk variants
Power
• Adaptive test (SKAT-A, Burden-A)– Perform worse than SKAT-C and Burden-C
• Results for a region of size 5 kb were similar
Real data
• CD NOD2 sequence data – 453 cases, 103 controls– 60 single nucleotide variations (9 of them have >
MAF 0.05)– Because only pooled frequency counts available
for each variants, sequencing data were simulated.
• Autism LRP2 sequencing data– 430 cases, 379 controls
Real data
• The combination methods powerful than others
Discussion
• The proposed combination methods– Partitioning rare/common– Powerful approach– Better than CMC (rare/common partitioning)– Better than original Burden and SKAT test – Extend to family-based designs
Discussion
• T1D HLA region – SKAT (2.7e-43)– Wald test (6.7e-49)– Likelihood ratio test (8.9e-221)
• LD between regions • Multiple different components within a region
• Thanks
Linear SKAT vs individual variant test statistics
• Linear SKAT (lower) and individual variant test (upper) is equivalent
• Three disease model for power comparison