Kun:LabNotes/CpgSeq/2011-10-14
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Analysis of BSPP data from Geographic Astrophy (GA) patients
- 96 blood samples from cases and controls were provided by Kang Zhang's group.
- Noi performed the BSPP capture and the sequencing was done in BioGem (110929_SN1001)
Summary statistics on sequencing, mapping and CpG methylation calling
Noi should fill in this part.
Regression analysis
- The working directory is on genome-miner
/home/kunzhang/mQTL/KangZhang_GA
- One data set (2200001) was removed due to low capture efficiency and low sequencing depth.
- Methylation levels from the rest of 95 samples (turned out to be 94) were combined into a matrix (up to 20% missing values was allowed):
allBED2Matrix.pl BED 76 1 0.1 > KangZhang_GA_BSPP_95_methylationMatrix.txt &
- I ended up getting a matrix containing 136,417 variable CpG sites.
- I wrote a Perl Script to perform linear regression on five factors: disease status, sex, age, BMI, smoking: GA_methylation_regression.pl.
./GA_methylation_regression.pl KangZhang_GA_BSPP_95_methylationMatrix.txt > KangZhang_GA_BSPP_95_methylation_regression.txt &
- Sites that might be significantly contributed by one of these factors were reported.
- I used a stringent cutoff of 3.5 for the t-statistics on reporting the data in individual files (allowed for more detailed analysis in R).
- The test statistics for all the tested sites were also reported in a separate file (KangZhang_GA_BSPP_95_methylation_regression.txt).
- If I simply use a cutoff for |t-value| of 2.5 (equivalent to p=0.014), then I can count how many Cpg sites are significantly correlated with each factor.
Factor # sites GA 1515 Sex 2718 Age 2310 BMI 1861 Smoking 2002 GA+Age 249 (somewhat expected as age is a major contributor of GA) GA+Sex 53 GA+BMI 28 GA+Smoking 34 It's important to note that no correction for multiple testing was done here. Even with the least stringent FDR correction, no site is significant.