 Casecontrol association ttest(case/control or binomial phenotype) 
 Carry out linear regression analysis (continuous phenotype) 
 Carry out logistic regression analysis(case/control or binomial phenotype) 
 Calculate the Pearson's correlation coefficients (continuous phenotype) 
 Convert DNA methlytion betavalue to SMP menoypte (MM MU or UU) 
 Chisqsquare test for epiallele 2 (phenotype)*2 ( M vs. U) table 
 Identify the type of SMP allele association for each DNA methylation loci 
Calculate the MAF and HadyWeinberg pvalue for each DNA methylation loci  
Give a summary statistic for each DNA methylation loci 
Epigenomewide meplotype association test  
 Calculated the MD coefficient MDprime and MD rsquare 
 Identify the MD blocks and calculate the frequency of meplotype 
Calculate the frequency of methylecomtypes in MD block rigion  
calculated the general MD coefficient gDprime and grsquare  
Identify the general MD blocks and calculate the frequency of methylecomtype 
Carry out epigenome wide metaanalysis 
Calculate the frequency of methylecomtypes in MD block rigion(default:'matrix')  
The sliding window's size(default:20)  
Specify a threshold to change the betavalues into two level(H and L)  
Do not sort the DNA methylation loci 
EWAS can carry out T test for single DNA methylation locus and calculate the Tp values
java jar ewas.jar t.test input example_ewas.txt
or
java jar ewas.jar t.test input example_ewas.txt output outFile
input file ：
please see example_ewas.txt or download example_ewas.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
From the fourth to the last:
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: the number of valid case samples.
column 5: the number of valid control samples.
column 6: the average of DNA methylation level in case samples.
column 4: the average of DNA methylation level in control samples.
column 5: Fold change.
column 6: T statistic.
column 7: p value.
EWAS can Carry out simple linear regression analysis for each methylation loci with continuous phenotypes.
java jar ewas.jar linear input example_linear.txt
or
java jar ewas.jar linear input example_linear.txt output outFile
input file ：
please see example_linear.txt or download example_linear.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the continuous phenotype of sample, such as : age
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: total sample number.
column 5: valid sample number.
column 6: missing rate.
column 7: linear regression coefficient.
column 8: standard error.
column 9: p value.
EWAS can Carry out logistic regression analysis for each methylation loci with binomial phenotype.
java jar ewas.jar logistic formula phenotype~marker input example_logistic.txt
or
java jar ewas.jar logistic formula phenotype~marker input example_logistic.txt output outFile
input file ：
please see example_logistic.txt or download example_logistic.zip for more information.
The first row in EWAS input data file, with a phenotype name starting with "#",is the header line. From the fourth to the last is the phenotype of sample, such as disease state.
1
: case
0
: control
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
form column 2 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
column 1: the ID of the DNA methylation locus.
column 2: total sample number.
column 3: valid sample number.
column 4: intercept.
column 5: standard error of intercept.
column 6: Zvalue of intercept.
column 7: Pvalue of intercept.
column 8: marker's logistic regression coefficient.
column 9: standard error of marker's logistic regression coefficient.
column 10: Zvalue of logistic marker's regression coefficient.
column 11: p value of logistic marker's regression coefficient.
EWAS can Calculate the Pearson's correlation coefficients for each methylation loci with continuous phenotype.
java jar ewas.jar cor input example_cor.txt
or
java jar ewas.jar cor input example_cor.txt output outFile
input file ：
please see example_cor.txt or download example_cor.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the continuous phenotype of sample, such as : age
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: total sample number.
column 5: valid sample number.
column 6: missing rate.
column 7: Pearson's correlation coefficient.
column 8: 95% CI of Pearson's correlation coefficient.
column 9: z value.
column 10: 95% CI of z value.
column 11: standard error of z value.
column 12: p value of z value.
EWAS can Convert DNA methlytion betavalue to SMP menoypte use two threshold.
java jar ewas.jar SMP.convert input example_ewas.txt
or
java jar ewas.jar SMP.convert input example_ewas.txt output outFile threshold 0.3 0.7
input file ：
please see example_ewas.txt or download example_ewas.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
0
: unknown/miss 1
: unaffected/normal/control group 2
: affected/disease/case group column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. MU etc.) represents the mplotype of samples in the DNA methylation locus.
EWAS can Carry out Chisqsquare test for SMP meplotypes , 2 (phenotype)*2 ( M vs. U) table.
java jar ewas.jar SMP.allele_chisq input out_smp.txt
or
java jar ewas.jar SMP.allele_chisq input out_smp.txt output outFile
input file ：
please see out_smp.txt or download out_smp.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. MU etc.) represents the methylation genotype of samples in the DNA methylation locus.
OutFile ：
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: total sample number.
column 5: case sample number.
column 6: control sample number.
column 7: missing sample number.
column 8: case M number.
column 9: control M number.
column 10: case U number.
column 11: control U number.
column 12:chisqure statistic.
column 13:chisqure p value.
column 14: OR.
column 15: 95% CI.
Attention:  for one SMP , if  case  control  
M  a  b  
U  c  d 


OR>1, risk factor  
OR=1, has no effect  
OR<1, protective factor 
EWAS can Identify the type of SMP allele association for each DNA methylation loci.
java jar ewas.jar SMP.aa input out_smp.txt
or
java jar ewas.jar SMP.aa input out_smp.txt output outFile
input file ：
please see out_smp.txt or download out_smp.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. MU etc.) represents the mplotype of samples in the DNA methylation locus.
OutFile ：
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: total sample number.
column 5: total sample number with unkown information.
column 6: case sample number with unkown information.
column 7: control sample number with unkown information.
column 8: total number ratio with MM meplotype.
column 9: case number ratio with MM meplotype.
column 10: control number ratio with MM meplotype.
column 11: total number ratio of M.
column 12: case number ratio of M.
column 13: control number ratio of M.
column 14: SMP association type.
column 15: case SMP association type.
column 16: control SMP association type.
EWAS can Calculate the MAF and HadyWeinberg pvalue for each DNA methylation loci.
java jar ewas.jar SMP.HW input out_smp.txt
or
java jar ewas.jar SMP.HW input out_smp.txt output outFile
input file ：
please see out_smp.txt or download out_smp.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. MU etc.) represents the mplotype of samples in the DNA methylation locus.
OutFile ：
column 111:
column 1222:
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: total sample number.
column 5: all sample number with unkown meplotype.
column 6: case sample number with unkown meplotype.
column 7: control sample number with unkown meplotype.
column 8: all sample number with MM meplotype.
column 9: case sample number with MM meplotype.
column 10: control sample number with MM meplotype.
column 11: all sample number with MU meplotype.
column 12: case sample number with MU meplotype.
column 13: control sample number with MU meplotype.
column 14: all sample number with UU meplotype.
column 15: case sample number with UU meplotype.
column 16: control sample number with UU meplotype.
column 17: MAF.
column 18: case MAF.
column 19: control MAF.
column 20: HadyWeinberg p value.
column 21: case HadyWeinberg p value.
column 22: control HadyWeinberg p value.
EWAS can Give a summary statistic for each DNA methylation loci.
java jar ewas.jar SMP.summary input out_smp.txt
or
java jar ewas.jar SMP.summary input out_smp.txt output outFile
input file ：
please see out_smp.txt or download out_smp.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. MU etc.) represents the mplotype of samples in the DNA methylation locus.
OutFile ：
column 116:
column 1731:
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: total sample number.
column 5: all sample number with unkown meplotype.
column 6: case sample number with unkown meplotype.
column 7: control sample number with unkown meplotype.
column 8: all sample number with MM meplotype.
column 9: case sample number with MM meplotype.
column 10: control sample number with MM meplotype.
column 11: all sample number with MU meplotype.
column 12: case sample number with MU meplotype.
column 13: control sample number with MU meplotype.
column 14: all sample number with UU meplotype.
column 15: case sample number with UU meplotype.
column 16: control sample number with UU meplotype.
column 17: MAF.
column 18: case MAF.
column 19: control MAF.
column 20: HadyWeinberg p value.
column 21: case HadyWeinberg p value.
column 22: control HadyWeinberg p value.
column 23: total number ratio with MM meplotype.
column 24: case number ratio with MM meplotype.
column 25: control number ratio with MM meplotype.
column 26: total number ratio of M.
column 27: case number ratio of M.
column 28: control number ratio of M.
column 29: SMP association type.
column 30: case SMP association type.
column 31: control SMP association type.
EWAS can scan the entire genome and identify the association between combinations of methylation levels (meplotype) and diseases.
java jar ewas.jar meplotype input example_meplotype.txt
or
java jar ewas.jar meplotype input example_meplotype.txt output outFile
input file ：
please see example_meplotype.txt or download example_meplotype.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
From the fourth to the last:
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. M/U) represents the methylation status of the DNA methylation locus.
column 3:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 4:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 5 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the methylation genotype in each sample.
OutFile ：
column 19
column 1015
column 1:
block number.
column 2:
chromosome number.
column 3:
the start position.
column 4:
the end position.
column 5:
the methylation haplotype.
column 6:
the total frequency of the methylation haplotype.
column 7:
the frequency of the methylation haplotype in case samples.
column 8:
the frequency of the methylation haplotype in control samples.
column 9:
the number of this methylation haplotype and others in case samples.
column 10:
the number of this methylation haplotype and others in control samples.
column 11:
the chisquare statistic.
column 12:
the Pvalue of chisquare test.
column 13:
OR.
column 14:
95%CI of OR.
column 15:
methylation locis in the blocks.
Attention:  for one meplotype , if  case  control  
this meplotype  a  b  
other meplotype  c  d 


OR>1, risk factor  
OR=1, has no effect  
OR<1, protective factor 
For a list of DNA methylation loci, EWAS can calculate the MD coefficient Dprime and rsquare. We provided two types of output formate: matrix and list.
java –jar ewas.jar MD input example_md.txt
or
java –jar ewas.jar MD input example_md.txt MDFormat matrix output outFile
input file ：
please see example_md.txt or download example_md.zip for more information.
The first row in EWAS input data file is the header line. From the fifth to the last is the status of sample.
From the fifth to the last:
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. M/U) represents the methylation status of the DNA methylation locus.
column 3:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 4:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 5 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the methylation status of samples in the DNA methylation locus.
OutFile ：
The first row and the first colum for each martrix is the DNA methylation loci. The data in the matrix is Dprime or rsquare.
java –jar ewas.jar MD input example_md.txt MDFormat list
or
java –jar ewas.jar MD input example_md.txt MDFormat list output outFile
OutFile ：
column 1:
is the ID of the first DNA methylation locus.
column 2:
is the ID of the second DNA methylation locus.
column 3:
is the MD coefficient Dprime.
column 4:
is the MD coefficient and rsquare.
EWAS can identify the MD blocks and calculate the frequency of meplotype in the blocks.
java –jar ewas.jar –block input example_meplotype.txt
or
java –jar ewas.jar –block input example_meplotype.txt output outFile
input file ：
please see example_meplotype.txt or download example_meplotype.zip for more information.
The first row in EWAS input data file is the header line. From the fifth to the last is the status of sample.
From the fifth to the last:
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. M/U) represents the methylation status of the DNA methylation locus.
column 3:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 4:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 5 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the methylation status of samples in the DNA methylation locus.
OutFile ：
column 1:
block#.
column 2:
chromosome number.
column 3:
start position of the block.
column 4:
end position of the block.
column 5:
the meplotype in blocks.
column 6:
frequency of methylecomtype in total sapmles.
column 7:
methylation loci in the blocks.
EWAS can scan the entire genome and identify the association between combinations of methylation levels (methylecomtype) and diseases.
java jar ewas.jar methylocomtype input example_ewas.txt
or
java jar ewas.jar methylocomtype WindowSize 20 input example_ewas.txt threshold 0.5 output outFile
input file :
please see example_ewas.txt or download example_ewas.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
From the fourth to the last:
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
column 1:
block#.
column 2:
the methylecomtype in blocks.
column 3:
chromosome number.
column 4:
the start position.
column 5:
the end position.
column 6:
the total sample number.
column 7:
the number of methylecomtype in total sapmles.
column 8:
the number of methylecomtype in cases.
column 9:
the number of methylecomtype in controls.
column 10:
frequency of methylecomtype in total sapmles.
column 11:
frequency of methylecomtype in cases.
column 12:
frequency of methylecomtype in controls.
column 13:
the chisquare statistic.
column 14:
the Pvalue of chisquare test.
column 15:
OR.
column 16:
95%CI of OR.
column 17:
methylation loci in the blocks.
Attention:  for one meplotype , if  case  control  
this meplotype  a  b  
other meplotype  c  d 


OR>1, risk factor  
OR=1, has no effect  
OR<1, protective factor 
For a list of DNA methylation loci, EWAS can calculate the general MD coefficient gDprime and grsquare. We provided two types of output formate: matrix and list.
java –jar ewas.jar gMD input example_gMD.txt
or
java –jar ewas.jar gMD input example_gMD.txt gMDFormat matrix output outFile
input file ：
please see example_gMD.txt or download example_gMD.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
From the fourth to the last:
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
The first row and the first colum for each martrix is the DNA methylation loci. The data in the matrix is gDprime or grsquare.
java –jar ewas.jar gMD input example_gMD.txt gMDFormat list
or
java –jar ewas.jar gMD input example_gMD.txt gMDFormat list output outFile
OutFile ：
column 1:
is the ID of the first DNA methylation locus.
column 2:
is the ID of the second DNA methylation locus.
column 3:
is the MD coefficient gDprime.
column 4:
is the MD coefficient and grsquare.
EWAS can identify the general MD blocks and calculate the frequency of methylecomtype in the blocks.
java –jar ewas.jar –gblock input example_gblock.txt
or
java –jar ewas.jar –gblock WindowSize 20 input example_gblock.txt thereshold 0.5 output outFile
input file ：
please see example_gblock.txt or download example_gblock.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
From the fourth to the last:
0
unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
column 1:
block#.
column 2:
the methylecomtype in blocks.
column 3:
chromosome number.
column 4:
start position of the block.
column 5:
end position of the block.
column 6:
the total sample number.
column 7:
the number of methylecomtype in total sapmles.
column 8:
frequency of methylecomtype in total sapmles.
column 9:
methylation loci in the blocks.
We firstly sort the DNA methylation loci based on their physical position. Then change the betavalues into two levels (H: high DNA methylation level and L: low DNA methylation level).
java jar ewas.jar sort input example_ewas.txt
or
java jar ewas.jar sort input example_ewas.txt output outFile
input file ：
please see example_ewas.txt or download example_ewas.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last is the status of sample.
0
: unknown/miss
1
: unaffected/normal/control group
2
: affected/disease/case group
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
OutFile ：
0
: unknown/miss 1
: unaffected/normal/control group 2
: affected/disease/case group column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
(e.g. 0.492 0.708 0.483 etc.) represents the betavalue of samples in the DNA methylation locus.
EWAS can Give a summary statistic for each DNA methylation loci.
java jar ewas.jar meta input example_meta.txt
or
java jar ewas.jar meta input example_meta.txt output outFile
input file ：
please see example_meta.txt or download example_meta.zip for more information.
The first row in EWAS input data file is the header line. From the fourth to the last: every two column represent one study.
column 1:
(e.g. cg00000165) is the ID or name of a DNA methylation locus.
column 2:
(e.g. chr1) represents the chromosome number of the DNA methylation locus.
column 3:
(e.g. 91194674) represents physical position of the DNA methylation locus.
form column 4 to end:
every two column represent one study, first column is regression coefficients, the next column is its standerd error .
OutFile ：
column 1: the ID of the DNA methylation locus.
column 2: chromosome number.
column 3: physical position of the DNA methylation locus.
column 4: total study number.
column 5: valid study number.
column 6: Qtest statistic.
column 7: Qtest p value.
column 8: I2statistic.
column 9: Pooled effect statistics in fixed model.
column 10: 95% CI of pooled effect statistics in fixed model.
column 11: Standard error of pooled effect statistics in fixed model.
column 12: Ztest statistic of pooled effect statistics in fixed model..
column 13: P value for ztest statistic of pooled effect statistics in fixed model..
column 14: Pooled effect statistics in random model.
column 15: 95% CI of pooled effect statistics in random model.
column 16: Standard error of pooled effect statistics in random model.
column 17: Ztest statistic of pooled effect statistics in random model.
column 18: P value for ztest statistic of pooled effect statistics in random model.
 The input file name 
 The output file name 
 Sort the DNA methylation loci 
 Start analysis without sorting the DNA methylation loci 
 Casecontrol association ttest(case/control or binomial phenotype) 
 Carry out linear regression analysis (continuous phenotype) 
 Carry out logistic regression analysis(case/control or binomial phenotype) 
 Calculate the Pearson's correlation coefficients (continuous phenotype) 
 Convert DNA methlytion betavalue to SMP menoypte (MM MU or UU) 
 Chisqsquare test for SMP meplotypes 2 (phenotype)*2 ( M vs. U) table 
 Identify the type of SMP allele association for each DNA methylation loci 
Calculate the MAF and HadyWeinberg pvalue for each DNA methylation loci  
Give a summary statistic for each DNA methylation loci 
Epigenomewide meplotype association test  
 Calculated the MD coefficient MDprime and MD rsquare 
 Identify the MD blocks and calculate the frequency of meplotype 
Calculate the frequency of methylecomtypes in MD block rigion  
calculated the general MD coefficient gDprime and grsquare  
Identify the general MD blocks and calculate the frequency of methylecomtype 
Carry out epigenome wide metaanalysis 
Calculate the frequency of methylecomtypes in MD block rigion(default:'matrix')  
The sliding window's size(default:20)  
Specify a threshold to change the betavalues into two level(H and L)  
Do not sort the DNA methylation loci 