**Conditioning Adaptive
Combination of P-values (conADA) Method
**

**To Analyze Case-Parent Trios with or without Population Controls
**

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# If you use this code to analyze data, please cite the following paper:

# Lin W-Y, Liang Y-C. Conditioning adaptive combination of P-values method to analyze case-parent trios with or without population controls. Scientific Reports, 6: 28389. (2016)

# Any questions or comments, please contact: Wan-Yu Lin, linwy@ntu.edu.tw, Institute of Epidemiology and Preventive Medicine, National Taiwan University College of Public Health

# Thank you.

##########################################################################################

**For case-parent trios (with or without population controls):**

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**

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The R code to implement the conADA method Example file: trios.txt Example file: PC.txt

In R, the code to implement this function is: (the R package "rvTDT" needs to be installed first)

source('conADA.R')

# If you do not have population controls:

TrioPC(file1 = "trios.txt" , min.sim = 10, max.sim = 1000) # wait for ~5 seconds

# If you have population controls:

TrioPC(file1 = "trios.txt", file2 = "PC.txt" , min.sim = 10, max.sim = 1000) # wait for ~5 seconds

where 'trios.txt' is the data file of trios, in which the first 1/3 rows are the genotypes of the affected children, the second 1/3 rows are the genotypes of mothers, and the last 1/3 rows are the genotypes of fathers. The genotypes are coded as 0, 1, or 2, which are the numbers of minor alleles;

'PC.txt' is the data file of population controls, in which the number of rows = the number of population controls, and the number of columns = the number of variant sites. 'PC.txt' and 'trios.txt' should have the same number of columns.

The following input elements of this function are:

min.sim = 10 (the minimum number of permutations in the sequential Monte Carlo permutation [Besag & Clifford, 1991])

max.sim = 1000 (the maximum number of permutations in the sequential Monte Carlo permutation [Besag & Clifford, 1991])

Output is the the P-value of the conADA test.

Thanks for your interest.

Return to Wan-Yu Lin's homepage

# If you use this code to analyze data, please cite the following paper:

# Lin W-Y, Liang Y-C. Conditioning adaptive combination of P-values method to analyze case-parent trios with or without population controls. Scientific Reports, 6: 28389. (2016)

# Any questions or comments, please contact: Wan-Yu Lin, linwy@ntu.edu.tw, Institute of Epidemiology and Preventive Medicine, National Taiwan University College of Public Health

# Thank you.

##########################################################################################

In R, the code to implement this function is: (the R package "rvTDT" needs to be installed first)

source('conADA.R')

# If you do not have population controls:

TrioPC(file1 = "trios.txt" , min.sim = 10, max.sim = 1000) # wait for ~5 seconds

# If you have population controls:

TrioPC(file1 = "trios.txt", file2 = "PC.txt" , min.sim = 10, max.sim = 1000) # wait for ~5 seconds

where 'trios.txt' is the data file of trios, in which the first 1/3 rows are the genotypes of the affected children, the second 1/3 rows are the genotypes of mothers, and the last 1/3 rows are the genotypes of fathers. The genotypes are coded as 0, 1, or 2, which are the numbers of minor alleles;

'PC.txt' is the data file of population controls, in which the number of rows = the number of population controls, and the number of columns = the number of variant sites. 'PC.txt' and 'trios.txt' should have the same number of columns.

The following input elements of this function are:

min.sim = 10 (the minimum number of permutations in the sequential Monte Carlo permutation [Besag & Clifford, 1991])

max.sim = 1000 (the maximum number of permutations in the sequential Monte Carlo permutation [Besag & Clifford, 1991])

Output is the the P-value of the conADA test.

Thanks for your interest.

Return to Wan-Yu Lin's homepage