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