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Source for Paper:


A Computational Model for the Inheritance Pattern of Genomic Imprinting for Complex Traits.

Description:

We develop a new computational model for quantifying the effects of genomic imprinting on complex traits and studying how genomic imprinting transmits from one generation to next. The model was founded on two-way reciprocal backcrosses which allow for separate tracing of allelic transmission from maternal and paternal parents. We use traditional quantitative genetic principles to define genetic parameters that describe genomic imprinting effects and their interactions with other types of genetic effects. A number of genetically meaningful hypothesis tests are generated to ask and address fundamental questions in quantitative inheritance and variation.

To cite this article:

Chenguang Wang, Zhong Wang, Li Wang, Junjia Zhu, Daniel R. Prows,Rongling Wu. A computational model for the inheritance pattern of genomic imprinting for complex traits.

Download:

Two R source files are available in the following link.



Installation:

The zipped file includes two R files, one is core algorithm for the imprinting model, another is a plot utility. It is unnecessary to install them. just load them by the source command. Because we use ginv function to calculate the inverse of a matrix, the MASS library should be installed in advance.

Any OS:

>source("imp2_model.r");
>source("imp2_plot.r");

Sample Script:

The following snippets show how to call the main function in R.

Example 1

The following example shows how to generate a simulated dataset and run this simulate under the following null hypothesis:
(a == d == i == i1 == i2 == i3 == i4 == i5 == i6 == i7 == i8 == I1 == I2 == I3 == I4==0)

par <- IMP2.param();
dat <- IMP2.simulate(par);
ret <- IMP2.hp_test(dat);
summary(ret, options=list( bOutputPDF=1 ) );


Example 2

The following example shows how to load a real data and scan all markers under the following null hypothesis:
(a == d == i == i1 == i2 == i3 == i4 == i5 == i6 == i7 == i8 == I1 == I2 == I3 == I4==0)

par <- IMP2.load("IMP2.marker.csv", "IMP2.data.csv", options=list(log_pheno=1));
ret <- IMP2.hp_test(dat);
summary(ret, options=list( bOutputPDF=1 ) );


Example 3

The following example shows how to scan all markers under the following null hypothesis:
( i1 == i2 == I1 == 0)

#load the real data or simulate a data object before scanning all markers.
ret <- IMP2.hp_test(dat, hp=c(5,6,13) );
summary(ret, options=list( bOutputPDF=1 ) );

Last updated:

03/14/2011 Version 1.0


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