[Genabel-commits] r2002 - tutorials/GenABEL_general

[noreply at r-forge.r-project.org]

Author: lckarssen
Date: 2015-07-08 21:51:55 +0200 (Wed, 08 Jul 2015)
New Revision: 2002

Modified:
   tutorials/GenABEL_general/intro.Rnw
Log:
Summary: Removed unnecessary white space at the ends of lines in the Overview chapter of the GenABEL tutorial.


Modified: tutorials/GenABEL_general/intro.Rnw
===================================================================
--- tutorials/GenABEL_general/intro.Rnw	2015-07-08 19:49:32 UTC (rev 2001)
+++ tutorials/GenABEL_general/intro.Rnw	2015-07-08 19:51:55 UTC (rev 2002)
@@ -5,68 +5,68 @@
 {\bf This introduction is outdated: now the \GA{} is the project, the
   suite, and the package, see \url{http://www.genabel.org/developers}}
 
-\GA{} is an R library developed to facilitate Genome-Wide 
-Association (GWA) analysis of binary and quantitative traits. 
-\GA{} is implemented as an R library. R is a free, open source 
-language and environment for general-purpose statistical analysis 
-(available at \url{http://www.r-project.org/}). It implements powerful 
-data management and analysis tools. Though it is not strictly necessary 
-to learn everything about R to run \GA{}, it is highly recommended 
+\GA{} is an R library developed to facilitate Genome-Wide
+Association (GWA) analysis of binary and quantitative traits.
+\GA{} is implemented as an R library. R is a free, open source
+language and environment for general-purpose statistical analysis
+(available at \url{http://www.r-project.org/}). It implements powerful
+data management and analysis tools. Though it is not strictly necessary
+to learn everything about R to run \GA{}, it is highly recommended
 as this knowledge will improve flexibility and quality of your analysis.
 
-Originally \GA{} was developed to facilitate GWA analysis of quantitative 
-traits using data coming from extended families and/or collected form 
-genetically isolated populations. 
-At the same time \GA{} implements a large number of procedures used 
-in analysis of population-based data; it supports analysis of 
-binary and quantitative tarits, and of survival 
-(time-till-event) data. 
+Originally \GA{} was developed to facilitate GWA analysis of quantitative
+traits using data coming from extended families and/or collected form
+genetically isolated populations.
+At the same time \GA{} implements a large number of procedures used
+in analysis of population-based data; it supports analysis of
+binary and quantitative tarits, and of survival
+(time-till-event) data.
 Most up-to-date information about \GA{} can be found at the web site
 \url{http://www.genabel.org}.
 
-This tutorial was originally written to serve as a set of exercises for the 
-"Advances in population-based studies of complex genetic disorders" 
+This tutorial was originally written to serve as a set of exercises for the
+"Advances in population-based studies of complex genetic disorders"
 (GE03) course of the Netherlands Institute of Health Sciences (Nihes).
 
-If you read this tutorial not as a part of the GE03 course, and you 
-are eager to start with you GWA analysis without reading all the 
-not-so-strictly-necessary staff, start directly from the 
-section \ref{sec:GWA} ("\nameref{sec:GWA}").  
+If you read this tutorial not as a part of the GE03 course, and you
+are eager to start with you GWA analysis without reading all the
+not-so-strictly-necessary staff, start directly from the
+section \ref{sec:GWA} ("\nameref{sec:GWA}").
 
-Otherwise, you can start with R basics and simple association analyses 
-using few SNPs in section \ref{sec:introR}, 
+Otherwise, you can start with R basics and simple association analyses
+using few SNPs in section \ref{sec:introR},
 "\nameref{sec:introR}".
-In the next section, \ref{sec:workgwaaclass} 
-("\nameref{sec:workgwaaclass}") you will learn 
-how to work with the \texttt{gwaa.data-class}, which is 
-used to store GWA data in \GA{} and will perform some 
+In the next section, \ref{sec:workgwaaclass}
+("\nameref{sec:workgwaaclass}") you will learn
+how to work with the \texttt{gwaa.data-class}, which is
+used to store GWA data in \GA{} and will perform some
 simple large-scale analyses.
 
-In the next section, \ref{sec:GWA} ("\nameref{sec:GWA}"), 
-you will do quality control of genetic data and do 
+In the next section, \ref{sec:GWA} ("\nameref{sec:GWA}"),
+you will do quality control of genetic data and do
 association analysis under realistic conditions.
-This section is the core of this tutorial. 
+This section is the core of this tutorial.
 
-The section \ref{sec:strat} ("\nameref{sec:strat}") is 
+The section \ref{sec:strat} ("\nameref{sec:strat}") is
 dedicated to analysis in the presence of population
-stratification and analysis of family-based data. 
+stratification and analysis of family-based data.
 
-Genetic data imputations are covered in the section 
+Genetic data imputations are covered in the section
 \ref{sec:impute}, "\nameref{sec:impute}".
 
-The last section, \ref{sec:reg} ("\nameref{sec:reg}"), is 
-dedicated to analysis of haplotype association and analysis 
-of SNP interactions. 
+The last section, \ref{sec:reg} ("\nameref{sec:reg}"), is
+dedicated to analysis of haplotype association and analysis
+of SNP interactions.
 
-%Appendix \ref{sec:GWAprotocol} oulines the formal step-by-step 
-%protocol for GWA analysis. 
-Information on importing the data from 
-different file formats to \GA{} is given in appendix 
+%Appendix \ref{sec:GWAprotocol} oulines the formal step-by-step
+%protocol for GWA analysis.
+Information on importing the data from
+different file formats to \GA{} is given in appendix
 \ref{sec:dataimport} ("\nameref{sec:dataimport}").
 Answers to exercises are provided at the end of the respective chapters.
 
-Experienced R users start directly with 
-the section (\ref{sec:workgwaaclass}, "\nameref{sec:workgwaaclass}"). 
+Experienced R users start directly with
+the section (\ref{sec:workgwaaclass}, "\nameref{sec:workgwaaclass}").
 
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