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authorPeter Carbonetto2017-07-07 11:20:56 -0500
committerGitHub2017-07-07 11:20:56 -0500
commit86e96ede4ff0955bb2d03ac6c1bd7562a3984955 (patch)
tree33120540091e7d16b58f389a13949df397535912 /doc
parentb3747413e6c5c8cd447e979157880676da66a342 (diff)
parentb9758364059d52e153a9f1b4fcae3bc3f3e68422 (diff)
downloadpangemma-86e96ede4ff0955bb2d03ac6c1bd7562a3984955.tar.gz
Merge pull request #51 from genenetwork/spacing
Spacing fixes.
Diffstat (limited to 'doc')
-rw-r--r--doc/manual.tex30
1 files changed, 15 insertions, 15 deletions
diff --git a/doc/manual.tex b/doc/manual.tex
index d47aa22..c0119dc 100644
--- a/doc/manual.tex
+++ b/doc/manual.tex
@@ -100,11 +100,11 @@ available open-source numerical libraries.
Stephens (2014). Efficient multivariate linear mixed model
algorithms for genome-wide association studies. Nature Methods. 11:
407-409.
-
+
\item Bayesian sparse linear mixed models \\ Xiang Zhou, Peter
Carbonetto and Matthew Stephens (2013). Polygenic modeling with
Bayesian sparse linear mixed models. PLoS Genetics. 9(2): e1003264.
-
+
\item Variance component estimation with individual-level or summary
data\\ Xiang Zhou (2016). A unified framework for variance component
estimation with summary statistics in genome-wide association
@@ -212,10 +212,10 @@ effects terms ($\mathbf X\boldsymbol\beta$). These two parameters are
defined as follows:
\begin{align*}
\mbox{PVE}(\bbeta, \bu, \tau) &
- :=\frac{\mbox{V}(\bX\bbeta+\bu)}{\mbox{V}(\bX\bbeta+\bu)+\tau^{-1}}, \\
+ :=\frac{\mbox{V}(\bX\bbeta+\bu)}{\mbox{V}(\bX\bbeta+\bu)+\tau^{-1}}, \\
\mbox{PGE}(\bbeta, \bu)
& :=\frac{\mbox{V}(\bX\bbeta)}{\mbox{V}(\bX\bbeta+\bu)}, \\
-\intertext{where}
+\intertext{where}
\mbox{V}({\bf x}) &:=\frac{1}{n}\sum_{i=1}^n (x_i-\overline {x})^2.
\end{align*}
@@ -429,7 +429,7 @@ BIMBAM mean genotype file from IMPUTE genotype files
(\url{http://www.stats.ox.ac.uk/~marchini/software/gwas/file_format.html})\cite{Howie:2009}:
%
\begin{verbatim}
-cat [impute filename] | awk -v s=[number of samples/individuals]
+cat [impute filename] | awk -v s=[number of samples/individuals]
'{ printf $2 "," $4 "," $5; for(i=1; i<=s; i++) printf "," $(i*3+3)*2+$(i*3+4); printf "\n" }'
> [bimbam filename]
\end{verbatim}
@@ -815,7 +815,7 @@ chr rs ps n_mis n_obs allele1 allele0 af beta se
\end{verbatim}
%
-The 11 columns are: chromosome numbers, snp ids, base pair positions on the chromosome, number of missing individuals for a given snp, number of non-missing individuals for a given snp, minor allele, major allele, allele frequency, beta estimates, standard errors for beta, and $p$ values from the Wald test.
+The 11 columns are: chromosome numbers, snp ids, base pair positions on the chromosome, number of missing individuals for a given snp, number of non-missing individuals for a given snp, minor allele, major allele, allele frequency, beta estimates, standard errors for beta, and $p$ values from the Wald test.
\subsection{Estimate Relatedness Matrix from Genotypes}
@@ -1009,7 +1009,7 @@ ped format or the BIMBAM format are:
\begin{verbatim}
./gemma -bfile [prefix] -k [filename] -lmm [num] -n [num1] [num2] [num3] -o [prefix]
-./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num]
+./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num]
-n [num1] [num2] [num3] -o [prefix]
\end{verbatim}
@@ -1041,7 +1041,7 @@ missing, one can impute these missing values before association tests:
\begin{verbatim}
./gemma -bfile [prefix] -k [filename] -predict -n [num1] [num2] [num3] -o [prefix]
-./gemma -g [filename] -p [filename] -a [filename] -k [filename] -predict
+./gemma -g [filename] -p [filename] -a [filename] -k [filename] -predict
-n [num1] [num2] [num3] -o [prefix]
\end{verbatim}
@@ -1154,10 +1154,10 @@ below:
%
\begin{verbatim}
h pve rho pge pi n_gamma
-4.777635e-01 5.829042e-01 4.181280e-01 4.327976e-01 2.106763e-03 25
-5.278073e-01 5.667885e-01 3.339020e-01 4.411859e-01 2.084355e-03 26
-5.278073e-01 5.667885e-01 3.339020e-01 4.411859e-01 2.084355e-03 26
-6.361674e-01 6.461678e-01 3.130355e-01 3.659850e-01 2.188401e-03 25
+4.777635e-01 5.829042e-01 4.181280e-01 4.327976e-01 2.106763e-03 25
+5.278073e-01 5.667885e-01 3.339020e-01 4.411859e-01 2.084355e-03 26
+5.278073e-01 5.667885e-01 3.339020e-01 4.411859e-01 2.084355e-03 26
+6.361674e-01 6.461678e-01 3.130355e-01 3.659850e-01 2.188401e-03 25
5.479237e-01 6.228036e-01 3.231856e-01 4.326231e-01 2.164183e-03 27
\end{verbatim}
%
@@ -1196,9 +1196,9 @@ The basic usages for association analysis with either the PLINK binary
ped format or the BIMBAM format are:
\begin{verbatim}
-./gemma -bfile [prefix] -epm [filename] -emu [filename] -ebv [filename] -k [filename]
+./gemma -bfile [prefix] -epm [filename] -emu [filename] -ebv [filename] -k [filename]
-predict [num] -o [prefix]
-./gemma -g [filename] -p [filename] -epm [filename] -emu [filename] -ebv [filename]
+./gemma -g [filename] -p [filename] -epm [filename] -emu [filename] -ebv [filename]
-k [filename] -predict [num] -o [prefix]
\end{verbatim}
@@ -1544,7 +1544,7 @@ source code for detailed examples.
\item \textcolor{red}{-ci [num]} \quad specify fitting algorithm to compute the standard errors. (default 1; valid value 1-2; 1: MQS-HEW; 2: MQS-LDW.)
\end{itemize}
%
-
+
\clearpage
\bibliographystyle{plain}