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author | Peter Carbonetto | 2017-07-07 11:20:56 -0500 |
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committer | GitHub | 2017-07-07 11:20:56 -0500 |
commit | 86e96ede4ff0955bb2d03ac6c1bd7562a3984955 (patch) | |
tree | 33120540091e7d16b58f389a13949df397535912 /doc | |
parent | b3747413e6c5c8cd447e979157880676da66a342 (diff) | |
parent | b9758364059d52e153a9f1b4fcae3bc3f3e68422 (diff) | |
download | pangemma-86e96ede4ff0955bb2d03ac6c1bd7562a3984955.tar.gz |
Merge pull request #51 from genenetwork/spacing
Spacing fixes.
Diffstat (limited to 'doc')
-rw-r--r-- | doc/manual.tex | 30 |
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} |