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| -rw-r--r-- | README.md | 50 | ||||
| -rw-r--r-- | doc/manual.tex | 21 |
2 files changed, 32 insertions, 39 deletions
diff --git a/README.md b/README.md index 68e2d05..b7a2bb8 100644 --- a/README.md +++ b/README.md @@ -25,7 +25,9 @@ Debian, Conda, Homebrew and GNU Guix. With Guix you find the latest version [here](http://git.genenetwork.org/guix-bioinformatics/guix-bioinformatics) as it is the version we use every day on http://genenetwork.org. For -installation instructions see also [INSTALL.md](INSTALL.md). +installation instructions see also [INSTALL.md](INSTALL.md). We use +continous integration builds on Travis-CI for Linux (amd64 & arm64) +and MacOS (amd64). *(The above image depicts physiological and behavioral trait loci identified in CFW mice using GEMMA, from [Parker et al, Nature @@ -51,19 +53,19 @@ Genetics, 2016](https://doi.org/10.1038/ng.3609).) 1. Fast assocation tests implemented using the univariate linear mixed model (LMM). In GWAS, this can correct for population structure and -sample nonexchangeability. It also provides estimates of the +sample non-exchangeability. It also provides estimates of the proportion of variance in phenotypes explained by available genotypes (PVE), often called "chip heritability" or "SNP heritability". 2. Fast association tests for multiple phenotypes implemented using a multivariate linear mixed model (mvLMM). In GWAS, this can correct for -populations tructure and sample nonexchangeability jointly in multiple -complex phenotypes. +population structure and sample (non)exchangeability - jointly in +multiple complex phenotypes. 3. Bayesian sparse linear mixed model (BSLMM) for estimating PVE, phenotype prediction, and multi-marker modeling in GWAS. -4. Estimation of variance components ("chip heritability") partitioned +4. Estimation of variance components ("chip/SNP heritability") partitioned by different SNP functional categories from raw (individual-level) data or summary data. For raw data, HE regression or the REML AI algorithm can be used to estimate variance components when @@ -74,7 +76,8 @@ MQS algorithm to estimate variance components. To install GEMMA you can -1. Download the precompiled binaries (64-bit Linux and Mac only) +1. Download the precompiled or Docker binaries + from [releases](https://github.com/genetics-statistics/GEMMA/releases). 2. Use existing package managers, see [INSTALL.md](INSTALL.md). @@ -89,20 +92,16 @@ numerical libraries. 1. Fetch the [latest stable release][latest_release] and download the file appropriate for your platform. -2. For .tar.bz2 files unpack the tar ball +2. For Docker images, install Docker, load the image into Docker and + run with something like - tar xvjf gemma-$version-installer.tar.bz2 - - run the installer - - ./install.sh ~/gemma - - and run gemma - - ~/gemma/bin/gemma + docker run -w /run -v ${PWD}:/run ed5bf7499691 gemma -gk -bfile example/mouse_hs1940 3. For .gz files run `gunzip gemma.linux.gz` or `gunzip -gemma.linux.gz` to unpack the file. +gemma.linux.gz` to unpack the file. And make sure it is executable with + + chmod u+x gemma-linux + ./gemma-linux ## Run GEMMA @@ -132,24 +131,25 @@ Above example files can be downloaded from GEMMA has a wide range of debugging options which can be viewed with ``` -gemma -h 14 - DEBUG OPTIONS + -check enable checks (slower) -no-fpe-check disable hardware floating point checking -strict strict mode will stop when there is a problem -silence silent terminal display -debug debug output -debug-data debug data output + -nind [num] read up to num individuals + -issue [num] enable tests relevant to issue tracker -legacy run gemma in legacy mode ``` -typically when running gemma you should use -debug which includes relevant -checks. +typically when running gemma you should use -debug which includes +relevant checks. When compiled for debugging the debug version of +GEMMA gives more information. -For performances you may want to use the -no-check option -instead. Also check the build optimization notes in -[INSTALL.md](INSTALL.md). +For performance you may want to use the -no-check option. Also check +the build optimization notes in [INSTALL.md](INSTALL.md). ## Help @@ -192,7 +192,7 @@ studies.](https://doi.org/10.1101/042846) *Annals of Applied Statistics*, in pre ## License -Copyright (C) 2012–2018, Xiang Zhou and team. +Copyright (C) 2012–2020, Xiang Zhou and team. The *GEMMA* source code repository is free software: you can redistribute it under the terms of the diff --git a/doc/manual.tex b/doc/manual.tex index 555d766..dc0aadf 100644 --- a/doc/manual.tex +++ b/doc/manual.tex @@ -75,7 +75,7 @@ association studies (GWAS). It fits a univariate linear mixed model (LMM) for marker association tests with a single phenotype to account for population stratification and sample structure, and for estimating the proportion of variance in phenotypes explained (PVE) by typed -genotypes (i.e. "chip heritability") \cite{Zhou:2012}. It fits a +genotypes (i.e. ``chip heritability'' or ``SNP heritability'') \cite{Zhou:2012}. It fits a multivariate linear mixed model (mvLMM) for testing marker associations with multiple phenotypes simultaneously while controlling for population stratification, and for estimating genetic correlations @@ -139,8 +139,8 @@ score). GEMMA obtains either the maximum likelihood estimate (MLE) or the restricted maximum likelihood estimate (REML) of $\lambda$ and $\beta$, and outputs the corresponding $p$ value. -In addition, GEMMA estimates the PVE by typed genotypes or ``chip -heritability". +In addition, GEMMA estimates the PVE by typed genotypes or ``chip or +SNP heritability''. \subsubsection{Multivariate Linear Mixed Model} GEMMA can fit a multivariate linear mixed model in the following form: @@ -307,19 +307,12 @@ platform. The binary executable of GEMMA works well for a reasonably large number of individuals (say, for example, the ``-eigen " option works -for at least 45,000 individuals). Due to the outdated computation -environment the software was compiled on, however, for larger sample -size and for improved computation efficiency, it is recommended to -compile GEMMA on user's own modern computer system. +for at least 45,000 individuals). If you want to compile GEMMA by yourself, you will need to download the source code, and you will need a standard C/C++ compiler such as -GNU gcc, as well as the GSL and LAPACK libraries. You will need to -change the library paths in the Makefile accordingly. A sample -Makefile is provided along with the source code. For details on -installing GSL library, please refer to -\url{http://www.gnu.org/s/gsl/}. For details on installing LAPACK -library, please refer to \url{http://www.netlib.org/lapack/}. +GNU gcc, as well as GSL and OpenBLAS libraries. A sample +Makefile is provided along with the source code. \newpage @@ -334,7 +327,7 @@ genotypes and using BIMBAM files for phenotypes) will result in unwanted errors. BIMBAM format is particularly useful for imputed genotypes, as PLINK codes genotypes using 0/1/2, while BIMBAM can accommodate any real values between 0 and 2 (and any real values if -paired with ``-notsnp" option). In addition, to estimate variance +paired with ``-notsnp'' option). In addition, to estimate variance components using summary statistics, GEMMA requires two other input files: one contains marginal z-scores and the other contains SNP category. |