GWAMA(1)

✨ NAME ✨

Okay bestie, meet GWAMA 🧚 It's short for Genome-Wide Association Meta Analysis, and it's the tool you reach for when you're combining GWA study results 💖

📜 SYNOPSIS 📜

GWAMA [options]

🦋 DESCRIPTION 🦋

Okay so GWAMA (Genome-Wide Association Meta Analysis) does meta-analysis on the results of GWA studies, whether your phenotype is binary or quantitative 💜 It runs both fixed-effect and random-effect meta-analyses, and it handles directly genotyped SNPs and imputed ones 💅

For binary traits, it works with the allelic odds ratio and its 95% confidence interval 🎀 For quantitative phenotypes, you give it the allelic effect size and standard error instead 📏

It's giving versatile, because you can analyse results from every genetic model: multiplicative, additive, dominant, and recessive 🌈

It also has error trapping, so it can catch strand alignment errors and allele flipping 🚨 And it runs tests of heterogeneity of effects between studies, so you can see how much the studies actually agree 🔍 No cap, it's a whole detective 🕵️‍♀️

🎀 OPTIONS 🎀

• -i, --filelist=filename — Point it at your studies' result files 📂 Default = gwama.in
• -o, --output=fileroot — Set the file root for your analysis output 🌸 Default = gwama (gwama.out, gwama.gc.out)
• -r, --random — Turns on random effect correction 🦋 Default = disabled
• -gc, --genomic_control — Uses genomic control to adjust your studies' result files 🧹 Default = disabled
• -gco, --genomic_control_output — Applies genomic control to the meta-analysis summary, so the results of the meta-analysis get corrected for gc ✨ Default = disabled
• -qt, --quantitative — Switches to the quantitative trait version, which uses the BETA and SE columns 📏 Default = binary trait
• -m, --map=filename — Picks the file name for your marker map 🗺️
• -t, --threshold=0-1 — The p-value threshold for showing direction in the summary effect directions 🔥 Default = 1
• --no_alleles — Tells it no allele information was given, so it expects the EA to always be the same 🙈
• --indel_alleles — Allele labels can be more than one letter long 🧬 No strand checks happen here, so no peeking at strands 🙅‍♀️
• --sex — Runs the gender-differentiated and gender-heterogeneity analysis, using the method from the Magi, Lindgren & Morris 2010 paper 👑 Your gender info has to live in the filelist file, in the second column after the file names, and it's either M or F 💖
• --filter — Filters based on a column header 🎯 You need 3 arguments: column name, equation [>,<,>=,<=,==,!=], and filter value. You can stack multiple filters, periodt 💅
• --name_marker — Alternative header for the marker name column 🏷️
• --name_strand — Alternative header for the strand column 🧭
• --name_n — Alternative header for the sample size column 🔢
• --name_ea — Alternative header for the effect allele column 💜
• --name_nea — Alternative header for the non-effect allele column 🩷
• --name_eaf — Alternative header for the effect allele frequency column 📊
• --name_beta — Alternative header for the beta column 🌈
• --name_se — Alternative header for the std. err. column 📐
• --name_or — Alternative header for the OR column ⭐
• --name_or_95l — Alternative header for the OR 95L column ⬇️
• --name_or_95u — Alternative header for the OR 95U column ⬆️
• -h, --help — Shows you this help 🆘
• -v, --version — Prints the GWAMA version number 🔢 Honestly, bestie, this manual page is probably not enough for you 😭 For a quick reminder of gwama's options, use the help option, or head to its home page for the online documentation or the downloadable manual 🌸

🔗 SEE ALSO 🔗

http://www.geenivaramu.ee/en/tools/gwama 🦋

🫶 AUTHOR 🫶

GWAMA was written by Reedik Magi and Andrew P Morris 👑

Version 2.2.2, August 2016, GWAMA(1) 🌈 That's the whole tea, bestie, now go run your meta-analysis 💅✨