Understand Mixed Effect Models: Watch A Short Tutorial on Linear Mixed Model Association Testing in Genetics by Noah Zaitlen during a Winter School.
- Saige: fits generalized mixed model, imbalanced case-control outcome, rare variants
- Regenie: fits generalized mixed model, fast, imbalanced case-control outcome, rare variants
- An all-in-one tool for genotype data (QC, LD, PCA, association testing, ...)
- download
- Input file formats: plink, bgen, gen, vcf
- Mainly heritability estimation, but also QC and PCA
- download
- Input file formats: binary plink files
- Tool to wrangle processed genetic data (bgen, gen, vcf, plink)
- download: Data handling, converting file formats, combining datasets
- QC
- PCA
- Input file formats: many (vcf, gen, bgen, plink, ...) check here
- Tool to wrangle "raw" genetic data (bed, bam, vcf, ...)
- download
- Cheatsheet
- Association testing
- download
- Association testing
- download
- LD estimation
- download
- LD estimation
- download
- Fine-mapping
- download:
- finemap howto
- SumHer
- heritability estimation
- uses summary statistics as input
- LDAK is for individual data
- see also http://dougspeed.com/heritability-model/
- https://openmendel.github.io/ (statistical genetic analysis tool in Julia)
- https://hail.is/ (genomic data analysis tool based on Python)
- https://ritchielab.org/software/plato-download (organising, QC and analysis of genetic data)
- QC: Data quality control, handling, transformation
- LD: Linkage disequilibrium
- PCA: Principal component analysis
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GWAS Atlas: Collection of summary statistics: browse and compare GWAS results.
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MR Base: A platform for Mendelian randomisation using summary data from genome-wide association studies (also has an R package).
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FUMA GWAS: Functional Mapping and Annotation of Genome-Wide Association Studies. Takes summary statistics as input.
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Imputation servers: Sanger Imputation Service and Michigan Imputation Server.
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Locuscompare: comparison of EQTL and GWAS summary statistics (using rsid and p-values).
Packages are either hosted on CRAN or Bioconductor.
- A tutorial for implementing a GWAS in R by the Foulkes Lab.
- Task view on CRAN.
- R-packages that deal with genetics or genomics.
- manhattanly for interactive Manhattan plots.
- qqman: for Manhattan plots (see DYI solution in here).
- rsnps: interface to SNP datasets. Check vignette.
- ggman: Well done Manhattan plot in a ggplot2 look.
- GWAS.utils: helper functions for genotype data and summary statistics.
- gaston: genetic data handeling + modelling (variety of models)
Related to Manhattan plots:
- Crafting Manhattan plots by Yan Holtz for the R graph gallery.