Posts Tagged ‘ GWAS ’

A New Dimension to Principal Components Analysis

October 27, 2011
By
A New Dimension to Principal Components Analysis

In general, the standard practice for correcting for population stratification in genetic studies is to use principal components analysis (PCA) to categorize samples along different ethnic axes.  Price et al. published on this in 20...

Read more »

More Command-Line Text Munging Utilities

May 19, 2011
By
More Command-Line Text Munging Utilities

In a previous post I linked to gcol as a quick and intuitive alternative to awk. I just stumbled across yet another set of handy text file manipulation utilities from the creators of the BEAGLE software for GWAS data imputation and analysis. In additio...

Read more »

PLINK/SEQ for Analyzing Large-Scale Genome Sequencing Data

May 4, 2011
By

PLINK/SEQ is an open source C/C++ library for analyzing large-scale genome sequencing data. The library can be accessed via the pseq command line tool, or through an R interface. The project is developed independently of PLINK but it's syntax will be f...

Read more »

Annotated Manhattan plots and QQ plots for GWAS using R, Revisited

April 25, 2011
By

Last year I showed you how to create manhattan plots, and later how to highlight regions of interest, using ggplot2 in R. The code was slow, required a lot of memory, and was difficult to maintain and modify. I finally found time to rewrite the code u...

Read more »

Using R + Bioconductor to Get Flanking Sequence Given Genomic Coordinates

April 12, 2011
By

I'm working on a project using next-gen sequencing to fine-map a genetic association in a gene region. Now that I've sequenced the region in a small sample, I'm picking SNPs to genotype in a larger sample. When designing the genotyping assay the lab wi...

Read more »

Prune GWAS data in R

March 29, 2011
By
Prune GWAS data in R

Hansong Wang, our biostats professor here at the Hawaii Cancer Center, generously gave me some R code that goes through a SNP annotation file (i.e. a mapfile) and selects SNPs that are at least a certain specified distance apart. You might want to do t...

Read more »

New GenABEL Website, and more *ABEL software

March 18, 2011
By

The *ABEL suite of R packages and software for genetic analysis has grown substantially since the appearance of GenABEL and the previously mentioned ProbABEL R packages. There are now a handful of useful R packages and other software utilities facilita...

Read more »

Efficient Mixed-Model Association eXpedited (EMMAX) to Simutaneously Account for Relatedness and Stratification in Genome-Wide Association Studies

June 9, 2010
By

A few months ago I covered an algorithm called EMMA (Efficient Mixed-Model Association) implemented in R for simultaneously correct for both population stratification and relatedness in an association study. This method/software is very useful because ...

Read more »

Mixed linear model approach adapted for genome-wide association studies

May 6, 2010
By

A few weeks ago I covered an R package for efficient mixed model regression that is capable of simultaneously accounting for both population stratification and relatedness to compute unbiased estimates of standard errors and p-values for genetic associ...

Read more »

Efficient Mixed-Model Association in GWAS using R

April 13, 2010
By

I recently did an analysis for the eMERGE network where I had lots of individuals from a small town in central Wisconsin where many of the subjects were related to one another. The subjects could not be treated as independent, but I could not use a fam...

Read more »

Sponsors

Mango solutions



plotly webpage

dominolab webpage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training

datasociety

http://www.eoda.de





ODSC

ODSC

CRC R books series





Six Sigma Online Training









Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)