Association analysis

[davidonlaptop]

Description

This feature adds the --assoc, --model, --fisher, --linear, --logistic, --ci, --counts, --fisher, --cell, --within, --mh, --mh2, --bd, --homog, --gene-drop, --T2, --qt-means, --gxe, --covar, --reference-allele, --beta, --standard-beta, --genotypic, --hethom, --dominant, --recessive option(s) based on the input file described in #2.

This feature also generates the following file formats: ASSOC, FISHER, MODEL, BEST.PERM, BEST.MPERM, GEN.PERM, TREND.PERM, DOM.PERM, REC.PERM, CMH, CMH2, HOMOG, T2.PERM, T2.MPERM, QASSOC, ASSOC.PERM, ASSOC.MPERM, QASSOC.MEANS, QASSOC.GXE, ASSOC.LINEAR, ASSOC.LOGISTIC. For more info, check: http://pngu.mgh.harvard.edu/~purcell/plink/anal.shtml

Scope

The scope of this feature may be too large to implement completely during MGL804. However, many of the file formats seems to share a similar structure... Anyway, the scope needs to be refined with Beatriz.

From a development point of view, it is interesting to note that feature is very distinct from the rest of the other features, so it would be easy to implement in parallel to the other features.

Note: I stopped listing the options and file formats at the section Covariates and interactions.

Analysis

Add a comment to this issue with:

• plink version used as reference (1.07 or 1.90 beta 3)
• relevant C++ function name(s) and the file name(s) where they appear
• Document the structure of all file format(s) described above generated by plink.
• Document the algorithm and/or mathematical formula to compute all the missing fields described above:

Design

Add a comment to this issue describing how this will be implemented in Spark, and how it differs from plink.

Also update the class diagram on the wiki page describing PLink formats (when incomplete) and add a class diagram describing the models implemented in Scala for this feature on the wiki page on the MGL804 formats.

Implementation

The implementation should integrates with the models implemented in scala by this project and use:

• Scala
• Spark RDD
• Spark MLlib / GraphX (if appropriate)