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arati_mejdal

Staff

Joined:

May 21, 2014

By Request, JMP Genomics 3.1 Includes EIGENSTRAT Method

In the course of one week last summer, we had three separate requests for implementing the EIGENSTRAT method in JMP Genomics. Coincidentally, our graduate student intern had just completed a paper for her preliminary PhD exam on the randomly but auspiciously assigned topic, “statistical methods of adjusting for population structure in genetic association analysis” -- which, of course, included an evaluation of EIGENSTRAT.


The method of genomic control (Devlin and Roeder, 1999) was currently available in the software for adjusting the Armitage trend test for population structure, and EIGENSTRAT seemed to be a worthy alternative to include because it offers increased power in certain situations. Given all the interest, we felt compelled to implement this method in JMP Genomics, and since we already had code in place for computing eigenvectors in the JMP Genomics Principal Components Analysis process, half the work was done.


The hard part was figuring out the best way to calculate a regression coefficient for each of the 500K to 1 million (or more!) SNPs from a genome-wide association study in a timely manner. Ultimately, the SAS procedure IML was used for this calculation due to its speed over the REG procedure in this case. The resulting PCA for Population Stratification process is making its debut in JMP Genomics 3.1.

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