This document summarizes work done by the Julia Labs at MIT on genomics data analysis and optimization of principal component analysis algorithms for genome-wide association studies. It describes how a native Julia implementation of PCA was able to reduce computation time for finding the top 10 principal components of a 80,000x40,000 genotype matrix from over 2,900 seconds to just 81 seconds. It also discusses how custom matrix-vector multiplication functions allowed the same computation speed while using 32x less memory by reading directly from a compressed data format. Future work directions include more complex analytics, improved data imputation methods, out-of-core matrix operations, and accessing different data formats.