This talk will discuss how modern data mining techniques can be imported into statistical genetics. Most relevant models now invoke high-dimensional optimization. Penalization and set projection give sparsity. Separation of variables gives parallelization. Time permitting, these ideas will be illustrated by several examples: estimation of ethnic ancestry, genotype imputation via matrix completion, conversion of imputed genotypes into haplotypes, matrix completion discriminant analysis, estimation in the linear mixed model, iterative hard thresholding in GWAS, and sparse principal components analysis.