New Paper: Simultaneous SNP Selection and Adjustment for Population Structure in High Dimensional Prediction Models
![Table 1. Simulation study results. Mean (standard deviation) from 200 simulations stratified by the number of causal SNPs (null, 1%), the overlap between causal SNPs and kinship matrix (no overlap, all causal SNPs in kinship), and true heritability (10%, 30%). For all simulations, sample size is n = 1000, the number of covariates is p = 5000, and the number of SNPs used to estimate the kinship matrix is k = 10000. TPR at FPR = 5% is the true positive rate at a fixed false positive rate of 5%. Model Size () is the number of selected variables in the training set using the high-dimensional BIC for ggmix and 10-fold cross validation for lasso and twostep. RMSE is the root mean squared error on the test set. Estimation error is the squared distance between the estimated and true effect sizes. Error variance (σ2) for twostep is estimated from an intercept only LMM with a single random effect and is modeled explicitly in ggmix. For the lasso we use [28] as an estimator for σ2. Heritability (η) for twostep is estimated as from an intercept only LMM with a single random effect where and are the variance components for the random effect and error term, respectively. η is explictly modeled in ggmix. There is no positive way to calculate η for the lasso since we are using a PC adjustment. show less](https://i0.wp.com/www.impactt-microbiome.ca/wp-content/uploads/2020/05/journal.pgen_.1008766.t001.png?fit=800%2C443&ssl=1)
ggmix, general penalized linear mixed effects model with a single random effect for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models.