1<p>P-value <0.0001;</p>2<p>Root mean square errors (RMSE) for the models using the training (T) and validation (V) data sets;</p>3<p>Root means square covariance (RMSC) for the bivariate model using the training (T) and validation (V) data sets;</p>4<p>MA, Model adequacy as defined in Eqs. 3 and 4, for the univariate and bivariate analyses, respectively.</p
<p>SNPs were divided into 6 models based on MAF or haplotype. AUC (A) and TPR (B) were calculated us...
<p>For the simulated datasets developed from 3-SNP models, average fraction over all 100 datasets of...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...
Motivation: Using simulation studies for quantitative trait loci, we evaluate the prediction quality...
<p>“Lasso” represents model (2), “Linear low-rank” represents model (3), and “Nonlinear low-rank” re...
<p>N-FHS = Number of records from Framingham, N-GEN = Number of records from GENEVA. G-BLUP uses 400...
<p>“Lasso” represents model (2), “Linear low-rank” represents model (3), and “Nonlinear low-rank” re...
<p>Odds ratios (black squares) from the complete multivariate model (“chromstate+eqtl [M3]”) for fea...
<p>Models are: K-nearest-neighbor (KNN); Regularized Linear model (Elastic-Net); single best-SNP (Si...
<p>The proportion of simulated traits for which the -log10(p) value of the causal SNP is above the t...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
<p>(A) QQ plot of -log10 p values from SNP set tests using different SNP weights under the null simu...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...
<p><sup>*</sup>Four samples from phenotype categories 3 and 4 lacked data in one of the following SN...
<p>Note: ‘Gene’ is the corresponding gene for each SNP; ‘beta’ is the standardized regression coeffi...
<p>SNPs were divided into 6 models based on MAF or haplotype. AUC (A) and TPR (B) were calculated us...
<p>For the simulated datasets developed from 3-SNP models, average fraction over all 100 datasets of...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...
Motivation: Using simulation studies for quantitative trait loci, we evaluate the prediction quality...
<p>“Lasso” represents model (2), “Linear low-rank” represents model (3), and “Nonlinear low-rank” re...
<p>N-FHS = Number of records from Framingham, N-GEN = Number of records from GENEVA. G-BLUP uses 400...
<p>“Lasso” represents model (2), “Linear low-rank” represents model (3), and “Nonlinear low-rank” re...
<p>Odds ratios (black squares) from the complete multivariate model (“chromstate+eqtl [M3]”) for fea...
<p>Models are: K-nearest-neighbor (KNN); Regularized Linear model (Elastic-Net); single best-SNP (Si...
<p>The proportion of simulated traits for which the -log10(p) value of the causal SNP is above the t...
<p>Results for feature selection, model selection and validation, using the two selection criteria a...
<p>(A) QQ plot of -log10 p values from SNP set tests using different SNP weights under the null simu...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...
<p><sup>*</sup>Four samples from phenotype categories 3 and 4 lacked data in one of the following SN...
<p>Note: ‘Gene’ is the corresponding gene for each SNP; ‘beta’ is the standardized regression coeffi...
<p>SNPs were divided into 6 models based on MAF or haplotype. AUC (A) and TPR (B) were calculated us...
<p>For the simulated datasets developed from 3-SNP models, average fraction over all 100 datasets of...
Background Genomic selection is particularly beneficial for difficult or expensive to measure traits...