Unlike previous bioinformatics disease-loci prediction approaches, this weighting method in our framework directly modeled both biological knowledge and statistic association significances emerging from GWAS to produce optimal weights for the prioritization.
If heritability, and thus accuracy is reduced, the accuracy of the other loci reduces as well and the overall accuracy reduces more than expected from single locus predictions.
Various attempts have been made to use highly polygenic risk scores based on these non-significant loci for prediction in different diseases, with varying degrees of success (30– 30).
Exonerate predictions are processed to produce a unique gene structure per genomic loci: identical predictions are considered just once, and predictions which are completely included within the boundaries of another are discarded.
However, using large numbers of loci made predictions and the use of the variable selection criteria reliable.
Imputation at this locus elevated prediction accuracy from 83.14% to 86.67%.
The contigs produced by Velvet were postprocessed using Oases yielding 85,555 loci (gene predictions) comprising a total of 117,594 extended contigs with an average length of 1,374 bp.
Under the generative model shown previously, spatial ancestry inference is reduced to inferring the M ancestral locations given data for an admixed haplotype, followed by posterior decoding in the HMM to obtain locus-specific predictions.
Both internal validation methods create objects of class 'mdr', a list of the final selected model loci and its prediction accuracy, the top models and their prediction accuracies, and the high-risk/low-risk characterization of the final model.
