Although markers identified by genome-wide association studies have individually
strong statistical significance, their performance in prediction remains limited. Our
goal was to use animal breeding genomic prediction models to predict additive genetic
contributions for systolic blood pressure (SBP) using whole genome sequencing data
with different validation designs.
The additive genetic contributions of SBP were estimated via linear mixed model. Rare
variants (MAF<0.05) were collapsed through the k-means method to create a
"collapsed single-nucleotide polymorphisms." Prediction of the additive genomic
contributions of SBP was conducted using genomic Best Linear Unbiased Predictor
(GBLUP) and BayesCπ. Estimates of predictive accuracy were compared
using common single-nucleotide polymorphisms (SNPs) versus common and collapsed SNPs,
and for prediction within and across families.
The additive genetic variance of SBP contributed to 18% of the phenotypic variance
(h2 = 0.18). BayesCπ had slightly better
prediction accuracies than GBLUP. In both models, within-family predictions had
higher accuracies both in the training and testing set than didacross-family design.
Collapsing rare variants via the k-means method and adding to the common SNPs did not
improve prediction accuracies. The prediction model, including both pedigree and
genomic information, achieved a slightly higher accuracy than using either source of
Prediction of genetic contributions to complex traits is feasible using whole genome
sequencing and statistical methods borrowed from animal breeding. The relatedness of
individuals between the training and testing set strongly affected the performance of
prediction models. Methods for inclusion of rare variants in these models need more