Accurate prediction of genetic values for complex traits by whole-genome resequencing

Abstract

Whole-genome resequencing technology has improved rapidly during recent years and is expected to improve further such that the sequencing of an entire human genome sequence for $1000 is within reach. Our main aim here is to use whole-genome sequence data for the prediction of genetic values of individuals for complex traits and to explore the accuracy of such predictions. This is relevant for the fields of plant and animal breeding and, in human genetics, for the prediction of an individual's risk for complex diseases. Here, population history and genomic architectures were simulated under the Wright-Fisher population and infinite-sites mutation model, and prediction of genetic value was by the genomic selection approach, where a Bayesian nonlinear model was used to predict the effects of individual SNPs. The Bayesian model assumed a priori that only few SNPs are causative, i.e., have an effect different from zero. When using whole-genome sequence data, accuracies of prediction of genetic value were >40% increased relative to the use of dense approximately 30K SNP chips. At equal high density, the inclusion of the causative mutations yielded an extra increase of accuracy of 2.5-3.7%. Predictions of genetic value remained accurate even when the training and evaluation data were 10 generations apart. Best linear unbiased prediction (BLUP) of SNP effects does not take full advantage of the genome sequence data, and nonlinear predictions, such as the Bayesian method used here, are needed to achieve maximum accuracy. On the basis of theoretical work, the results could be extended to more realistic genome and population sizes.

Figure 1.—

The accuracy of the predictions of total genetic value in the TEST1 data set as a function of the marker density (d) in SNPs per morgan. The densities evaluated were d = 1000, 11,000, and 33,000, and the training data contained T = 200 individuals.

Figure 2.—

The posterior probability of a SNP being fitted in the model and the simulated QTL variance plotted against the position along the chromosome for three randomly picked 3-QTL⁺ data sets (plotted are the 100 SNPs that surround the 3 causative SNPs, resulting in three segments in each graph (separated by ).

Figure 3.—

The posterior probability of fitting a causative SNP vs. the genetic variance explained by this SNP. (a and b) The 3-QTL⁺ and 30-QTL⁺ data, with T = 200 records; (c) the 6-QTL⁺ data with 400 records.