The least squares prediction or ‘profile scoring’29 method is commonly used for prediction of genetic risk. Although simple to apply it does not have desirable statistical properties and an arbitrary p-value threshold is used for the selection of SNPs that go in the predictor. Moreover, estimation of SNP effects one at a time is not an optimal approach1, 39–44. This is because SNP effects are correlated and accounting for LD in the profile scoring method requires SNP selection on arbitrary thresholds. Methods that model the distribution of SNP effects40 and the correlation between SNPs in the presence of single as well as multiple causal variants will be more accurate1, 39–43, 45. In human applications, sometimes only genome-wide significant SNPs are included in the predictor15, 46–49, yet greater accuracy results from the use of less stringent thresholds1, 37, 40 and in animal and plant breeding it is typical to use all available SNPs. Better SNP estimation methods exist and are used in plant and animal breeding1, 2, 37, 44, 50 and such methods have been proposed for applications to human data1,
