Besides, there were differences in the age structure (more elderly test readers over 45 and from 15 25 years old) and the educational level (more test readers with a higher level of education) between the test population and the wider German population.
But in this case, it is necessary to extrapolate to account for potential differences between the test population and the exposed population, which for many infectious pathogens include individuals with immune systems weakened by age, immunosuppression, cytotoxic drugs, or pre-existing disease.
Many factors may influence prediction accuracy [ 17, 56]: marker density, training population size, the level of linkage disequilibrium in the population, the effective size of the population, the relatedness between the training and the test populations, the heritability of the trait, the architecture of the trait.
We needed this period to allow discriminating between the tested populations.
We used DNASP 5.0 to assess the mutation patterns and Fst between the tested populations (Librado and Rozas 2009).
All the preceding work was focused on the question: Are there any metabolites that differ in concentration levels between samples from the test population indicative of a specific pathophysiology?
Other factors that affect the accuracy of imputation include levels of linkage disequilibrium (LD) in the population, the degree of similarity between the reference population and the test population and, to some extent, the size of the reference population [ 7- 10].
In this context, it is important to take into account that prediction accuracy of genomic selection is not only driven by the linkage disequilibrium between molecular markers and QTL, but also exploits genetic relatedness between members of the training and the test population [ 12].
However, this curve is present only when using Onge themselves as one reference; moreover, the amplitude is independent of whether CEU, CHB, YRI, GIH (HapMap Gujarati), or Great Andamanese is used as the second reference, as expected if the weighted LD is due to correlation between LD and allele frequencies in the test population alone (and independent of the reference allele frequencies).
It is expected that the use of double haploid sib test populations increases the accuracy of genomic selection for any candidate population because use of double haploids achieves a more accurate estimation of the SNP effects in the test population by increasing the variance of the genomic relationships between the test and candidate population.
