Sentence examples for the aim panels from inspiring English sources

Training set populations refer to the population groups used to design the AIM panels.

As a result, we developed and preset the AIM panels for each database individually.

This decision was based on the fact that most of the AIM panels available were designed to identify ancestry from main population groups.

Secondly, for all the five measures, as the AIM panels included more markers that were less ancestry informative, the mean and standard deviation of estimation errors increased.

Overall, the AIM panels chosen by In performed the best, giving the lowest bias and RMSE, whereas those by FIC performed the worst across the real dataset and the simulated datasets.

Most of the AIM panels available in the literature have been designed by way of selecting SNPs from large genomic databases (e.g. HapMap) showing skewed population frequencies between the ancestral populations targeted.

The AIMs panels are listed within the IBC resource site (http://bmic.upenn.edu/cvdsnp/updates/ancestry_informative_markers-ibc-v1.xls).

We also generated an additional panel (2 k random panel) by randomly sub-sampling 10% of the 21 k random panel to match the marker density of the AIMs panels.

We then randomly selected one-third of the SNPs to obtain a random marker panel (21 k random panel) that had 10-fold greater marker density than the AIMs panels.

The distribution of SNPs across the AIMs panels (one based on δ contained 2,076 AIMs (Additional file 1), the other based on F ST contained 1,923 AIMs (Additional file 2)) and two random marker panels (21 k random marker panel and 2 k random marker panel, Additional file 3) are shown in Table 1.

By using random subsets of AIMs from the top AIM panels of various sizes, we expect to select less informative markers and the estimate of the ancestry contribution is expected to become less accurate (or more biased) with more variability.