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Assessing the power of informative subsets of loci for population assignment: standard methods are upwardly biased
Author(s) -
ANDERSON E. C.
Publication year - 2010
Publication title -
molecular ecology resources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.96
H-Index - 136
eISSN - 1755-0998
pISSN - 1755-098X
DOI - 10.1111/j.1755-0998.2010.02846.x
Subject(s) - biology , statistical power , population , set (abstract data type) , classifier (uml) , allele frequency , statistics , allele , artificial intelligence , genetics , computer science , mathematics , demography , sociology , gene , programming language
It is well known that statistical classification procedures should be assessed using data that are separate from those used to train the classifier. This principle is commonly overlooked when the classification procedure in question is population assignment using a set of genetic markers that were chosen specifically on the basis of their allele frequencies from amongst a larger number of candidate markers. This oversight leads to a systematic upward bias in the predicted accuracy of the chosen set of markers for population assignment. Three widely used software programs for selecting markers informative for population assignment suffer from this bias. The extent of this bias is documented through a small set of simulations. The relative effect of the bias is largest when screening many candidate loci from poorly differentiated populations. Simple unbiased methods are presented and their use encouraged.