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Therefore, these GI prediction methods will tend to under-predict GIs.
Our results suggest that without correction for the prevalence bias induced by multifunctionality, these methods will tend to prioritize genes which have the best existing characterization, and be insufficiently sensitive to the choice of target disease.
Holden and Miller [ 4] emphasized that observational methods will tend to underestimate parenting stability.
This suggests that these methods will tend to yield inflated estimates in populations with low inbreeding coefficients, most likely due to isolated homozygous SNPs (Table 2).
Multiplier methods will tend to produce underestimates if the members of the target population that appear in administrative data are overrepresented in the sample of the target population akin to problems with capture-recapture when capture probabilities are correlated (28).
From Table 2, we can see that when identifying high confidence peaks, both the wavelet-based and our proposed peak detection method have reasonable results if they only used to recognize 80% of high confidence peaks, and both methods will tend to more false-positive results as criteria becomes more strict.
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The BB method will tend to provide increasingly dubious results because it will estimate erroneous space use frequencies attached to an increasing number of too long segments, whereas the BRB method, which filters them out, will tend to provide increasingly biased results in terms of habitat preferences because GPS failures are often habitat-dependent [28], [29].
To recap, Problem 1 means that parametric CIs for the LD method will tend to be slightly too narrow, with the effect being more pronounced for large numbers of loci.
Moreover, the accuracy and relevance of ancestral annotations (and thus of the whole analysis) may depend on the representativity of the sample of sequences; if certain annotations are clearly over-represented, then parsimony (and any ancestral reconstruction method) will tend to over-predict those annotations, thus producing erroneous results.
Haplotypes that are indeed frequent because of LD will not be taken into account properly by the method and will tend to wrongly provide increased evidence for HBD.
Important to keep in mind is that EM imputations, though a clear improvement over more traditional methods, will still tend to underestimate the standard errors [ 52].
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