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For haplotypes that are fairly well reconstructed, the analysis of inferred haplotypes is unbiased and is more powerful than the analysis based on expected haplotype probabilities [22].
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Furthermore, if some haplotypes can be expected to be inferred correctly or with only small error with respect to sensitivity and specificity as in the example of the CAPN10 haplotype mentioned, analyses based on individually inferred haplotype are most powerful.
This process tempers any highly localized fluctuations, which are not expected based on our expected size distribution of nonrecombined haplotypes at generation 100.
An expectation-substitution approach was used to assign expected haplotype counts based on unphased genotype data and to account for uncertainty in assignment [27].
Due to the lack of a gold standard, we can only provide an estimation of expected haplotype misclassification based on the frequencies of observed haplotypes.
The frequencies of expected haplotypes were estimated by using the statistical methodologies implemented by HPlus software [ 21], which infers haplotypes based on expectation-maximization with a modified progressive ligation computational algorithm.
Based on haplotype analysis, each individual haplotype-pair was estimated with haplotype frequencies greater than 5%.
LD (r2) measure was based on Composite Haplotype Method (CHM).
LD (r2) measure of adjacent pairs was based on Composite Haplotype Method (CHM).
A further detailed examination of this region is thus also warranted based on our haplotype analysis.
Analysis of molecular variance (AMOVA) [32], FST statistic [33], and measures of haplotype diversity based on estimated haplotype frequencies were computed using Arlequin v.3.11 software [34].
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