2021
DOI: 10.1101/2021.06.04.447093
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WeightedLD: The Application of Sequence Weights to Linkage Disequilibrium

Abstract: Sequence-weighting methods are commonly employed to account for biases in sequence datasets. We use a weighting scheme which considers the observed distinctiveness of sequences and apply it to calculations of linkage disequilibrium. Each sequence now contributes a weighted score to linkage disequilibrium measurements of pairwise loci. We demonstrate that this reduces the effect of uneven sampling, as underrepresented groups of sequences will each contribute more individually than redundant, similar sequences.

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Cited by 2 publications
(2 citation statements)
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“…Starting with a sequence alignment, we determined the pairwise LD r 2 associations for all variable sites using WeightedLD ( 72 ) without weighting. This method allowed us to easily exclude sites with any insertions or ambiguous characters, where we used the options –min-acgt 0.99 and –min-variability 0.05.…”
Section: Methodsmentioning
confidence: 99%
“…Starting with a sequence alignment, we determined the pairwise LD r 2 associations for all variable sites using WeightedLD ( 72 ) without weighting. This method allowed us to easily exclude sites with any insertions or ambiguous characters, where we used the options –min-acgt 0.99 and –min-variability 0.05.…”
Section: Methodsmentioning
confidence: 99%
“…Starting with a sequence alignment we determined the pairwise LD r 2 associations for all variable sites using WeightedLD 72 without weighting. This method allowed us to exclude sites with any insertions or ambiguous characters easily where we used the option --min-acgt 0.99 and --min- variability 0.05.…”
Section: Methodsmentioning
confidence: 99%