Author: Charles, Oscar J.; Roberts, Joseph; Breuer, Judith; Goldstein, Richard A.
Title: WeightedLD: The Application of Sequence Weights to Linkage Disequilibrium Cord-id: geuevfgj Document date: 2021_6_7
ID: geuevfgj
Snippet: 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, simi
Document: 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. Availability Source code for a python and rust implementation are freely available at under an MIT license at github.com/ojcharles/WeightedLD. Contact [email protected] or [email protected]
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