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Based on discussions with @ngaddis regarding relatedness filtering for GWAS, the implementation of this QC process will be refactored. Currently this process uses a combination of PLINK, an in-house R script for graph-based sample selection, and KING (for AAs only).
The new implementation will be based on recommendations by a GENESIS relatedness workflow and a GWASTools best practices guide. The main enhancements will be the simplification to only use R, and the use of the KING algorithm for all ancestries.
Previously it was discussed offline whether KING was a suitable method for admixed individuals. According to this publication, it is biased but will generally be suitable for QC purposes:
KING-robust was developed for relatedness inference in samples from populations with discrete substructure, and it is a consistent estimator of the kinship coefficient for a pair of outbred individuals from the same subpopulation. This estimator, however, will generally be negatively biased for pairs of individuals that have different ancestries. Despite this bias, the KING-robust estimator is typically able to separate close relatives with similar ancestry from unrelated individuals
The text was updated successfully, but these errors were encountered:
Based on discussions with @ngaddis regarding relatedness filtering for GWAS, the implementation of this QC process will be refactored. Currently this process uses a combination of PLINK, an in-house R script for graph-based sample selection, and KING (for AAs only).
The new implementation will be based on recommendations by a GENESIS relatedness workflow and a GWASTools best practices guide. The main enhancements will be the simplification to only use R, and the use of the KING algorithm for all ancestries.
Previously it was discussed offline whether KING was a suitable method for admixed individuals. According to this publication, it is biased but will generally be suitable for QC purposes:
The text was updated successfully, but these errors were encountered: