Genotype Quality Control with 'PLINK'
Predicting sample superpopulation ancestry
Identification of individuals with outlying missing genotype or hetero...
Identification of SNPs showing a significant deviation from Hardy-Wein...
Identification of SNPs with low minor allele frequency
Identification of related individuals
Identification of individuals with discordant sex information
Identification of SNPs with high missingness rate
Check and construct PLINK sample and marker filters
Checking the path of the loading matrix
Check PLINK software access
Check PLINK2 software access
Check and construct individual IDs to be removed
Create plink dataset with individuals and markers passing quality cont...
Converting PLINK v1.9 data files into PLINK v2.0 data files
Predicting sample superpopulation ancestry
Evaluate results from PLINK missing genotype and heterozygosity rate c...
Evaluate results from PLINK IBD estimation.
Evaluate results from PLINK sex check.
Overview of per sample QC
Overview of per marker QC
Quality control for all individuals in plink-dataset
Quality control for all markers in plink-dataset
plinkQC: Genotype Quality Control with 'PLINK'
Pruning of SNPs in Linkage Disequilibrium
Remove related individuals while keeping maximum number of individuals
Renaming variants
Running functions to format data for ancestry prediction
Projecting the study data set onto the PC space of the reference datas...
Run PLINK heterozygosity rate calculation
Run PLINK missingness rate calculation
Run PLINK IBD estimation
Run PLINK sexcheck
Test lists for different properties of numerics
Genotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. 'plinkQC' facilitates genotype quality control for genetic association studies as described by Anderson and colleagues (2010) <doi:10.1038/nprot.2010.116>. It makes 'PLINK' basic statistics (e.g. missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions accessible from 'R' and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed to generate a new, clean dataset. Removal of individuals based on relationship status is optimised to retain as many individuals as possible in the study. Additionally, there is a trained classifier to predict genomic ancestry of human samples.
Useful links