Package index • plinkQC

Main quality control functions

perIndividualQC(): Quality control for all individuals in plink-dataset

perMarkerQC(): Quality control for all markers in plink-dataset

overviewPerIndividualQC(): Overview of per sample QC

overviewPerMarkerQC(): Overview of per marker QC

cleanData(): Create plink dataset with individuals and markers passing quality control

Individual quality control functions

Functions for step-by-step per-individual quality control

check_sex(): Identification of individuals with discordant sex information

check_relatedness(): Identification of related individuals

check_het_and_miss(): Identification of individuals with outlying missing genotype or heterozygosity rates

Marker quality control functions

Functions for step-by-step per-marker quality control

check_snp_missingness(): Identification of SNPs with high missingness rate

check_maf(): Identification of SNPs with low minor allele frequency

check_hwe(): Identification of SNPs showing a significant deviation from Hardy-Weinberg- equilibrium (HWE)

Quality control helper functions

Helper functions for step-by-step per-individual quality control: accesible to the user, but recommended use via per-individual check_* functions.

run_check_sex(): Run PLINK sexcheck

evaluate_check_sex(): Evaluate results from PLINK sex check.

run_check_heterozygosity(): Run PLINK heterozygosity rate calculation

run_check_missingness(): Run PLINK missingness rate calculation

evaluate_check_het_and_miss(): Evaluate results from PLINK missing genotype and heterozygosity rate check.

run_check_relatedness(): Run PLINK IBD estimation

evaluate_check_relatedness(): Evaluate results from PLINK IBD estimation.

General helper functions

checkPlink(): Check PLINK software access

checkPlink2(): Check PLINK2 software access

testNumerics(): Test lists for different properties of numerics

relatednessFilter(): Remove related individuals while keeping maximum number of individuals

checkFiltering(): Check and construct PLINK sample and marker filters

rename_variant_identifiers(): Renaming variants

pruning_ld(): Pruning of SNPs in Linkage Disequilibrium

convert_to_plink2(): Converting PLINK v1.9 data files into PLINK v2.0 data files

Identifying Ancestry

ancestry_prediction(): Predicting sample superpopulation ancestry

evaluate_ancestry_prediction(): Predicting sample superpopulation ancestry

run_ancestry_prediction(): Projecting the study data set onto the PC space of the reference dataset

run_ancestry_format(): Running functions to format data for ancestry prediction