Genotyping arrays enable the direct measurement of an individuals genotype at thousands of markers. Subsequent analyses such as genome-wide association studies rely on the high quality of these marker genotypes.
Anderson and colleagues introduced a protocol for data quality control in genetic association studies heavily based on the summary statistics and relatedness estimation functions in the PLINK software . PLINK is a comprehensive, open-source command-line tool for genome-wide association studies (GWAS) and population genetics research . It’s main functionalities include data management, computing individual- and marker- level summary statistics, identity-by-state estimation and association analysis.
Integration with R is achieved through its R plugin or PLINK/SEQ R Package . While the plugin is limited to operations yielding simple genetic marker vectors as output, the PLINK/SEQ R Package is limited in the functionalities it can access.
plinkQC facilitates genotype quality control for genetic association studies as described by . It wraps around plink basic statistics (e.g. missing genotyping rates per individual, allele frequencies per genetic marker) and relationship functions and generates a per-individual and per-marker quality control report. Individuals and markers that fail the quality control can subsequently be removed with plinkQC 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.
plinkQC depends on the PLINK (version \(\ge\) 1.9), which has to be manually installed prior to the usage of plinkQC. It assumes the genotype have already been determined from the original probe intensity data of the genotype array and is available in plink format.
The protocol is implemented in three main functions, the per-individual quality control (
perIndividualQC), the per-marker quality control (
perMarkerQC) and the generation of the new, quality control dataset (
The per-individual quality control with
perIndividualQC wraps around these functions: (i)
check_sex: for the identification of individuals with discordant sex information, (ii)
check_heterozygosity_and_missingness: for the identification of individuals with outlying missing genotype and/or heterozygosity rates, (iii)
check_relatedness: for the identification of related individuals, (iv)
check_ancestry: identification of individuals of divergent ancestry.
The per-marker quality control with
perMarkerQC wraps around these functions: (i)
check_snp_missingnes: for the identifying markers with excessive missing genotype rates, (ii)
check_hwe: for the identifying markers showing a significant deviation from Hardy-Weinberg equilibrium (HWE), (iii)
check_maf: for the removal of markers with low minor allele frequency (MAF).
In the following, genotype quality control with plinkQC is applied on a small example dataset with 200 individuals and 10,000 markers (provided with this package). The quality control is demonstrated in three easy steps, per-individual and per-marker quality control followed by the generation of the new dataset. In addition, the functionality of each of the functions underlying
perIndividualQC is demonstrated at the end of this vignette.
perIndividualQC, one simply specifies the directory where the data is stored (qcdir) and the prefix of the plink files (i.e. prefix.bim, prefix.bed, prefix.fam). In addition, the names of the files containing information about the reference population and the merged dataset used in
check_ancestry have to be provided: refSamplesFile, refColorsFile and prefixMergedDataset. Per default, all quality control checks will be conducted.
In addition to running each check,
perIndividualQC writes a list of all fail individual IDs to the qcdir. These IDs will be removed in the computation of the
perMarkerQC. If the list is not present,
perMarkerQC will send a message about conducting the quality control on the entire dataset.
NB: To reduce the data size of the example data in
plinkQC, data.genome has already been reduced to the individuals that are related. Thus the relatedness plots in C only show counts for related individuals only.
NB: To demonstrate the results of the ancestry check, the required eigenvector file of the combined study and reference datasets have been precomputed and for the purpose of this example will be copied to the
qcdir. In practice, the
qcdir will often be the same as the
indir and this step will not be required.
perIndividualQC displays the results of the quality control steps in a multi-panel plot.
fail_individuals <- perIndividualQC(indir=indir, qcdir=qcdir, name=name, refSamplesFile=paste(indir, "/HapMap_ID2Pop.txt", sep=""), refColorsFile=paste(indir, "/HapMap_PopColors.txt", sep=""), prefixMergedDataset="data.HapMapIII", path2plink=path2plink, do.run_check_ancestry = FALSE, interactive=TRUE, verbose=TRUE)
overviewperIndividualQC depicts overview plots of quality control failures and the intersection of quality control failures with ancestry exclusion.
perMarkerQC applies its checks to data in the specified directory (qcdir), starting with the specified prefix of the plink files (i.e. prefix.bim, prefix.bed, prefix.fam). Optionally, the user can specify different thresholds for the quality control checks and which check to conduct. Per default, all quality control checks will be conducted.
perMarkerQC displays the results of the QC step in a multi-panel plot.
overviewPerMarkerQC depicts an overview of the marker quality control failures and their overlaps.
After checking results of the per-individual and per-marker quality control, individuals and markers that fail the chosen criteria can automatically be removed from the dataset with
cleanData, resulting in the new dataset qcdir/data.clean.bed,qcdir/data.clean.bim, qcdir/data.clean.fam. For convenience,
cleanData returns a list of all individuals in the study split into keep and remove individuals.
The identification of individuals with discordant sex information helps to detect sample mix-ups and samples with very poor genotyping rates. For each sample, the homozygosity rates across all X-chromosomal genetic markers are computed and compared with the expected rates (typically $<$0.2 for females and $>$0.8 for males). For samples where the assigned sex (PEDSEX in the .fam file) contradicts the sex inferred from the homozygosity rates (SNPSEX), it should be checked that the sex was correctly recorded (genotyping often occurs at different locations as phenotyping and misrecording might occur). Samples with discordant sex information that is not accounted for should be removed from the study. Identifying individuals with discordant sex information is implemented in
check_sex. It finds individuals whose SNPSEX != PEDSEX. Optionally, an extra data.frame with sample IDs and sex can be provided to double check if external and PEDSEX data (often processed at different centers) match. If a mismatch between PEDSEX and SNPSEX was detected, by SNPSEX == Sex, PEDSEX of these individuals can optionally be updated.
check_sex depicts the X-chromosomal heterozygosity (SNPSEX) of the samples split by their (PEDSEX).
The identification of individuals with outlying missing genotype and/or heterozygosity rates helps to detect samples with poor DNA quality and/or concentration that should be excluded from the study. Typically, individuals with more than 3-7% of their genotype calles missing are removed. Outlying heterozygosity rates are judged relative to the overall heterozygosity rates in the study, and individuals whose rates are more than a few standard deviations (sd) from the mean heterozygosity rate are removed. A typical quality control for outlying heterozygoisty rates would remove individuals who are three sd away from the mean rate. Identifying related individuals with outlying missing genotype and/or heterozygosity rates is implemented in
check_het_and_miss. It finds individuals that have genotyping and heterozygosity rates that fail the set thresholds and depicts the results as a scatter plot with the samples’ missingness rates on x-axis and their heterozygosity rates on the y-axis.
The identification of individuals of divergent ancestry can be achieved by combining the genotypes of the study population with genotypes of a reference dataset consisting of individuals from known ethnicities (for instance individuals from the Hapmap or 1000 genomes study ). Principal component analysis on this combined genotype panel can be used to detect population structure down to the level of the reference dataset (for Hapmap and 1000 Genomes, this is down to large-scale continental ancestry). Identifying individuals of divergent ancestry is implemented in
check_ancestry. Currently, check ancestry only supports automatic selection of individuals of European descent. It uses information from principal components 1 and 2 to find the center of the European reference samples. All study samples whose euclidean distance from the centre falls outside a specified radius are considered non-European.
check_ancestry creates a scatter plot of PC1 versus PC2 color-coded for samples of the reference populations and the study population.
Markers with excessive missingness rate are removed as they are considered unreliable. Typically, thresholds for marker exclusion based on missingness range from 1%-5%. Identifying markers with high missingness rates is implemented in
snp_missingness. It calculates the rates of missing genotype calls and frequency for all variants in the individuals that passed the
## Markers with deviation from HWE Markers with strong deviation from HWE might be indicative of genotyping or genotype-calling errors. As serious genotyping errors often yield very low p-values (in the order of \(10^-50\)), it is recommended to choose a reasonably low threshold to avoid filtering too many variants (that might have slight, non-critical deviations). Identifying markers with deviation from HWE is implemented in
check_hwe. It calculates the observed and expected heterozygote frequencies per SNP in the individuals that passed the
perIndividualQC and computes the deviation of the frequencies from Hardy-Weinberg equilibrium (HWE) by HWE exact test.
Markers with low minor allele count are often removed as the actual genotype calling (via the calling algorithm) is very difficult due to the small sizes of the heterozygote and rare-homozygote clusters. Identifying markers with low minor allele count is implemented in
check_maf. It calculates the minor allele frequencies for all variants in the individuals that passed the
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