In genetic analyses, the term ‘batch effect’ refers to systematic differences caused by batch heterogeneity. Controlling this unintended effect is the most important step in quality control (QC) processes that precede analyses. Currently, batch effects are not appropriately controlled by statistics, and newer approaches are required.
The proposed method was used to assess genotyping data obtained using an Affymetrix Human Mapping array of 3,619 subjects consisting of 1,074 patients with Alzheimer’s disease, 296 with mild cognitive impairment (MCI), and 1,153 controls. The samples were gathered from seven different hospitals in Korea and they were genotyped in five discrete sets. Furthermore, one hundred subjects were genotyped twice and these data were used to evaluate performance. The data had a batch effect and batches were clustered using K-medoid clustering using the averages of the probe intensity measurements for each batch. We called the subjects’ genotypes in these different clusters separately. The proposed method improves the accuracy of called genotypes without the need to filter a lot of subjects and SNPs, and therefore is a reasonable approach for controlling batch effects. We implemented R functions(createUpstreamCode and downstreamQC) and it can be downloaded from http://healthstat.snu.ac.kr/.
We proposed a new strategy that detects batch effects with probe intensity measurement and calls genotypes in the presence of batch effects. The application of the proposed method to real data shows that it produces a balanced approach. Furthermore, the proposed method can be extended to various scenarios with a simple modification.
createUpstreamCode function creates codes needed for Axiom SNP chip calling and upstream QC. Automatically generated codes should be run manually by users. It is the purpose of user convenience by helping users look into the codes of each step.
Requirements
Linux
Axiom APT
R {SNPolisher}
Python
Annotation File
Input Parameter
Parameter
Description
wd
Directory where callng and QC works
celDir
Directory where cel(*.cel) file exist
celName
Format of the cel file
aptDir
Location of Axiom APT program
aptLibDir
Location of Axiom APT - R libraries
DQC_thr
Threshold of DQC value
smpCallRate_thr
Threshold of sample call rate
plateCallRate_thr
Trheshold of plate call rate
platePassRate_thr
Threhold of plate pass rate
annoDir
Location of annotation file
Output
The function creates following subfolders under wd and code.sh, code.R or code.py are automatically stored in them.
Folder
Code
Description
1.celfile_list
code.sh
List the *.cel files located in the celDir
2.Generate_DQC
code.sh
Generate DQC value of each sample using APT
3.Sample_QC_DQC
code.R
Remove samples of which DQC value is lower than DQC_thr
4.Generate_CallRate
code.sh
Generate sample call rates using APT
5.Sample_QC_CallRate
code.R
Remove samples of which call rate is lower than smpCallRate_thr
6.Palte_QC
code.R
Remove plates of which average call rate is lower than plateCallRate_thr or plate pass rate is lower than platePassRate_thr
7.Genotype_calling
code.sh
Genotype passing samples and plates using APT
8.SNPolisher
code.R
Classify SNP
9.Call2plink
code1.R
Prepare for converting APT outputs to Plink format(*.ped/*.map)
code2.py
Convert APT outputs to plink format (*.ped/*.map)
All we need to do following downstream QC is downstreamQC funtion.
Individaul QC
1) Identification of individuals with discordant sex information
2) Identification of individuals with elevated missing data rates
3) Identification of individuals with outlying heterozygosity rate
4) Identification of duplicated or related individuals
SNP QC
1) identification of SNPs with significantly different missing genotype rates
between cases and controls
2) identification of SNPs demonstrating a significant deviation from
Hardy-Weinberg equilibrium(HWE)
3) the removal of all makers with a very low minor allele frequency
4) identification of SNPs with significantly different maf between plates
Requirements
PLINK
Input Parameter
Parameter
Description
wd
Working directory
plinkDir
Location of PLINK program
genotypeDir
Directory of genotype file
genotypeFile
File name of genotype file
phenoDir
Directory of phenotype file if separately exists
qcDir
Specify a direcotory where the results would be stored
ind_qc_sex
Whether include 'Discordant sex information QC'
ind_qc_sex_thr
If ind_qc_sex=TRUE, set threshold(refer to PLINK --check-sex)
ind_qc_miss
Whether include 'Missnig rate QC'
ind_qc_miss_thr
If ind_qc_miss=TRUE, set threshold (refer to PLINK --missing)
ind_qc_het
Whether include 'heterozygosity QC'
ind_qc_het_thr
If ind_qc_het=TRUE, set threshold (refer to PLINK --het)
ind_qc_relate
Whether include 'Relateness QC'
ind_qc_relate_thr
If ind_qc_relate=TRUE, set threshold (refer to PLINK --indep-pairwise)
snp_qc_miss_cs_cntrl
Whether include 'missing genotype rates b/w cases and controls QC'
snp_qc_miss_cs_cntrol_thr
If snp_qc_miss_cs_cntrl=TRUE, set threshold (refer to PLINK --test-missing)
snp_qc_miss
Whether include 'missing genotype rates QC'
snp_qc_miss_thr
If snp_qc_miss_cs_cntrl=TRUE, set threshold (refer to PLINK --geno)
snp_qc_hwe
Whether include 'HWE QC'
snp_qc_hwe_thr
If snp_qc_miss_cs_cntrl=TRUE, set threshold (refer to PLINK --hwe)
snp_qc_hwe_incl_nonctrl
If snp_qc_miss_cs_cntrl=TRUE, whether include case calculating hwe (refer to PLINK --hwe include-nonctrl)
snp_qc_maf
Whether include 'MAF QC'
snp_qc_maf_thr
If snp_qc_miss_cs_cntrl=TRUE, set threshold (refer to PLINK --maf)