a genotyping array is a cost - effective and efficient platform for the identification of variants in a large population [ 1 , 2 ] . until now , genome - wide association studies ( gwass )
most identified variants are among the common variants ( minor allele frequency ( maf )  0.05 ) found throughout the population , even though detection of rare genetic variants in human populations is an important aspect of understanding pathophysiological variability in complex diseases [ 4 , 5 ] .
next - generation sequencing technologies are effective methods to detect rare variants in the human genome , but these technologies have higher cost per sample compared with a genotyping array .
also , because next - generation sequencing has a high noise - to - signal ratio , experimental validation is necessary .
exome - based genotyping arrays , such as the illumina infinium humanexome beadchip ( hereafter referred to as exome chip ) and the affymetrix axiom exome array , were recently introduced .
prices of these platforms are lower than for other kinds of genotyping arrays . moreover , exome - based genotyping has the capacity to discover rare variants in exon regions associated with complex diseases [ 79 ] .
genotype calling algorithms based on clustering methods are vulnerable to detecting rare variants . at times
, clustering of rare or low - frequency variants results in spurious genotype calls . the number of variants remaining after quality control process can be different according to diverse genotype calling methods .
in addition , because of racial differences in allele frequencies of polymorphic variants , criteria for variants selection should be different according to ethnicities of populations .
grove et al . proposed a list of best practices for genotype calling for exome chip methods following a study with 62,266 participants from seven genotyping centers included in the cohorts for heart and aging research in genomic epidemiology ( charge ) consortium .
this genotype calling method suggested based on charge consortium presented criteria for identified variants that may contain clustering errors .
also , this study was largely based on western populations , as those of african american ( n = 13,605 ) , caucasian ( n = 43,869 ) , and hispanic ancestry ( n = 2,129 ) were represented in relatively large numbers , whereas only 777 asian samples of 62,266 were analyzed .
the subjects recruited for exome chip design listed in a web site , exome chip design - genome analysis wiki ( http://genome.sph.umich.edu/wiki/exome_chip_design ) , were also mainly comprised of european ancestry , but a small fraction of asians including 327 chinese individuals . in addition , guo et al . have suggested a protocol for exome chip data consisting of 39,000 samples mainly composed of european and african ancestry .
therefore , additional information from asian samples is needed to provide efficient background data for genotype calling for asian populations .
here , we propose a practical method for accurate genotype calling using exome chip technologies with data from 14,647 korean samples .
further , we provide workflow for manual reclustering and present a change in genotype calling quality following the use of custom cluster files .
we performed a two - stage analysis as part of a novel genotype calling method . in the first stage , we established an efficient genotype calling process for 14,647 korean - based data points and built a custom cluster file using quality control process that included manual reclustering for low - frequency cluster data .
the korean individuals belong to three cohorts that comprise the korean genome epidemiology study ( koges ) : ansan / ansung ( n = 8,012 ) , health examinee ( hexa , n = 3,448 ) , and the cardiovascular association ( cavas ) cohort ( n = 3,187 ) . in the second stage , we applied knowledge from our custom cluster file to independent samples , namely , 804 korean diabetic retinopathy samples recruited from the seoul national university bundang hospital . written informed consents
the study was approved by the institutional review board of korea centers for disease control and prevention and seoul university bundang hospital .
each genotype was automatically clustered once by illumina genomestudio v2011.1 software , and then we applied criteria that have been used in the charge consortium for manual reclustering and visual inspection .
when we applied criteria provided by the charge consortium , over half of variants were identified to be required for manual reclustering . in response , we adjusted a few criteria scores to fit our data set .
call frequency rates in all categories decreased from 1 to 0.99 and maf in the ab category cluster error increased from 0 to 0.0002 ( table 1 ) .
the customized cluster files ( .egt ) , which have cluster information containing manual reclustering criteria , were generated by genomestudio .
sample and variant quality control of reclustered genotype data was performed following extraction of data using the plink format .
we rearranged three types of misclassified clusters using genomestudio as follows : ( 1 ) small number of variants misclassified with the ab genotype ( supplementary figure  2a in supplementary material available online at http://dx.doi.org/10.1155/2015/421715 ) , ( 2 ) some misclassified variants with a lower norm r score and wide norm theta distribution ( supplementary figure  2b ) , and ( 3 ) a short range for a cluster boundary ( supplementary figure  2c ) . to test the effects of our customized cluster file , we performed automated clustering on patient data for 804 koreans with diabetic retinopathy using a customized cluster file instead of the default cluster file .
first , relatives defined based on identity - by - descent and samples with sex inconstancies were excluded through the plink options of  -  -genome  and  -  -check - sex  , respectively . also , samples with low call rate ( < 99% ) were thrown out by option  -  -mind  .
next , plink option  -  -geno 0.95 ( call rate < 95% )  was used to remove variants with completely missing data or having a low call rate . also , additional variants were excluded based on deviation from hardy - weinberg equilibrium ( hwe ) , a below - threshold p value ( hwe p < 0.000001 ) , and minor allele count ( mac )
duplicated variants were removed . for performance comparisons of manual reclustering , we compared exome chip data with or without reclustering process to whole - exome sequencing data .
exome chip data were extracted to plink format and quality control process was performed as described above . and whole - exome sequencing data for 185 samples which were the same as our exome chip samples was produced with the illuminahiseq2000 platform ( 70x coverage ) .
also off - target variants of whole - exome sequencing data were removed from each exome chip data for further analysis .
in addition , overall accuracies of heteroalleles and nonreference concordances were calculated by our in - house customized python script .
maf of all genotypes was calculated using option  -  -freq  within the plink tool .  
monomorphic variants accounted for > 70% of those found in hexa ( n = 3,448 ) and cavas ( n = 3,187 ) samples . from all 14,647 samples , we found that 66% of the data was accounted for by monomorphic variants .
with regard to sample selection , samples were excluded through the quality control process as follows : ( 1 ) samples that were estimated as relatives based on identity - by - descent ( n = 579 ) , ( 2 ) sex inconsistency ( fail , n = 55 ) , and ( 3 ) low call rate per sample ( < 99% , n = 61 ) . regarding variant selection , variants which are monomorphic with maf > 0.001% ( n = 162,757 ) , deviated from hardy - weinberg equilibrium ( p value < 10 , n = 1,185 ) , and with low call rate per genotype ( < 95% , n = 24 ) were excluded .
consequently , 77,204 snps of 14,056 samples remained . to evaluate the variant calling accuracy of our method , we calculated overall concordance with frequency rates between exome chip and whole - exome sequencing data .
after quality control process , a total of 78,114 and 77,204 variants remained before and after manual reclustering , respectively . consequently , among those , 29,959 and 29,850 from before and after reclustering , respectively , were extracted for comparing performance .
mean and standard deviation of overall accuracy rate were calculated by maf categories such as common , low - frequency , rare , and all variants .
overall accuracy rate showed that results of manual reclustering were more accurate than those of absence of manual reclustering ( table 3 ) .
particularly , overall concordance of common variants was improved from 0.9730 to 0.9856 ( table 3 ) . moreover , to evaluate the efficiency of identifying rare variants , we calculated nonreference concordance and accuracy of heteroalleles on extreme rare variants by mac ranging from 2 to 5 .
overall , nonreference concordance of after reclustering was similar to those of before reclustering . but with regard to accuracy of heteroalleles by mac , accuracy of after quality control was significantly improved compared to those of before quality control ( supplementary table  1 ) . to examine discrepancies between custom versus default cluster files , we used 804 additional korean samples . for variants and sample selection
, we used the same quality control criteria as that in the first stage of analysis . from the quality control process ,
the remaining number of variants using custom and default cluster files included 50,076 and 50,540 variants , respectively .
a total of 48,956 variants were shared between the two cluster files , whereas 1,120 and 1,584 variants were uniquely found in the custom versus default cluster file , respectively ( figure 1 ) .
supplementary figure  2 shows that 1,120 misclassified variants were reclustered using our custom cluster file . using annovar , we examined the discrepancy of the functional category of variants comparing data from before and after we applied our quality control guidelines .
the rate of exonic variants using our guidelines ( 87.13% ) was less than that seen implementing our guidelines ( 77.31% ) , whereas the rate of nongenic ( intergenic and intronic ) variants found using our guidelines was higher than that before implementing our guidelines ( supplementary table  2 ) . moreover , relative proportions of splicing and exonic - splicing variants using our guidelines decreased by 0.39% and 0.67% , whereas the abundances of other variants increased .
supplementary figure  1 shows the distribution of variants on each chromosome ; variants found using our guidelines were not biased to a particular chromosome .
exome chip methodologies constitute a unique platform to perform large - scale gwass . not only is the price of an exome chip lower than that of other kinds of genotyping arrays , but also an exome chip focuses on the discovery of rare variants in exonic regions , which can be associated with complex diseases .
they suggested that a custom cluster file ( .egt ) be applied to attain more accurate genotype data .
however , because the humanexome beadchip was developed based on the genetics of a caucasian population , substantial variants were monomorphic when we conducted gwass using other ethnic samples , such as koreans ( table 2 ) .
therefore , proper genotype calling guidelines that consider ethnic differences can be important attributes of large - scale gwass using exome chips . here
, we propose a suitable exome chip genotype calling process to apply to korean samples . to reduce the number of variants that were excluded by the automated genotype calling method , we paid attention to two issues in the process of quality control .
first , we adjusted several scores of automated clustering criteria such as call frequency rate and maf of ab cluster error .
when we applied automated clustering criteria recommended by the charge consortium , more than half of variants were identified to be requiring manual reclustering using those filters ( table 1 ) .
therefore , remaining data were too small to conduct a gwas with respect to rare variants which can be a cause of rare diseases . to resolve this problem
, we lowered several cut - offs . through this process the number of excluded variants decreased by 46,070 variants ( table 1 ) .
however , the increased number of remaining variants due to lowered quality control threshold compared to charge consortium guideline did not affect overall accuracy of variants ( table 3 and supplementary table  1 ) .
we remedied three types of representative misclassification patterns , which could be reclassified among 46,070 variants ( supplementary figure  2 ) .
as shown in supplementary figure  2 , our correction process was robust enough to detect low - frequency and rare variants . from this correction process
, 5,038 variants were restored as additional variants . consequently , 77,204 variants were used as background data within the customized cluster file ( .egt ) . to evaluate the accuracy of the genotype call , we compared concordance rates between exome chip and whole - exome sequencing data obtained from the 185 same individuals .
more than 99% of variants were the same genotype as each other ( table 3 ) .
particularly , the concordance rate for rare variants was close to 99.9% , supporting the robustness of our data for rare variants .
we applied the customized cluster file ( .egt ) to genotype calling for 804 independent individuals .
when we compared the remaining variants using the custom cluster file to those using the default cluster file , 48,956 variants were shared between the cluster files .
only 1,120 variants were uniquely found in our custom cluster files . the fact that we identified a relatively small number of variants may be attributable to the relatively small sample size with respect to finding a rare variant .
therefore , it remains possible that our customized cluster file would identify a larger number of variants if applied to larger sample sizes .
as shown in supplementary table  2 , we could not find bias of the number of functional variants after our approach .
taken together , we think that our approach can provide larger number of variants with high accuracy for association analysis in korean exome chip data
. moreover , prior knowledge data ( .egt ) can be valuable reference information for performing variant clustering of exome chip data .
our study can provide practical quality control guidelines for exome array - based gwas using korean population .
although there is a limitation due to the recruitment of korean samples only , we firstly tried to apply alternative criteria for quality control of exome chip data produced with a large scale of asian population ( more than 10,000 individuals ) .
consequently , we could substantially improve the call rate of asian - specific rare variants . considering similar allele distributions between koreans and the chinese / japanese
, our alternatively suggested criteria using a large scale of korean population may be applied to other east asian populations .
further investigations might be needed whereas our criteria for quality control would be applied to other asian ancestry , such as southeastern asians .
we suggest practical guidelines for exome chip quality control for variant analysis in korean populations .
moreover , ours is the first study to describe the practical use of variant calling and genotype clustering in korean populations .
our study allows an efficient method to detect exonic variants as well as low - frequency and rare variants .
furthermore , the modified criteria for genotype quality control and clustering we suggested might be possible to be extensively applied to other east asian populations .
 exome - based genotyping arrays are cost - effective and have recently been used as alternative platforms to whole - exome sequencing . however , the automated clustering algorithm in an exome array has a genotype calling problem in accuracy for identifying rare and low - frequency variants . to address these shortcomings , we present a practical approach for accurate genotype calling using the illumina infinium humanexome beadchip . 
 we present comparison results and a statistical summary of our genotype data sets . 
 our data set comprises 14,647 korean samples . 
 to solve the limitation of automated clustering , we performed manual genotype clustering for the targeted identification of 46,076 variants that were identified using genomestudio software . to evaluate the effects of applying custom cluster files , we tested cluster files using 804 independent korean samples and the same platform . 
 our study firstly suggests practical guidelines for exome chip quality control in asian populations and provides valuable insight into an association study using exome chip . 
1. Introduction
2. Materials and Methods
3. Results
4. Discussion
5. Conclusion