Technical Note: Genotyping

Infinium® Genotyping Data Analysis
A guide for analyzing Infinium genotyping data using the GenomeStudio® Genotyping Module

Introduction

GenCall Score Cutoff

Illumina DNA Analysis BeadChips using the Infinium Assay provide
researchers unparalleled genomic access and accuracy for analyzing
genetic variation. Illumina’s portfolio of powerful products offers
many benefits to researchers. In particular, BeadChips provide highly
multiplexed sample analysis of 300,000 to more than 5,000,000
markers simultaneously. The density of arrayed features used for thishigh level of parallel analysis also allows for a high level of intrinsic
assay redundancy. Each locus is assayed with a minimum 12 to
18fold redundancy for each sample, yielding a highly robust intensity
estimate that enables highly accurate genotype calls. Another strong
contributor to data quality is outlier removal when averaging raw
intensity data.

The first GenomeStudio parameter that should be optimized to obtain
the highest genotyping accuracy is the GenCall score cutoff, ornocall threshold. The GenCall score is a quality metric calculated foreach genotype (data point), and ranges from 0 to 1. GenCall scores
generally decrease in value the further a sample is from the center ofthe cluster to which the data point is associated. The nocall thresholdis the lower bound for calling genotypes relative to its associatedcluster. Illumina FastTrack Genotyping Project Managers typically use anocall threshold of 0.15 with Infinium data. This means that genotypes
with a GenCall score less than 0.15 are not assigned genotypes
because they are considered too far from the cluster centroid to makereliable genotype calls. They are instead assigned a “no call” for that
locus. No calls on successful DNAs at successful loci contribute tolowering the call rate for the overall project. The standard 0.15 no-callthreshold for Infinium data was determined using projects with trio andreplicate information. A range of no-call thresholds were evaluatedto optimize the call rate without compromising reproducibility or
Mendelian consistency. The nocall threshold in GenomeStudio canbe changed by selecting Tools | Options | Project | Nocall Threshold.
Sample and SNP statistics must be recalculated after an adjustment
to the nocall threshold.

Each SNP is analyzed independently to identify genotypes. Genotypes
are called by comparing customer-generated data with those in
the supplied cluster file. Genotype calls are highly accurate andunambiguous for high-quality samples. Generally, high-quality datawith 99.5% call rates can be expected. However, accuracy is highly
sample dependent. When samples do not perform as expected,
experimenters can choose to reprocess (requeue) these samples toconfirm or potentially improve results. Poorly performing samples canbe systematically excluded from the project before recalling
genotypes. Additionally, some samples (e.g., samples that have been
whole-genome amplified) may not fit standard cluster positions well. A
custom-generated cluster file may improve the call rate and accuracy
for these aberrant cases.
This technical note describes the method used by the Illumina
FastTrack Genotyping Services Group for analyzing Infinium
genotyping data with the Illumina GenomeStudio Genotyping Module
to optimize call rates. Analysis begins with preliminary sample quality
evaluation to determine which samples may require reprocessing or
removal. If a custom cluster file is required, clustering should be done
after removal of failed or suboptimal samples. After selected samples
have been reprocessed and included in the analysis, sample quality
can be evaluated in more detail. If necessary, individual locus analysis,
where loci are identified by sorting on per-locus metrics such as callrate or cluster separation, can optimize data quality before generating
a final report.
Note: For polyploid samples, the Polyploid Clustering Module isused rather than the standard Genotyping Module. The Polyploid
Clustering Module identifies clusters in polyploid organisms like wheat
and potato, for which the standard diploid clustering algorithm is notparticularly efficient. This module performs cluster assignment but
does not assign genotypes because the assignment of genotypes inpolyploid species is highly dependent on the population and biology ofthe organism. Further information on using this module can be foundin the Performing Clustering in the GenomeStudio Polyploid Clustering
Module user guide1.

Identifying and Reprocessing Poorly
Performing Samples
There are two options to ensure optimal analysis with samples
that have not performed well. A sample may be reprocessed in an
attempt to improve the results, or the sample can be excluded before
reclustering. An initial assessment of DNA sample quality, as described
here, can indicate which samples, if any, should be reprocessed.
This procedure should be used after the Infinium Genotyping assay iscomplete and all BeadChips are imaged in a project.
1.

Open a new project in the GenomeStudio GT Module.

2.

Load your data from a sample sheet or from LIMS using the
SNP manifest file (*.bpm) and standard cluster file (*.egt) that
correspond to the Infinium product used. For instructions on
loading data from a sample sheet, refer to the GenomeStudio GT
Module User Guide (part # 11319113).

3.

After the GenomeStudio tables are populated with data, click the
Calculate button ( ) above the Samples Table. This updates
the average call rate in the Call Rate column for each sample
across all loci. Average call rate expectations for standard human
products are typically greater than 99%. Refer to the productliterature for specific call rate specifications.

4.

To identify problematic samples, create scatter plots of call rate
as a function of sample number (Figure 1), and 10% GenCall(10% GC) score as a function of sample call rate (Figure 2). To

Technical Note: Genotyping
Figure 1: Scatter Plot of Call Rates Across a
Set of Samples

Figure 3: Example of a Successfully
Reclustered SNP

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Call Rate

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Viewing the call rate of all samples quickly reveals poorly performing
samples with abnormally low call rates (green circle).

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Figure 2: Scatter Plot of 10% GC Score
Compared to Call Rates of a Sample Set

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Call Rate0

Poorly performing samples are obvious outliers from most of the samples
when 10% GC Score is plotted against sample call rate (green circle).

create graphs, use the graphing functions in GenomeStudio, or
export the Sample Table data and reopen it in another graphing
program.
Poorly 0.74
performing0.75
samples canbe identified
as having
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low sample call rates and low 10% GC scores. Samples withlow call rates and 10% GC scores that are outliers from the main
population should be considered for reprocessing.
However, if there are samples expected to have large amounts ofloss of heterozygosity (LOH) or copy number variation (CNV), such as
tumor or WGA samples, there may be outliers in call rate and 10%
GC. This may be due to biological reasons and not because of poor
DNA quality. Therefore, these samples are not good candidates
for reprocessing.
In human projects, if call rates below 99% are observed acrossmost samples, the measured sample intensities may not match theintensities from the standard cluster file provided by Illumina. To test forthis possibility, recluster SNPs on good samples only and recalculate
the average call rate. Save this new preliminary cluster file with ameaningful name (e.g., Preliminary_Hap550_Project Name.egt). If thesample call rate is still low after reclustering samples with the highest
call rates and 10% GC scores, there could be a systematic nonbiological issue affecting data quality.

51
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Norm Theta

Three distinct clusters exist but are poorly represented by the standard
cluster file. This leads to an initial SNP call frequency of 39.6% for this locus(before). After reclustering, the cluster positions better represent the data
and yield a call frequency of 99.7% (after).

Determining Final Sample Set
After poorly performing samples have been reprocessed, the new
requeued samples should be screened in GenomeStudio for the best
performing instance of each sample. The higher-quality instance ofeach sample (across all SNPs) should be retained for analysis. If bothinstances of a sample perform poorly, the entire sample should be
excluded from the project before generating final reports. A procedurefor including only samples that perform best follows:1)

Load reprocessed DNA samples into the existing GenomeStudio
project by either selecting File | Update Project from LIMS or File |Load Additional Samples.

2)

Select the appropriate sample sheet and data repository.

3)

After all additional samples have been included, click the 		Calculate button.

4)

Select Analysis | Exclude Samples by Best Run. This
automatically excludes the lower quality requeued samples
throughout the entire project.

Technical Note: Genotyping
5)

To determine whether any additional samples should be excluded
from the project, reevaluate samples as described in theprevious section.

6)

When the GenomeStudio project contains only final and uniquesamples, update the SNP statistics.

Locus Analysis and Reclustering
Standard cluster files provided with Infinium products identify expected
intensity levels of genotype classes for each SNP. For human projects,
comparing sample intensities to this cluster file is sufficient for
generating high-quality data. Standard cluster files for standard humanInfinium products are created using a diverse set of over 100 samples
from the Caucasian (CEU), Asian (CHB+JPT), and Yoruban (YRI)
HapMap populations. Therefore, much of the genetic diversity in these
populations is incorporated. Use this standard file when call rates arehigh and the genetic diversity of a sample set is well represented by
the standard cluster file.

BeforeSpecial consideration must be given to mitochondrial (mtDNA) and Y
chromosome (Ychr) SNPs. The GenomeStudio clustering algorithms
do not automatically accommodate loci that lack heterozygous
clusters. The mtDNA (Figures 5 and 6) and Ychr SNPs (Figures 7
and 8) must be manually edited following reclustering. Before
reclustering all SNPs, evaluate the mtDNA and Ychr SNPs to decide
whether the predefined Illumina standard cluster positions areappropriate for the current project samples. If appropriate, exclude
these loci from reclustering.

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All or a subset of loci can be reclustered to generate a custom cluster
file. To recluster all SNPs in the GenomeStudio project select Analysis| Cluster All SNPs. To recluster a subset of SNPs, highlight relevantrows of the SNP Table, rightclick the SNP Table, and select Cluster
Selected SNPs. An alternative method is to use filters to identify
and select SNPs that should be excluded from reclustering. Consult
the GenomeStudio Framework User Guide (part # 11318815) for
information about how to create filters for various metrics in the Full
Data Table, SNP Table, or Samples Table. Generating a custom cluster
file will require a thorough evaluation of newly defined SNP cluster
positions to ensure the accuracy of the genotype data. See Evaluatingand Editing SNP Cluster Positions later in this document.
An important consideration in the decision to recluster is that the
GenomeStudio clustering algorithms require approximately 100
samples to produce cluster positions that are representative of
the overlying population. Therefore, in projects with fewer than
100 different samples, it is best to use the standard cluster file forcalling genotypes.

Figure 4: Example of an Unsuccessfully
Reclustered SNP

0However, in some situations, sample intensities might not overlay
perfectly onto the standard cluster positions. Reclustering some or
all SNPs can optimize the GenomeStudio software’s ability to callgenotypes and may result in higher overall call rates (Figure 3). In somecases, reclustering is not helpful and the locus must be zeroed(Figure 4). Reclustering using samples from a particular project will
redefine cluster positions, making them more representative of thesamples in the project.

1

Norm ThetaAt this locus, project samples form diffuse clusters and do not correlate
well with the standard cluster positions (before). The genotype calls areunreliable because of poor cluster separation. Reclustering on this locus
does not improve data quality. If not zeroed automatically by the algorithm(as shown), this locus should be zeroed based on the low score for cluster
separation.

Evaluating and Editing SNP Cluster Positions
To identify loci that should be manually edited or zeroed due topoor performance (< 1%), evaluate newly reclustered SNPs using
the metrics listed in the SNP Table. These metrics are based on
all samples for each locus, and thus provide overall performance
information for each locus. To find loci that may require editing or
removal before generating final reports, sequentially sorting the SNP
Table by columns and screening loci by each quality metric identified
in Table 1 is recommended. To determine hard cutoffs and grey zones,
sort data by one column at a time and explore values starting at theextremes of the ranges. A hard cutoff, defined as the level above (orbelow) which most of the loci are unsuccessful, can be applied andthe loci zeroed when necessary. To improve analysis further, the greyzone, defined to contain loci that are 80–90% successful, can be
manually adjusted. The upper (or lower) limit of the grey zone is thepoint at which all loci are successful. SNPs falling in the grey zone
should be visually evaluated and either accepted, zeroed, or manuallyedited by moving cluster positions. Hard cutoffs and grey zones may
not transfer between projects because they are highly dependent on

Technical Note: Genotyping
Figure 5: A Successfully Edited
Mitochondrial SNP

sample quality. Additional metrics may be required with each new
project and should be determined on an individual basis.

Before

The choice to edit loci manually should be carefully considered. Anychanges made should be consistent with principles of population
genetics and prevent introduction of subjective bias into the data set.
Additionally, visually inspecting cluster plots and manually editing locican be very time consuming. Increasing the number of SNPs used
for data generation is certainly beneficial, but the process shouldbe evaluated for diminishing returns. For example, with projects
requiring large numbers of SNPs such as genome-wide association
studies (GWAS), applying hard cutoffs and zeroed SNPs may be fully
adequate, excluding only 1–3% of the markers in the panel while
significantly increasing data quality.

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After

The following procedure describes a method for evaluating the qualityof newly created cluster positions by sequentially sorting the SNP
Table by various column metrics. For each step, it may be helpful todetermine and record hard cutoff and grey zone thresholds.

5

1.

For projects with 100 samples or more, sort the SNP Table byCluster Sep. Cluster Sep measures the separation between
the three genotype clusters in the theta dimension and varies
from 0–1. Evaluate individual SNPs for overlapping clusters,
starting with those having low Cluster Sep. If clusters are well
separated, the SNP can be manually edited (Figure 9). SNPs withoverlapping clusters should be zeroed (Figure 10).

2.

Sort the SNP Table by call frequency (Call_Freq). Call_Freq
is the proportion of all samples at each locus with call scores
above the nocall threshold. The value varies from 0–1. Evaluate
SNPs starting with those having low Call_Freq values. Zero the
SNP if the low call frequency cannot be attributed to a potential
biological effect (Figures 11 and 12).

3.

Sort the SNP Table by AB R Mean, the mean normalized
intensity (R) of the heterozygote cluster. This metric helps identify
SNPs with low intensity data and has values increasing from 0.
Evaluate SNPs from low to high AB R Mean and zero any SNPs
where the intensities are too low for genotypes to be called
reliably (Figure 13).

4.

Sort the SNP Table by AB T Mean, the mean of the normalized
theta values of the heterozygote cluster. This value ranges
from 0–1. Evaluate SNPs with AB T Mean ranging from 0–0.2
and 1–0.8 (or more, if necessary) to identify SNPs where theheterozygote cluster has shifted toward the homozygotes.
Edit the SNP if clusters can be reliably separated (Figure 14).
Otherwise, zero the locus.

5.

To identify errors in Mendelian inheritance (MI), sort the SNP
Table by columns PPC Errors and PC Errors. Both columns
measure deviations from expected allelic inheritance patterns inmatched parent and child samples. Values range from zero to
two times the maximum number of trios included in the project.
In the GenomeStudio SNP Graph, MI errors are displayed as
circles (o) for parents and a cross (×) for a child. Sort by thiscolumn and evaluate all SNPs with one or more errors. Zero
SNPs with both MI errors and ambiguous clusters (Figure 15).
SNPs associated with copy number polymorphisms may exhibit
PPC and PC errors that are biologically meaningful. Therefore,

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Figure 6: An Unsuccessful Mitochondrial SNP

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The clustering is ambiguous and genotypes are unreliable. This SNP shouldbe zeroed.

Technical Note: Genotyping
Figure 7: A Successfully Edited
Y-Chromosome SNPloci with errors should be zeroed only if the clustering is also
ambiguous (Figure 16).

The SNP Table metric, Het Excess, is an indicator of the quantityof excess heterozygote calls relative to expectations based on
HardyWeinberg Equilibrium. This metric varies from 1 (complete
deficiency of heterozygotes) to 1 (100% heterozygotes). Evaluate
SNPs with Het Excess values less than 0.3 or greater than 0.2(or more, if necessary). If clusters are unambiguous (Figure 19),
edit the SNP and retain it for the final report. Otherwise, the SNPshould be zeroed (Figure 20). Because males are not expected
to be heterozygous for X chromosome loci, the Het Excess
metric might not be relevant for Xlinked SNPs. To evaluate X
chromosome SNPs, see step 9.

8.

9.

The SNP Table column, Minor_Freq, measures the SNP minor
allele frequency and can help identify loci where homozygotes
have been incorrectly identified. Minor_Freq values vary from 0–1
and all SNPs with Minor_Freq less than 0.1 should be evaluatedto detect false homozygotes. A SNP can be edited unless clusters
overlap or cannot be separated unambiguously (Figure 21).
Because males are not expected to be heterozygous for any
X-linked markers, these loci should be evaluated while taking
gender into account. In GenomeStudio, the gender of eachsample is estimated using X chromosome SNPs (Gender Est
column in the Samples Table). Males can be selected in the
Samples Table and marked with a different color on the SNPGraph to evaluate SNPs on the X chromosome. If males arecalled heterozygote at X chromosome SNPs, clusters may need
to be manually adjusted so that they are called homozygote(Figure 22). However, if clustering is ambiguous, the SNP shouldbe zeroed (Figure 23).

To watch a pre-recorded webinar featuring cluster evaluation,
reclustering, and cluster editing, visit the Support Webinars list at
icom.illumina.com/Webinar/Index. Select View Recorded Webinars,
and choose the Infinium Exome Webinar.

Final Report
After completing the suggested data analysis and editing SNP
clusters, calculate sample statistics by clicking the Calculator buttonin the Samples Table toolbar. If necessary, also update sample
reproducibility and heritability statistics by selecting Analysis | Update
Heritability/Reproducibility Errors. The GenomeStudio project is now
final. Save changes using File | Save Project Copy As. The newly
created cluster file (*.egt) can be exported from the GenomeStudio
project by selecting File | Export Cluster Positions.
The Final Report Wizard lets you export the genotyping data from
GenomeStudio for use in downstream analysis applications. To run
the Final Report Wizard, select Analysis | Reports | Report Wizard andBefore0.80

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7.

The Rep Errors column of the SNP Table measures the
reproducibility of genotype calls for replicate samples at each
SNP. A range from 0 to the maximum number of replicates is
included in the project. Sort by this column and evaluate all SNPs
with one or more errors (Figures 17 and 18). Reproducibility
errors are displayed as squares in the GenomeStudio
SNP Graph.

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Female samples have very low intensities, a broad range of theta values,
and are incorrectly called AB after reclustering (before). After manually
adjusting the cluster positions, female samples are not called, and the two
homozygous clusters correctly define male genotypes (after).

Figure 8: An Unsuccessful
Y Chromosome SNP

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The clustering is ambiguous and the genotypes are unreliable. This SNP
should be zeroed.

Technical Note: Genotyping
Table 1: GenomeStudio Clustering Algorithm Metrics
Single-Variable Metrics
Variable

Hard Cut Off

Grey Zone

Call_Freq*< 0.97≤ 0.99Rep Errors>2>0

P-P-C Errors>2>0

Cluster Sep≤ 0.3< 0.45

AA R Mean

≤ 0.2

< 0.4

AB R Mean

≤ 0.2

< 0.4

BB R Mean

≤ 0.2

< 0.4

–

≤ 0.3

10%_GC_Score

Notes
User-definedSome good quality SNPs identified in this range

Hex Excess

–

> 0.2

Hardy Weinberg equilibrium

A/B_Freq–≥ 0.4

Hardy Weinberg equilibrium

AB T Mean–(< 0.2) or (> 0.8)

Identifies nearby polymorphisms

Multi-Variable Metrics
Filter Condition

Variable

Hard Cut Off

Grey Zone

A/A_Freq = 1

AA T Mean

> 0.3

> 0.2

AA homozygote clustering far from axis

A/A_Freq = 1

AA T Dev> 0.06> 0.04

Wide AA homozygote clustering

B/B_Freq = 1

BB T Mean

< 0.7

< 0.8

BB homozygote clustering far from axis

B/B_Freq = 1

BB T Dev> 0.06> 0.04

Wide BB homozygote clustering

A/A_ or B/B_Freq = 0

AB T Dev–> 0.05

Identifies possible nearby polymorphisms

A/B_Freq = 0

Minor_Freq (MAF)†–>00.998 > Call_Freq > 0.990

Minor_Freq (MAF)–< 0.05

Notes

Identifies heterozygote clusters incorrectly classified
as homozygote
Low MAF clusters identified* Y chromosome markers should be excluded from call rate metric before analysis.
†

X chromosome, Y chromosome, and mtDNA SNPs should be excluded from the Minor_Freq metric before analysis.

To increase data quality, various quality metrics defined in the GenomeStudio clustering algorithm are considered. Loci are either zeroed automatically (Hard Cutoff)
or visually inspected for accuracy (Grey Zone). These metrics allow evaluation of cluster width, deviation, and intensity, helping to identify errors in cluster assignment.
Metric definitions can be found in the appendix at the end of this document. Note: The metric values in this table are for human studies. Non-human studies may
require different cut-off values.

Technical Note: Genotyping
Figure 9: Example of a Successful SNP Locus

Figure 11: A Successful Locus with Low
Call Frequency
Before

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Clusters are sufficiently separated so that clustering is not ambiguous.
Manually editing this locus can increase the call frequency.

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Figure 10: Example of an Unsuccessful Locus
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AB and BB clusters overlap and genotyping cannot be considered reliable.
This SNP should be zeroed.

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Although this SNP has a call frequency that falls below expectations, it
has a phenotype that may indicate the presence of a deletion or a third
polymorphic allele. There are two AA clusters (possibly AA and A/), an ABcluster, and two BB clusters (possibly BB and B/). In this case, samples
that are not called (the black samples along the X-axis) may represent a bio
logically meaningful failure of those samples (e.g., /). Illumina recommends
nocalling the A/ and B/ clusters.

Technical Note: Genotyping
Figure 12: An Unsuccessful Locus with Low
Call Frequency

Figure 15: An Unsuccessful Locus withMI Errors
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AB and BB clusters are not distinct and genotyping cannot be considered
reliable. This SNP should be zeroed.

This locus exhibits six MI errors and ambiguous clusters (left BB cluster
could be AB). This locus should be zeroed.

Figure 16: Example of a SNP With
Erroneous Trios

Figure 13: SNP with Low Intensities

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Intensities (Norm R) are too low for genotypes to be reliably called. This
SNP should be zeroed.

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Clustering is not ambiguous and the locus has a phenotype that may
indicate the presence of a chromosomal deletion or third polymorphic allele.
This locus might be of special interest.

Figure 17: A Successful Locus with a
Reproducibility Error

Figure 14: A Successful SNP Locus With
High AB T Mean Value

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This locus can be manually edited to include more AA samples to improvecall frequency. There is no ambiguity in clusters at this locus.

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There is one erroneous genotype (the AA replicate should be AB, and could
be manually no-called) and the clustering is not ambiguous at this locus.

Technical Note: Genotyping
Figure 18: A Failed Locus with Reproducibility
ErrorsFigure 21: Example of a SNP with
Minor_Freq of 10

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AB and BB clusters overlap. Genotypes cannot be called reliably so thislocus should be zeroed.

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This SNP has been incorrectly clustered with a Minor_Freq of 0. In this case,clusters are too close together for genotypes to be called and the locus
should be zeroed.

Figure 19: Example of a SNP with HighHeterozygote Excess

Figure 22: Example of X Chromosome SNP
that Can Be Successfully Edited

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1

1.20
1
0.80
0.60
0.40
0.20
0

Norm Theta-0.20

Clusters can be separated and are not ambiguous. If the algorithm
minimum cluster separation parameters allow it, this locus can be manuallyedited to call genotypes correctly.

146
0

828
0.20

0.40

1036
0.60

0.80

1

Norm Theta

The clustering algorithm called males AB at this X chromosome SNP. The
males (light blue) that are called heterozygote should be manually edited to
be included in the AA cluster.

Figure 20: Example of a SNP with
Heterozygote Deficiency

Figure 23: Example of an Unsuccessful
X Locus

1
0.60
0.50

0.60

Norm R

Norm R

0.80

0.40
0.20

0.40
0.30
0.20
0.10

0

1
0

0
0.20

0.40

2159
0.60

0.80

1

Norm Theta

The clustering is ambiguous (left BB cluster could be AB). This locus shouldbe zeroed.

0
-0.10

153
0

260
0.20

0.40

0.60

1530
0.80

1

Norm Theta

Male AB and BB clusters (light blue) overlap and genotyping cannot be
considered reliable.

Technical Note: Genotyping
follow the onscreen instructions. Filter the project data for excluded
samples and nonzeroed SNPs, group, or other attributes. The Final
Report Wizard also allows you to include SNP annotation and choose
from among various formats to accommodate most genotyping
analysis packages.

Summary
By identifying problematic samples and loci in a systematic manner,a project data set can be analyzed comprehensively. This takes fulladvantage of the robustness of Illumina Genotyping BeadChips ingenotyping experiments. The sample workflow described in thisdocument is a method recommended by Illumina to optimize final
data quality. By editing loci that are not clustered or called correctly,
collected data can be fully utilized. When editing is not possible forunsuccessful loci or samples, excluding them from the data set
ensures that the remaining data are of the highest quality.

Appendix: DNA Report Column Definitions#No_Calls: The total number of genotypes in each sample witha GenCall score below the nocall threshold as defined in the
project options. Genotypes that are not called are shown on the
GenomeStudio SNP Graph as black points falling outside of the darkly
shaded regions.
#Calls: The total number of genotypes in each sample with a GenCall
score above the nocall threshold.
Call_Freq: Call_Freq is equal to #Calls / (#No_Calls + #Calls). Call_Freq
is equivalent to Call Rate in the GenomeStudio Samples Table.
A/A_Freq: For each sample, the number of AA genotype calls divided
by #Calls.
A/B_Freq: For each sample, the number of AB genotype calls divided
by #Calls.
B/B_Freq: For each sample, the number of BB genotype calls divided
by #Calls.
R: The normalized R-value of a SNP in a specific sample.
AA R Mean: Mean of the normalized R-values for the AA genotypes.
AB R Mean: Mean of the normalized R-values for the AB genotypes.

GenCall Score: This score is a quality metric that indicates thereliability of the genotypes called. A GenCall score value is calculated
for every genotype and can range from 0.0 to 1.0. GenCall scores
are calculated using information from the sample clustering algorithm.
Each SNP is evaluated based on the angle of the clusters, dispersionof the clusters, overlap between clusters, and intensity. Genotypes
with lower GenCall scores are located furthest from the center of acluster and have lower reliability. There is no global interpretation of a
GenCall score as it depends on the clustering of samples at each SNP.
Clustering is affected by many different variables, including the qualityof the samples and loci.
50%_GC_Score: For each sample, this represents the 50th percentileof the distribution of GenCall scores across all called genotypes. For
SNPs across all samples, this is referred to as the 50%_GC_Score.
For samples across all loci, it is referred to as p50GC in the Samples
Table.
10%_GC_Score: For each sample, this represents the 10th percentileof the distribution of GenCall scores across all called genotypes. For
SNPs across all samples, this is referred to as the 10%_GC_Score.
For samples across all loci, it is referred to as p10GC in the Samples
Table. Note: Call frequency, 50% GenCall score, and 10% GenCall
score are useful metrics for evaluating the quality and performance ofDNA samples in an experiment.
0/1: GenomeStudio calculates a threshold from the distribution of
10%_GC_Score values across all samples in the DNA report. A ‘1’
is assigned to samples whose 10%_GC_Score is at or above thisthreshold. A ‘0’ is assigned to samples whose 10%_GC_Score is
below this threshold. The equation defining this threshold is 0.85 • 90th
percentile of 10%_GC_Score values for all samples in DNA Report(i.e., 0.85 • 90th percentile of column K in the DNA report).

Additional Information
Learn more about GenomeStudio and other analysis software
products from Illumina at www.illumina.com/software/genomestudio_
software.ilmn.

References
1.

Performing Clustering in the GenomeStudio Polyploid Clustering Module,
supportres.illumina.com/documents/downloads/software/genomestudio/
genomestudio_polyploid_clustering_15044760_a.pdf.

BB R Mean: Mean of the normalized R-values for the BB genotypes.
Minor_Freq: If the number of AA calls is less than the number of BB
calls for a sample, the frequency for the minor allele A is: [(2 • AA) +AB] divided by [2 • (AA+AB+BB)] across all called loci for that sample.

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Pub. No. 970-2007-005 Current as of 31 January 2014