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Genotyping Console™ Software

Genotyping Console™ (GTC) Software makes primary data analysis easy with simple drop-down menus and visualization tools that help you get to your data quickly and dig deeper for quality checking and biological meaning.

GTC Software integrates single nucleotide polymorphism (SNP), copy number polymorphism (CNP) genotyping, rare copy number variation (CNV) identification, and cytogenetic analyses into one application. It generates genotyping calls, copy number calls for CNV regions and individual probe sets, loss of heterozygosity (LOH) data, cluster graphs (Figure 1), and quality control metrics.

Download Information and Instructions

Download the latest version of Genotyping Console - version 4.0 ( 64-bit | 32-bit )

Figure 1: Graphical representation of sample and processing information in the cluster plot
Figure 1
Figure 1: Graphical representation of sample and processing information in the cluster plot
Figure 2: Flexible SNP filtering capabilities
Figure 2
Figure 2: Flexible SNP filtering capabilities

Data are displayed in tabular and graphic formats, allowing you to easily share your results with others. Flexible SNP filtering (Figure 2) and export tools are included to enable downstream statistical analysis.

GTC Software version 4.0 enables you to:

Figure 3: Heat map view of a CNP region
Figure 3
Figure 3: Heat map view of a CNP region
  • Perform SNP genotyping analysis on data generated from Axiom™ Genome-Wide Array Plates, the Affymetrix® Genome-Wide Human SNP Array 5.0 and SNP Array 6.0, and GeneChip® Human Mapping 100K and 500K Array Sets
  • Call genotypes and cluster data
  • Calculate copy number state calls for use in statistical association analyses and high-resolution CNP mapping (Figure 3)
  • Quickly identify features of interest in your data set with scatter plots, line graphs, and heat map graphs
  • Seamlessly integrate with third-party software packages
  • Analyze data from a range of Affymetrix® Arrays while providing data analysis continuity
  • New in GTC Software version 4.0:

    • Use on 32- and 64-bit Windows operating systems
    • Export forward strand allele calls
    • Graphically represent sample and processing information in the cluster graph for quick data review and troubleshooting
    • Improve your data comparison workflow by merging genotype results from multiple arrays within an array set into a single export file
    • Easily export data in a PLINK-compatible format

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    For research use only. Not for use in diagnostic procedures.

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SNP Genotyping Algorithms

Genotyping Console™ Software uses the following algorithms:

  • Axiom™ GT1 genotyping algorithm for Axiom Genome-Wide Human Array Plates
  • Robust Linear Model with Mahalanobis Distance Classifier1 (RLMM) with a Bayesian step2 (BRLMM) algorithm for GeneChip® Human Mapping 100K and 500K Array Sets
  • BRLMM using perfect-match probes (BRLMM-P) algorithm for the Affymetrix® Genome-Wide SNP Array 5.0
  • Birdseed3 v1 or v2 algorithm for the Affymetrix® Genome-Wide SNP Array 6.0

The Axiom GT1 algorithm

Based on the BRLMM-P algorithm, the new Axiom GT1 algorithm incorporates a multi-channel processing method as well as substantial improvements and features for pre-processing and genotype calling. These improvements provide high performance on the ligation assay-based genotyping platform and allow for flexible adaption to future array plate genotyping products.

Preprocessing is improved by an artifact reduction layer which reduces the impact of spatially localized artifacts on genotyping performance. Many of the genotype clustering and calling improvements are leveraged from those developed for the DMET™ Plus Assay and are described in the white paper, Single-sample analysis methodology for the DMET™ Plus Product4. Clusters are now represented as two-dimensional Gaussians and resistance to non-Gaussian cluster behavior has been improved. As usual, training data has been used to generate SNP-specific models that represent the cluster properties learned for each marker. Unlike the DMET Plus Assay, which is designed to call in a single-sample mode without adapting to the data, the Axiom GT1 default behavior uses dynamic clustering to adapt the clusters to the observed data. Although a single sample can be run by itself, more samples allow the algorithm to learn more from the training data.

The Birdseed Algorithm

The Birdseed v2 algorithm, developed by researchers at the Broad Institute of Harvard and MIT, is an evolution of the RLMM genotyping algorithm. Specifically designed for the Genome-Wide Human SNP Array 6.0, Birdseed v2 performs a multiple-chip analysis to estimate signal intensity for each allele of each SNP, fitting probe-specific effects to increase precision (similar to the BRLMM-P algorithm developed for the SNP Array 5.0). It then makes genotype calls by fitting a Gaussian mixture model in the two-dimensional A-signal vs. B-signal space, using SNP-specific models to improve accuracy.

CNP Genotyping Algorithm

The copy number variation results table provides copy number polymorphism states and confidence values.
Figure 1
The copy number variation results table provides copy number polymorphism states and confidence values.

Developed in collaboration with the Broad Institute of Harvard and MIT, the Canary algorithm5 calculates CNP copy number state calls for approximately 1,100 regions with known copy number variation at a frequency greater than 1 percent. These CNP calls can then be exported and treated like a SNP genotype (AA, AB, or BB) for statistical association analysis. Genotyping Console Software provides a confidence metric for each CNP call to help you perform quality control on your data (Figure 1).

Enhanced GC Waviness Correction Algorithm

Variation in chromosomal GC content is an intrinsic property of the human genome and is known to induce sample-specific variability correlated to the percentage of local chromosomal GC content. It is a well-known, systematic issue that occurs on multiple platforms. It creates variability in signal intensity that may increase false positives for copy number predictions of duplications and deletions.

Autosomes of one sample without and with GC correction.
Figure 2
Autosomes of one sample without and with GC correction

The enhanced GC waviness correction algorithm6, based on a smoothing method, reduces the waviness patterns of signal intensities due to sample-specific, genome-wide GC distribution and thereby reduces false positive results within a sample. This correction provides better data quality for copy number analysis and, more importantly, improves the accuracy of CNV detection, leading to more precise copy number segmentation (Figure 2).

References

  1. Rabbee, N., et al. A genotype calling algorithm for Affymetrix SNP arrays. Bioinformatics 22:7-12 (2006).
  2. Affymetrix, Inc. BRLMM: An Improved Genotype Calling Method for the Mapping 500K Array Set (2006).
  3. Korn, J., et al. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nature Genetics 40(10):1253-60 (2008).
  4. Affymetrix, Inc. Single-sample analysis methodology for the DMET™ Plus Product (2009).
  5. Affymetrix, Inc. Affymetrix® Canary Algorithm Version 1.0 (2008).
  6. Affymetrix, Inc. Copy Number Algorithm with Built-in GC Waviness Correction in Genotyping Console™ Software (2009).

For research use only. Not for use in diagnostic procedures.

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SNP Cluster Graph

Figure 1: Graphical representation of sample and processing information in the cluster plot
Figure 1
Figure 1: Graphical representation of sample and processing information in the cluster plot

Following QC filtering, you can use the SNP filter to create a customized list containing SNPs of interest for further investigation. This allows for quick visualization and review of genotype clusters and genotyping results. Using the flexible display options, by color and shape independently, you can further analyze your genotyping results to find patterns in your data (Figure 1).

Genotyping Console Browser

igure 3: Chromosome view of two DiGeorge Syndrome samples compared to reference
Figure 3
igure 3: Chromosome view of two DiGeorge Syndrome samples compared to reference
Figure 2: Whole-genome karyoview of one sample, with gains and losses of segments in blue and red triangles, respectively
Figure 2
Figure 2: Whole-genome karyoview of one sample, with gains and losses of segments in blue and red triangles, respectively

The GTC Browser supports copy number and loss of heterozygosity (LOH) analyses* for the GeneChip® Mapping 100K and 500K Array Sets and the Affymetrix® Genome-Wide SNP Array 6.0. Chromosomal aberrations and LOH results are represented graphically in a genome-wide karyoview and individual chromosome views.

Karyoview displays the entire genome visually, with chromosomal ideograms arranged vertically. Within this view, cytobands on each ideogram are represented and annotated. Gains and losses are represented by blue and red triangles, respectively (Figure 2).

The chromosome view feature displays an individually selected chromosome along with the accompanying data graphs and annotation tracks. Direct links to external public databases, such as the Database of Genomic Variants, are available (Figure 3).

*For data analysis options for the Affymetrix® Cytogenetics Research Solution, please click here.

Heat Map Viewer

Figure 4: Heat map view of a CNP region with three distinct CNP allele calls
Figure 4
Figure 4: Heat map view of a CNP region with three distinct CNP allele calls

The heat map viewer displays copy number intensity values from each probe set, enabling you to quality control your data and to discover de novo copy number variations (CNVs). As many as 450 samples can be simultaneously displayed for visual inspection. You can zoom and navigate to different parts of the chromosomes and dynamically sort the samples by intensity value or CNV call (if available).

Using the Broad Institute map containing 1,100 known copy number polymorphism (CNP) regions, you can compare copy number calls for each CNP region across samples and survey large quantities of genomic data to detect de novo CNVs. You can also load custom maps into the viewer to compare copy number intensity values across samples and identify novel CNVs.

From the heat map viewer, you can link directly to external databases, including the UCSC Genome Browser, Ensembl, and the Database of Genomic Variants.

For research use only. Not for use in diagnostic procedures.

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