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Affymetrix^® Genotyping Console^™ Software

Genotyping Console™ (GTC) is the first commercially available software that integrates SNP genotyping, copy number polymorphism (CNP) genotyping and rare CNV identification in one data analysis application. The latest version of Genotyping Console provides proven tools for genotyping and cytogenetic analysis alongside cutting-edge tools for CNP genotyping. GTC makes analysis easy with simple drop-down menus and visualization tools that let you get to your data fast and dig deeper for quality checking and biological meaning.

Download Information and Instructions

Download the latest Genotyping Console - version 3.0.1

New! Copy Number and LOH Analysis with Built-in Enhanced GC Waviness Correction

Genotyping Console now includes algorithms and visualization tools for copy number and LOH analysis and CNP analysis. CNP calls can be exported, just like SNP calls, and analyzed for statistical association (Figure 1).

Figure 1

Figure 2

GTC also embedded an enhanced GC waviness correction algorithm. This correction algorithm specifically handles the sample-specific wavy artifact of the human genome due to genome-wide GC-rich regions. As a result, it provides better data quality for copy number analysis and LOH analysis, and more importantly, improves the accuracy of CNV detection, leading to more precise copy number segmentation (Figure 2).

GTC also gives you unique tools for cytogenetic analysis. From the GTC Browser, Figure 2 illustrates two regions of uniparental disomy, a type of abnormality common to Prader-Willi and Angelman Syndrome.

Genotyping Analysis

Figure 4

Figure 3

GTC 3.0.1 features a new CNP genotyping algorithm (Canary) and the first high-resolution CNP map of approximately 1,100 regions of known copy number variation, both developed by the Broad Institute.

In addition, GTC streamlines whole-genome genotyping analysis with a built-in quality control metric, Contrast Quality Control (CQC) that predicts genotyping performance. Version 3.0.1 implements robust genotyping algorithms—including Birdseed v2 for Affymetrix® Genome-Wide Human SNP Array 6.0, BRLMM-P for the SNP Array 5.0 and BRLMM for the GeneChip® 500K Array Set—as well as cluster visualization (Figure 3) and flexible SNP filtering capabilities (Figure 4). With Genotyping Console, you can generate per-SNP cluster graphs as well as pdf reports for SNP genotyping analysis.

These analysis tools enable you to truly bridge CNP genotyping and classical SNP genotyping analysis in one genome-wide association study on the SNP Array 6.0 platform.

Related Products

CNP Genotyping Algorithms (Canary)

Figure 1

Figure 2

Genotyping Console™ 3.0.1 implements a new CNP genotyping algorithm, called Canary, which was developed in collaboration with the Broad Institute of Harvard and MIT. Canary provides 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 provides a confidence metric for each CNP call (Figure 2).

Enhanced GC Waviness Correction Algorithm

GC wave is an intrinsic property of the human genome. This known genome-wide inheritance wavy artifact is sample specific and highly correlates with the percentage of GC content within a sample. It is a well-known systematic issue which is platform independent. It creates variability in signal intensity and may increase false positives for copy number predictions of both duplications and deletions.

Figure 3

The enhanced GC waviness correction algorithm in GTC 3.0.1, 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 3).

Birdseed v2 Algorithm

Developed by researchers at the Broad Institute of Harvard and MIT, the Birdseed v2 algorithm is an evolution of the RLMM genotyping algorithm. It performs a multiple-chip analysis to estimate signal intensity for each allele of each SNP, fitting probe-specific effects to increase precision (like 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.

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.

Heat Map Viewer

Figure 1

The new heat map functionality enables the generation of population-specific custom CNV maps. It allows you to compare copy number results across samples using intensity values from each probe set within a particular CNV region and to survey large quantities of genomic data to detect de novo CNV regions. The heat map viewer displays copy number intensity values and copy number state calls for each CNP region.

As many as 450 samples can be simultaneously displayed for visual inspection using the heat map feature, which also allows you to zoom and navigate to different parts of the chromosomes and dynamically sort the samples by intensity value or CNP call.

You can also load custom maps into the heat map for comparison against copy number data, or use the viewer to investigate de novo CNV regions outside of the 1,100 known CNP regions defined in the Broad CNP map. GTC 3.0.1 also provides easy links to external databases, including the UCSC genome browser, Emsembl and the Database of Genomic Variants.

Genotyping Console Browser

The GTC Browser is designed to support copy number and loss of heterozygosity (LOH) analysis. The visualization of chromosomal aberrations and LOH is facilitated by the analysis algorithms within GTC 3.0.1, which provide graphical results in a genome-wide karyoview and individual chromosome views.

Figure 3

Figure 2

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).

Chromosome view displays an individually selected chromosome along with the accompanying data graphs and annotation tracks. It also has the ability to link to external public databases (Figure 3).