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The SNP Analysis Package offers a powerful and straightforward way of testing for genotypic association against either cases vs. controls or quantitative traits using one or more statistical measures under any one of several genetic model assumptions. These tests can be run individually or simultaneously while also correcting for stratification and applying multiple testing corrections (including permutation testing).
Supported genetic models include basic allele tests, genotypic tests, and the additive, dominant and recessive genetic models. Test statistics include the correlation/trend test, the Armitage trend test (including the exact Armitage trend test), Pearson's chi-squared test, Fisher's exact test, odds ratio with confidence intervals, analysis of deviance (ANODEV), the F-test and linear or logistic regression.
Interactively explore linkage disequilibrium (LD) and haplotypes in an innovative and powerful interface. You can view LD plots from one or more populations and explore them side-by-side with association results. For haplotype analysis it is easy to define and modify haplotype blocks from an LD plot or spreadsheet, compute haplotype and diplotype frequency tables, and perform a number of haplotype association tests, including per-block and per-haplotype methods. Read more about LD & Haplotype Analysis »
The SNP Package of SVS 7 incorporates advanced regression technologies that enable you to perform linear and logistic regression, stepwise regression (both backward elimination and forward selection), and permutation tests with numeric variables and recoded genotypes. You can use a moving window along with numeric or categorical covariates, against a single dependent variable. Regressions may either be performed with all variables and covariates together ("full model") or with some of the covariates grouped into a "reduced model" (yielding a full-vs-reduced model p-value).
SVS comes with an array of resources for learning and utilizing the software to get the most out of your data including tutorials, add-on scripts, example data and projects, and much more.
Mixed linear model analysis in SVS is a powerful utility to not only perform a regression analysis on genotype data while correcting for cryptic relatedness and pedigree structure, it also provides new capabilities to make regression analysis on genotypic data easier. SVS includes: Genomic Best Linear Unbiased Predictors (GBLUP) [Taylor2013] and GWAS mixed linear model analysis in the form of linear regression (fixed effects only), mixed model GWAS using a single locus (EMMAX) [Kang2010], and multi-locus mixed model GWAS (MLMM) [Segura2012]. GWAS mixed linear model analysis uses a kinship matrix to correct for cryptic relatedness as a random effect and can include any additional fixed effects in the model.
ROH analysis is a novel analytic method that first identifies patterned clusters of SNPs demonstrating extended homozygosity (runs of homozygosity or "ROHs") and then employs both genome-wide and regionally-specific statistical tests for association to disease. This approach can identify chromosomal segments that may harbor rare, penetrant recessive loci.
Anticipating association studies with possibly hundreds of millions of markers generated per sample by next generation sequencing, the core architecture of SVS 7 has been completely reinvented to efficiently handle datasets of virtually any size on a desktop computer. Smart memory management and data caching ensures you will experience accelerated performance at every step. Further, SNP data is stored in a remarkably sparse data storage format enabling you to rapidly import large-scale whole-genome data, analyze it with conventional hardware, and efficiently share projects among collaborators. Your entire GWAS study can fit on a single USB Flash drive!
The sheer size and complexity of genome data makes it extremely difficult to work with. SVS 7 eliminates the hassles with real-time spreadsheet manipulation, data editing, and enrichment. Easily combine multiple sample sets and data of different types, from different arrays, or even platforms. Quickly recode genotypes based on a specified genetic model, flip DNA strands, transcode from AB to AGCT formats, and more. Further, an integrated spreadsheet editor facilitates data editing and transformation on a grand scale.
High quality data is critical for quality results. To ensure your data is of the highest quality, the SNP Analysis Package provides the most comprehensive set of conventional and state-of-the-art quality assurance tools to ensure your data is of the highest quality. Here's a sample of what you can do:
The SVS 7 integrated genome browser offers exceptional flexibility in how you visualize SNP data and present results. You can easily compare SNP association results against haplotype, examine linkage disequilibrium, generate cluster plots of allele intensities, create Manhattan plots of whole genome data, and more. The genome browser immediately puts your data in genomic context and enables you to link to online databases for further investigation of a region, gene, or marker. When you finalize the view you want a number of publication quality formats are available, including scalable vector graphics.
The core architecture of SVS has been designed to efficiently handle datasets of virtually any size and type on a desktop computer. SVS natively supports over 70 different file formats and over 40 export formats to streamline data management, ensuring you spend most of your time on the more important aspects of analysis.
Real-time spreadsheet manipulation, data editing, and enrichment help eliminate the hassles of working with large-scale, complex data. Easily combine multiple sample sets and data of different types, from different arrays, or even platforms. Further, an integrated spreadsheet editor facilitates data editing and transformations on a grand scale.
Genomic Build, Marker Map, and Annotation Management
SVS provides a robust set of tools for working with and managing genomic information. Easily switch among a wide-variety of supported species and genomic builds and apply genetic marker maps to ensure all analyses and visualizations are accurate based on correct genomic coordinates. Further, genomic annotations can be used to enrich analyses with visualization alongside data for greater context in the genome browser. SVS provides real-time network access to an expanding list of genomic annotations. You can also use your own custom annotations from private sources or public databases, such as UCSC, RefSeq, and Ensemble.
Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java. SVS gives you fully programmatic access to most SVS functionality via a Python scripting interface enabling you to automate workflows, interoperate with other programs, and develop more robust data management and manipulation routines. Also included is the mature statistical and numeric packages of NumPy and SciPy giving you a broad base of standardized test statistics to add your own methods as well as the 2D plotting library, matplotlib, for generating a near limitless number of publication quality plots and other visualizations. More about Python Scripting in SVS »