Genome-Wide Association Studies
GWAS continues to be an effective method for identifying disease susceptible genes in humans and other organisms. SNP & Variation Suite empowers users to run basic and advanced SNP analyses, incorporating a number of intuitive workflows to lead you beyond single marker associations.
Powerful Genotype Association Testing and Statistics
SVS offers a powerful and straightforward way of testing for genotypic association against either dichotomous 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 allele comparison, genotype comparison, 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.
Meta-Analysis takes the results of two or more GWAS studies for multiple SNPs or markers, and standard meta-analysis statistics are then performed on each SNP and the results compiled into one spreadsheet. SVS can perform meta-analysis on results created within the SVS software or from third-party software programs or a combination of the two. Results for a fixed-effects model, random-effects model and tests for heterogeneity between studies are automatically computed for every meta-analysis performed.
Linkage Disequilibrium and Haplotype Analysis
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 and trend regression, including per-block and per-haplotype methods.
SVS incorporates advanced regression technologies that enable you to perform linear and logistic regression, stepwise regression (both backward elimination and forward selection), gene by environment interatction regression, 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).
Mixed Linear Model Analysis
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 additional fixed effects in the model.
Runs of Homozygosity (ROH) Analysis
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.
Unparalleled Performance with Virtually Unlimited Data
Anticipating association studies with possibly tens of millions of markers generated per sample by DNA sequencing, the core architecture of SVS has been completely reinvented to efficiently handle micro-array and whole-exome datasets with thousands of samples desktop computer. Datasets can scale to whole-genomes with thousands of samples as the amount of RAM increases. 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.
Real-Time Editing, Manipulation, and Enrichment
The sheer size and complexity of genome data makes it extremely difficult to work with. SVS 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.
Comprehensive Quality Assurance
High quality data is critical for quality results. To ensure your data is of the highest quality, SVS 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:
- One-step SNP filtering on call rates, HWE, and MAF
- LD pruning
- Cryptic relatedness checks
- Mendelian error checks
- Autosome heterozygosity
- Visual inspection of allele intensities
- Population stratification
- SNP concordance
- Gender misidentification
- Multidimensional outlier detection
- Chromosomal aberration screening
- And more...
SVS integrates GenomeBrowse as its visualization engine and 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. GenomeBrowse 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.
Learn more about GenomeBrowse as a stand-alone tool »
Case Study - John Curtin, University of Manchester
"SVS is easy to use. That's sometimes underrated, but it's important. Frankly, you could spend forever learning how to use open-source tools."
Getting More From GWAS
by Dr. Andreas SchererDownload the eBook »
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