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Admixture and Blaine Bettinger
SNP Analysis
From basic to advanced analyses, SVS 7 incorporates a number of intuitive workflows to lead you beyond single marker associations. With support for case-control and quantitative traits, whole genome and candidate gene data, you can run a breadth of statistical tests under several genetic models. Advanced regression can help elucidate even the most complex gene-gene and gene-environment interactions.
The following tutorial leads you through an entire SNP analysis workflow on a whole-genome scale from data preparation and quality assurance to association testing and regression.
Genotype Statistics
Within a separate window or while performing genotype association tests, you have the option to calculate a number of basic genotype statistics for each marker, including call rate, minor allele frequency, Hardy-Weinberg Equilibrium (HWE) P-value, Fisher’s Exact Test for HWE P-value, Signed HWE Correlation R, and allele and genotype counts.
Recoding Genotypes
Genotype data typically comes in the generic AB format. SVS 7 has a new option that can take generic AB data and recode it in a number of ways. Encoding options include DD, Dd, dd based on in-sample calculations of major and minor alleles or integer representations (0, 1, 2) based on the additive, dominant, or recessive model. The latter is important for including genotypes as covariates for advanced regression analysis (below). You can also flip DNA strands, or transcode generic AB alleles to their respective AGCT format.
Genotype Association Testing
SVS 7 offers a straightforward way of testing for genetic 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).
Genetic Models and Test Statistics
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, Fischer's exact test, odds ratio with confidence intervals, analysis of deviance (ANODEV), the F-test and linear or logistic regression.
Regression Analysis
SVS 7 incorporates an advanced regression module that enables 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).
» More about Regression Analysis
Multiple Testing Corrections
It is possible to obtain a good test statistic value by chance alone. Multiple testing corrections help ensure this is not the case. SVS 7 offers a number of methods to help control for false-positives, including: Bonferroni adjusted p-values, false-discovery rate (FDR), Simes' method, and single and full scan permutation testing. See the General Statistics section of the SVS Manual for more details on these statistics.
Visualization
A new dynamic analytic visualization tool with integrated genome browser offers exceptional flexibility in how you visualize SNP data and present results. You can easily compare SNP association results against haplotype or copy number variation (CNV) association results and linkage disequilibrium, generate cluster plots of allele intensities, create Manhattan plots of whole genome data, and more. When you finalize the view you want a number of publication quality formats are available, including scalable vector graphics.
» More about Data Visualization
