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Interactively explore LD across the entire genome, at a chromosome level, or around a specific marker or gene of interest. You can add LD plots to other plots (e.g., association results) to assess the correlation structure around significant markers. You can even view multiple LD plots together within the same genome browser to compare LD in your study population to that of a similar population (e.g. HapMap populations). It is also useful for comparing higher and lower density arrays, or comparing haplotype blocks
among two or more populations.
SVS makes it easy to automatically compute haplotype blocks based on correlation between markers and then manually manipulate them within an LD plot using a simple drag-and-drop interface. For any defined block in an LD plot, haplotype frequencies and case/control association results can be computed in real-time. Importantly, all block definitions can be saved and reloaded for subsequent analyses, freeing you to explore various block configurations without losing any work or ever having to start over.
From a given haplotype block in an LD plot you can generate four separate frequency tables, enabling you to interrogate haplotype and diplotype frequency estimations for individual samples as well as overall haplotype frequency estimations across all samples.
Robust haplotype association methods are available from a spreadsheet for case/control traits. Using pre-defined block definitions or a moving window approach, you can perform haplotype association across your entire dataset employing both per-haplotype (for each haplotype in a block) and per-block tests. Several test statistics and multiple testing corrections are available, ensuring you get the most power out of multi-marker associations.
Now included in SVS is Haplotype Trend Regression (HTR), which takes one or more block(s) of genotypic markes and for each block of markers, estimates haplotypes and then regresses their by-sample haplotype probabilities against a depedent variable. The regression may be linear or logistics, may be stepwise if desired, and may involve fixed numeric or categorical covariates and/or interaction terms.