Haplotypic Frequency Estimation
Calculate haplotype frequencies for specific loci using the expectation maximization (EM) algorithm or composite haplotype method (CHM).
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Haplotypic Association Analysis
Get extra power by performing haplotype analysis using haplotypes as binary predictors or with a moving window approach.
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Haplotype Trend Regression
Haplotype trend regression (HTR) provides a unified moving window approach for testing association of haplotype frequencies with discrete and continuous phenotypes. HTR fits an additive effects model of haplotypes. By adding the Golden Helix Regression Analysis Module to HelixTree you can also adjust your regression model for non-genetic covariates.
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Linkage Disequilibrium (LD)
HelixTree includes an interactive LD plot, enabling you to quickly determine the extent of correlation between marker pairs. LD blocks can be easily located and selected to compute haplotype frequencies and tagging SNPs.
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Multi-Locus Genetic Association Analysis
The two-loci genetic p-value plot shows the statistical significance of performing associations on pairs of genetic markers with the selected response variable. HelixTree attempts a categorical split upon every possible pair of genetic variables in a node and then reports the corresponding raw p-value and adjusted p-values.
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Copy Number Association Analysis (Add-on)
The Copy Number Analysis Module (CNAM) employs a proprietary optimal segmenting algorithm that can detect copy number variations with astonishing accuracy. In fact, its unique multivariate method can detect copy number variations at single marker resolution! In conjunction with HelixTree you can perform association analysis on both copy number variation covariates as well as log ratios themselves.
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Runs of Homozygosity Association (Add-on)
Available with the Whole Genome Analysis Module is a novel methodology that enables you to identify patterned clusters of SNPs demonstrating extended homozygosity, referred to as runs of homozygosity (ROHs). With HelixTree you can then employ both genome-wide and regionally-specific association tests using various ROH covariates.
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Regression Analysis (Add-on)
HelixTree provides a powerful Regression Analysis Module to test allelic and haplotypic associations in the presence of confounding phenotypic variables. The Regression Module supports both linear and logistic regression. A typical workflow uses stepwise regression to find confounding phenotypic variables, fixes those regressors and then finds significantly associated haplotypes or individual SNPs.
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Gene-Gene, Gene-Environment Interactions

Often times a SNP hypothesized to be highly associated with a disease is determined not to be significant as a main effect. This may be due to other factors (genetic or environmental) confounding the results. HelixTree provides a robust set of tools to uncover complex gene-gene (epistatic) and gene-environment interactions and/or correlations.

Recursive Partitioning (RP)
Uncover conditional gene-gene and gene-environment associations with Golden Helix FIRMPlus™ technology, an enhanced version of RP based on the statistical hypothesis testing methodology known as Formal Inference-Based Recursive Modeling (FIRM).

RP enables you to interactively build dendrogram-like decision tree models of your data. This is a powerful method for genetic association studies and has proven to be extremely effective for uncovering complex relationships among mixed variable types (binary, continuous, nominal, categorical and genetic). RP can be viewed as conditional gene finding. Once a split is made based upon a given covariate (genetic or environmental), then subsequent analysis is conditional on the presence or absence of that covariate.
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Forest of Random Trees Analysis
While one model of your data can be informative, HelixTree allows you to average multiple models by creating a “forest” of random trees. By analyzing a forest of random trees it is possible to understand both correlation and interaction effects among mixed types of variables. Random trees can also be used to create cluster plots for finding subgroups within your data set, which are homogenous in nature, thus sharing many common characteristics.
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Observation Distance Matrix from Random Trees
The Observation Distance matrix is suitable for analysis by external clustering or multidimensional scaling algorithms and is based on the idea that when two observations end up together in a small subset deep within a tree, the descriptors that drive their response are shared, and hence the observations are similar in response space.
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Correlation Interaction Analysis
By creating a forest of random trees with the multiple tree function, it is possible to detect both correlation and interaction effects - even among variables of mixed types (nominal, continuous, binary, ordinal, genetic).
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Multivariate Analysis
HelixTree also offers multivariate RP, enabling the simultaneous analysis of multiple phenotypes.
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