💻 Instructions
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+### Single-marker association analysis (i.e. GWAS):
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+[
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+### Kernelized haplotype association analysis:
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+### Kernelized single-marker association analysis (i.e. Kernelized GWAS):
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## References :
Example of results :
-The gif and captured image below show an example of results, for +
+
Result examples :
+The gif and captured images below show result examples, for
LLGTH using the example dataset, that one might expect with
khamix:
+
+Haplotype association analysis:
+
+Single-marker association analysis (i.e. GWAS):
+
+
+Kernelized haplotype association analysis:
+
+
Kernelized single-marker association analysis (i.e. Kernelized +GWAS):
+
References :
diff --git a/readme.md b/readme.md index 8a2f72d..d424254 100644 --- a/readme.md +++ b/readme.md @@ -16,7 +16,7 @@ where : * $Y$ represents the vector of $n$ measured phenotypes for the analyzed trait. * $\beta$ denotes the vector of fixed effects. * $h$ represents the vector of random effects of haplotypes following a multivariate normal distribution, i.e. $h \sim N_{n_h}(0,H_{\sigma^2_{h}}) $, where $H=I_{n_h}$ is the covariance matrix describing the identity-by-state (IBS) status between haplotypes. - * $u$ denotes the vector of random polygenic effects following a multivariate normal distribution, i.e. $u \sim N_{n}(0,K_{\sigma^2_{K}})$, where $K$ is the genomic covariance matrix also known as the Gram matrix. $K$ can be constructed using either the VanRaden (2008) method which represents a linear additive kernel, or a Gaussian kernel which is a non-linear universal approximator capable of modeling epistatic effects (Jacquin $\textit{et al.}$, 2016). + * $u$ denotes the vector of random polygenic effects following a multivariate normal distribution, i.e. $u \sim N_{n}(0,K_{\sigma^2_{K}})$, where $K$ is the genomic covariance matrix also known as the Gram matrix. $K$ can be constructed using either the VanRaden (2008) method which represents a linear additive kernel, or a Gaussian kernel which is a non-linear universal approximator capable of modeling additive and epistatic effects (Jacquin $\textit{et al.}$, 2016). * $X$, $Z_h$, and $Z_u$ represent the design matrices that relate fixed and random effects to the measured phenotypes. * $\varepsilon$ denotes the vector of residuals.@@ -95,13 +95,33 @@ where : * Final note: The ```khamix``` folder can be duplicated multiple times, with each copy renamed, to analyze multiple traits in parallel. For example, folders like ```khamix_LLGTH```, ```khamix_NBL```, ```khamix_TIL```, etc., can be created from the provided data set. -## Example of results : +## Result examples : -The gif and captured image below show an example of results, for ```LLGTH``` using the example dataset, that one might expect with ```khamix```: +The gif and captured images below show result examples, for ```LLGTH``` using the example dataset, that one might expect with ```khamix```: - +### Haplotype association analysis: -[
]()
+
+
+[