Article Non-additive Effects in Genomic Selection
Article Orthogonal Estimates of Variances for Additive, Dominance an...
I have read several papers on the same but I haven't yet succeeded in finding one with a script I can use on my data
Here is the answer but if You are interested, I can write complete code and we publish together. Do you know Python? It is better to write code in python
Non-additive effects in genomic selection refer to the genetic interactions between alleles at different loci that cannot be explained by the additive effects of individual alleles. These interactions can have important implications for the accuracy of genomic prediction, and there are several statistical methods and R-scripts that have been developed to take non-additive effects into account.
Here are a few R packages that can be used to model non-additive effects in genomic selection:
1. rrBLUP: rrBLUP is a popular R package for genomic prediction that can be extended to model non-additive effects using the genomic best linear unbiased prediction (GBLUP) method. The package includes a function called "MME" that can be used to construct the genomic relationship matrix with non-additive effects.
2. BGLR: BGLR is another R package for genomic prediction that can model non-additive effects using a Bayesian approach. The package includes a function called "BGLR" that can be used to fit a Bayesian sparse linear mixed model with non-additive effects.
3. MixP: MixP is an R package that can be used to model both additive and non-additive effects in genomic prediction. The package includes a function called "mixp" that can be used to fit a mixed model that includes both additive and non-additive effects.
4. AlphaSimR: AlphaSimR is a simulation tool for breeding programs that can be used to simulate datasets with non-additive effects. The package includes a function called "add.nonadd" that can be used to add non-additive effects to a simulated dataset.
These are just a few examples of R packages that can be used to model non-additive effects in genomic selection. It is important to note that the choice of method will depend on the specific research question and the nature of the data being analyzed.
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Thank you very much for your response Mohammad Imam
A very great proposal on writing the complete code. However, I'm yet to get familiar with Python.
You can learn from this video or I can write complete code in R, you will compile and exceute.
https://www.youtube.com/watch?v=rfscVS0vtbw
A complete code in R is the best option right now. I would appreciate that. Learning Python is on my agenda too but I'm yet to allocate time for it. Thanks for the video.
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