<p>Yes, you can run an additive model (i.e., B*AA = 2*B*Aa, aa = B*AA - 4*Aa) or a dominance model (B*AA = B*Aa). You can test both of these and see which is a better fit to your data.</p>
<p>However, it is practically known already that the model will be at least predominantly additive.</p>

CC BY-SA 4.0
2023-10-11T15:39:33.887Z
2023-10-11T15:39:33.887Z
BigMistake
<p>My experiment is based on a quantitative continuous variable levels (B12 vitamin) in a sample of patients retrospectively selected based on values that can take (under our criteria):</p>
<ul>
<li>normal</li>
<li>low levels: under the physiological or normal reference values but not enough to be considered deficiency</li>
<li>deficiency (which is associated with poor nutrition, gastric disease, etc.). These data are excluded.</li>
</ul>
<p>We are interested in <strong>low levels</strong>. Our hypothesis is <strong>to prove if mutations in 10 genes can be associated with these physiological low levels.</strong> These physiological low levels are quantified under the format of maximum and minimum value of B12. The genes can take 3 possible categories:</p>
<ul>
<li>wt: absence of mutation</li>
<li>het: half of mutation</li>
<li>hom: full mutation</li>
</ul>
<p>Hypothesis:</p>
<ol>
<li>I would like, if possible, to try something like a genetic risk score, <strong>if multiple het or hom states</strong> in multiple genes can be additive. I don't know how to approach this, because there can be multiple permutations, and maybe permutation 1 has a stronger association with low levels than permutation 2.</li>
<li>Any ideas on how to carry this out? I don't really know if mutations are additive or opposite.
Not sure if some clustering approach can be helpful here, like PCA or euclidean distance, linear discriminant analysis... Any advice is welcome.</li>
</ol>
<p>My idea is to execute a summary of levels of B12 for each gene and see in a boxplot possible differences, perhaps look for some association (or correlation), but I doubt it.</p>
<p>Any idea or previous input or experiment like this would be tremendously welcome.</p>

CC BY-SA 4.0
2023-05-18T16:44:21.960Z
The solution is supervised machine learning (ML) and is fairly standard analytics for precisely this problem.  PCA or euclidean distance, will not prove this especially distance methods. Discriminant analysis is okay. What you need are complex regression analytics and that broadly includes ML. So IMO  the project needs an analyst. I hope thats helpful and again its just my opinion
M__
CC BY-SA 4.0
2023-05-18T23:06:15.143Z
Could you recommend me something to begin?
Javier Hernando
CC BY-SA 4.0
2023-05-19T00:09:45.917Z
I personally would recommend collaboration, unless someone already published an algorithm specific for purpose. Its a bit like a specialist/expert in military logistics wanting to perform a "commando assault", i.e. it would be really hard, particularly if the key source of help was online.
M__
<point-mutation><polygenic-risk-score>
How to associate point mutations to quantitative protein levels
# How to associate point mutations to quantitative protein levels

(Asked by: username_0 on 2023-05-18)

My experiment is based on a quantitative continuous variable levels (B12 vitamin) in a sample of patients retrospectively selected based on values that can take (under our criteria):

* normal
* low levels: under the physiological or normal reference values but not enough to be considered deficiency
* deficiency (which is associated with poor nutrition, gastric disease, etc.). These data are excluded.

We are interested in **low levels**. Our hypothesis is **to prove if mutations in 10 genes can be associated with these physiological low levels.** These physiological low levels are quantified under the format of maximum and minimum value of B12. The genes can take 3 possible categories:

* wt: absence of mutation
* het: half of mutation
* hom: full mutation

Hypothesis:

1. I would like, if possible, to try something like a genetic risk score, **if multiple het or hom states** in multiple genes can be additive. I don't know how to approach this, because there can be multiple permutations, and maybe permutation 1 has a stronger association with low levels than permutation 2.
2. Any ideas on how to carry this out? I don't really know if mutations are additive or opposite.
   Not sure if some clustering approach can be helpful here, like PCA or euclidean distance, linear discriminant analysis... Any advice is welcome.

My idea is to execute a summary of levels of B12 for each gene and see in a boxplot possible differences, perhaps look for some association (or correlation), but I doubt it.

Any idea or previous input or experiment like this would be tremendously welcome.

**Tags:** point-mutation, polygenic-risk-score
> **Comments:**
> **username_1**: The solution is supervised machine learning (ML) and is fairly standard analytics for precisely this problem.  PCA or euclidean distance, will not prove this especially distance methods. Discriminant analysis is okay. What you need are complex regression analytics and that broadly includes ML. So IMO  the project needs an analyst. I hope thats helpful and again its just my opinion  
> **username_0**: Could you recommend me something to begin?  
> **username_1**: I personally would recommend collaboration, unless someone already published an algorithm specific for purpose. Its a bit like a specialist/expert in military logistics wanting to perform a "commando assault", i.e. it would be really hard, particularly if the key source of help was online.  

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## 1 answers

### Answer by username_2 on 2023-10-11. Score: 1

Yes, you can run an additive model (i.e., B\*AA = 2\*B\*Aa, aa = B\*AA - 4\*Aa) or a dominance model (B\*AA = B\*Aa). You can test both of these and see which is a better fit to your data.

However, it is practically known already that the model will be at least predominantly additive.


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