<p>I've got the answer to my question at <a href="https://www.biostars.org/p/410466/" rel="nofollow noreferrer">https://www.biostars.org/p/410466/</a> by <a href="https://www.biostars.org/u/9575/" rel="nofollow noreferrer">chrchang522</a></p>
<blockquote>
<p>&quot;The main regression executed by Plink was introduced by EIGENSTRAT in ~2006; see <a href="https://www.nature.com/articles/ng1847" rel="nofollow noreferrer">https://www.nature.com/articles/ng1847</a> . This is actually straightforward to write in R/Python from scratch; the harder part is optimizing the implementation for large datasets.</p>
<p>The Firth regression added to Plink 2.0 to improve handling of rare variants and imbalanced binary phenotypes was motivated by <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4049324/" rel="nofollow noreferrer">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4049324/</a> .</p>
<p>Mixed linear models provide better statistical power when you have lots of close relatives in your dataset, but are much trickier to solve; actually, this is still a significant research area. Two tools covering parts of the current state-of-the-art are SAIGE (<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6119127/" rel="nofollow noreferrer">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6119127/</a> ; handles imbalanced binary phenotypes, but relatively slow) and fastGWA (<a href="https://www.nature.com/articles/s41588-019-0530-8" rel="nofollow noreferrer">https://www.nature.com/articles/s41588-019-0530-8</a> ; great speed, but doesn't support dosage data yet and uses a misspecified model for binary phenotypes).&quot;</p>
</blockquote>

CC BY-SA 4.0
2019-12-07T14:44:42.057Z
2020-01-08T15:16:39.133Z
2020-06-18T08:30:41.743Z
Bruno Ambrozio
<p><em>This question was also asked on <a href="https://www.biostars.org/p/410466/" rel="nofollow noreferrer">Biostars</a></em></p>

<p>I'm trying to understand what is the best algorithm for GWAS nowadays.
I know we have many tools available like Plink and Hail, but currently, what is the best algorithm if I won't use any them? Let's say, write down a script in R or Python from scratch. Which statistical algorithm should I use?
Is it Linear mixed models (LMMs)? I'm confused as we can have binary phenotypes (case/control) or quantitative phenotypes. LMM seems to address quantitative ones, but can it be used for case/control as well? 
Actually, what is the state of the art for both/each of them? Pair-reviewed papers as references will be appreciated.
Thanks!</p>

CC BY-SA 4.0
2019-12-02T00:36:44.440Z
To clarify: you're asking what the best algorithm is, assuming you don't want to use the well-tested and validated algorithms that other people are using?
gringer
CC BY-SA 4.0
2019-12-03T13:29:47.090Z
It can be whatever anyone is using. I just want to know what is the algorithm behind the scene for binary or quantitative phenotypes.
Bruno Ambrozio
CC BY-SA 4.0
2019-12-03T18:48:31.507Z
This is not a good fit for a Bioinformatics Stack Exchange question, as the question is too broad and primarily opinion-based. I'd recommend that you ask something like this on [reddit](reddit.com/r/bioinformatics). The best questions on BSE deal with specific problems that people are having. If you think your question *is* specific, please edit it to make that more obvious.
gringer
CC BY-SA 4.0
2019-12-07T14:45:27.957Z
Sorry, but I disagree. I'm not looking for opinions, but the algorithm that, mathematically, (through statistical tests) prooves to be the state-of-the-art. I've got the answer, check it out below. Thanks anyway!
Bruno Ambrozio
<r><gwas>
What is the state of the art for GWAS in terms of the statistical algorithm for either Case/control and Quantitative traits?
# What is the state of the art for GWAS in terms of the statistical algorithm for either Case/control and Quantitative traits?

(Asked by: username_0 on 2019-12-01)

*This question was also asked on [Biostars](https://www.biostars.org/p/410466/)*

I'm trying to understand what is the best algorithm for GWAS nowadays.
I know we have many tools available like Plink and Hail, but currently, what is the best algorithm if I won't use any them? Let's say, write down a script in R or Python from scratch. Which statistical algorithm should I use?
Is it Linear mixed models (LMMs)? I'm confused as we can have binary phenotypes (case/control) or quantitative phenotypes. LMM seems to address quantitative ones, but can it be used for case/control as well?
Actually, what is the state of the art for both/each of them? Pair-reviewed papers as references will be appreciated.
Thanks!

**Tags:** r, gwas
> **Comments:**
> **username_1**: To clarify: you're asking what the best algorithm is, assuming you don't want to use the well-tested and validated algorithms that other people are using?  
> **username_0**: It can be whatever anyone is using. I just want to know what is the algorithm behind the scene for binary or quantitative phenotypes.  
> **username_1**: This is not a good fit for a Bioinformatics Stack Exchange question, as the question is too broad and primarily opinion-based. I'd recommend that you ask something like this on [reddit](reddit.com/r/bioinformatics). The best questions on BSE deal with specific problems that people are having. If you think your question *is* specific, please edit it to make that more obvious.  
> **username_0**: Sorry, but I disagree. I'm not looking for opinions, but the algorithm that, mathematically, (through statistical tests) prooves to be the state-of-the-art. I've got the answer, check it out below. Thanks anyway!  

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## 1 answers

### Answer by username_0 on 2019-12-07. Score: 2 | Accepted answer

I've got the answer to my question at <https://www.biostars.org/p/410466/> by [chrchang522](https://www.biostars.org/u/9575/)

> "The main regression executed by Plink was introduced by EIGENSTRAT in ~2006; see <https://www.nature.com/articles/ng1847> . This is actually straightforward to write in R/Python from scratch; the harder part is optimizing the implementation for large datasets.
>
> The Firth regression added to Plink 2.0 to improve handling of rare variants and imbalanced binary phenotypes was motivated by <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4049324/> .
>
> Mixed linear models provide better statistical power when you have lots of close relatives in your dataset, but are much trickier to solve; actually, this is still a significant research area. Two tools covering parts of the current state-of-the-art are SAIGE (<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6119127/> ; handles imbalanced binary phenotypes, but relatively slow) and fastGWA (<https://www.nature.com/articles/s41588-019-0530-8> ; great speed, but doesn't support dosage data yet and uses a misspecified model for binary phenotypes)."


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