<p>This is done by downsampling. Take the 1000 genomes, set some genotypes as missing <code>./.</code>, impute them using GLIMPSE, then measure correlation between the genotype dosages in the imputed dataset and the same sample at high coverage - in this case, the 1000 genomes sequenced to 30x coverage by the NY Genome Centre.</p>

CC BY-SA 4.0
2022-09-08T12:39:37.853Z
Is the correlation a Pearson correlation coefficient?
winni2k
CC BY-SA 4.0
2022-09-08T12:46:26.883Z
@winni2k judging by this yes I think so https://github.com/odelaneau/GLIMPSE/blob/master/concordance/src/utils/basic_stats.h#L110
user438383
CC BY-SA 4.0
2022-09-07T17:35:05.630Z
2022-09-08T12:37:56.857Z
2022-09-08T12:37:56.857Z
user438383
<p>There is a graph in the link <a href="https://odelaneau.github.io/GLIMPSE/rsquare_eur.html" rel="nofollow noreferrer">https://odelaneau.github.io/GLIMPSE/rsquare_eur.html</a>. This graph shows the imputation accuracy of certain MAF bins. Now I understand that <span class="math-container">$r^2$</span> is the correlation between imputed genotype and true genotype. What I don't understand is how they calculate this <span class="math-container">$r^2$</span> correlation. Can anyone suggest to me how I can define the r^2 correlation between true genotype and imputed genotype? I suppose it depends on the individual who defines his own <span class="math-container">$r^2$</span>. So, it may differ for the different algorithms. However, different aspects or different <span class="math-container">$r^2$</span> definitions will help me understand and represent this graph slightly differently.</p>

<correlation><imputation>
correlation between imputed genotype and true genotype
# correlation between imputed genotype and true genotype

(Asked by: username_0 on 2022-09-07)

There is a graph in the link <https://odelaneau.github.io/GLIMPSE/rsquare_eur.html>. This graph shows the imputation accuracy of certain MAF bins. Now I understand that $r^2$ is the correlation between imputed genotype and true genotype. What I don't understand is how they calculate this $r^2$ correlation. Can anyone suggest to me how I can define the r^2 correlation between true genotype and imputed genotype? I suppose it depends on the individual who defines his own $r^2$. So, it may differ for the different algorithms. However, different aspects or different $r^2$ definitions will help me understand and represent this graph slightly differently.

**Tags:** correlation, imputation

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## 1 answers

### Answer by username_1 on 2022-09-07. Score: 2 | Accepted answer

This is done by downsampling. Take the 1000 genomes, set some genotypes as missing `./.`, impute them using GLIMPSE, then measure correlation between the genotype dosages in the imputed dataset and the same sample at high coverage - in this case, the 1000 genomes sequenced to 30x coverage by the NY Genome Centre.

> **Comments:**
> **username_2**: Is the correlation a Pearson correlation coefficient?  
> **username_1**: @winni2k judging by this yes I think so https://github.com/odelaneau/GLIMPSE/blob/master/concordance/src/utils/basic_stats.h#L110  

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