<p>Basically, yes, you will get better results if you use a reference panel which contains samples with similar ancestry to your targets. Imputation works by matching haplotypes in your target samples to those in a reference panel. If the haplotypes in the target don’t exist in the reference panel, the variants won’t be imputed accurately.</p>
<blockquote>
<p>We demonstrated that using TOPMed sequencing data as the imputation
reference panel improves genotype imputation quality in these
populations[Hispanic/Latino and African], which subsequently enhanced gene-mapping power for
complex traits. For rare variants with minor allele frequency (MAF) &lt;
0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation
quality, compared to the state-of-the-art 1000 Genomes Project Phase 3
and Haplotype Reference Consortium reference panels.</p>
</blockquote>
<p>From <a href="https://pubmed.ncbi.nlm.nih.gov/31869403/" rel="nofollow noreferrer">this paper</a>.</p>

CC BY-SA 4.0
2023-05-29T17:56:14.297Z
Is it still acceptable to use a general reference panel like 1000 Genomes Project Phase 3, or has everyone switched to ancestry-specific by now?
BigMistake
CC BY-SA 4.0
2023-05-29T18:03:57.583Z
Most people use TopMed now using a remote server, or use their own custom ancestry specific one,  I don’t think many serious studies use 1000 genomes anymore
user438383
CC BY-SA 4.0
2023-05-29T22:10:34.480Z
TopMed is similar to 1000 genomes in that they are multiethnic. I agree that TopMed is better for other reasons. However, the question of "does a multiethnic panel which includes some samples of ethnicity X improves accuracy for ethnicity X over a panel without ethnicity X?" is a different question than "if the panel consisted of only ethnicity X, would that improve accuracy over the multiethnic panel?"
BigMistake
CC BY-SA 4.0
2023-05-29T22:21:58.807Z
Well yes as the abstract says, E.g. Latino ancestry individuals are imputed better using topmed because they it contains Latino samples. You can only get good imputation of non-common variants if you have individuals of matched ancestry in the reference panel.
user438383
CC BY-SA 4.0
2023-05-29T23:07:48.737Z
Right. The study does a good job answering the first question, but doesn't address the second question specifically. (Which is where 1000 genomes might have an advantage if using an online imputation server as it allows for ancestry-specific imputation whereas TopMed doesn't.)
BigMistake
CC BY-SA 4.0
2023-05-29T23:08:49.770Z
Why do you think topmed isn’t ancestry specific and 1000 genomes is? Actually what do you mean by ‘ancestry specific’ exactly anyway?
user438383
CC BY-SA 4.0
2023-05-29T23:25:29.370Z
On online imputation servers, TopMed only has a multiethnic panel option. 1000 genomes allows you to select a single population, for example EAS, to use. Ancestry-specific here means data from a single ancestry group as opposed to data from multiple ancestry groups.
BigMistake
CC BY-SA 4.0
2023-05-30T16:41:46.703Z
@BigMistake Jonathan Marchini did 1000g imputation. He observed that pooling all samples together achieved accuracy than imputing in individual populations independently. Don't split by population.
user172818
CC BY-SA 4.0
2023-05-31T05:18:44.673Z
Thanks! This is what I was wondering. That definitely makes it easier. Seems like sample size outweighs the specificity of LD patterns for now. If you happen to have a link to where he tried that, I'd appreciate it.
BigMistake
CC BY-SA 4.0
2024-03-07T00:42:13.910Z
@user172818 Any chance you have a paper or Blog I can refer to ? Our lab was pondering over the same question. Also based on what BigMistake said.
Sam
CC BY-SA 4.0
2023-05-24T08:53:37.907Z
2023-05-29T22:20:39.133Z
2023-05-29T22:20:39.133Z
user438383
<p>in my experience you should use a publicly cited reference genome like GRCh37 or GRCh38</p>
<p><a href="https://www.sciencedirect.com/science/article/pii/S0888754317300058" rel="nofollow noreferrer">https://www.sciencedirect.com/science/article/pii/S0888754317300058</a></p>
<p><a href="https://gatk.broadinstitute.org/hc/en-us/articles/360035890951-Human-genome-reference-builds-GRCh38-or-hg38-b37-hg19" rel="nofollow noreferrer">https://gatk.broadinstitute.org/hc/en-us/articles/360035890951-Human-genome-reference-builds-GRCh38-or-hg38-b37-hg19</a></p>

CC BY-SA 4.0
2023-05-23T20:32:00.743Z
I use GRCh37 but I need to use a reference panel in addition to a reference genome
BigMistake
CC BY-SA 4.0
2023-05-24T08:49:17.793Z
The question asks about a reference panel, not a reference genome.
user438383
CC BY-SA 4.0
2023-05-23T20:21:06.983Z
2023-05-23T20:21:06.983Z
zhiguang
<p>I'm wondering if imputation, specifically <a href="http://faculty.washington.edu/browning/beagle/beagle.html" rel="nofollow noreferrer">Beagle</a>, needs a reference panel that matches the sample's ancestry group. For example, Beagle documentation suggests the 1000 Genomes Project phase 3 reference panel, which contains all ancestry groups. Is this acceptable to use to impute the genome of, say, an East Asian, African, and European?</p>

<imputation><1000genomes>
Should genotype imputation be ancestry specific?
# Should genotype imputation be ancestry specific?

(Asked by: username_0 on 2023-05-23)

I'm wondering if imputation, specifically [Beagle](http://faculty.washington.edu/browning/beagle/beagle.html), needs a reference panel that matches the sample's ancestry group. For example, Beagle documentation suggests the 1000 Genomes Project phase 3 reference panel, which contains all ancestry groups. Is this acceptable to use to impute the genome of, say, an East Asian, African, and European?

**Tags:** imputation, 1000genomes

---

## 2 answers

### Answer by username_1 on 2023-05-24. Score: 1

Basically, yes, you will get better results if you use a reference panel which contains samples with similar ancestry to your targets. Imputation works by matching haplotypes in your target samples to those in a reference panel. If the haplotypes in the target don’t exist in the reference panel, the variants won’t be imputed accurately.

> We demonstrated that using TOPMed sequencing data as the imputation
> reference panel improves genotype imputation quality in these
> populations[Hispanic/Latino and African], which subsequently enhanced gene-mapping power for
> complex traits. For rare variants with minor allele frequency (MAF) <
> 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation
> quality, compared to the state-of-the-art 1000 Genomes Project Phase 3
> and Haplotype Reference Consortium reference panels.

From [this paper](https://pubmed.ncbi.nlm.nih.gov/31869403/).

> **Comments:**
> **username_0**: Is it still acceptable to use a general reference panel like 1000 Genomes Project Phase 3, or has everyone switched to ancestry-specific by now?  
> **username_1**: Most people use TopMed now using a remote server, or use their own custom ancestry specific one,  I don’t think many serious studies use 1000 genomes anymore  
> **username_0**: TopMed is similar to 1000 genomes in that they are multiethnic. I agree that TopMed is better for other reasons. However, the question of "does a multiethnic panel which includes some samples of ethnicity X improves accuracy for ethnicity X over a panel without ethnicity X?" is a different question than "if the panel consisted of only ethnicity X, would that improve accuracy over the multiethnic panel?"  
> **username_1**: Well yes as the abstract says, E.g. Latino ancestry individuals are imputed better using topmed because they it contains Latino samples. You can only get good imputation of non-common variants if you have individuals of matched ancestry in the reference panel.  
> **username_0**: Right. The study does a good job answering the first question, but doesn't address the second question specifically. (Which is where 1000 genomes might have an advantage if using an online imputation server as it allows for ancestry-specific imputation whereas TopMed doesn't.)  
> **username_1**: Why do you think topmed isn’t ancestry specific and 1000 genomes is? Actually what do you mean by ‘ancestry specific’ exactly anyway?  
> **username_0**: On online imputation servers, TopMed only has a multiethnic panel option. 1000 genomes allows you to select a single population, for example EAS, to use. Ancestry-specific here means data from a single ancestry group as opposed to data from multiple ancestry groups.  
> **username_2**: @BigMistake Jonathan Marchini did 1000g imputation. He observed that pooling all samples together achieved accuracy than imputing in individual populations independently. Don't split by population.  
> **username_0**: Thanks! This is what I was wondering. That definitely makes it easier. Seems like sample size outweighs the specificity of LD patterns for now. If you happen to have a link to where he tried that, I'd appreciate it.  
> **username_3**: @user172818 Any chance you have a paper or Blog I can refer to ? Our lab was pondering over the same question. Also based on what BigMistake said.  

---

### Answer by username_4 on 2023-05-23. Score: 0

in my experience you should use a publicly cited reference genome like GRCh37 or GRCh38

<https://www.sciencedirect.com/science/article/pii/S0888754317300058>

<https://gatk.broadinstitute.org/hc/en-us/articles/360035890951-Human-genome-reference-builds-GRCh38-or-hg38-b37-hg19>

> **Comments:**
> **username_0**: I use GRCh37 but I need to use a reference panel in addition to a reference genome  
> **username_1**: The question asks about a reference panel, not a reference genome.  

---

