<p>This is all relevant for data 0.5x-1x coverage.</p>
<p>Assuming you have genotype likelihoods data, if you want to phase low-coverage data, the most suitable options are GLIMPSE, Beagle4 and QUILT. Of the three, GLIMSPE and QUILT are the most recent and similar in performance. shapeit, eagle and minimac aren't suitable for low-coverage data.</p>
<p>I have found <a href="https://pubmed.ncbi.nlm.nih.gov/33414550/" rel="nofollow noreferrer">GLIMPSE</a> to be fast and memory efficient, but it requires several different steps; breaking the genome into chunks, imputing, reforming the chunks and then sampling from the haplotypes to phase the data. It has a fairly good tutorial <a href="https://odelaneau.github.io/GLIMPSE/tutorial_b38.html#run_preliminaries" rel="nofollow noreferrer">here</a>.</p>
<p><a href="https://www.nature.com/articles/s41588-021-00877-0" rel="nofollow noreferrer">QUILT</a> has fewer stages and faster, but is unlikely to make much of a difference unless you are working on very large datasets. It has a tutorial <a href="https://github.com/rwdavies/QUILT" rel="nofollow noreferrer">here</a>.</p>
<p>Which one you use probably doesn't matter a whole lot, but I would probably suggest using QUILT as it has fewer stages and is slightly more recent.</p>

CC BY-SA 4.0
2021-12-27T17:09:27.680Z
I am interested in using Beagle. But it doesn't have good documentation and tutorial. Can you suggest any?
Shafayet Rahat
CC BY-SA 4.0
2021-12-27T17:12:59.020Z
Only beagle4 is suitable for low coverage data and it’s pretty old and doesn’t scale well, so I’d advise against using it unless you have a good reason to?
user438383
CC BY-SA 4.0
2021-12-27T17:18:45.983Z
Also, I guess Shapit is only for phasing. I found some tutorial on Shapit+impute2 https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#examples. What do you suggest about it?
Shafayet Rahat
CC BY-SA 4.0
2021-12-27T17:22:33.710Z
Glimpse or Quilt which one is much older?
Shafayet Rahat
CC BY-SA 4.0
2021-12-27T17:24:12.807Z
Also, there is a new Beagle5.2 version. Will it be suitable for low pass sequencing?
Shafayet Rahat
CC BY-SA 4.0
2021-12-27T17:55:40.170Z
GLIMSE is slightly older, Beagle5.2 is not suitable for low coverage and neither is the shapeit2 and impute2 tutorial you linked.
user438383
CC BY-SA 4.0
2021-12-27T18:59:09.930Z
Thanks for your comment. But I found a paper on the performance of phasing and imputation tools https://www.karger.com/Article/Fulltext/489758. The data here has pretty low depth(4x). It suggests that beagle 4 or shapeit+impute2 has a good performance. Can you tell me more elaborately why are you telling that shapeit+impute2 is not a good choice for low pass sequencing? Maybe add your suggestion in the answer?
Shafayet Rahat
CC BY-SA 4.0
2021-12-27T19:09:30.923Z
I suppose it depends on what you consider 'low-pass'. I am referring to data around 0.5-1x coverage, so the results from that paper aren't really relevant to that case, and it predates the release of GLIMPSE and QUILT. Please read the papers I linked for more details. I think that’s everything I’ve got to say about this, as comments are not for extending questions and discussions.
user438383
CC BY-SA 4.0
2021-12-27T21:21:02.973Z
That said, if you want to add in more specific detail about the coverages you are interested in, I will amend the question.
user438383
CC BY-SA 4.0
2022-01-17T10:10:54.750Z
The author of shapeit (Olivier) once told me that shapeit2 imputation simply doesn't work below 4x. I have no reason to doubt that assessment.
winni2k
CC BY-SA 4.0
2022-01-22T13:04:07.570Z
I investigated a few different ways of running beagle v3 (same algorithm as v4) for this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4522619/ i found that beagle scaled really poorly in run time with ref panel size. This is why we went just with asian 1kg haplotypes. I think beagle could work for you. There's also quite a bit about that testing in my thesis: https://ora.ox.ac.uk/objects/uuid:19ce0c8e-1d65-44ff-b56c-77ee849b2167
winni2k
CC BY-SA 4.0
2022-01-22T13:11:41.783Z
That said, the imputation using beagle on 11k whole genome samples (1.7x) took around 300k cpu hours. I would expect quilt to require less compute time and probably get you similar results.
winni2k
CC BY-SA 4.0
2021-12-27T16:59:23.767Z
2021-12-27T19:15:06.760Z
2021-12-27T19:15:06.760Z
user438383
<p>I am new to low-pass whole genome sequencing and have the basic idea of phasing and imputation. I have .vcf file after calling <a href="https://gatk.broadinstitute.org/hc/en-us/articles/360037225632-HaplotypeCaller" rel="nofollow noreferrer">haplotypecaller</a> tools from GATK. After searching the phasing and imputation tools I've encountered some tools like Beagle, Minimac, Shapeit, Glimpse, Eagle. The problem is that I didn't find enough hands-on material on how to process a .vcf from unphased to phased and then to an imputed one.</p>
<p>Can anyone suggests any hands-on tutorial regarding phasing and imputation?</p>

CC BY-SA 4.0
2022-01-17T10:06:10.400Z
What is your sample size, organism, and average coverage per sample?
winni2k
CC BY-SA 4.0
2022-01-17T16:12:40.557Z
total sequences read 4093666, Human cell, average coverage (0.1x-6x).
Shafayet Rahat
CC BY-SA 4.0
2022-01-19T14:47:38.490Z
ok, here is the tricky bit: are you trying to impute germline or somatic mutations? I have tried beagle with somatic mutations, and it simply doesn't (and shouldn't) work. If you are using germline mutations, what is your reference panel size?
winni2k
CC BY-SA 4.0
2022-01-21T06:25:43.223Z
I am trying to impute germline. I'm trying to impute it with 1000kg phase3 reference panel.
Shafayet Rahat
CC BY-SA 4.0
2022-01-21T06:41:09.417Z
1000 genome phase3 reference panel.
Shafayet Rahat
<vcf><imputation><phasing>
Tutorials on phasing and imputing low-coverage sequencing data
# Tutorials on phasing and imputing low-coverage sequencing data

(Asked by: username_0 on 2021-12-27)

I am new to low-pass whole genome sequencing and have the basic idea of phasing and imputation. I have .vcf file after calling [haplotypecaller](https://gatk.broadinstitute.org/hc/en-us/articles/360037225632-HaplotypeCaller) tools from GATK. After searching the phasing and imputation tools I've encountered some tools like Beagle, Minimac, Shapeit, Glimpse, Eagle. The problem is that I didn't find enough hands-on material on how to process a .vcf from unphased to phased and then to an imputed one.

Can anyone suggests any hands-on tutorial regarding phasing and imputation?

**Tags:** vcf, imputation, phasing
> **Comments:**
> **username_1**: What is your sample size, organism, and average coverage per sample?  
> **username_0**: total sequences read 4093666, Human cell, average coverage (0.1x-6x).  
> **username_1**: ok, here is the tricky bit: are you trying to impute germline or somatic mutations? I have tried beagle with somatic mutations, and it simply doesn't (and shouldn't) work. If you are using germline mutations, what is your reference panel size?  
> **username_0**: I am trying to impute germline. I'm trying to impute it with 1000kg phase3 reference panel.  
> **username_0**: 1000 genome phase3 reference panel.  

---

## 1 answers

### Answer by username_2 on 2021-12-27. Score: 3 | Accepted answer

This is all relevant for data 0.5x-1x coverage.

Assuming you have genotype likelihoods data, if you want to phase low-coverage data, the most suitable options are GLIMPSE, Beagle4 and QUILT. Of the three, GLIMSPE and QUILT are the most recent and similar in performance. shapeit, eagle and minimac aren't suitable for low-coverage data.

I have found [GLIMPSE](https://pubmed.ncbi.nlm.nih.gov/33414550/) to be fast and memory efficient, but it requires several different steps; breaking the genome into chunks, imputing, reforming the chunks and then sampling from the haplotypes to phase the data. It has a fairly good tutorial [here](https://odelaneau.github.io/GLIMPSE/tutorial_b38.html#run_preliminaries).

[QUILT](https://www.nature.com/articles/s41588-021-00877-0) has fewer stages and faster, but is unlikely to make much of a difference unless you are working on very large datasets. It has a tutorial [here](https://github.com/rwdavies/QUILT).

Which one you use probably doesn't matter a whole lot, but I would probably suggest using QUILT as it has fewer stages and is slightly more recent.

> **Comments:**
> **username_0**: I am interested in using Beagle. But it doesn't have good documentation and tutorial. Can you suggest any?  
> **username_2**: Only beagle4 is suitable for low coverage data and it’s pretty old and doesn’t scale well, so I’d advise against using it unless you have a good reason to?  
> **username_0**: Also, I guess Shapit is only for phasing. I found some tutorial on Shapit+impute2 https://mathgen.stats.ox.ac.uk/impute/impute_v2.html#examples. What do you suggest about it?  
> **username_0**: Glimpse or Quilt which one is much older?  
> **username_0**: Also, there is a new Beagle5.2 version. Will it be suitable for low pass sequencing?  
> **username_2**: GLIMSE is slightly older, Beagle5.2 is not suitable for low coverage and neither is the shapeit2 and impute2 tutorial you linked.  
> **username_0**: Thanks for your comment. But I found a paper on the performance of phasing and imputation tools https://www.karger.com/Article/Fulltext/489758. The data here has pretty low depth(4x). It suggests that beagle 4 or shapeit+impute2 has a good performance. Can you tell me more elaborately why are you telling that shapeit+impute2 is not a good choice for low pass sequencing? Maybe add your suggestion in the answer?  
> **username_2**: I suppose it depends on what you consider 'low-pass'. I am referring to data around 0.5-1x coverage, so the results from that paper aren't really relevant to that case, and it predates the release of GLIMPSE and QUILT. Please read the papers I linked for more details. I think that’s everything I’ve got to say about this, as comments are not for extending questions and discussions.  
> **username_2**: That said, if you want to add in more specific detail about the coverages you are interested in, I will amend the question.  
> **username_1**: The author of shapeit (Olivier) once told me that shapeit2 imputation simply doesn't work below 4x. I have no reason to doubt that assessment.  
> **username_1**: I investigated a few different ways of running beagle v3 (same algorithm as v4) for this paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4522619/ i found that beagle scaled really poorly in run time with ref panel size. This is why we went just with asian 1kg haplotypes. I think beagle could work for you. There's also quite a bit about that testing in my thesis: https://ora.ox.ac.uk/objects/uuid:19ce0c8e-1d65-44ff-b56c-77ee849b2167  
> **username_1**: That said, the imputation using beagle on 11k whole genome samples (1.7x) took around 300k cpu hours. I would expect quilt to require less compute time and probably get you similar results.  

---

