<p>I'm assuming that this is such a common question that I don't have to do my own imputation...</p>
<p>I have a SNP from a paper that is associated with a specific trait... Actually I have 8 of them:
<a href="https://www.ebi.ac.uk/gwas/publications/33307546" rel="nofollow noreferrer">https://www.ebi.ac.uk/gwas/publications/33307546</a></p>
<p>However, when I search my SNP data from 23andMe (v3 NOT v5), only one of them is in the set of SNPs used by that chip, namely rs2109069.</p>
<p>I'm wondering how I find the closest SNP in my v3 data for the remaining 7 SNPs missing in that data.</p>
<p>Is there a quick way to find a 'tag' SNP in v3 for a given 'random' SNP?</p>
<p>I'm aware that a tag SNP will only be as good (on average) as the LD between it and the given SNP.</p>

CC BY-SA 4.0
2022-02-08T13:27:17.233Z
What is a "closest SNP"? You mean in distance? Why would that be relevant? Don't you want to look for pathogenic or at least LP or VUS variants? Why would the nearby SNP be of interest?
terdon
CC BY-SA 4.0
2022-02-09T18:26:44.597Z
Sorry, thought it was clear. SNP x is associated with a trait but isn't on the SNP-Chip. SNP y is in LD with X and *is* on the chip.
Dan Bolser
CC BY-SA 4.0
2022-02-09T19:07:36.130Z
So you want to try and guess at the variants your sample carries based on their linkage disequilibrium values? And is this using the raw, unfiltered data of 23&me? (if the answer is yes, please don't do this, their raw data are not supposed to be used that way and are not reliable at all).
terdon
CC BY-SA 4.0
2022-02-11T09:04:59.530Z
I don't understand... you mean I cant run 23andMe v3 data through imputation?
Dan Bolser
CC BY-SA 4.0
2022-02-11T09:11:23.660Z
No, I mean you shouldn't trust 23&me data beyond the filtered results they show you. If you have downloaded the full, unfiltered dataset, that will contain _loads_ of false positive calls that should not be considered. Imputation won't be a problem, I just find it a bit odd that you would use what is essentially a statistical approach (using LD values as a proxy to guess what variants are really present) when looking at a single, specific sample. Is getting proper sequencing data so you can actually see what you have in the loci of interest not an option?
terdon
CC BY-SA 4.0
2022-02-14T10:52:53.540Z
Yes it's not an option. Please can you provide a reference for the fact that 23andMe 'unfiltered' data contains a lot of false positives?

I had understood that SNP-Chip accuracy was very high when validated by WGS techniques.
Dan Bolser
CC BY-SA 4.0
2022-02-14T11:07:51.587Z
I don't have a reference, but I have looked at the unfiltered results of four different (healthy) samples and all of them had variants that, if true, would have made these healthy individuals very, very sick or, more likely, dead. Variant calls need to be filtered and the 23&me unfiltered results are just a list of variants with no information on call quality, coverage, fastq quality or anything that can be used to sort false positives from true calls. That's why they only show the filtered results.
terdon
CC BY-SA 4.0
2022-02-14T11:55:12.773Z
You're talking about sequencing based variation calls. I'm talking about SNP-Chip based calls. As far as I know, 23andMe isn't doing any sequencing commercially.
Dan Bolser
CC BY-SA 4.0
2022-02-14T12:54:57.570Z
All I can tell you is that in all 4 cases I have checked, the _unfiltered_ data, the dataset you can download but is not shown in their web interface, was riddled with obviously unreliable calls.
terdon
CC BY-SA 4.0
2022-02-15T16:08:10.280Z
That makes no sense. For example, I ran my own 23andMe data through GenomePrep (results here: https://supfam.mrc-lmb.cam.ac.uk/GenomePrep/results.html?id=48318966999) and you can see that 80% of the calls match the reference. Sure 20% don't, but I wouldn't be me if they didn't.
Dan Bolser
CC BY-SA 4.0
2022-02-15T16:12:48.013Z
As you wish. I can only share my experience based on what I have seen and my ~7 years of working in NGS variant calling in a clinical setting. I would never trust _unfiltered_ 23&me results. Their filtered stuff is fine, but _unfiltered_ variant calls are generally not reliable.
terdon
CC BY-SA 4.0
2022-02-16T16:12:05.617Z
That's true when it comes to GBS data, but as I said, 23andMe DON'T do GBS, they use SNP-Chips, which are generally regarded as very reliable.
Dan Bolser
CC BY-SA 4.0
2022-02-16T16:19:33.897Z
And I'm sure their filtered data are indeed reliable. Not so their unfiltered data, at least not in my experience. There's a reason they don't show the unfiltered results, after all. Here's a paper that seems to confirm my experience (I only glanced at the abstract): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879796/: "_SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation._"
terdon
CC BY-SA 4.0
2022-02-17T10:01:00.877Z
This is weird... You don't seem to be understanding what I'm saying, but you clearly are very good in the field, so I'm confused. Perhaps we should have a call, because we seem to be talking over each other. e.g. of course a SNP chip won't see a rare variant... it's totally beside the point... Somehow I think we're talking about different things.
Dan Bolser
CC BY-SA 4.0
2022-02-17T10:17:37.210Z
Heh, I'm getting the same impression :)  Feel free to ping me in [the site's chat room](https://chat.stackexchange.com/rooms/58858/bioinformatics). Might be easier there.
terdon
CC BY-SA 4.0
2022-02-17T16:11:35.687Z
Also, while I do have experience with NGS in general and NGS in a human clinical setting in particular, I do not have any to speak of with SNP chips. I just know that the _unfiltered_ 23&me data I have seen have consistently been crap. I may very well be wrong or may have misunderstood what you're doing entirely.
terdon
<imputation><snp-chip>
Given a 'random' rsID, how can I find the closest (LD) SNP in a given set of SNPs?
# Given a 'random' rsID, how can I find the closest (LD) SNP in a given set of SNPs?

(Asked by: username_0 on 2022-02-07)

I'm assuming that this is such a common question that I don't have to do my own imputation...

I have a SNP from a paper that is associated with a specific trait... Actually I have 8 of them:
<https://www.ebi.ac.uk/gwas/publications/33307546>

However, when I search my SNP data from 23andMe (v3 NOT v5), only one of them is in the set of SNPs used by that chip, namely rs2109069.

I'm wondering how I find the closest SNP in my v3 data for the remaining 7 SNPs missing in that data.

Is there a quick way to find a 'tag' SNP in v3 for a given 'random' SNP?

I'm aware that a tag SNP will only be as good (on average) as the LD between it and the given SNP.

**Tags:** imputation, snp-chip
> **Comments:**
> **username_1**: What is a "closest SNP"? You mean in distance? Why would that be relevant? Don't you want to look for pathogenic or at least LP or VUS variants? Why would the nearby SNP be of interest?  
> **username_0**: Sorry, thought it was clear. SNP x is associated with a trait but isn't on the SNP-Chip. SNP y is in LD with X and *is* on the chip.  
> **username_1**: So you want to try and guess at the variants your sample carries based on their linkage disequilibrium values? And is this using the raw, unfiltered data of 23&me? (if the answer is yes, please don't do this, their raw data are not supposed to be used that way and are not reliable at all).  
> **username_0**: I don't understand... you mean I cant run 23andMe v3 data through imputation?  
> **username_1**: No, I mean you shouldn't trust 23&me data beyond the filtered results they show you. If you have downloaded the full, unfiltered dataset, that will contain _loads_ of false positive calls that should not be considered. Imputation won't be a problem, I just find it a bit odd that you would use what is essentially a statistical approach (using LD values as a proxy to guess what variants are really present) when looking at a single, specific sample. Is getting proper sequencing data so you can actually see what you have in the loci of interest not an option?  
> **username_0**: Yes it's not an option. Please can you provide a reference for the fact that 23andMe 'unfiltered' data contains a lot of false positives?  I had understood that SNP-Chip accuracy was very high when validated by WGS techniques.  
> **username_1**: I don't have a reference, but I have looked at the unfiltered results of four different (healthy) samples and all of them had variants that, if true, would have made these healthy individuals very, very sick or, more likely, dead. Variant calls need to be filtered and the 23&me unfiltered results are just a list of variants with no information on call quality, coverage, fastq quality or anything that can be used to sort false positives from true calls. That's why they only show the filtered results.  
> **username_0**: You're talking about sequencing based variation calls. I'm talking about SNP-Chip based calls. As far as I know, 23andMe isn't doing any sequencing commercially.  
> **username_1**: All I can tell you is that in all 4 cases I have checked, the _unfiltered_ data, the dataset you can download but is not shown in their web interface, was riddled with obviously unreliable calls.  
> **username_0**: That makes no sense. For example, I ran my own 23andMe data through GenomePrep (results here: https://supfam.mrc-lmb.cam.ac.uk/GenomePrep/results.html?id=48318966999) and you can see that 80% of the calls match the reference. Sure 20% don't, but I wouldn't be me if they didn't.  
> **username_1**: As you wish. I can only share my experience based on what I have seen and my ~7 years of working in NGS variant calling in a clinical setting. I would never trust _unfiltered_ 23&me results. Their filtered stuff is fine, but _unfiltered_ variant calls are generally not reliable.  
> **username_0**: That's true when it comes to GBS data, but as I said, 23andMe DON'T do GBS, they use SNP-Chips, which are generally regarded as very reliable.  
> **username_1**: And I'm sure their filtered data are indeed reliable. Not so their unfiltered data, at least not in my experience. There's a reason they don't show the unfiltered results, after all. Here's a paper that seems to confirm my experience (I only glanced at the abstract): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7879796/: "_SNP chips are extremely unreliable for genotyping very rare pathogenic variants and should not be used to guide health decisions without validation._"  
> **username_0**: This is weird... You don't seem to be understanding what I'm saying, but you clearly are very good in the field, so I'm confused. Perhaps we should have a call, because we seem to be talking over each other. e.g. of course a SNP chip won't see a rare variant... it's totally beside the point... Somehow I think we're talking about different things.  
> **username_1**: Heh, I'm getting the same impression :)  Feel free to ping me in [the site's chat room](https://chat.stackexchange.com/rooms/58858/bioinformatics). Might be easier there.  
> **username_1**: Also, while I do have experience with NGS in general and NGS in a human clinical setting in particular, I do not have any to speak of with SNP chips. I just know that the _unfiltered_ 23&me data I have seen have consistently been crap. I may very well be wrong or may have misunderstood what you're doing entirely.  

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## 0 answers

