PRS Results from my Genomics Data (mostly from impute.me)

I haven't had a whole lot of personal experience with Polygenic Risk Score (PRS) estimates, so I thought it was interesting when I found a couple options for re-analysis of my own genomics data (for selected examples):

Association	SNP chip (impute.me) (Folkersen et al. 2020)	Other Re-Analysis Options	23andMe Results
Type 2 Diabetes (No)	(Type 2 Diabetes, 146 variants) Average / Above Average (23andMe-V3, 12/19) Average / Above Average (AncestryDNA, 12/19)	MySeq 1.000 risk ratio [error] (Nebula lcWGS) 0.955 risk ratio (Genos Exome, 3 variants) 1.089 risk ratio (Veritas WGS, 6 variants)	"Typical Risk" of 23% (directly from 23andMe, PRS with 1,244 loci) [actually, slightly lower than normal] Reduces to less than 1% when age, height, weight, fast food consumption, and exercise rate are taken into consideration (also from 23andMe)
Ulcerative Colitis (once, so I think really "no")	(23 variants, and 116 variants) Both Below Average and Above Average Risk, for different PRS (23andMe-V3, 12/19) Both Below Average and Above Average Risk, for different PRS (AncestryDNA, 12/19)
Anxiety Disorder (Yes, but getting better)	(6 variants) Average / Above Average (23andMe-V3, 12/19) Average / Above Average (AncestryDNA, 12/19)
Migraine (Periodic)	(26 variants, and 21 variants) 2 PRS (Average and Above Average) (23andMe-V3, 12/19) 2 PRS (Average and Above Average) (AncestryDNA, 12/19)
Eye Color (Light Brown)		DNA.land Likely to have Brown Eyes (23andMe-V3, 12/19) Likely to have Brown Eyes (23andMe-V3_V5, 12/19) Likely to have Brown Eyes (AncestryDNA, 12/19)	23andMe reports that I am expected to have "brown or hazel eyes" based upon 1 SNP (rs12913832)
Hair Color (Light Brown)	See Below (Roughly 25% Red and 50% Blonde)		For "Light or Dark Hair", 23andMe reports that I have "Likely Dark" Hair (using 42 SNPs) For "Red Hair" 23andMe reports that I am "Unlikely to have red hair" (using 3 MC1R SNPs: rs1805007, rs1805008, and another custom MCR1 probe)
Height (180 cm)	See Below	DNA.land 171 cm: "Likely Taller than Average" (23andMe-V3, 12/19) 171 cm: "Likely Taller than Average" (23andMe-V3_V5, 12/19) 171 cm: "Likely Taller than Average" (AncestryDNA, 12/19)

Individual SNP risks were reported (from impute.me).  While I had a bit of a hard time finding the precise overall risk estimate (without trying to sum / multiply separate risks), this might be OK in terms of getting a sense of whether I was an outlier or not.  For example, being above or below average for "Type 2 Diabetes" seemed to vary (unless you say most people were under something like a null distribution for "average" risk).  In other words, I thought the following plots (which you could see for various traits) were interesting:

impute.me Type 2 Diabetes PRS (23andMe V3)

impute.me Ulcerative Colitis (1st entry, 23andMe V3)

impute.me Ulcerative Colitis (2nd entry, 23andMe V3)

impute.me Anxiety Disorder PRS (23andMe V3)

impute.me Migraine-Broad PRS (23andMe V3)

impute.me Migraine PRS (23andMe V3)

impute.me Hair Color (23andMe V3 + Ancestry DNA, respectively)

impute.me Height (23andMe V3)

I thought the anxiety disorder result was interesting for 2 reasons.  First, I have had issues with anxiety problems (for example, you can click here for notes, even though they are primarily related to PatientsLikeMe).  Second, notice the environmental component is larger than the genetics component.  This matches my concerns that I expressed in this review of "blueprint".  For example, I would say the predictive power from birth has some notable limitations (such as difficulties in the need to take medication at any given point in your life).

While I am not sure if the exact right term was used (since I thought "Ulcerative Colitis" was a condition, rather than a symptom).  However, I was hospitalized for Ulcerative Colitis (even though that was a one time occurrence caused from E. coli with Shiga toxin).

I also get migraines.

I don't have Type 2 Diabetes, but I provided that because I also had other PRS results to compare.  Similarly, if others have suggestions where I can quickly compare to the impute.me PRS results, please let me know and I would be very happy to add them!

For example, I did add DNA.land (and 23andMe) Eye Color and Height based upon a Twitter response.  While I think height is one of the more heritable traits, DNA.land couldn't guess my actual height within a few inches (and there is a noticeable spread of points for the impute.me plot above).  Even though DNA.land gave lower confidence to other predictions, I would say these have been "fair" rather than "high" confidence (and everything else probably should have been "low" confidence).  I am close to the diagonal for the impute.me plot, but I don't know if the scale is 1:1.  For example, my DNA.land height prediction was off by 3-4 inches.  However, to be fair, note that the highest and lowest percentiles for high don't have overlap (there are not any points in the upper-left or bottom-right regions of the scatter plot, even though those make up a smaller fraction of the population).

For comparison, here is the distribution of score for DNA.land (where my true height was greater than anything on the density distribution - perhaps because this was height scaled for female percentiles?):

For impute.me, the predicted hair color shows blondness on the x-axis and redness on the y-axis.  The cyan circle is my actual color (which I filled in), and the while circle is my predicted color.  I think my hair color used to be lighter than it is now (and I think the shade that I reported for myself was a bit too dark), so that is closer to the genetic prediction (perhaps half-way between).

It may be worth noting that 23andMe could predict that I had brown hair and eyes (although I think that covers most people and you need the more rare traits to better calculate accuracy - for example, Francis Collins said that his 23andMe report indicated he had brown eyes when he really had blue eyes, at least 10 years ago).

Again, for comparison, here is the distribution of DNA.land scores for eye color:

I didn't add the AncestryDNA density plots since they looked qualitatively similar to the 23andMe V3 plots (and, on another computer, I had an issue with the percent variance explained appearing in a pie chart that was harder to read).  I also originally intended to test my updated 23andMe genotypes (V3+V5), but I got an error saying that data was already uploaded (from my V3 chip).  However, perhaps I can test those results later, and see if they are still similar.

With a $5 donation, the turn-around time for processing was 1-3 days.

For Genos Exome and Veritas WGS data, I used the BWA-MEM Re-Aligned GATK Variant calls.  However, I think the main conclusion from looking at my diabetes results was that I was of average risk, and I don't believe my own genetic diabetes PRS risk assessment was great without taking additional factors into consideration (for 23andMe, that was a difference between 23% and 1%, after considering BMI, diet, and exercise).

This essentially matches Supplementary Figure S12 for this paper (whose title I respectfully believe can give the reader the wrong impression, and there is at least one objective error that I believe needs to be corrected), where absolute risk explained was usually very low (usually explaining less than 15% of the variation for a trait).  You can also see that the variability explained by "this score" for the impute.me PRS above is estimated to be less than half of the genetic component.

I think the preprint by Brockman et al. 2021 might also have some additional relevant information for this discussion.

In somewhat different contexts, you can also see some notes / concerns about percentiles / indices in the posts on Nebula and basepaws lcWGS results.

Change Log:

12/5/2019 - public post
12/7/2019 - add DNA.land results based upon Twitter reply from Debbie Kennett; revise wording in post
12/8/2019 - mention possible scaling for female height; also fix date for previous log entry.
6/25/2020 - add links to posts with Nebula and basepaws results.  Minor formatting changes.
7/7/2020 - add reference to impute.me paper

4/22/2021 - add reference to another paper

2/4/2024 - change column labels to be more precise

My Genome-wide, Broad-Level Super-Population Ancestry was Robust, but I Observed Some False Positives in Smaller or Specific Segments

You can get an idea of the specific (country) assignments for ancestry in the various sub-folders on GitHub as well as sometimes in reports that I uploaded to my personal genome project page.

First, the good news: most companies indicate that I am mostly of European Ancestry, which is correct.

Second, the mixed news: while there were some findings that were correct, I had concerns about emphasizing a non-trivial false positive rate for some of the more specific ancestry predictions.  For example, I respectfully believe it is inappropriate for 23andMe to encourage travel destinations based upon their ancestry results.

To some extent, the names themselves sometimes indicate a limit to precision.  For example, if the category is "British & Irish" or "French & German," then you already don't have 1 country for a travel recommendation.  While I do have both British and Irish ancestry (and accordingly, those have the best specific marker evidence), I am a little concerned about the basis of some of the more specific assignments that I currently see.  For example, does overall population affect the density of likeihood that I had relatives from London?  If so, I think that would be kind of like assuming I live in either LA or NYC because I am from the United States (technically, I do live in the greater LA area, but I was born in Cincinnati and raised in Atlanta - plus, I think this is probably sufficient to make my point).  Also, it looks like that density plot is somewhat contradictory with the marker status, even within 23andMe.

While this sort of thing may be hard to firmly prove (for example, convergence between companies does not necessarily indicate the result is accurate, which we saw in a different way for my cystic fibrosis result), I have examples of the sort of things which I did or did not consider to be accurate below.

While some of these could be correct, I think it may sometimes be best to think of them like "hypotheses".

Positive Examples of More Specific (Relatively Recent) Ancestry

• AncestryDNA predicted that I had more recent relatives in Tennessee, which is correct (on my mother's side).  However, even that may have had some limits to precision, given that the 1925-1950 interval seems less relevant to what I know.
• 23andMe predicted that I had relatives living in Kingston Parish less than 200 years ago.  This could be correct.  Based upon my other family members, I can tell that this comes from my father's side with a relatively robust prediction of ~2-3% African ancestry (with large segments on multiple chromosomes).
• I thought I had heard that my Great-Great-Grandfather (my Grandfather's Grandfather) was supposed to have been born from family that moved from the Caribbean to the United States (but I don't currently have confirmation of that).  
• I also have consistent reports of Y-chromosome lineage E-M123.  While I am not sure if that is completely consistent with what I have described above, the greater African ancestry could be coming from Great-Great-Grandfather's father's mother's side (and/or his mother's side).

Effect of Filtering 23andMe Ancestry for Results with Higher Confidence Threshold

• While I believe the above explanation for my African ancestry is plausible, there were 2 other specific ancestry predictions that I didn't think were right (and, in fact, those could be filtered by increasing the confidence threshold to 90%)

23andMe V3 Chip Ancestry Results (3/21/2019, 50% Confidence)

23andMe V3 Chip Ancestry Results (3/21/2019, 90% Confidence)

As noted in the GitHub notes, the East Asian & Native American and South Asian results go away with the higher confidence threshold (90%, instead of the default 50%).

What is not as clear from the above plots is that I also have notes of my percent Scandinavian ancestry varying from 11% to 3% (both with the V3 chip, at various times), and this is something that I think should have been called "Broadly European" instead of being assigned to a country that I believe is incorrect).  Accordingly, my Scandinavian ancestry also disappears if I change the confidence interval.  For other 23andMe customers, note the pull-down in the upper-right of the above screenshots.  That is how you can get the more conservative predictions (even though, in my opinion, I think it should be the other way around, where you have to opt-in for more speculative results).

I can also perform chromosome painting re-analysis with RFMix with 1000 Genomes reference samples, which I have shown below:

The overall picture is still that I am of mostly European ancestry.  You now start to get some small SAS (South Asian) predictions, but I think this is consistent with my general suggestion that the smaller segments are more likely to be false positives.

Also, all the segments of African ancestry should be coming from my father's side.  So, even though I think the above plot is good for some sense of overall estimates (for large segments), there is some sort of issue with phasing for my large SHAPEIT/RFMix chr14 segments (all the red should be 1 of my 2 copies of Chromosome 14, similar to my 23andMe results).  However, to be fair, there are chromosome-discordant 50% confidence results in my 23andMe data (with the smaller segments on Chromosome 3), which are on the same chromosome for this particular SHAPEIT/RFMix result (although that may also vary with different random seeds on different days).

Going back to the official 23andMe results, I purchased an upgraded V5 chip, and you can see those results below.

23andMe V5 Chip Ancestry Results (7/11/2019, 50% Confidence)

23andMe V5 Chip Ancestry Results (7/11/2019, 90% Confidence)

You now get a East Asian and Native American segment that remains with the higher confidence threshold.  However, using the same rationale as the SAS RFMix segments, I think the 0.1% segment on chr3 (surrounded by regions that were filtered with the higher confidence threshold) should receive less emphasis based upon the size of the segment.  So, if you ignore that (or just look at the most common ancestry prediction), the results for the V3 and V5 chips are consistent with each other (and other companies) with the broad conclusion that I am of mostly European ancestry.

On the flip side, I should also have some Spanish ancestry, which I don't see with the 90% confidence threshold.  However, I can see that ancestry with 50% confidence on chromosome 3 - in fact, that segment is estimated to be larger (2.1% versus 1.3%) in a later ancestry estimate.  So, unless that ancestry is being represented in another way (defined less precisely), this could be an example of a false negative with the higher confidence threshold.


Free Alternate Ancestry Prediction Options

• I describe these in more detail on the 1000 Genomes re-analysis page for my 23andMe data (on GitHub).  However, I provide the general links here:
• DNA.land (for now?)
• GENOtation
• GEDmatch
• impute.me
• MySeq
• Again, it might be possible to have a false positive from multiple programs.  However, I think it is an overall good thing that you have these free options for re-analysis available.

To be clear, I am defining a difference between ancestry and relatedness.  In 23andMe, these are even in different sections ("Ancestry" versus "Family & Friends").  As mentioned in another blog post (please scroll towards the bottom), I believe the close family predictions should be accurate (even though I got a weird result when I uploaded my 23andMe data to FamilyTreeDNA).

However, to be clear, I think "specific" closely related individual predictions should be accurate (and I could in fact verify predicted relatives up to the range of second cousin on 23andMe and AncestryDNA), and this is the different than the more distant "specific" country assignments.  This matches 23andMe's definition of a "close relative."  However, it is hard for me to assess the accuracy of the confidence estimates for increasingly distant "DNA relative" predictions.

Update Log:

8/4/2019 - public post date
8/6/2019 - minor changes
8/14/2019 - minor changes
8/15/2019 - mention issue of RFMix phasing for African ancestry
8/16/2019 - minor changes
9/15/2019 - change title to just refer to myself
9/16/2019 - add link about DNA.land
10/18/2019 - mention aunt with Turner Syndrome (later removed, along with entire section)
10/22/2019 - minor change
12/2/2019 - add links to inpute.me and MySeq
1/27/2020 - add note about Great-Great-Grandfather (later removed)
1/29/2020 - modify notes (based upon what I could verify, even though I will probably have more revisions)
2/1/2020 - further modify notes
2/2/2020 - further modify notes
2/4/2020 - modify content throughout post (including changing the name of the section related to changing the 23andMe confidence thresholds, as well as removing some other details and the section about my mom's chromosome X)
2/5/2020 - additional changes in wording
2/6/2020 - minor changes
3/10/2020 - minor change

Predicting HLA Types for Array and High-Throughput Sequencing Data

My previous link to my HLA-assignments with varying technologies has the most important table in the middle of the page.  So, I am mostly reproducing that here to make the information easier to view.

	SNP2HLA	HIBAG	bwakit	HLAminer
HLA-A	A*01, A*02 (23andMe) A*01, A*02 (Genes for Good) A*01, A*02 (AncestryDNA)	A*01, A*02 (23andMe) A*01, A*02 (AncestryDNA)	A*01, A*02 (Genos Exome BWA-MEM)	A*01, A*02 (Genos Exome BWA-MEM) A*01, A*68 (Genos Exome BWA)
HLA-B	B*08, B*40 (23andMe) B*08, B*40 (Genes for Good) B*08, B*40 (AncestryDNA)	B*08, B*40 (23andMe) B*08, B*40 (AncestryDNA)	B*08, B*40 (Genos Exome BWA-MEM)	B*08, B*40 (Genos Exome BWA-MEM) B*08, B*41 (Genos Exome BWA)
HLA-C	C*03, C*07 (23andMe) C*03, C*07 (Genes for Good) C*03, C*07 (AncestryDNA)	C*03, C*07 (23andMe) C*03, C*07 (AncestryDNA)	C*03, C*07 (Genos Exome BWA-MEM)	C*03, C*07 (Genos Exome BWA-MEM) C*03, C*07 (Genos Exome BWA)
HLA-DRB1	DRB1*01, DRB1*03 (23andMe) DRB1*01, DRB1*03 (Genes for Good) DRB1*01, DRB1*03 (AncestryDNA)	DRB1*03, DRB1*11 (23andMe) DRB1*03, DRB1*15 (AncestryDNA)	DRB1*04, DRB1*04 (Genos Exome BWA-MEM)	DRB1*01, DRB1*15 (Genos Exome BWA-MEM) DRB1*01, DRB1*15 (Genos Exome BWA)
HLA-DQA1	DQA1*05, DQA1*05 (23andMe) DQA1*01, DQA1*05 (Genes for Good) DQA1*01, DQA1*05 (AncestryDNA)	DQA1*05, DQA1*05 (23andMe) DQA1*01, DQA1*05 (AncestryDNA)	DQA1*03, DQA1*03 (Genos Exome BWA-MEM)	DQA1*02, DQA1*03 (Genos Exome BWA-MEM) DQA1*02, DQA1*03 (Genos Exome BWA)
HLA-DQB1	DQB1*02, DQB1*05 (23andMe) DQB1*02, DQB1*02 (Genes for Good) DQB1*02, DQB1*05 (AncestryDNA)	DQB1*02, DQB1*03 (23andMe) DQB1*03, DQB1*06 (AncestryDNA)	DQB1*03, DQB1*03 (Genos Exome BWA-MEM)	DQB1*02, DQB1*03 (Genos Exome BWA-MEM) DQB1*02, DQB1*03 (Genos Exome BWA)

In other words, my HLA-A / HLA-B / HLA-C types could be identified more robustly than the HLA-D genotypes (which I don't know, since I haven't gotten a regular blood test).  However, my understanding is that those types have a greater priority in defining organ transplant matches (although I'm currently encountering some difficulty finding the reference for that).

The GitHub link also goes a little deeper into how 23andMe is using 2 SNPs to represent 2 haplotypes (across genes) for celiac disease (which I found surprising, but that is done for other diagnostics as well).  I am mostly leaving that out of this section, but I did think it was interesting that HLA was used in 23andMe's "Meet Your Genes" when the SNPs are actually intronic / intergenic (with respect to the RefSeq annotations).

My 23andMe report indicated that I was DQ8-positive but DQ2-negative for my celiac disease risk.  In terms of defining the 2 genes used to define my DQ8-positive status I coloring matching assignments above in magenta (HLA-DQA1*03 and HLA-DQB1*0302).

Here is a screenshot for the variants tested by 23andMe (where I have the "C" variant for rs7454108, for the marker described as "HLA-DQ8"):

Again, as described here, a positive HLA-DQ8 status is defined by having HLA-DQA1*03 and HLA-DQB1*0302.

More recently, I collected Illumina Whole Genome Sequencing data where unaligned reads were provided (from Sequencing.com), along with some amount of PacBio HiFi data from Dante Labs.  There are some parts of the results that are not especially clear to me and I am interested to learn about additional options for analysis.  However, I believe those results are consistent with me having at least one DQB1*03 allele.

In terms of what appears to be consistent between the PacBio data and Illumina Whole Genome Sequencing data, the T1K results from the Sequencing.com Illumina reads indicates that the related HLA-DQB1 allele should be HLA-DQB1*03:02:01.

I ordered additional GlutenID testing from Targeted Genomics, with an uploaded subfolder on GitHub.  However, I believe the potential problem with using this for validation is that the DQ8-DQ8 result is based upon the same 1 SNP as my 23andMe result (rs7454108).  So, I will continue to look into additional validation options.

I am interested to learn more about the broader trends if certain HLA types are harder to assign and/or impute than other HLA types.  I have some notes mentioned in this Disqus comment.  Comments containing relevant feedback is also welcome on this blog post.

Update Log:

8/4/2019 - public post date
8/6/2019 - minor changes
8/15/2019 - add coloring for HLA-DQ8

2/11/2024 - add information / links to Whole Genome Sequencing data (Illumina from Sequencing.com and PacBio HiFi from Dante Labs)

2/18/2024 - add screenshot from 23andMe + dbSNP link; add additional HLA-DQ8 sentence; add small paragraph for Illumina WGS T1K result; add link to Disqus comment; fix minor typos + add tags

2/27/2024 - minor change in column header

3/19/2024 - minor change in column header; add link to GlutenID results

Additional Analysis of AncestryDNA Data

NOTE: Getting Advice About Genetic Testing

I have a friend that asked me what extra information she could get from her raw AncestryDNA data.  I've studied by own 23andMe data extensively, so I thought it would be useful to show what tools can also be applied to raw data from other genetic testing services.

I have not purchased an AncestryDNA kit for myself, so all the analysis that I performed was for somebody else.  Therefore, I will not mention any specific results from this analysis.

First, you may find it useful to convert your raw data to 23andMe format.  You can use this Perl script (AncestryDNA_to_23andMe.pl) to convert your file.  The Perl script can be run using instructions similar to those provided here.

Here are some potentially useful tools to learn more from your AncestryDNA data:

1) Interpretome - free, but requires 23andMe format file
2) Custom scripts - free, but requires 23andMe format file (and probably some comfort with programming)
3) Promethease - can directly use raw AncestryDNA file, but it costs $5.  I would probably recommend trying to use the free options first.

At first, I wasn't certain how well these strategies would work.  For example, I thought AncestryDNA might focus on non-functional regions that wouldn't affect disease risk.  However, I think there is a decent amount of additional information that can be gained from the raw data.

For example, all the functions that I tested in Interpretome worked properly.  Additionally, I found 2,581 AncestryDNA SNPs were listed in the GWAS Catalog.  Of those 2,581 SNPs, 1,337 were risk allele the individual that I tested.  This is less than I had for my 23andMe data (3,050 with GWAS Catalog annoations, 1,626 risk alleles within GWAS Catalog variants), but I think it is decent for an assay that don't provide any health reports to the user.

For those that are interested, here is a venn diagram of the overlapping 23andMe variants (V3 23andMe chip, AncestryDNA result from a few months ago):

You can see that the 23andMe chip covers ~50% more of the genome, but the AncestryDNA chip still covers a fair number of nucleotides.  My only real problem is the lack of labeling data from X, Y, and MT chromosomes as such in the raw data.  There are chromosomes listed as "23" and "25," so I initially guessed that "24" is the Y-chromosome.  However, I noticed that randomly selected rsIDs from chromosomes "23" and "25" both came from the X-chromosome.  So, I don't see a simple way to change those into a standard format.

Nevertheless, I think the information from chromosomes 1-22 provide a lot of information to review.  I hope this post helps AncestryDNA customers find something interesting!