Hi there,
I am running QC on some GWAS data on 48 samples that we are planning to do linkage analysis with. I have run the --het command in PLINK to check for excess heterozygostity and/or consanguinity.
For every sample I am getting a negative F statistic, indicating there is more heterozygosity that expected. But I'm wondering - how much heterozygosity is too much heterozygosity? Is there a threshold that would indicate sample contamination or is it solely based on the distribution amongst the 48 samples?
Here is an example of the -- het results file for a subset of the samples:
IID O(HOM) E(HOM) N(NM) F
1 292543 295900 388776 -0.03604
2 299349 302200 396946 -0.03016
3 298893 302000 396663 -0.03272
4 299188 302600 397491 -0.03591
5 298827 302200 396937 -0.0354
6 274894 283200 372565 -0.09318
7 298750 302500 397353 -0.03951
8 298737 302300 397082 -0.03761
9 299640 302600 397511 -0.03138
Any help would be greatly appreciated!
Thanks,
Caragh
2 answers
According to this link:
"The estimate of F can sometimes be negative. Often this will just reflect random sampling error, but a result that is strongly negative (i.e. an individual has fewer homozygotes than one would expect by chance at the genome-wide level) can reflect other factors, e.g. sample contamination events perhaps."
Ram when you said these negative values are "negligible" I assume you meant that those individuals would not need to be removed.
However, I feel it would be helpful if you could provide a more objective definition of negligible (i.e. provide numbers)? At what values do these negative scores no longer become "negligible" (e.g. -0.5, -1.8 something else) and should be removed?
That's a wonderful question, and unfortunately I do not have a strong reason for picking a definite threshold. Part of it is owing to what we saw in the majority of the samples that were sequenced, and part of it was just practice that was carried over. We had a threshold of around -0.2. Samples with a lot of contamination would drop out in other steps of the pipeline as well, and by the time we got to measuring an F statistic, one would rarely see values cross -0.25 - the pipeline discarded samples at stages including sample prep, sequencing, and other metrics were also used pre-F stat QC stages. For example, we would use a filter to obtain a high quality set of variants that contributed to the F statistic (and other such statistics), so any underlying condition that affected the sample would strongly affect what we saw.
All said and done, it was still subjective to an extent in that it worked for our consortium.
Thank you for that detailed response.
It makes sense that those samples suffering from contamination are dropping out in other (upstream) steps of the QC pipeline.
Furthermore, certain things we do in bioinformatics analysis can often be subjective in the sense that they are practices carried over (within a lab/consortium/sub-field) and not necessarily objective standards (i.e. benchmarking) but they just work
According to this link:
"The estimate of F can sometimes be negative. Often this will just reflect random sampling error, but a result that is strongly negative (i.e. an individual has fewer homozygotes than one would expect by chance at the genome-wide level) can reflect other factors, e.g. sample contamination events perhaps."
When you say these negative values are "negligible" what does that mean exactly? Do you mean that the contamination is negligible; that these individuals do not need to be removed? Or do you mean something else?
I found a slide-deck from the Armidale Genetics Summer Course (2016) and on slide 41 you can see that Heterozygosity scores (F) range from 0.21 to 0.26 and potential outliers have been flagged < 0.23 and >0.24.
So from that example above it seems very much like the outliers were chosen based on the scores from the entire dataset rather than some fixed value.
Say for example that one has their heterozygosity values ranging like this: 
.
Where should one choose cutoffs?
At what values do these negative scores no longer become "negligible" (e.g. -0.025 or -0.15)?
If one has very low F score for say one or two samples (e.g. around -1.8) that is clearly a case of contamination of that individuals genotype and should be removed.
However, does that say anything about the reliability of the raw data as a whole (all of the sampled genotypes)?
I believe this adds to the top-level question by seeking greater clarity on what @Caragh asked: "how much heterozygosity is too much heterozygosity? Is there a threshold that would indicate sample contamination or is it solely based on the distribution amongst the 48 [n] samples?
Excellent point Ram, I've added it as a comment now and apologize for posting as an answer originally - it was not.
I feel your answer could be enriched by expanding upon what you meant when you say "negligible". Could you please, in a more objective way, define what that means? (e.g. providing numbers). Looking forward to your sage advice.
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