Question: 1. How did it come to be that the alternate nucleotide was more frequent than the reference nucleotide?
2. How does one account for this phenomenon when designing a strategy to filter for variants of interest? Should I go through the complicated process of selecting those individuals who DO NOT have the variant and calculate that the REFERENCE frequency in the population is probably around (1 - esp6500siv_all)?

I am researching a rare disease and have whole exome sequence data with the corresponding variant calls. Each variant call has been passed to annovar and among other data, we have looked up the frequency of the variant in the esp6500siv2_all data. Clearly a variant that was observed to have a high frequency in our sample but that had low frequency in esp6500siv2_all would be of disproportionate interest.

Low and behold I was surprised to find that 13% of the all of our variants (4055 out of 32131) had an allele frequency that was greater than 0.5. How can that be? I expected that all the allele frequencies would be <0.5.

I had thought that the variants would be akin to a minor allele frequency (MAF). Clearly I was wrong. I pulled 3 random variants from among the variants that had more than 0.5 frequency, to check them against the Exome Variant Server.

avsnp147 Chr Start End Ref Alt Gene.refGene esp6500siv2_all
1: rs3803530 15 89632842 89632842 C A KIF7 0.5373
2: rs621383 3 125118840 125118840 T C SLC12A8 0.9988
3: rs633561 11 64229857 64229857 A G NUDT22 0.9418

Looking up at NHLBI Exome Sequencing Project (ESP) [Exome Variant Server][2] and using All Allele

1. rs3803530: C>A; A=6984/C=6014 which means A is 6984/(6984+6014) or 0.537
2. rs621383: T>C; C=12479/T=15 which means C is 12479/(12479+15) or 0.999
3. rs633561: A>G; G=12240/A=756 which means G is 12240/(12240+756) or 0.941

[1]: https://drive.google.com/file/d/1jA7Azm2f1ss_rw3NoVgMSJ75tBqAf_qt/view?usp=sharing
[2]: http://evs.gs.washington.edu/EVS/

Answer: This is due to the fact that the very reference genomes that we use for re-alignment are themselves based on individuals who carry rare risk alleles. Thus, when we call variants against these genomes, we are, at many loci, comparing against rare disease risk alleles.

As the best/worst example (depending on your point of view), hg19 / GRCh37 was used for more than a decade as the primary reference genome, yet ~70% of the genomic sequence of this genome was based on a single individual from the Buffalo area, New York, USA. Amongst the many 1 000s of rare disease susceptibility alleles that this individual carried was one called *Factor V Leiden*, which statistically significantly increases the risk of deep vein thrombosis (DVT). If you're researching DVT (I was), you have to be aware of this.

Thus, if I perform exome-seq on an individual who does not have *Factor V Leiden* and re-align the data to hg19 / GRCh37, the *Factor V Leiden* variant position will show a SNV because the reference allele in my patient sample (which doesn't increase risk of DVT) is being compared against the disease allele that's contained in the very reference genome against which I'm re-aligning my data. Without careful screening, I may assume that my patient has increased risk of DVT, erroneously so.

There was a publication on this listed in PubMed but it's very difficult to find, even by Google. It's a critical problem yet has not received the attention that it deserves.

**Edit June 2, 2021: much later, I found it: THE REFERENCE HUMAN GENOME DEMONSTRATES HIGH RISK OF TYPE 1 DIABETES AND OTHER DISORDERS**

The situation improved with hg38 / GRCh38, as this reference build was based on much more individuals, but the same problems still persist, broadly speaking.

So, you really have to get to know your target panel and all of these nuances related to whatever variants you're studying., particularly if you're dealing with live patient data.

Kevin

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**Update 3rd January 2018**

It has come to my attention that there is an automated method to search for these types of variants in your VCF:

- http://rmahunter.bioinf.me/
- https://github.com/bioinf/RMAhunter