The problems with the VCF model
This and the next posts were mostly written on the plane when I felt tired and did not have Internet connections. The logical flow is not very clear. In addition, I have to admit that I have not thought through the topic when I was writting it up. Now I have a clearer picture after I finish the posts. I will still put them online for a historical record.
VCF represents a variant by substituting the reference allele sequence with the variant allele. Substitution is a type of edit. In fact, HGVS, GVF and most other variant formats or representations are edit-based. However, edit-based representations have an intrinsic problem: edits are determined by the alignment between the variant allele and the reference allele, and alignments are affected by the scoring system. As a result, one allele sequence could be represented in multiple ways. Here is an example:
Ref: AAGCTA--CTAG----CT AAGCTA------CTAGCT Allele: AAGCTAGACTAGGAAGCT or AAGCTAGACTAGGAAGCT (2 gap opens, 0 mismatch) (1 gap open, 2 mismatches)
In this example, the allele sequence is the same, but in the VCF format, it could be represented in two different ways. This is a serious problem. Some blame VCF but this is not exactly fair. We have had the problem for over a decade. We were not aware of it only because we haven’t dealt with so many sites and samples.
A possible solution to the one-variant-multiple-representation problem is to use a context-based representation. I will not go into details in this post except pointing out that adopting a context-based representation requires a huge shift in the modeling of genetic variants and will take time. Those who are interested in this topic should read the preprint by Benedict et al (2014).
Multiple alleles per record
In VCF, we frequently have to squeeze multiple alleles in one VCF line; otherwise we will not be able to represent a diploid genotype with two ALT alleles sometimes. Then what is the rule to combine multiple alleles? The VCF spec doesn’t specify. Conventionally, we merge all overlapping alleles into one VCF line. Why couldn’t we promote this convention into the spec? It is because the convention leads to various issues.
Firstly, merging overlapping alleles doesn’t practically work with long deletions. Then when should we merge and when shouldn’t? There are no consistent solutions.
Secondly, merging is sensitive to rare variants. Suppose we have two common SNPs at position 1002 and 1005 among 100,000 samples. The two SNPs will be put on separate VCF lines as they have no overlaps. However, if the 100,001st sample has a deletion from position 1001 to 1006, we will have to squash the two SNPs and the one deletion into one VCF line, but this complicates the annotation and the analysis of the two SNPs. Such a scenario may happen often given many samples.
Thirdly, merging is not always possible when data are unphased. Still consider the example above. If the two SNPs at 1002 and 1005 are unphased, we will not know how to join them for each sample. (TODO: how bcftools merge works?)
Fourthly, merging multi-sample VCFs will add many “./.” or “0/0” genotypes. The resultant VCF is usually much larger than the sum of input. Merging is not scalable. Strictly speaking, the space inefficiency is caused by the dense representation of VCF, which I will come back shortly.
As a consequence of the points above, VCF merging as is required by the multi-allele-per-record representation is a complex, expensive, inconsistent and indeterministic operation. It effectively creates a boundary between VCFs produced from different projects and hampers data integration. In addition, the multi-allele-per-record representation also makes annotation harder because we annotate individual alleles, not a VCF line. Furthermore, not every VCF follows the non-overlapping convention. The inconsistencies between VCFs are frequently troublesome.
The VCF representation also raises a serious theoretical concern: what does each VCF line stand for? It is in fact this conceptual ambiguity that leads to all the problems in this section.
VCF encodes a matrix of genotypes (site-by-sample) with a dense representation whereby it explicitly gives the genotype of every cell. For many samples, this matrix is sparse in the sense that the vast majority of cells are “0/0” (if the VCF is produced by multi-sample calling, including gVCF merging) or “./.” (if produced by merging). The more samples, the more sites, the more sparse the matrix, and the more space and computation VCF costs. VCF is not scalable.
I have listed three major problems with the VCF model: edit-based representation, multiple allele per record and dense representation. While I am not sure how to solve the first problem without disruptively trashing our established practices on variants, a few believe it should be possible to solve the other two problems. This will be explained in my second post of this sequel.
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