I am trying to understand the benefits of joint genotyping and would be grateful if someone could provide an argument (ideally mathematically) that would clearly demonstrate the benefit of joint vs. single-sample genotyping.
This is what I've gathered from other resources (Biostars, GATK forums, etc.)
- Joint-genotyping helps control FDR because errors from individually genotyped samples are added up, and amplified when merging call-sets (by Heng Li on https://www.biostars.org/p/10926/)
If someone understands this, can you please clarify what is the difference on the overall FDR rate between the two scenarios (again, with an example ideally)
- Greater sensitivity for low-frequency variants - By sharing information across all samples, joint calling makes it possible to “rescue” genotype calls at sites where a carrier has low coverage but other samples within the call set have a confident variant at that location. (from https://software.broadinstitute.org/gatk/documentation/article.php?id=4150)
I don't understand how the presence of a confidently called variant at the same locus in another individual can affect the genotyping of an individual with low coverage. Is there some valid argument that allows one to consider reads from another person as evidence of a particular variant in a third person? What are the assumptions for such an argument? What if that person is from a different population with entirely different allele frequencies for that variant?
Having read several of the papers (or method descriptions) that describe the latest haplotype-aware SNP calling methods (HaplotypeCaller, freebayes, Platypus) the overall framework seems to be:
- Establish a prior on the allele frequency distribution at a site of interest using one (or combination) of: non-informative prior, population genetics model-based prior like Wright Fisher, prior based on established variation patterns like dbSNP, ExAC, or gnomAD.
- Build a list of plausible haplotypes in a region around the locus of interest using local assembly.
- Select haplotype with highest likelihood based on prior and reads data and infer the locus genotype accordingly.
At which point(s) in the above procedure can information between samples be shared or pooled? Should one not trust the AFS from a large-scale resource like gnomAD much more than the distribution obtained from other samples that are nominally party of the same "cohort" but may have little to do with each other because of different ancestry, for example?
I really want to understand the justifications and benefits offered by multi-sample genotyping and would appreciate your insights.