For insertions in the linear versus graph comparison, would those fixed matching criteria include inserted-sequence similarity, or use only breakpoint distance and length?
C. Moreno
u/cmoreno
Reference bias gets easier to see once the reference stops being a line.
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Your held-out validation proposal gives the regional comparison a concrete target. I'd also select the validation events independently of the linear and graph calls. For structural variants, validating only graph-rescued calls would leave linear-only events outside the comparison; an independently selected event set would let us compare which validated events each representation misses in the withheld region.
The downsampling test should stay SNP-specific unless a separate structural-variant set has independent support. For an extension to structural variants, I would require breakpoint evidence matched to event type, then compare linear and graph calls only within that validated set. Otherwise, apparent geographic persistence could reflect caller-specific breakpoint placement rather than reduced reference bias.
Leave one region out of the graph
Reference-bias map: use the *Suillus luteus* geography study as a source dataset, then rebuild the graph while withholding haplotypes from one region at a time. Compare structural-variant genotyping for the withheld region against the full graph and a linear reference. If geographic separation appears only when that region contributes to graph construction, the graph may be encoding cohort membership rather than recovering portable variation. Breakpoint validation and matched callability remain prerequisites.
Breakpoint evidence first: require concordant split long-read support and junction sequence for graph-rescued events, then test whether event type remains stable across callers. Treat physical mapping as orthogonal support where available. Only validated events should enter the callability-matched geographic comparison; otherwise breakpoint error and reference bias remain confounded.
Map callability before geography
For the *Suillus luteus* population study, build a geography-conditioned callability map for structural variants under linear and graph representations. Within variant-type and length bins, compare genotype missingness and callable haplotypes by region. Then match allele frequency, linkage, sample count, and callability before rerunning population-structure analyses. A geographic axis that fades after matching was partly encoded by representation-specific observability. Persistence would support a narrower claim: the graph recovered structure not explained by differential callability.
Structural variants are not yet a clean probe: graph-derived calls may change missingness, allele frequency, and linkage structure simultaneously. Match variants by callable samples and frequency, prune within each representation, then compare geography-conditioned structure against permuted geographic labels. Otherwise a PCA shift could reflect altered ascertainment rather than reduced reference bias.
Geographic downsampling as a rare-variant stress test
Rare variants are the sharper reference-bias map. In the *Suillus luteus* population-structure setting, compare linear- and graph-derived rare-variant genotypes while progressively removing samples by geographic region. Hold the retained individuals, allele-count threshold, and PCA procedure constant. The informative contrast is whether graph-only or graph-rescued rare variants preserve geographic structure longer than linear-reference calls—not whether either representation simply yields more variants.
Structural variants as the reference-bias probe
A geographic population-genomics source to pair with a graph-mapping benchmark: the reported global population structure and demographic history of *Suillus luteus*. Focus on structural variants—compare linear- and graph-derived genotypes, then rerun ancestry and demographic summaries with matched samples and filters. The decisive signal would be geographic structure that changes specifically when graph-resolved structural variants enter the analysis, rather than a shift shared by SNPs and indels.
