Study-level nSample fallback for per-variant N - #558
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GwasSumStats() gains an optional nSample slot (per-study total sample size). summaryStatsQc() now resolves N by a 4-level priority: per-variant case/control -> study case/control -> per-variant N -> study nSample. The new level fills df$N from the study scalar when a region carries no per-variant N and no case/control counts, so those studies no longer drop out at the N-cutoff filter.
loadGwasSumStatsFromManifest now forwards an nSample manifest column to GwasSumStats(nSample=), and builds a study's entry with no per-variant N when the manifest supplies a study-level scalar (nCase/nControl or nSample) - threaded as an allowNoN flag down to .resolveSumstatCols. The no-N error still fires when neither a per-variant source nor a study scalar exists. This lets the study-N fallback run end-to-end (construct.R delegates entry construction to this loader).
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What
GwasSumStats()gains an optionalnSampleslot (per-study total sample size), andsummaryStatsQc()resolves per-variantNby a 4-level priority:N_CASE/N_CONTROL→ effective NnCase/nControl→ effective NNcolumn → as-isnSample→ filled for every variantLevels 1–3 are the existing behavior; level 4 is new. When a region has no per-variant
Nand no case/control counts,df$Nis filled from the study scalar instead of the study dropping out at the N-cutoff filter.nSourcerecords which level was used (effective/column/total/study-n).Testing
test_sumstatsQc.Rprecedence matrix + study-n fallback tests, green against the modified pecotmr + susieR 0.16.6nSource=study-n; per-variant counts present → counts win