Table 2 Filters, priors, and findings
From: Filtering genetic variants and placing informative priors based on putative biological function
| Â | Filter | Prior | Conclusions | Annotation |
|---|---|---|---|---|
Almeida et al [36] | ||||
| Â | Functional annotation, LD-corrected effective number of tests | None | LD-correction in WGS reduces multiple-testing burden by 85Â %, significant associations: PFH14 with SBP, MAP4 with DBP | Location: ANNOVAR; functional annotation: PolyPhen, SIFT |
Liu et al [37] | ||||
| Â | IBD sharing | None | No significances, ZPLD1 had strongest evidence | IBD mapping: BEAGLE; functional annotation: CADD |
Kim and Wei [27] | ||||
|  | Sliding window on MAF ≤5 % SNVs | SNV-weights: based on MAF or regulatory importance | Significant association: SNUPN | Functional annotation: ENCODE, RegulomeDB, PolyPhen2 |
Zhang et al [28] | ||||
|  | Genes, exome-sequence | SNV-weights: up-weight protein binding sites, apply direction weights | Top-ranked genes differ between weighted burden tests LRT, C-α, CMC; but good overlap with literature | ANNOVAR, variant tools; random forest classifiers assign SNVs to protein binding sites; DSSP, PSAIA, DOMINO |
Malzahn et al [30] | ||||
| Â | Gene covering LD-blocks | SNV-weights: using MAF | SKAT: power depends on SNV weights, exploiting LD is very beneficial, optimal strategy for joint testing rare and common SNVs depends on LD structure | Haploview with HapMap data for LD-calculation |
Overall weight: on rare SNV variance component in SKAT | ||||
Ho et al [33] | ||||
|  | Rare SNVs in genes with >1 and <50 rare SNVs (MAF < 0.01) | p value weights: improve gene ranking | Power of burden tests improved by incorporating phenotype associated gene expression into p value weights | Genes: hg19; GO biological process categories |