Filtering genetic variants and placing informative priors based on putative biological function

BMC Genomic Data

Table 1 Statistical tests and analyzed data

	Marker data	Data set	Statistical tests	Covariates	Trait(s)
Almeida et al [36]
	Sequence	Family study	Single-variant regression in SOLAR	Smoking, BP medication, PC1-3, sex, age, age², sexage, sexage²	Real SBP and DBP at first time point, own simulated trait for H₀
Liu et al [37]
	Chr3: GWASmp and sequence	Unrelated individuals (from family study)	Regress pairwise DBP residual difference and sum on IBD sharing status; sequence data analyses by SKAT-O	Sex, age, smoking, PC 1-3	Real DBP at first time point
Kim and Wei [27]
	Sequence	Family study	Informative SNV weights in burden test T5 and SKAT; with R: seqMeta	Age, sex, smoking, BP medication	Real SBP at earliest available measurement
Zhang et al [28]
	Exome sequence	Unrelated individuals (large Hispanic sample)	LRT, C- $α$ , CMC on informatively weighted SNV burden	None	Simulated HT status; real SBP, DBP with cutoffs for case-control status
Malzahn et al [30]
	Sequence and GWASmp	Family study	SKAT with R (coxme, kinship2, QuadCompForm); strategies for joint testing of rare and common SNVs	Sex, age, sex*age; subjects not on BP medication	Real and simulated SBP at first time point
Ho et al [33]
	Sequence and GWASmp	Family study, including gene expression data	Seq-aSum-VS burden test; regression on gene expression data; gene set enrichment analysis	PC1-3	Average real SBP and DBP

BP blood pressure, Chr Chromosome, CMC Combined multivariate collapsing, DBP diastolic blood pressure, GWASmp genome-wide association study marker panel, HT hypertension, IBD identity-by-descent, LRT likelihood ratio test, PC principal component, SBP systolic blood pressure, SKAT sequence kernel association test, SNV single nucleotide variant, Seq-aSum-VS sequential sum

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