Table 1.

Genotypes, phenotypes, and quality control filters applied by authors of accepted papers in the Population-Based Association group

Contribution	Genotypes	Phenotypes	Quality control
Blue et al. [2]	GWSNPA data for odd-numbered autosomes from 959 subjects in 20 pedigrees WES data for odd-numbered autosomes from 464 subjects in 20 pedigrees	Longitudinal SBP, real and simulated phenotypes	Support vector machine filter, exclusion of variants with more than 10 % missing calls, extracted with VCFtools
Datta et al. [9]	WES data within ULK4 and MAP4 from 1943 unrelated subjects	Cases were defined as persons with a SBP >140 mm Hg, DBP >90 mm Hg or taking antihypertension medication. Other persons, including individuals with a missing medication field, were treated as controls	Exclusion of variants with more than 25 % missing calls or a MAF >0.001, leaving 70 ULK4 and 18 MAP4 variants for analysis
Fernández-Rhodes et al. [7]	GWSNPA data for odd-numbered autosomes from 959 subjects in 20 pedigrees	Hypertension phenotype PHEN simulated based on 984 variants with main SBP effects, and 3 CYP3A43 variants that interacted with medication but showed no main effect	Excluded 92 individuals with missing phenotype data; monomorphic and singleton variants were filtered out. Only the last SBP measurement was considered
González-Silos et al. [1]	WES variants in chromosome 3 from 407 samples with information on blood pressure medication out of 1943 unrelated samples	DBP	Reference and alternative allele counts (AD fields in the FORMAT tag of the vcf file), genotype (GT field in the FORMAT tag) and average genotype quality (GQ field in the FORMAT tag), extracted with VCFtools. Nonbiallelic, monomorphic and variants with a MAF <0.003 were excluded, leaving 8957 variants for analysis
Oh [5]	WES data in MAP4 from 1943 unrelated subjects	Log-transformed baseline measurements of SBP and DBP	Exclusion 92 individuals with missing phenotype data, monomorphic and singleton variants were filtered out
Schwantes-An et al. [6]	WES data in odd-numbered autosomes from 1943 unrelated subjects	Four traits were simulated by the authors under a null hypothesis of no genetic association. The fifth trait was Q1 provided	Alternative allele counts (NALTT field) were extracted with VCFtools and converted to 2-allele genotype calls. Nonbiallelic and monomorphic variants, and variants with more than 5 % missing calls were excluded, leaving 313,340 variants for analysis
Shin et al. [3]	WES data in MAP4 from 1943 unrelated subjects	Real data: Cases were defined as persons with SBP >140 mm Hg, DBP >90 mm Hg or taking antihypertension medication. Other persons, including individuals with a missing medication field, were treated as controls	Excluded 92 individuals with missing phenotype data
			Predicted alternative allele counts (DOSAGE field) were extracted with VCFtools; monomorphic variants were filtered out, leaving 90 variants for analysis
		Simulated phenotypes: Null trait Q1 (dichotomomized) and PHEN, both with disease prevalence of 17.8 %
Thompson and Fardo [4]	Variants in TNN, LEPR, GSN, TCIRG1, and FLT3 including 100,000 base pairs upstream and downstream	Simulated phenotypes Q1 and PHEN on 1943 unrelated subjects	Data extracted with VCFtools; monomorphic variants were filtered out
Wang et al. [8]	WES data 5 kb within, up- and downstream of MAP4 from 1943 unrelated subjects	Simulated data, including a null trait (25 variants have true SBP effects)	Excluded 81 subjects without age information; monomorphic and low-coverage (<20×) variants were filtered out, leaving 94 variants

DBP diastolic blood pressure, GWSNPA genome-wide single nucleotide polymorphism array, MAF minor allele frequency, NALTT number of nonreference alleles for each individual thresholded, SBP systolic blood pressure, VCF variant call format, WES whole exome sequence