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Table 2 Contributions of the “Genetics of Gene Expression and Phenotype” subgroup

From: Gene expression in large pedigrees: analytic approaches

   

Real data

   

First author

Sample size

Relatedness correction

Phenotype

Genotype

Simulated data

Analytic approach

Results

Ainsworth [27]

R 638

GWAS: FaST-LMM

Covariate adjusted mean SBP, DBP

427,952 GWAS SNPs

Analyzed but not presented

Pairwise association, WGCNA to identify variables for causal modeling in SEM and BUF

Weak significance, high concordance between SEM and BUF

WGCNA SEM, BUF: none

Pitsillides [24]

R 267

Linear mixed effects models

DBP, SBP

12,296,048 SNVs from WGS

Not used

Test of enrichment of cis-eQTL in known BP loci and regulatory regions; pairwise association of expression and BP

Many highly significant eQTL. Enrichment of eQTL in known BP loci and regulatory regions

Tong [26]

U 142

None needed

SBP, DBP, HTN, adjusted for covariates

6,956,910 SNVs from WGS in 17,558 genes

Not used

Similarity-based test for joint effects of genotype, gene expression, phenotype

Weak significance, but some benefit from using genotypes and gene expression

Radkowski [25]

R 340

None made

HTN at several time points; change in BP

Not used

Not used

Change in BP (in individuals with no HTN) modeled as a function of gene expression and covariates

7 potentially predictive HTN gene expression probes identified in 6 genes

  1. BP blood pressure; BUF Bayesian unified framework; DBP diastolic blood pressure; eQTL expression quantitative trait locus; GWAS genome-wide association study; HTN hypertension; R related individuals; SBP systolic blood pressure; SEM structural equation modeling; SNP single nucleotide polymorphism; SNV single nucleotide variant; U unrelated individuals; WGCNA weighted gene correlation network analysis; WGS whole-genome sequencing