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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