Single nucleotide polymorphism-single nucleotide polymorphism interactions among inflammation genes in the genetic architecture of blood pressure in the Framingham Heart Study.

BACKGROUND: Hypertension is a major global health burden, but, although systolic and diastolic blood pressure (BP) each have estimated heritability of at least 30%, <3% of their variance has been attributed to particular genetic variants. Few studies have shown interactions between pairs of single nucleotide polymorphisms (SNPs) to be associated with BP. Although many studies use a Bonferroni correction for multiple testing to control type I error, thereby potentially reducing power, false discovery rate (FDR) approaches are also used in genome-wide studies. Renal ion balance genes have been associated with BP regulation, but, although inflammation has been studied in connection with BP, few studies have reported associations between inflammation genes and BP. METHODS: We analyzed SNP-SNP interactions among 31 SNPs from genes involved in renal ion balance and 30 SNPs from genes involved in inflammation using data from the Framingham Heart Study. RESULTS: No evidence of association was found for interactions among renal ion balance SNPs for either systolic or diastolic BP. A group of 3 interactions involving 6 inflammation genes (IKBKB-NFKBIA, IKBKE-CHUK, and ADIPOR2-RETN) showed evidence of association with diastolic BP with an FDR of 4.2%; no single interaction reached experiment-wide significance. CONCLUSIONS: This study identified promising and biologically plausible candidates for interactions between inflammation genes that may be associated with DBP. Analysis using the FDR may allow detection of signals in the presence of modest noise (false positives) that a stringent approach based on Bonferroni-corrected P value thresholds may miss.

Multigene interactions and the prediction of depression in the Wisconsin Longitudinal Study.

OBJECTIVES: Single genetic loci offer little predictive power for the identification of depression. This study examined whether an analysis of gene-gene (G × G) interactions of 78 single nucleotide polymorphisms (SNPs) in genes associated with depression and age-related diseases would identify significant interactions with increased predictive power for depression. DESIGN: A retrospective cohort study. SETTING: A survey of participants in the Wisconsin Longitudinal Study. PARTICIPANTS: A total of 4811 persons (2464 women and 2347 men) who provided saliva for genotyping; the group comes from a randomly selected sample of Wisconsin high school graduates from the class of 1957 as well as a randomly selected sibling, almost all of whom are non-Hispanic white. PRIMARY OUTCOME MEASURE: Depression as determine by the Composite International Diagnostic Interview-Short-Form. RESULTS: Using a classification tree approach (recursive partitioning (RP)), the authors identified a number of candidate G × G interactions associated with depression. The primary SNP splits revealed by RP (ANKK1 rs1800497 (also known as DRD2 Taq1A) in men and DRD2 rs224592 in women) were found to be significant as single factors by logistic regression (LR) after controlling for multiple testing (p=0.001 for both). Without considering interaction effects, only one of the five subsequent RP splits reached nominal significance in LR (FTO rs1421085 in women, p=0.008). However, after controlling for G × G interactions by running LR on RP-specific subsets, every split became significant and grew larger in magnitude (OR (before) → (after): men: GNRH1 novel SNP: (1.43 → 1.57); women: APOC3 rs2854116: (1.28 → 1.55), ACVR2B rs3749386: (1.11 → 2.17), FTO rs1421085: (1.32 → 1.65), IL6 rs1800795: (1.12 → 1.85)). CONCLUSIONS: The results suggest that examining G × G interactions improves the identification of genetic associations predictive of depression. 4 of the SNPs identified in these interactions were located in two pathways well known to impact depression: neurotransmitter (ANKK1 and DRD2) and neuroendocrine (GNRH1 and ACVR2B) signalling. This study demonstrates the utility of RP analysis as an efficient and powerful exploratory analysis technique for uncovering genetic and molecular pathway interactions associated with disease aetiology.