Investigation of gene-gene interactions in cardiac traits and serum fatty acid levels in the LURIC Health Study

Abstract

Epistasis analysis elucidates the effects of gene-gene interactions (G×G) between multiple loci for complex traits. However, the large computational demands and the high multiple testing burden impede their discoveries. Here, we illustrate the utilization of two methods, main effect filtering based on individual GWAS results and biological knowledge-based modeling through Biofilter software, to reduce the number of interactions tested among single nucleotide polymorphisms (SNPs) for 15 cardiac-related traits and 14 fatty acids. We performed interaction analyses using the two filtering methods, adjusting for age, sex, body mass index (BMI), waist-hip ratio, and the first three principal components from genetic data, among 2,824 samples from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study. Using Biofilter, one interaction nearly met Bonferroni significance: an interaction between rs7735781 in XRCC4 and rs10804247 in XRCC5 was identified for venous thrombosis with a Bonferroni-adjusted likelihood ratio test (LRT) p: 0.0627. A total of 57 interactions were identified from main effect filtering for the cardiac traits G×G (10) and fatty acids G×G (47) at Bonferroni-adjusted LRT p < 0.05. For cardiac traits, the top interaction involved SNPs rs1383819 in SNTG1 and rs1493939 (138kb from 5' of SAMD12) with Bonferroni-adjusted LRT p: 0.0228 which was significantly associated with history of arterial hypertension. For fatty acids, the top interaction between rs4839193 in KCND3 and rs10829717 in LOC107984002 with Bonferroni-adjusted LRT p: 2.28×10 −5 was associated with 9-trans 12-trans octadecanoic acid, an omega-6 trans fatty acid. The model inflation factor for the interactions under different filtering methods was evaluated from the standard median and the linear regression approach. Here, we applied filtering approaches to identify numerous genetic interactions related to cardiac-related outcomes as potential targets for therapy. The

Using GWAS summary data to impute traits for genotyped individuals

“…Nevertheless, since marginal SNP-trait association estimates in GWAS summary data measure only linear relationships between the SNPs and trait, the current use of GWAS summary data is limited to exploiting only linear SNP-trait associations; it is unknown how to use GWAS summary data for nonlinear SNP-trait association analysis. For example, given a GWAS summary dataset (and a reference panel of individual-level genotypes), it seems impossible to detect SNP-SNP interactions 16 , 17 or to build a PRS model accounting for possibly nonlinear and epistatic SNP effects by taking advantage of many emerging powerful nonlinear machine learning models such as random forests (RFs) 18 , 19 , 20 , 21 , 22 and deep learning. 23 , 24 These nonlinear SNP-trait association and prediction analyses are expected to shed more light on the genetic architecture of complex traits, deepen mechanistic understanding of common diseases, and thus advance translational applications of genetics.…”

Genetic Contributors of Incident Stroke in 10,700 African Americans With Hypertension: A Meta-Analysis From the Genetics of Hypertension Associated Treatments and Reasons for Geographic and Racial Differences in Stroke Studies

“…SNTG1 encodes a protein that mediates gammaenolase trafficking to the plasma membrane and enhances its neurotrophic activity (UniProt, 2021). An epistasis analysis performed in 2,800 EAs found an association between an SNTG1 variant and a history of arterial HTN (Zhou et al, 2020). Tissue specificity of our top identified genes, specifically SNTG1 and NTM, showed differential expression in specific brain tissues, compared to other available tissues from GTEx RNA sequence data.…”

Fatty acids and risk of dilated cardiomyopathy: A two-sample Mendelian randomization study

“…Participants were filtered to remove individuals under the age of 18; persons with absent covariates including age, waist–hip ratio, BMI, and sex; and samples with a < 99% sample call rate. Finally, a total of 687,262 SNPs were obtained from 3,061 samples ( 25 ). (d) Other genetic data were acquired from 5,662 controls in the Pakistan Myocardial Infarction Risk Study (PROIS) and 13,814 UK participants in the INTERVAL study ( 26 ).…”