Novel EDGE encoding method enhances ability to identify genetic interactions

Abstract

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

Flexibly encoded GWAS identifies novel nonadditive SNPs in individuals of African and European ancestry

“…As previously described 12 , EDGE assigns a flexible calculated heterozygous to homozygous alternate ratio of risk, as α, to the heterozygous genotype based on the inheritance model each SNP represents in the α-calculation dataset using a codominant (dummy) encoding with covariates.…”

“…In the current study, we illustrated the use of Elastic Data-Driven Encoding (EDGE) 12 in identifying the genotype-phenotype associations with SNPs functioning as an additive and nonadditive inheritance patterns. Previously, EDGE was applied to a gene-gene interaction in helping with identifying SNPs' interactions with nonadditive inheritance patterns for common diseases 12 , but hasn't been incorporated with a single variant association test. EGDE provided an opportunity to allow the assignment of a unique, flexible, and data-informed risk to the heterozygous genotype.…”

Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing

“…Any possible additive or dominant relationship between a trait and a biallelic locus can be modeled by a combination of additive and complete dominance effects, and the presence of dominance in this wider non-additive sense is therefore testable by comparing the fit of a purely additive model to a model with both additive and dominance effects. Computational methods to detect dominance in GWAS have been developed by our group [18] and others [19][20][21][22][23][24][25][26].…”

Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart Failure

“…Our TRACE method challenges this oversimplification by incorporating categorical main and GxE effects into each SNP's encoding prior to linear regression against the phenotype. This encoding is based loosely on the EDGE method [15], which essentially uses target encoding [16] to incorporate categorical genetic effects into each SNP's encoding. However (letting T refer to an arbitrary test statistic), while target encoding generally substitutes x = T(y|x), our TRACE encoding generalizes T(y|x) to T(y|x, E), where E is an environmental factor.…”