Natural and orthogonal model for estimating gene–gene interactions applied to cutaneous melanoma

Abstract

Epistasis, or gene–gene interaction, results from joint effects of genes on a trait; thus, the same alleles of one gene may display different genetic effects in different genetic backgrounds. In this study, we generalized the coding technique of a natural and orthogonal interaction (NOIA) model for association studies along with gene–gene interactions for dichotomous traits and human complex diseases. The NOIA model which has non-correlated estimators for genetic effects is important for estimating influencing from multiple loci. We conducted simulations and data analyses to evaluate the performance of the NOIA model. Both simulation and real data analyses revealed that the NOIA statistical model had higher power for detecting main genetic effects and usually had higher power for some interaction effects than the usual model. Although associated genes have been identified for predisposing people to melanoma risk: HERC2 at 15q13.1, MC1R at 16q24.3 and CDKN2A at 9p21.3, no gene–gene interaction study has been fully explored for melanoma. By applying the NOIA statistical model to a genome-wide melanoma dataset, we confirmed the previously identified significantly associated genes and found potential regions at chromosomes 5 and 4 that may interact with the HERC2 and MC1R genes, respectively. Our study not only generalized the orthogonal NOIA model but also provided useful insights for understanding the influencing of interactions on melanoma risk.

Melanoma genetics

“…Epistasis can also be linked to multiple genes, where a certain genetic background may be essential for subsequent gene expression 81. A number of epistatic mutations likely contribute to the polygenic inheritance of melanoma.…”

A statistical method for joint estimation of cis-eQTLs and parent-of-orign effects using an orthogonal framework with RNA-seq data

“…This is the first time the performance of the natural and orthogonal models in detecting POE with RNA-seq data are evaluated. This project is an extension of the natural and orthogonal interaction model (NOIA) proposed by Albarez-Castro et al, in 2007[12], based on which we have extensively worked on the framework to the estimation of statistical epistasis, geneenvironmental interactions and imprinting effect in genotype-phenotype mapping for quantitative traits and qualitative traits[8,13,14]. In previous findings, we proved that the one-locus Stat-POE model for quantitative trait was orthogonal in certain conditions including when HWE was satisfied[8].…”

HERC Ubiquitin Ligases in Cancer

“…Pheochromocytoma and paraganglioma HERC2 mutations [19] T-cell prolymphocytic leukemia HERC2 mutations [13] Cutaneous melanoma SNPs in HERC2 gene increase susceptibility [20][21][22][23] Gene-gene interactions between HERC2 gene and IL31RA and DDX4 genes [24] Epistatic effects between HERC2 and VDR genes [25] Cutaneous squamous cell carcinoma SNPs in HERC2 gene impact on time to develop the tumor in organ transplant recipients [26] Uveal melanoma SNPs in HERC2 gene increase susceptibility [27] Non-small-cell lung cancer Worse prognosis in patients expressing high HERC2 mRNA levels [28] Breast cancer Enhanced BRCA1 degradation [29,30] Gastric and colorectal carcinomas HERC2 mutations [31] Osteosarcoma Negative correlation of SOX18 overexpression and HERC2 mRNA levels [18] HERC3 Glioblastoma Degradation of SMAD7 and activation of the TGFβ signaling [32] Gastric and colorectal carcinomas HERC3 mutations [31] Osteosarcoma Negative correlation of SOX18 overexpression and HERC3 mRNA levels [18] HERC4…”