A Genome-Wise Association Study of A Quantitative Alcoholism Trait Using A Full Genetic Model Identifies Significant SNP Heritability From Dominance And Epistatic Interactions

Abstract

Most psychiatric diseases are complex diseases involving polygenes, environment, and their interactions. Although genome-wide association studies (GWASs) revolutionized the genetic dissection of many diseases, only a very small portion of heritability has been accounted for, i.e. there was a remarkable limitation as missing heritability. Here, we demonstrate that perturbations arising from gene-gene interactions, including dominance and epistasis, and conditions of comorbidity substantially influence the complex trait of alcohol dependence symptom count (ADSC). We conducted a GWAS of ADSC by using a mixed liner approach in a full genetic model considering additive, dominance, epistasis and their interactions with ethnicity, as well as conditions of comorbid substance dependences, in the dataset of the Study of Addiction: Genetics and Environment with 3838 subjects. Twenty quantitative trait single nucleotide polymorphisms (QTSs) showed significant associations with ADSC (experiment-wise P < 0.05 based on permutation test), including four in previously reported genes (ADH1C, PKNOX2, CPE, and KCNB2), supporting the overall validity of the analysis. Two QTSs within or near ADH1C showed significant association in a dominance inheritance mode, and increased the phenotype value of ADSC when the effect of comorbid opiate or marijuana dependence was controlled. Significant association was also identified in variants within 4 novel genes (RGS6, FMN1, NRM, and BPTF), 2 noncoding RNA and 2 epistasis loci. QTS rs7616413, located near PTPRG encoding a protein tyrosine phosphatase (PTP) receptor, interacted with rs10090742 within ANGPT1 encoding a PTP, in an additive × additive or dominance × additive manner. The detected QTSs contributed to about 20% of total heritability, in which dominance and epistasis effects accounted for over 50%. In comparison, the original analysis of the dataset using the conventional approach of GWAS failed to detect a genome-wide association with alcohol dependence. Our analysis discloses a much larger number of SNPs or epistasis for a quantitative trait of a psychiatric disorder, and may represent an important stride toward solving the problem of missing heritability. This approach can be employed in GWASs and precision medicine of many other psychiatric disorders such as schizophrenia, major depression et al.