Exome-Wide Association Study Reveals Several Susceptibility Genes and Pathways Associated With Acute Coronary Syndromes in Han Chinese

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.

Gene and pathway based burden analyses in familial lymphoid cancer cases: Rare variants in immune pathway genes

Genome-wide association studies have revealed common genetic variants with small effect sizes associated with diverse lymphoid cancers. Family studies have uncovered rare variants with high effect sizes. However, these variants explain only a portion of the heritability of these cancers. Some of the missing heritability may be attributable to rare variants with small effect sizes. We aim to identify rare germline variants associated with familial lymphoid cancers using exome sequencing. One case per family was selected from 39 lymphoid cancer families based on early onset of disease or rarity of subtype. Control data was from Non-Finnish Europeans in gnomAD exomes (N = 56,885) or ExAC (N = 33,370). Gene and pathway-based burden tests for rare variants were performed using TRAPD. Five putatively pathogenic germline variants were found in four genes: INTU, PEX7, EHHADH, and ASXL1. Pathway-based association tests identified the innate and adaptive immune systems, peroxisomal pathway and olfactory receptor pathway as associated with lymphoid cancers in familial cases. Our results suggest that rare inherited defects in the genes involved in immune system and peroxisomal pathway may predispose individuals to lymphoid cancers.

Genetic Variation of Migration Inhibitory Factor Gene rs2070766 Is Associated With Acute Coronary Syndromes in Chinese Population

Genetic variation of macrophage migration inhibitory factor (MIF) gene has been linked to coronary artery disease. We investigated an association between the polymorphism of MIF gene rs2070766 and acute coronary syndromes (ACS) and the predictive value of MIF gene variation in clinical outcomes. This study involved in 963 ACS patients and 932 control subjects from a Chinese population. All participants were genotyped for the single nucleotide polymorphism (SNP) of MIF gene rs2070766 using SNPscan™. A nomogram model using MIF genetic variation and clinical variables was established to predict risk of ACS. Major adverse cardiovascular events (MACE) were monitored during a follow-up period. The frequency of rs2070766 GG genotype was higher in ACS patients than in control subjects (6.2 vs 3.8%, p = 0.034). Multivariate logistic regression analysis revealed that individuals with mutant GG genotype had a 1.7-fold higher risk of ACS compared with individuals with CC or CG genotypes. Using MIF rs2070766 genotypes and clinical factors, we developed a nomogram model to predict risk of ACS. The nomogram model had a good discrimination with an area under the curve of 0.781 (95% CI: 0.759-0.804), concordance index of 0.784 (95% CI: 0.762-0.806) and well-fitted calibration. During the follow-up period of 25 months, Kaplan-Meier curves demonstrated that ACS patients carrying GG phenotype developed more MACE compared to CC or CG carriers (p < 0.05). GG genotype of MIF gene rs2070766 was associated with a higher risk of ACS in a Chinese population. The GG genotype carriers in ACS patients had worse clinical outcomes compared with those carrying CC or CG genotype. Together with rs2070766 genetic variant of MIF gene, we established a novel nomogram model that can provide individualized prediction for ACS.