Widespread Cumulative Influence of Small Effect Size Mutations on Yeast Quantitative Traits

Gene Interactions in Human Disease Studies-Evidence Is Mounting

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets

Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes.

Effects of Different Exercise Types on Chrna7 and Chrfam7a Expression in Healthy Normal Weight and Overweight Type 2 Diabetic Adults

Purpose: Considering that the CHRNA7 and CHRFAM7A genes can be modulated by acute or chronic inflammation, and exercise modulates inflammatory responses, the question that arises is whether physical exercise could exert any effect on the expression of these genes. Thus, the aim of this work is to identify the effects of different types of exercises on the expression of the CHRNA7, CHRFAM7A and tumor necrosis factor-α (TNF-α) in leukocytes of healthy normal weight (HNW), and overweight with type 2 diabetes (OT2D) individuals. Methods: 15 OT2D and 13 HNW participants (men and women, from 40 to 60 years old) performed in a randomized crossover design three exercise sessions: aerobic exercise (AE), resistance exercise (RE) and combined exercise (CE). Blood samples were collected at rest and post-60-min of the exercise sessions. The leukocytes were the analysis of the CHRNA7, CHRFAM7A and (TNF-α) gene expression. Results: At baseline, OT2D had higher CHRFAM7A and TNF-α expression compared to HNW. No statistical differences were observed between groups for CHRNA7; however, the HNW group presented almost twice as many subjects with the expression of this gene (24% vs. 49%). Post exercise, the CHRFAM7A increased in AE, RE and CE for HNW, and in AE and CE for OT2D. There was no significant difference for TNF-α and CHRNA7 expression between any type of exercise and group. Conclusions: Our study shows that OT2D individuals presented higher baseline expression of TNF-α and CHRFAM7A, besides evidence of decreased CHRNA7A expression in leukocytes when compared with HNW. On the other hand, acutely physical exercise induces increased CHRFAM7A expression, especially when the aerobic component is present.

Variation in the modality of a yeast signaling pathway is mediated by a single regulator

Bimodal gene expression by genetically identical cells is a pervasive feature of signaling networks and has been suggested to allow organisms to hedge their ‘bets’ in uncertain conditions. In the galactose-utilization (GAL) pathway of Saccharomyces cerevisiae, gene induction is unimodal or bimodal depending on natural genetic variation and pre-induction conditions. Here, we find that this variation in modality arises from regulation of two features of the pathway response: the fraction of cells that show induction and their level of expression. GAL3, the galactose sensor, controls the fraction of induced cells, and titrating its expression is sufficient to control modality; moreover, all the observed differences in modality between different pre-induction conditions and among natural isolates can be explained by changes in GAL3’s regulation and activity. The ability to switch modality by tuning the activity of a single protein may allow rapid adaptation of bet hedging to maximize fitness in complex environments.