A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection

Individual Genetic Heterogeneity

Genetic variation has been widely covered in literature, however, not from the perspective of an individual in any species. Here, a synthesis of genetic concepts and variations relevant for individual genetic constitution is provided. All the different levels of genetic information and variation are covered, ranging from whether an organism is unmixed or hybrid, has variations in genome, chromosomes, and more locally in DNA regions, to epigenetic variants or alterations in selfish genetic elements. Genetic constitution and heterogeneity of microbiota are highly relevant for health and wellbeing of an individual. Mutation rates vary widely for variation types, e.g., due to the sequence context. Genetic information guides numerous aspects in organisms. Types of inheritance, whether Mendelian or non-Mendelian, zygosity, sexual reproduction, and sex determination are covered. Functions of DNA and functional effects of variations are introduced, along with mechanism that reduce and modulate functional effects, including TARAR countermeasures and intraindividual genetic conflict. TARAR countermeasures for tolerance, avoidance, repair, attenuation, and resistance are essential for life, integrity of genetic information, and gene expression. The genetic composition, effects of variations, and their expression are considered also in diseases and personalized medicine. The text synthesizes knowledge and insight on individual genetic heterogeneity and organizes and systematizes the central concepts.

Compositional epistasis detection using a few prototype disease models

We study computational approaches for detecting SNP-SNP interactions that are characterized by a set of "two-locus, two-allele, two-phenotype and complete-penetrance" disease models. We argue that existing methods, which use data to determine a best-fitting disease model for each pair of SNPs prior to screening, may be too greedy. We present a less greedy strategy which, for each given pair of SNPs, limits the number of candidate disease models to a set of prototypes determined a priori.

Leveraging epigenomics and contactomics data to investigate SNP pairs in GWAS

Although Genome Wide Association Studies (GWAS) have led to many valuable insights into the genetic bases of common diseases over the past decade, the issue of missing heritability has surfaced, as the discovered main effect genetic variants found to date do not account for much of a trait's predicted genetic component. We present a workflow, integrating epigenomics and topologically associating domain data, aimed at discovering trait-associated SNP pairs from GWAS where neither SNP achieved independent genome-wide significance. Each analyzed SNP pair consists of one SNP in a putative active enhancer and another SNP in a putative physically interacting gene promoter in a trait-relevant tissue. As a proof-of-principle case study, we used this approach to identify focused collections of SNP pairs that we analyzed in three independent Type 2 diabetes (T2D) GWAS. This approach led us to discover 35 significant SNP pairs, encompassing both novel signals and signals for which we have found orthogonal support from other sources. Nine of these pairs are consistent with eQTL results, two are consistent with our own capture C experiments, and seven involve signals supported by recent T2D literature.

Leveraging putative enhancer-promoter interactions to investigate two-way epistasis in Type 2 Diabetes GWAS

We utilized evidence for enhancer-promoter interactions from functional genomics data in order to build biological filters to narrow down the search space for two-way Single Nucleotide Polymorphism (SNP) interactions in Type 2 Diabetes (T2D) Genome Wide Association Studies (GWAS). This has led us to the identification of a reproducible statistically significant SNP pair associated with T2D. As more functional genomics data are being generated that can help identify potentially interacting enhancer-promoter pairs in larger collection of tissues/cells, this approach has implications for investigation of epistasis from GWAS in general.

Discovering causal interactions using Bayesian network scoring and information gain

BACKGROUND

The problem of learning causal influences from data has recently attracted much attention. Standard statistical methods can have difficulty learning discrete causes, which interacting to affect a target, because the assumptions in these methods often do not model discrete causal relationships well. An important task then is to learn such interactions from data. Motivated by the problem of learning epistatic interactions from datasets developed in genome-wide association studies (GWAS), researchers conceived new methods for learning discrete interactions. However, many of these methods do not differentiate a model representing a true interaction from a model representing non-interacting causes with strong individual affects. The recent algorithm MBS-IGain addresses this difficulty by using Bayesian network learning and information gain to discover interactions from high-dimensional datasets. However, MBS-IGain requires marginal effects to detect interactions containing more than two causes. If the dataset is not high-dimensional, we can avoid this shortcoming by doing an exhaustive search.

RESULTS

We develop Exhaustive-IGain, which is like MBS-IGain but does an exhaustive search. We compare the performance of Exhaustive-IGain to MBS-IGain using low-dimensional simulated datasets based on interactions with marginal effects and ones based on interactions without marginal effects. Their performance is similar on the datasets based on marginal effects. However, Exhaustive-IGain compellingly outperforms MBS-IGain on the datasets based on 3 and 4-cause interactions without marginal effects. We apply Exhaustive-IGain to investigate how clinical variables interact to affect breast cancer survival, and obtain results that agree with judgements of a breast cancer oncologist.

CONCLUSIONS

We conclude that the combined use of information gain and Bayesian network scoring enables us to discover higher order interactions with no marginal effects if we perform an exhaustive search. We further conclude that Exhaustive-IGain can be effective when applied to real data.

Epistatic interaction between common AGT G(-6)A (rs5051) and AGTR1 A1166C (rs5186) variants contributes to variation in kidney size at birth

Low nephron number has been recognised as an important cardiovascular risk factor and recently a strong correlation between renal mass and nephron number has been demonstrated in newborns. The aim of this study was to investigate individual, as well as combined, effects of common variants of genes which encode for major components of the renin-angiotensin system (REN G10601A, AGT G(-6)A, ACE I/D, AGTR1 A1166C) on kidney size in healthy, full-term newborns. A significant additive main effect of the ACE I/D polymorphism, as well as an additive-by-additive interaction between AGT G(-6)A and AGTR1 A1166C variants, were found. The variance attributed to the epistatic effect was 27.9 ml(2)/m(4), which accounted for 73.8% of the interaction variance (37.8 ml(2)/m(4)), 66.4% of the genetic variance (42.0 ml(2)/m(4)) and 4.4% to the total phenotypic variance (628 ml(2)/m(4)). No other statistically significant main or epistatic effects were detected. Our results highlight the importance of considering gene-gene interactions as part of the genetic architecture of congenital nephron number, even when the loci do not show significant single locus effects. Unravelling the genetic determinants of low nephron number, along with early molecular screening, may well help to identify children at risk for cardiovascular disease.