Systematic detection of epistatic interactions based on allele pair frequencies

Cryptic female choice within individual males - A neglected component of the postmating sexual selection?

Cryptic female choice (CFC) is commonly assumed to act only in polyandrous mating systems, which allows females to bias fertilization towards the sperm of particular males. However, accumulated evidence has demonstrated that sperm show significant phenotypic and genotypic variation also within single ejaculates, which have important consequences for offspring phenotype and fitness. Here, I argue that these neglected sources of intra-male sperm variation often allow CFC to act also within individual males and facilitate fertilization bias towards genetically compatible (or otherwise preferred) sperm haplotypes. In this article, I explain prerequisites for within-male CFC, the criteria for demonstrating it and summarize accumulated evidence for this emerging selection process. Then, I evaluate prevalence of within-male CFC and review its potential evolutionary consequences. The aim of this article is to broaden the current definition of CFC by demonstrating that CFC has potential to act in all mating systems, in both internally and externally fertilizing species. Incorporation of the within-male CFC concept into the current models of sexual selection may provide novel insights into the deeper understanding of selective factors driving the evolution of mating systems and reproductive proteins. Finally, within-male CFC towards particular sperm haplotypes may increase our understanding of non-Mendelian inheritance.

Transmission distortion and genetic incompatibilities between alleles in a multigenerational mouse advanced intercross line

While direct additive and dominance effects on complex traits have been mapped repeatedly, additional genetic factors contributing to the heterogeneity of complex traits have been scarcely investigated. To assess genetic background effects, we investigated transmission ratio distortions (TRDs) of alleles from parent to offspring using an advanced intercross line (AIL) of an initial cross between the mouse inbred strains C57BL/6NCrl (B6N) and BFMI860-12 [Berlin Fat Mouse Inbred (BFMI)]. A total of 341 males of generation 28 and their respective 61 parents and 66 grandparents were genotyped using Mega Mouse Universal Genotyping Arrays. TRDs were investigated using allele transmission asymmetry tests, and pathway overrepresentation analysis was performed. Sequencing data were used to test for overrepresentation of nonsynonymous SNPs (nsSNPs) in TRD regions. Genetic incompatibilities were tested using the Bateson–Dobzhansky–Muller two-locus model. A total of 62 TRD regions were detected, many in close proximity to the telocentric centromere. TRD regions contained 44.5% more nsSNPs than randomly selected regions (182 vs 125.9 ± 17.0, P < 1 × 10⁻⁴). Testing for genetic incompatibilities between TRD regions identified 29 genome-wide significant incompatibilities between TRD regions [P_(BF) < 0.05]. Pathway overrepresentation analysis of genes in TRD regions showed that DNA methylation, epigenetic regulation of RNA, and meiotic/meiosis regulation pathways were affected independent of the parental origin of the TRD. Paternal BFMI TRD regions showed overrepresentation in the small interfering RNA biogenesis and in the metabolism of lipids and lipoproteins. Maternal B6N TRD regions harbored genes involved in meiotic recombination, cell death, and apoptosis pathways. The analysis of genes in TRD regions suggests the potential distortion of protein–protein interactions influencing obesity and diabetic retinopathy as a result of disadvantageous combinations of allelic variants in Aass, Pgx6, and Nme8. Using an AIL significantly improves the resolution at which we can investigate TRD. Our analysis implicates distortion of protein–protein interactions as well as meiotic drive as the underlying mechanisms leading to the observed TRD in our AIL. Furthermore, genes with large amounts of nsSNPs located in TRD regions are more likely to be involved in pathways that are related to the phenotypic differences between the parental strains. Genes in these TRD regions provide new targets for investigating genetic adaptation, protein–protein interactions, and determinants of complex traits such as obesity.

The Evolutionary Dynamics of Genetic Incompatibilities Introduced by Duplicated Genes in Arabidopsis thaliana

Although gene duplications provide genetic backup and allow genomic changes under relaxed selection, they may potentially limit gene flow. When different copies of a duplicated gene are pseudofunctionalized in different genotypes, genetic incompatibilities can arise in their hybrid offspring. Although such cases have been reported after manual crosses, it remains unclear whether they occur in nature and how they affect natural populations. Here, we identified four duplicated-gene based incompatibilities including one previously not reported within an artificial Arabidopsis intercross population. Unexpectedly, however, for each of the genetic incompatibilities we also identified the incompatible alleles in natural populations based on the genomes of 1,135 Arabidopsis accessions published by the 1001 Genomes Project. Using the presence of incompatible allele combinations as phenotypes for GWAS, we mapped genomic regions that included additional gene copies which likely rescue the genetic incompatibility. Reconstructing the geographic origins and evolutionary trajectories of the individual alleles suggested that incompatible alleles frequently coexist, even in geographically closed regions, and that their effects can be overcome by additional gene copies collectively shaping the evolutionary dynamics of duplicated genes during population history.

A Novel Test for Detecting SNP-SNP Interactions in Case-Only Trio Studies

Epistasis plays a significant role in the genetic architecture of many complex phenotypes in model organisms. To date, there have been very few interactions replicated in human studies due in part to the multiple-hypothesis burden implicit in genome-wide tests of epistasis. Therefore, it is of paramount importance to develop the most powerful tests possible for detecting interactions. In this work we develop a new SNP-SNP interaction test for use in case-only trio studies called the trio correlation (TC) test. The TC test computes the expected joint distribution of marker pairs in offspring conditional on parental genotypes. This distribution is then incorporated into a standard 1 d.f. correlation test of interaction. We show via extensive simulations under a variety of disease models that our test substantially outperforms existing tests of interaction in case-only trio studies. We also demonstrate a bias in a previous case-only trio interaction test and identify its origin. Finally, we show that a previously proposed permutation scheme in trio studies mitigates the known biases of case-only tests in the presence of population stratification. We conclude that the TC test shows improved power to identify interactions in existing, as well as emerging, trio association studies. The method is publicly available at www.github.com/BrunildaBalliu/TrioEpi.

Detecting epistasis in human complex traits

Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

Genetic incompatibilities are widespread within species

The importance of epistasis — non-additive interactions between alleles — in shaping population fitness has long been a controversial topic, hampered in part by lack of empirical evidence^1,2,3,4. Traditionally, epistasis is inferred based on non-independence of genotypic values between loci for a given trait. However epistasis for fitness should also have a genomic footprint^5,6,7. To capture this signal, we have developed a simple approach that relies on detecting genotype ratio distortion (GRD) as a signal for epistasis, and we confirm experimentally that instances of GRD represent loci with epistatic fitness effects. In applying this method to a large panel of Drosophila melanogaster recombinant inbred lines^{8, 9}, we conservatively estimate that any two haploid genomes in this study are expected to harbor 1.15 pairs of incompatible alleles. This observation has important implications for speciation genetics, as it indicates that the raw material to drive reproductive isolation is segregating contemporaneously within species and does not necessarily require, as proposed by the Dobzhansky–Muller model, the emergence of incompatible mutations independently derived and fixed in allopatry. The relevance of our result extends beyond speciation, as it demonstrates that epistasis is widespread but that it may often go undetected due to lack of statistical power or lack of genome-wide scope of the experiments.

GWIS--model-free, fast and exhaustive search for epistatic interactions in case-control GWAS

Background

It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci.

Results

We have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives.

Conclusions

We demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis.

Availability

A web-based version of the software used for this analysis is available at http://bioinformatics.research.nicta.com.au/gwis.

Systems genetics in "-omics" era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate "-omics" datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.