Search

Background: In statistical genetics, an important task involves building predictive models of the genotype-phenotype relationship to attribute a proportion of the total phenotypic variance to the variation in genotypes. Many models have been proposed to incorporate additive genetic effects into prediction or association models. Currently, there is a scarcity of models that can adequately account for gene by gene or other forms of genetic interactions and there is an increased interest in using marker annotations in genome-wide prediction and association analyses. In this paper, we discuss a hybrid modeling method which combines parametric mixed modeling and non-parametric rule ensembles. Results: This approach gives us a flexible class of models that can be used to capture additive, locally epistatic genetic effects, gene-by-background interactions and allows us to incorporate one or more annotations into the genomic selection or association models. We use benchmark datasets that cover a range of organisms and traits in addition to simulated datasets to illustrate the strengths of this approach. Conclusions: In this paper, we describe a new strategy for incorporating genetic interactions into genomic prediction and association models. This strategy results in accurate models, with sometimes significantly higher accuracies than that of a standard additive model. [ABSTRACT FROM AUTHOR]

For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the "missing heritability" of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson's disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated genegene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD). Results: In this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles. Conclusion: iLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. [ABSTRACT FROM AUTHOR]

Is evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses - a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic. [ABSTRACT FROM AUTHOR]

Genome-wide association (GWA) studies have identified a number of loci underlying variation in human serum uric acid (SUA) levels with the SLC2A9 gene having the largest effect identified so far. Gene-gene interactions (epistasis) are largely unexplored in these GWA studies. We performed a full pair-wise genome scan in the Italian MICROS population (n = 1201) to characterise epistasis signals in SUA levels. In the resultant epistasis profile, no SNP pairs reached the Bonferroni adjusted threshold for the pair-wise genome-wide significance. However, SLC2A9 was found interacting with multiple loci across the genome, with NFIA - SLC2A9 and SLC2A9 - ESRRAP2 being significant based on a threshold derived for interactions between GWA significant SNPs and the genome and jointly explaining 8.0% of the phenotypic variance in SUA levels (3.4% by interaction components). Epistasis signal replication in a CROATIAN population (n = 1772) was limited at the SNP level but improved dramatically at the gene ontology level. In addition, gene ontology terms enriched by the epistasis signals in each population support links between SUA levels and neurological disorders. We conclude that GWA epistasis analysis is useful despite relatively low power in small isolated populations. [ABSTRACT FROM AUTHOR]

The extent to which mitochondrial DNA (mtDNA) variation is involved in adaptive evolutionary change is currently being reevaluated. In particular, emerging evidence suggests that mtDNA genes coevolve with the nuclear genes with which they interact to form the energy producing enzyme complexes in the mitochondria. This suggests that intergenomic epistasis between mitochondrial and nuclear genes may affect whole-organism metabolic phenotypes. Here, we use crossed combinations of mitochondrial and nuclear lineages of the seed beetle Callosobruchus maculatus and assay metabolic rate under two different temperature regimes. Metabolic rate was affected by an interaction between the mitochondrial and nuclear lineages and the temperature regime. Sequence data suggests that mitochondrial genetic variation has a role in determining the outcome of this interaction. Our genetic dissection of metabolic rate reveals a high level of complexity, encompassing genetic interactions over two genomes, and genotype × genotype × environment interactions. The evolutionary implications of these results are twofold. First, because metabolic rate is at the root of life histories, our results provide insights into the complexity of life-history evolution in general, and thermal adaptation in particular. Second, our results suggest a mechanism that could contribute to the maintenance of nonneutral mtDNA polymorphism. [ABSTRACT FROM AUTHOR]

Background: Biological robustness results from redundant pathways that achieve an essential objective, e.g. the production of biomass. As a consequence, the biological roles of many genes can only be revealed through multiple knockouts that identify a set of genes as essential for a given function. The identification of such "epistatic" essential relationships between network components is critical for the understanding and eventual manipulation of robust systems-level phenotypes. Results: We introduce and apply a network-based approach for genome-scale metabolic knockout design. We apply this method to uncover over 11,000 minimal knockouts for biomass production in an in silico genome-scale model of E. coli. A large majority of these "essential sets" contain 5 or more reactions, and thus represent complex epistatic relationships between components of the E. coli metabolic network. Conclusion: The complex minimal biomass knockouts discovered with our approach illuminate robust essential systems-level roles for reactions in the E. coli metabolic network. Unlike previous approaches, our method yields results regarding high-order epistatic relationships and is applicable at the genome-scale. [ABSTRACT FROM AUTHOR]

Chromosomal inversion polymorphisms are thought to play a role in adaptive divergence, but the genes conferring adaptive benefits remain elusive. Here we study 2La, a common polymorphic inversion in the African malaria vector Anopheles gambiae. The frequency of 2La varies clinally and seasonally in a pattern suggesting response to selection for aridity tolerance. By hybridizing genomic DNA from individual mosquitoes to oligonucleotide microarrays, we obtained a complete map of differentiation across the A. gambiae genome. Comparing mosquitoes homozygous for the 2La gene arrangement or its alternative (2L+a), divergence was highest at loci within the rearranged region. In the 22 Mb included within alternative arrangements, two ~1.5 Mb regions near but not adjacent to the breakpoints were identified as being significantly diverged, a conclusion validated by targeted sequencing. The persistent association of both regions with the 2La arrangement is highly unlikely given known recombination rates across the inversion in 2La heterozygotes, thus implicating selection on genes underlying these regions as factors responsible for the maintenance of 2La. Polymorphism and divergence data are consistent with a model in which the inversion is maintained by migration-selection balance between multiple alleles inside these regions, but further experiments will be needed to fully distinguish between the epistasis (coadaptation) and local adaptation models for the maintenance of 2La. [ABSTRACT FROM AUTHOR]

Genetic studies of behavior have traditionally come in two flavors: quantitative genetic studies of natural variants and single-gene studies of induced mutants. Each employed different techniques and methods of analysis toward the common, ultimate goal of understanding how genes influence behavior. With the advent of new genomic technologies, and also the realization that mechanisms underlying behavior involve a considerable degree of complex gene interaction, the traditionally separate strands of behavior genetics are merging into a single, synthetic strategy. [ABSTRACT FROM AUTHOR]

The pathogenesis of all forms of psoriasis remains obscure. Segregation analysis and twin studies together with ethnic differences in disease frequency all point to an underlying genetic susceptibility to psoriasis, which is both complex and likely to reflect the action of a number of genes. We performed a genome wide analysis using a total of 271 polymorphic autosomal markers on 284 sib relative pairs identified within 158 independent families. We detected evidence for linkage at 6p21 (PSORS1) with a non-parametric linkage score (NPL)=4.7, p=2 x 10-6 and at chromosome 1p (NPL=3.6, p=1.9 x 10-4) in all families studied. Significant excess (p=0.004) paternal allele sharing was detected for markers spanning the PSORS1 locus. A further three regions reached NPL scores of 2 or greater, including a region at chromosome 7 (NPL 2.1), for which linkage for a number of autoimmune disorders has been reported. Partitioning of the data set according to allele sharing at 6p21 (PSORS1) favoured linkage to chromosomes 2p (NPL 2.09) and 14q (NPL 2.0), both regions implicated in previous independent genome scans, and suggests evidence for epistasis between PSORS1 and genes at other genomic locations. This study has provided linkage evidence in favour of a novel susceptibility locus for psoriasis and provides evidence of the complex mechanisms underlying the genetic predisposition to this common skin disease. [ABSTRACT FROM AUTHOR]

The article discusses how most early geneticists assumed that alleles have constant relative selective values and that the phenotype were a mosaic of unit characters. According to the author, this viewpoint was later changed when it was understood that the selective value of any gene depends on the rest of the genome and on various gene interactions (epistasis).

The results of recently completed genome-wide association studies have advanced knowledge of inflammatory bowel disease (IBD) genetics, including the identification of over 30 genes or loci associated with Crohn's disease (CD). The possibility of interactions between genes, referred to by the term epistasis, needs to be carefully considered as both genetic and functional studies in IBD move forward. We review a paper in this issue of the Journal that reports evidence of epistasis in CD, and we discuss important issues that arise when trying to determine whether there is indeed interaction between genes and what their potential implications for disease pathogenesis and clinical outcome might be.Am J Gastroenterol 2009; 104:1734–1736; doi:10.1038/ajg.2009.179; published online 12 May 2009 [ABSTRACT FROM AUTHOR]