General Epistatic Models of the Risk of Complex Diseases

The range of possible gene interactions in a multilocus model of a complex inherited disease is studied by exploring genotype-specific risks subject to the constraint that the allele frequencies and marginal risks are known. We quantify the effect of gene interactions by defining the interaction ratio, , where K_R is the recurrence risk to relatives with relationship R for the true model and is the recurrence risk to relatives for a multiplicative model with the same marginal risks. We use a Markov chain Monte Carlo (MCMC) procedure to sample from the space of possible models. We find that the average of C_R increases with the number of loci for both low frequency (p = 0.03) and higher frequency (p = 0.25) causative alleles. Furthermore, the probability that C_R > 1 is nearly 1. Similar results are obtained when more weight is given to risk models that are closer to the comparable multiplicative model. These results imply that, in general, gene interactions will result in greater heritability of a complex inherited disease than is expected on the basis of a multiplicative model of interactions and hence may provide a partial explanation for the problem of missing heritability of complex diseases.ALTHOUGH many genome-wide association studies (GWAS) have been performed and have found hundreds of SNPs associated with higher risk of complex inherited diseases, those SNPs so far account for only a small fraction of the inherited risk of those diseases (Altshuler et al. 2008). Several not mutually exclusive explanations have been proposed for the “missing heritability,” i.e., the heritability that is not yet accounted for by SNPs found in GWAS (Manolio et al. 2009): (i) common alleles of small effect that have not been found because GWAS done so far have been underpowered, (ii) low-frequency alleles of moderate effect that are difficult to find using HapMap SNPs, (iii) rare copy-number variants that are not in strong linkage disequilibrium (LD) with HapMap SNPs, (iv) inherited epigenetic factors that are not in strong LD with HapMap SNPs, and (v) interactions among causative alleles that conceal their true contribution to heritability. In this article we investigate the last possibility and determine the extent to which interactions may account for missing heritability.Our analysis is in the same spirit as that of Culverhouse et al. (2002). We assume that the risk of being affected by a complex disease is determined by an individual''s genotype at two or more loci and that the frequencies of causative alleles and the average risks for each one-locus genotype (the marginal risks) are known. Culverhouse et al. (2002) assumed the marginal risks were the same for all genotypes and all loci. In that case, causative alleles have odds ratios of 1; they contribute to risk only through their interactions. Culverhouse et al. found the risk function that maximized the heritability and showed that the maximum possible heritability attributable to interactions increased with the number of loci. They concluded that it is quite possible that interactions among loci that have no main effect could contribute substantially to the heritability of a complex disease and indeed could account for “virtually all the variation in affection status for diseases with any prevalence” (Culverhouse et al. 2002, p. 468).We generalize the analysis of Culverhouse et al. in three ways. First, we allow causative alleles to have odds ratios >1. Second, we explore the entire space of models instead of focusing only on the risk model that maximizes heritability. Third, we examine how the importance of gene interactions depends on the “distance” between a risk model and a comparable multiplicative model. We show that gene interactions can substantially increase the heritability of risk as measured by recurrence risk, K_R, and that the effect increases with the number of loci carrying causative alleles. Furthermore, we show that these results are true even if more weight is given to models that are closer to a comparable multiplicative model.Geometrically, the space of feasible genotype-specific risks subject to the aforementioned constraints (i.e., that the allele frequencies and marginal risks are known) corresponds to a high-dimensional convex polytope, and the computational problem of interest involves integrating a quadratic function over the polytope. The dimension of the polytope grows exponentially with the number of loci, and, therefore, analytic computation is intractable for more than two loci. Hence, we devise a Monte Carlo approach to tackle the problem. Note that, because of high dimensionality, rejection algorithms are not appropriate for this kind of problem. We instead employ a Markov chain Monte Carlo (MCMC) algorithm based on a random walk that always stays inside the polytope. We present empirical results for up to five loci and obtain a closed-form formula for the minimum of K_R over the polytope; the latter result applies to an arbitrary number of loci. Interestingly, the minimum of K_R decreases as the number L of loci increases, but the average of K_R over the polytope increases with L.     

Discover Protein Complexes in Protein-Protein Interaction Networks Using Parametric Local Modularity

Background  

Recent advances in proteomic technologies have enabled us to create detailed protein-protein interaction maps in multiple species and in both normal and diseased cells. As the size of the interaction dataset increases, powerful computational methods are required in order to effectively distil network models from large-scale interactome data.     

Epistatic Effects on Abdominal Fat Content in Chickens: Results from a Genome-Wide SNP-SNP Interaction Analysis

We performed a pairwise epistatic interaction test using the chicken 60 K single nucleotide polymorphism (SNP) chip for the 11^th generation of the Northeast Agricultural University broiler lines divergently selected for abdominal fat content. A linear mixed model was used to test two dimensions of SNP interactions affecting abdominal fat weight. With a threshold of P<1.2×10⁻¹¹ by a Bonferroni 5% correction, 52 pairs of SNPs were detected, comprising 45 pairs showing an Additive×Additive and seven pairs showing an Additive×Dominance epistatic effect. The contribution rates of significant epistatic interactive SNPs ranged from 0.62% to 1.54%, with 47 pairs contributing more than 1%. The SNP-SNP network affecting abdominal fat weight constructed using the significant SNP pairs was analyzed, estimated and annotated. On the basis of the network’s features, SNPs Gga_rs14303341 and Gga_rs14988623 at the center of the subnet should be important nodes, and an interaction between GGAZ and GGA8 was suggested. Twenty-two quantitative trait loci, 97 genes (including nine non-coding genes), and 50 pathways were annotated on the epistatic interactive SNP-SNP network. The results of the present study provide insights into the genetic architecture underlying broiler chicken abdominal fat weight.     

Using Biotic Interaction Networks for Prediction in Biodiversity and Emerging Diseases

Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease - Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases.     

Epistatic Gene-Based Interaction Analyses for Glaucoma in eMERGE and NEIGHBOR Consortium

Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.     

Genetic Association Analysis of Complex Diseases Incorporating Intermediate Phenotype Information

Genetic researchers often collect disease related quantitative traits in addition to disease status because they are interested in understanding the pathophysiology of disease processes. In genome-wide association (GWA) studies, these quantitative phenotypes may be relevant to disease development and serve as intermediate phenotypes or they could be behavioral or other risk factors that predict disease risk. Statistical tests combining both disease status and quantitative risk factors should be more powerful than case-control studies, as the former incorporates more information about the disease. In this paper, we proposed a modified inverse-variance weighted meta-analysis method to combine disease status and quantitative intermediate phenotype information. The simulation results showed that when an intermediate phenotype was available, the inverse-variance weighted method had more power than did a case-control study of complex diseases, especially in identifying susceptibility loci having minor effects. We further applied this modified meta-analysis to a study of imputed lung cancer genotypes with smoking data in 1154 cases and 1137 matched controls. The most significant SNPs came from the CHRNA3-CHRNA5-CHRNB4 region on chromosome 15q24–25.1, which has been replicated in many other studies. Our results confirm that this CHRNA region is associated with both lung cancer development and smoking behavior. We also detected three significant SNPs—rs1800469, rs1982072, and rs2241714—in the promoter region of the TGFB1 gene on chromosome 19 (p = 1.46×10⁻⁵, 1.18×10⁻⁵, and 6.57×10⁻⁶, respectively). The SNP rs1800469 is reported to be associated with chronic obstructive pulmonary disease and lung cancer in cigarette smokers. The present study is the first GWA study to replicate this result. Signals in the 3q26 region were also identified in the meta-analysis. We demonstrate the intermediate phenotype can potentially enhance the power of complex disease association analysis and the modified meta-analysis method is robust to incorporate intermediate phenotype or other quantitative risk factor in the analysis.     

Mapping Quantitative Trait Loci for Complex Binary Diseases Using Line Crosses

A composite interval gene mapping procedure for complex binary disease traits is proposed in this paper. The binary trait of interest is assumed to be controlled by an underlying liability that is normally distributed. The liability is treated as a typical quantitative character and thus described by the usual quantitative genetics model. Translation from the liability into a binary (disease) phenotype is through the physiological threshold model. Logistic regression analysis is employed to estimate the effects and locations of putative quantitative trait loci (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). Simulation studies show that properties of this mapping procedure mimic those of the composite interval mapping for normally distributed data. Potential utilization of the QTL mapping procedure for resolving alternative genetic models (e.g., single- or two-trait-locus model) is discussed.     

Discovery of New Candidate Genes Related to Brain Development Using Protein Interaction Information

Human brain development is a dramatic process composed of a series of complex and fine-tuned spatiotemporal gene expressions. A good comprehension of this process can assist us in developing the potential of our brain. However, we have only limited knowledge about the genes and gene functions that are involved in this biological process. Therefore, a substantial demand remains to discover new brain development-related genes and identify their biological functions. In this study, we aimed to discover new brain-development related genes by building a computational method. We referred to a series of computational methods used to discover new disease-related genes and developed a similar method. In this method, the shortest path algorithm was executed on a weighted graph that was constructed using protein-protein interactions. New candidate genes fell on at least one of the shortest paths connecting two known genes that are related to brain development. A randomization test was then adopted to filter positive discoveries. Of the final identified genes, several have been reported to be associated with brain development, indicating the effectiveness of the method, whereas several of the others may have potential roles in brain development.     

Targeting Ageing to Decrease Complex Non-Communicable Human Diseases

正During the last two centuries,there have been many spectacular advances in medical science,the main consequence of which has been the dramatically reduced burden of infectious diseases.While in the 1800s many people died before reaching adulthood,nowadays most people survive.Hence average life expectancy in 1800s was around 30—40,which was barely higher than it had been in Greek and Roman times(Finch,2010),but nowadays life expectancy in most modernised economies is     

Using Structural Information to Change the Phosphotransfer Specificity of a Two-Component Chemotaxis Signalling Complex

Two-component signal transduction pathways comprising histidine protein kinases (HPKs) and their response regulators (RRs) are widely used to control bacterial responses to environmental challenges. Some bacteria have over 150 different two-component pathways, and the specificity of the phosphotransfer reactions within these systems is tightly controlled to prevent unwanted crosstalk. One of the best understood two-component signalling pathways is the chemotaxis pathway. Here, we present the 1.40 Å crystal structure of the histidine-containing phosphotransfer domain of the chemotaxis HPK, CheA₃, in complex with its cognate RR, CheY₆. A methionine finger on CheY₆ that nestles in a hydrophobic pocket in CheA₃ was shown to be important for the interaction and was found to only occur in the cognate RRs of CheA₃, CheY₆, and CheB₂. Site-directed mutagenesis of this methionine in combination with two adjacent residues abolished binding, as shown by surface plasmon resonance studies, and phosphotransfer from CheA₃-P to CheY₆. Introduction of this methionine and an adjacent alanine residue into a range of noncognate CheYs, dramatically changed their specificity, allowing protein interaction and rapid phosphotransfer from CheA₃-P. The structure presented here has allowed us to identify specificity determinants for the CheA–CheY interaction and subsequently to successfully reengineer phosphotransfer signalling. In summary, our results provide valuable insight into how cells mediate specificity in one of the most abundant signalling pathways in biology, two-component signal transduction.     

Epistatic contributions to quantitative traits in Tribolium castaneum

Summary Triple-testcross experiments were used to analyze epistatic contributions to larva weight, pupa weight, pupa width and adult weight in Tribolium castaneum. Seven diverse inbred lines and the F₁. produced by crossing the two tester lines were examined for indications of epistasis. Larva weight was the only trait for which no significant epistasis was detected. There was significant epistasis for pupa weight in three of the inbred lines; for pupa width in four of the inbred lines; for adult weight in five of the inbred lines. Only one inbred line and the F₁ line failed to exhibit significant epistasis for any trait. Each inbred line had a unique pattern of epistasis, suggesting that a number of different loci were contributing to the detected epistasis.This paper (No. 76-5-158) is published with the approval of the Director of the Kentucky Agricultural Experiment Station.     

Epistatic contributions to quantitative traits in Tribolium castaneum

Summary Triple-testcross experiments were used to analyze epistatic contributions to % hatchability of eggs, age of pupation, number of eggs laid in 24-hour period, and survival from hatching to day 35. Seven diverse inbred lines and the F₁ produced by crossing the two tester lines were examined for the presence of epistasis. There was evidence of epistasis for each of the 4 traits in at least one of the 8 lines tested. Epistasis was a major source of variation in survival in all of the lines tested.     

Relating Diseases by Integrating Gene Associations and Information Flow through Protein Interaction Network

Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved in causing the diseases. Although not needed for understanding this paper, the raw results are available for download for further study at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/DiseaseRelations/.     

水稻CMS-DA育性恢复基因定位及其互作分析

在由210个测交组合组成的青早A／（协青早B／密阳46）F6群体中,构建了由129个RFLP、SSLP组成的连锁遗传图普。应用QTL分析方法,对水矮败型质雄性不育恢复基因进行了定位。检测到一个主效基因和3个效应较小的QTL（qRf－1、qRf－1、qRf－5）,这些基因这宰存在复杂的相互作用。