Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.

Multifactor effects and evidence of potential interaction between complement factor H Y402H and LOC387715 A69S in age-related macular degeneration.

BACKGROUND: Variants in the complement cascade genes and the LOC387715/HTRA1, have been widely reported to associate with age-related macular degeneration (AMD), the most common cause of visual impairment in industrialized countries. METHODS/PRINCIPAL FINDINGS: We investigated the association between the LOC387715 A69S and complement component C3 R102G risk alleles in the Finnish case-control material and found a significant association with both variants (OR 2.98, p = 3.75 x 10(-9); non-AMD controls and OR 2.79, p = 2.78 x 10(-19), blood donor controls and OR 1.83, p = 0.008; non-AMD controls and OR 1.39, p = 0.039; blood donor controls), respectively. Previously, we have shown a strong association between complement factor H (CFH) Y402H and AMD in the Finnish population. A carrier of at least one risk allele in each of the three susceptibility loci (LOC387715, C3, CFH) had an 18-fold risk of AMD when compared to a non-carrier homozygote in all three loci. A tentative gene-gene interaction between the two major AMD-associated loci, LOC387715 and CFH, was found in this study using a multiplicative (logistic regression) model, a synergy index (departure-from-additivity model) and the mutual information method (MI), suggesting that a common causative pathway may exist for these genes. Smoking (ever vs. never) exerted an extra risk for AMD, but somewhat surprisingly, only in connection with other factors such as sex and the C3 genotype. Population attributable risks (PAR) for the CFH, LOC387715 and C3 variants were 58.2%, 51.4% and 5.8%, respectively, the summary PAR for the three variants being 65.4%. CONCLUSIONS/SIGNIFICANCE: Evidence for gene-gene interaction between two major AMD associated loci CFH and LOC387715 was obtained using three methods, logistic regression, a synergy index and the mutual information (MI) index.

Ligand-signaled upregulation of Enterococcus faecalis ace transcription, a mechanism for modulating host-E. faecalis interaction.

Enterococcus faecalis, the third most frequent cause of bacterial endocarditis, appears to be equipped with diverse surface-associated proteins showing structural-fold similarity to the immunoglobulin-fold family of staphylococcal adhesins. Among the putative E. faecalis surface proteins, the previously characterized adhesin Ace, which shows specific binding to collagen and laminin, was detectable in surface protein preparations only after growth at 46 degrees C, mirroring the finding that adherence was observed in 46 degrees C, but not 37 degrees C, grown E. faecalis cultures. To elucidate the influence of different growth and host parameters on ace expression, we investigated ace expression using E. faecalis OG1RF grown in routine laboratory media (brain heart infusion) and found that ace mRNA levels were low in all growth phases. However, quantitative reverse transcription-PCR showed 18-fold-higher ace mRNA amounts in cells grown in the presence of collagen type IV compared to the controls. Similarly, a marked increase was observed when cells were either grown in the presence of collagen type I or serum but not in the presence of fibrinogen or bovine serum albumin. The production of Ace after growth in the presence of collagen type IV was demonstrated by immunofluorescence microscopy, mirroring the increased ace mRNA levels. Furthermore, increased Ace expression correlated with increased collagen and laminin adhesion. Collagen-induced Ace expression was also seen in three of three other E. faecalis strains of diverse origins tested, and thus it appears to be a common phenomenon. The observation of host matrix signal-induced adherence of E. faecalis may have important implications on our understanding of this opportunistic pathogen.

NMR structure of a complex between the VirB9/VirB7 interaction domains of the pKM101 type IV secretion system.

Type IV secretion (T4S) systems translocate DNA and protein effectors through the double membrane of Gram-negative bacteria. The paradigmatic T4S system in Agrobacterium tumefaciens is assembled from 11 VirB subunits and VirD4. Two subunits, VirB9 and VirB7, form an important stabilizing complex in the outer membrane. We describe here the NMR structure of a complex between the C-terminal domain of the VirB9 homolog TraO (TraO(CT)), bound to VirB7-like TraN from plasmid pKM101. TraO(CT) forms a beta-sandwich around which TraN winds. Structure-based mutations in VirB7 and VirB9 of A. tumefaciens show that the heterodimer interface is conserved. Opposite this interface, the TraO structure shows a protruding three-stranded beta-appendage, and here, we supply evidence that the corresponding region of VirB9 of A. tumefaciens inserts in the membrane and protrudes extracellularly. This complex structure elucidates the molecular basis for the interaction between two essential components of a T4S system.

The roles of electrostatic charge pairing in the interaction between cytochrome P-450 and NADPH-cytochrome P-450 reductase

The cytochrome P450 enzyme catalysis requires two electrons transferred from NADPH-cytochrome P450 reductase (reductase) to P450. Electrostatic charge-pairing has been proposed to be one of the major forces in the interaction between P450 and reductase. In order to obtain further insight into the molecular basis for the protein interaction, I used two methods, chemical modification and specific anti-peptide antibodies, to study the involvement and importance of charged amino acid residues. Acetylation of lysine residues of P450c and P450b by acetic anhydride dramatically inhibited the reductase-supported P450c-dependent ethoxycoumarin hydroxylation activity, but P450 activity supported by cumene hydroperoxide is relatively unchanged. The modification of lysine residues of P450c and P450b did not grossly disturb the protein conformation as revealed by several spectral studies. This differential effect of lysine modification on the P450 activity in the system reconstituted with reductase versus the system supported by cumene hydroperoxide suggested an important role for P450 lysine residues in the interaction with reductase. Using $\rm\sp{14}C$-acetic anhydride, P450 lysine residues were labelled and further identified on P450c and P450b. Those lysine residues are at position 97, 271, 279, and 407 for P450c, and 251, 384, 422, 433, and 473 for P450b. Alignment of those identified lysine residues on P450c and P450b with amino acid residues identified in other studies indicated those residues reside in three major sequence areas. Modification of arginine residues of P450b by phenylglyoxal and 2, 3-butanedione have no significant effect on P450 activity either supported by NADPH and reductase or supported by cumene hydroperoxide. Further studies using $\rm\sp{14}C$-phenylglyoxal reveals that no incorporation of phenylglyoxal into P450b was found. These results demonstrated a predominant role of lysine residues of P450 in the electrostatic interaction with reductase. To understand the protein binding sites on each of P450 and reductase, I generated three anti-peptide antibodies against regions on reductase and five anti-peptide antibodies against five putative reductase binding sites on P450c. These anti-peptide antibodies were affinity purified and characterized on ELISA and by Western blot analysis. Inhibition experiments using these antibodies demonstrated that regions 109-120 and 204-220 of reductase are probably the two major binding sites for P450. The association of reductase with cytochromes P450 and cytochrome c may rely on different mechanisms. The data from experiments using anti-peptide (P450c) antibodies supports the important role of P450c lysine residues 271/279 and 458/460 in the interaction with reductase. ^

Protein-protein interaction between phototaxis receptor sensory Rhodopsin I and its transducer HtrI

The molecular complex containing the seven transmembrane helix photoreceptor S&barbelow;ensory R&barbelow;hodopsin I&barbelow; (SRI) and transducer protein HtrI (H&barbelow;alobacterial Transducer for SRI&barbelow;) mediates color-sensitive phototaxis responses in the archaeon Halobacterium salinarum. Orange light causes an attractant response by a one-photon reaction and white light (orange + UV light) a repellent response by a two-photon reaction. Three aspects of SRI-HtrI structure/function and the signal transduction pathway were explored. First, the coupling of HtrI to the photoactive site of SRI was analyzed by mutagenesis and kinetic spectroscopy. Second, SRI-HtrI mutations and suppressors were selected and characterized to elucidate the color-sensing mechanism. Third, the signal relay through the transducer-bound histidine kinase was analyzed using an in vitro reconstitution system with known and newly identified taxis components. ^ Twenty-one mutations on HtrI were introduced by site-directed mutagenesis. Several replacements of charged residues perturbed the photochemical kinetics of SRI which led to the finding of a cluster of residues at the membrane/cytoplasm interface in HtrI electrostatically coupled to the photoactive site of SRI. We found by laser-flash kinetic spectroscopy that the transducer and these residues have specific effects on the light-induced proton transfer between the retinal chromophore and the protein. ^ One of the mutations showed an unusual mutant phenotype we called “inverted” signaling, in which the cell produces a repellent response to normally attractant light. Therefore, this mutant (E56Q of HtrI) had lost the color-discrimination by the SRI-HtrI complex. We used suppressor analysis to better understand the phenotype. Certain suppressors resulted in return of attractant responses to orange light but with inversion of the normally repellent response to white light to an attractant response. To explain this and other results, we formulated the Conformational Shuttling model in which the HtrI-SRI complex is poised in a metastable equilibrium of two conformations shifted in opposite directions by orange and white light. We tested this model by behavioral analysis (computerized cell tracking and motion study) of double mutants of inverting and suppressing mutations and the results confirmed the equilibrium-shift explanation. ^ We developed an in vitro system for measuring the effect of purified transducer on the histidine-kinase CheAH that controls the flagellar motor switch. The rate of kinase autophosphorylation was stimulated >2 fold in the reconstitution of the complete signal transduction system from purified components from H. salinarum. The in vitro assay also showed that the kinase activity was reduced in the absence and in the presence of high levels of linker protein CheWH. (Abstract shortened by UMI.) ^

Macromolecular interaction studies on FF1-3 of human CA150 and STAT1-importin alpha5 using biophysical and biochemical methods

Macromolecular interactions, such as protein-protein interactions and protein-DNA interactions, play important roles in executing biological functions in cells. However the complexity of such interactions often makes it very challenging to elucidate the structural details of these subjects. In this thesis, two different research strategies were applied on two different two macromolecular systems: X-ray crystallography on three tandem FF domains of transcription regulator CA150 and electron microscopy on STAT1-importin α5 complex. The results from these studies provide novel insights into the function-structure relationships of transcription coupled RNA splicing mediated by CA150 and the nuclear import process of the JAK-STAT signaling pathway. ^ The first project aimed at the protein-protein interaction module FF domain, which often occurs as tandem repeats. Crystallographic structure of the first three FF domains of human CA150 was determined to 2.7 Å resolution. This is the only crystal structure of an FF domain and the only structure on tandem FF domains to date. It revealed a striking connectivity between an FF domain and the next. Peptide binding assay with the potential binding ligand of FF domains was performed using fluorescence polarization. Furthermore, for the first time, FF domains were found to potentially interact with DNA. DNA binding assays were also performed and the results were supportive to this newly proposed functionality of an FF domain. ^ The second project aimed at understanding the molecular mechanism of the nuclear import process of transcription factor STAT1. The first structural model of pSTAT1-importin α5 complex in solution was built from the images of negative staining electron microscopy. Two STAT1 molecules were observed to interact with one molecule of importin α5 in an asymmetric manner. This seems to imply that STAT1 interacts with importin α5 with a novel mechanism that is different from canonical importin α-cargo interactions. Further in vitro binding assays were performed to obtain more details on the pSTAT1-importin α5 interaction. ^

Gene-gene interaction and gene-pathway analysis of genome-wide association for schizophrenia

Schizophrenia (SZ) is a complex disorder with high heritability and variable phenotypes that has limited success in finding causal genes associated with the disease development. Pathway-based analysis is an effective approach in investigating the molecular mechanism of susceptible genes associated with complex diseases. The etiology of complex diseases could be a network of genetic factors and within the genes, interaction may occur. In this work we argue that some genes might be of small effect that by itself are neither sufficient nor necessary to cause the disease however, their effect may induce slight changes to the gene expression or affect the protein function, therefore, analyzing the gene-gene interaction mechanism within the disease pathway would play crucial role in dissecting the genetic architecture of complex diseases, making the pathway-based analysis a complementary approach to GWAS technique. ^ In this study, we implemented three novel linkage disequilibrium based statistics, the linear combination, the quadratic, and the decorrelation test statistics, to investigate the interaction between linked and unlinked genes in two independent case-control GWAS datasets for SZ including participants of European (EA) and African (AA) ancestries. The EA population included 1,173 cases and 1,378 controls with 729,454 genotyped SNPs, while the AA population included 219 cases and 288 controls with 845,814 genotyped SNPs. We identified 17,186 interacting gene-sets at significant level in EA dataset, and 12,691 gene-sets in AA dataset using the gene-gene interaction method. We also identified 18,846 genes in EA dataset and 19,431 genes in AA dataset that were in the disease pathways. However, few genes were reported of significant association to SZ. ^ Our research determined the pathways characteristics for schizophrenia through the gene-gene interaction and gene-pathway based approaches. Our findings suggest insightful inferences of our methods in studying the molecular mechanisms of common complex diseases.^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.

Mechanism of subunit interaction and DNA binding of the heterotrimeric CCAAT binding factor CBF

Many eukaryotic promoters contain a CCAAT element at a site close ($-$80 to $-$120) to the transcription initiation site. CBF (CCAAT Binding Factor), also called NF-Y and CP1, was initially identified as a transcription factor binding to such sites in the promoters of the Type I collagen, albumin and MHC class II genes. CBF is a heteromeric transcription factor and purification and cloning of two of the subunits, CBF-A and CBF-B revealed that it was evolutionarily conserved with striking sequence identities with the yeast polypeptides HAP3 and HAP2, which are components of a CCAAT binding factor in yeast. Recombinant CBF-A and CBF-B however failed to bind to DNA containing CCAAT sequences. Biochemical experiments led to the identification of a third subunit, CBF-C which co-purified with CBF-A and complemented the DNA binding of recombinant CBF-A and CBF-B. We have recently isolated CBF-C cDNAs and have shown that bacterially expressed purified CBF-C binds to CCAAT containing DNA in the presence of recombinant CBF-A and CBF-B. Our experiments also show that a single molecule each of all the three subunits are present in the protein-DNA complex. Interestingly, CBF-C is also evolutionarily conserved and the conserved domain between CBF-C and its yeast homolog HAP5 is sufficient for CBF-C activity. Using GST-pulldown experiments we have demonstrated the existence of protein-protein interaction between CBF-A and CBF-C in the absence of CBF-B and DNA. CBF-B on other hand, requires both CBF-A and CBF-C to form a ternary complex which then binds to DNA. Mutational studies of CBF-A have revealed different domains of the protein which are involved in CBF-C interaction and CBF-B interaction. In addition, CBF-A harbors a domain which is involved in DNA recognition along with CBF-B. Dominant negative analogs of CBF-A have also substantiated our initial observation of assembly of CBF subunits. Our studies define a novel DNA binding structure of heterotrimeric CBF, where the three subunits of CBF follow a particular pathway of assembly of subunits that leads to CBF binding to DNA and activating transcription. ^