Information-theoretic identification of predictive SNPs and supervised visualization of genome-wide association studies

KIR2DS5 in the presence of HLA-C C2 protects against endometriosis

Endometriosis is defined as the presence of functional endometrial tissue outside the uterine cavity. Several hypotheses have attempted to explain the etiology and pathogenesis of endometriosis. Recently, it has been suggested that a defect of the natural killer (NK) activity in the recognition and lysis of endometrial cells is one of the crucial points in the development of this disease. Natural killer cells can express killer immunoglobulin-like receptors (KIR), which recognize class I human leukocyte antigens on target cells. We asked whether polymorphisms in KIR, HLA-C, and HLA-B genes are risk factors for endometriosis. We tested 153 women with endometriosis diagnosed on the basis of laparoscopic and histological examination, and 213 control healthy women, who gave birth to at least one child. The frequency of KIR genes in patients was similar to that in controls except for KIR2DS5, which exerted a protective effect only in HLA-C C2-positive individuals. Moreover, KIR2DS5-positive women with endometriosis had 13 times lower chance that the disease would occupy the peritoneum than KIR2DS5- and KIR2DS4del-negative ones (OR = 0.077, P = 0.0061). Similarly, KIR2DS4del-positive endometriotic persons had 11 times lower chance for peritoneal disease (OR = 0.094, P < 0.001). Negative linkage disequilibrium between KIR2DS5 and KIR2DS4del indicates that these genes are mutually exclusive. Our data suggest that KIR2DS5 may be associated with protection from endometriosis, whereas KIR2DS4del seems to be associated with higher disease stages, possibly by exclusion of protective KIR2DS5.

Killer immunoglobulin-like receptor (KIR) and HLA genotypes affect the outcome of allogeneic kidney transplantation

Background

Recipient NK cells may detect the lack of recipient's (i.e., self) HLA antigens on donor renal tissue by means of their killer cell immunoglobulin-like receptors (KIRs). KIR genes are differently distributed in individuals, possibly contributing to differences in response to allogeneic graft.

Methodology/Principal Findings

We compared frequencies of 10 KIR genes by PCR-SSP in 93 kidney graft recipients rejecting allogeneic renal transplants with those in 190 recipients accepting grafts and 690 healthy control individuals. HLA matching results were drawn from medical records. We observed associations of both a full-length KIR2DS4 gene and its variant with 22-bp deletion with kidney graft rejection. This effect was modulated by the HLA-B,-DR matching, particularly in recipients who did not have glomerulonephritis but had both forms of KIR2DS4 gene. In contrast, in recipients with glomerulonephritis, HLA compatibility seemed to be much less important for graft rejection than the presence of KIR2DS4 gene. Simultaneous presence of both KIR2DS4 variants strongly increased the probability of rejection. Interestingly, KIR2DS5 seemed to protect the graft in the presence of KIR2DS4fl but in the absence of KIR2DS4del.

Conclusions/Significance

Our results suggest a protective role of KIR2DS5 in graft rejection and an association of KIR2DS4 with kidney rejection, particularly in recipients with glomerulonephritis.

Predicting binding sites by analyzing allosteric effects

This chapter describes a method for analyzing the allosteric influence of molecular interactions on protein conformational distributions. The method, called Dynamics Perturbation Analysis (DPA), generally yields insights into allosteric effects in proteins and is especially useful for predicting ligand-binding sites. The use of DPA for binding site prediction is motivated by the following allosteric regulation hypothesis: interactions in native binding sites cause a large change in protein conformational distributions. Here, we review the reasoning behind this hypothesis, describe the math behind the method, and present a recipe for predicting binding sites using DPA.

Information-theoretic gene-gene and gene-environment interaction analysis of quantitative traits

Background

The purpose of this research was to develop a novel information theoretic method and an efficient algorithm for analyzing the gene-gene (GGI) and gene-environmental interactions (GEI) associated with quantitative traits (QT). The method is built on two information-theoretic metrics, the k-way interaction information (KWII) and phenotype-associated information (PAI). The PAI is a novel information theoretic metric that is obtained from the total information correlation (TCI) information theoretic metric by removing the contributions for inter-variable dependencies (resulting from factors such as linkage disequilibrium and common sources of environmental pollutants).

Results

The KWII and the PAI were critically evaluated and incorporated within an algorithm called CHORUS for analyzing QT. The combinations with the highest values of KWII and PAI identified each known GEI associated with the QT in the simulated data sets. The CHORUS algorithm was tested using the simulated GAW15 data set and two real GGI data sets from QTL mapping studies of high-density lipoprotein levels/atherosclerotic lesion size and ultra-violet light-induced immunosuppression. The KWII and PAI were found to have excellent sensitivity for identifying the key GEI simulated to affect the two quantitative trait variables in the GAW15 data set. In addition, both metrics showed strong concordance with the results of the two different QTL mapping data sets.

Conclusion

The KWII and PAI are promising metrics for analyzing the GEI of QT.

AMBIENCE: a novel approach and efficient algorithm for identifying informative genetic and environmental associations with complex phenotypes

We developed a computationally efficient algorithm AMBIENCE, for identifying the informative variables involved in gene-gene (GGI) and gene-environment interactions (GEI) that are associated with disease phenotypes. The AMBIENCE algorithm uses a novel information theoretic metric called phenotype-associated information (PAI) to search for combinations of genetic variants and environmental variables associated with the disease phenotype. The PAI-based AMBIENCE algorithm effectively and efficiently detected GEI in simulated data sets of varying size and complexity, including the 10K simulated rheumatoid arthritis data set from Genetic Analysis Workshop 15. The method was also successfully used to detect GGI in a Crohn's disease data set. The performance of the AMBIENCE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized MDR (GMDR), and pedigree disequilibrium test (PDT) methods. Furthermore, we assessed the computational speed of AMBIENCE for detecting GGI and GEI for data sets varying in size from 100 to 10(5) variables. Our results demonstrate that the AMBIENCE information theoretic algorithm is useful for analyzing a diverse range of epidemiologic data sets containing evidence for GGI and GEI.

Fast dynamics perturbation analysis for prediction of protein functional sites

Background

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D_x. Such regions are associated with functional sites.

Results

The Fast DPA algorithm, which accelerates DPA calculations, is motivated by an empirical observation that D_x in a normal-modes model is highly correlated with an entropic term that only depends on the eigenvalues of the normal modes. The eigenvalues are accurately estimated using first-order perturbation theory, resulting in a N-fold reduction in the overall computational requirements of the algorithm, where N is the number of residues in the protein. The performance of the original and Fast DPA algorithms was compared using protein structures from a standard small-molecule docking test set. For nominal implementations of each algorithm, top-ranked Fast DPA predictions overlapped the true binding site 94% of the time, compared to 87% of the time for original DPA. In addition, per-protein recall statistics (fraction of binding-site residues that are among predicted residues) were slightly better for Fast DPA. On the other hand, per-protein precision statistics (fraction of predicted residues that are among binding-site residues) were slightly better using original DPA. Overall, the performance of Fast DPA in predicting ligand-binding-site residues was comparable to that of the original DPA algorithm.

Conclusion

Compared to the original DPA algorithm, the decreased run time with comparable performance makes Fast DPA well-suited for implementation on a web server and for high-throughput analysis.

Information-theoretic metrics for visualizing gene-environment interactions

The purpose of our work was to develop heuristics for visualizing and interpreting gene-environment interactions (GEIs) and to assess the dependence of candidate visualization metrics on biological and study-design factors. Two information-theoretic metrics, the k-way interaction information (KWII) and the total correlation information (TCI), were investigated. The effectiveness of the KWII and TCI to detect GEIs in a diverse range of simulated data sets and a Crohn disease data set was assessed. The sensitivity of the KWII and TCI spectra to biological and study-design variables was determined. Head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and the pedigree disequilibrium test (PDT) methods were obtained. The KWII and TCI spectra, which are graphical summaries of the KWII and TCI for each subset of environmental and genotype variables, were found to detect each known GEI in the simulated data sets. The patterns in the KWII and TCI spectra were informative for factors such as case-control misassignment, locus heterogeneity, allele frequencies, and linkage disequilibrium. The KWII and TCI spectra were found to have excellent sensitivity for identifying the key disease-associated genetic variations in the Crohn disease data set. In head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, the results from visual interpretation of the KWII and TCI spectra performed satisfactorily. The KWII and TCI are promising metrics for visualizing GEIs. They are capable of detecting interactions among numerous single-nucleotide polymorphisms and environmental variables for a diverse range of GEI models.