Exploiting interactions among polymorphisms contributing to complex disease traits with boosted generative modeling.

Although there has been great success in identifying disease genes for simple, monogenic Mendelian traits, deciphering the genetic mechanisms involved in complex diseases remains challenging. One major approach is to identify configurations of interacting factors such as single nucleotide polymorphisms (SNPs) that confer susceptibility to disease. Traditional methods, such as the multiple dimensional reduction method and the combinatorial partitioning method, provide good tools to decipher such interactions amid a disease population with a single genetic cause. However, these traditional methods have not managed to resolve the issue of genetic heterogeneity, which is believed to be a very common phenomenon in complex diseases. There is rarely prior knowledge of the genetic heterogeneity of a disease, and traditional methods based on estimation over the entire population are unlikely to succeed in the presence of heterogeneity. We present a novel Boosted Generative Modeling (BGM) approach for structure-model the interactions leading to diseases in the context of genetic heterogeneity. Our BGM method bridges the ensemble and generative modeling approaches to genetic association studies under a case-control design. Generative modeling is employed to model the interaction network configuration and the causal relationships, while boosting is used to address the genetic heterogeneity problem. We perform our method on simulation data of complex diseases. The results indicate that our method is capable of modeling the structure of interaction networks among disease-susceptible loci and of addressing genetic heterogeneity issues where the traditional methods, such as multiple dimensional reduction method, fail to apply. Our BGM method provides an exploratory tool that identifies the variables (e.g., disease-susceptible loci) that are likely to correlate and contribute to the disease.     

Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer

One of the greatest challenges facing human geneticists is the identification and characterization of susceptibility genes for common complex multifactorial human diseases. This challenge is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes and with environmental exposures. We introduce multifactor-dimensionality reduction (MDR) as a method for reducing the dimensionality of multilocus information, to improve the identification of polymorphism combinations associated with disease risk. The MDR method is nonparametric (i.e., no hypothesis about the value of a statistical parameter is made), is model-free (i.e., it assumes no particular inheritance model), and is directly applicable to case-control and discordant-sib-pair studies. Using simulated case-control data, we demonstrate that MDR has reasonable power to identify interactions among two or more loci in relatively small samples. When it was applied to a sporadic breast cancer case-control data set, in the absence of any statistically significant independent main effects, MDR identified a statistically significant high-order interaction among four polymorphisms from three different estrogen-metabolism genes. To our knowledge, this is the first report of a four-locus interaction associated with a common complex multifactorial disease.     

Multilocus analysis of hypertension: a hierarchical approach

While hypertension is a complex disease with a well-documented genetic component, genetic studies often fail to replicate findings. One possibility for such inconsistency is that the underlying genetics of hypertension is not based on single genes of major effect, but on interactions among genes. To test this hypothesis, we studied both single locus and multilocus effects, using a case-control design of subjects from Ghana. Thirteen polymorphisms in eight candidate genes were studied. Each candidate gene has been shown to play a physiological role in blood pressure regulation and affects one of four pathways that modulate blood pressure: vasoconstriction (angiotensinogen, angiotensin converting enzyme - ACE, angiotensin II receptor), nitric oxide (NO) dependent and NO independent vasodilation pathways and sodium balance (G protein-coupled receptor kinase, GRK4). We evaluated single site allelic and genotypic associations, multilocus genotype equilibrium and multilocus genotype associations, using multifactor dimensionality reduction (MDR). For MDR, we performed systematic reanalysis of the data to address the role of various physiological pathways. We found no significant single site associations, but the hypertensive class deviated significantly from genotype equilibrium in more than 25% of all multilocus comparisons (2,162 of 8,178), whereas the normotensive class rarely did (11 of 8,178). The MDR analysis identified a two-locus model including ACE and GRK4 that successfully predicted blood pressure phenotype 70.5% of the time. Thus, our data indicate epistatic interactions play a major role in hypertension susceptibility. Our data also support a model where multiple pathways need to be affected in order to predispose to hypertension.     

Multifactor-dimensionality reduction versus family-based association tests in detecting susceptibility loci in discordant sib-pair studies

Complex diseases are generally thought to be under the influence of multiple, and possibly interacting, genes. Many association methods have been developed to identify susceptibility genes assuming a single-gene disease model, referred to as single-locus methods. Multilocus methods consider joint effects of multiple genes and environmental factors. One commonly used method for family-based association analysis is implemented in FBAT. The multifactor-dimensionality reduction method (MDR) is a multilocus method, which identifies multiple genetic loci associated with the occurrence of complex disease. Many studies of late onset complex diseases employ a discordant sib pairs design. We compared the FBAT and MDR in their ability to detect susceptibility loci using a discordant sib-pair dataset generated from the simulated data made available to participants in the Genetic Analysis Workshop 14. Using FBAT, we were able to identify the effect of one susceptibility locus. However, the finding was not statistically significant. We were not able to detect any of the interactions using this method. This is probably because the FBAT test is designed to find loci with major effects, not interactions. Using MDR, the best result we obtained identified two interactions. However, neither of these reached a level of statistical significance. This is mainly due to the heterogeneity of the disease trait and noise in the data.     

Studies on the mechanism of action of a proline-rich polypeptide complex (PRP): effect on the stage of cell differentiation

A proline-rich polypeptide complex (PRP) with immunoregulatory and procognitive activities shows beneficial effects in Alzheimer's disease (AD). The mechanism of action of PRP in AD is not yet clarified. Here, we present results of the effect of PRP on Vitamin D3-induced phenotypic (CD11b and CD14) and functional (phagocytic) differentiation/maturation of monocytes/macrophages using the premonocytic HL-60 cell line as a model. This cell line can be induced to differentiate into monocyte/macrophage cells by incubation with Vitamin D3. However, when Vitamin D3 was applied together with PRP, a 30-40% inhibition of the expression of the differentiation markers and an over-60% inhibition of phagocytic ability were observed. When PRP was administered to the cells after treatment with Vitamin D3, no attenuation of the differentiation/maturation process of the HL-60 cells was observed. This indicates that PRP affects the early stages of differentiation/maturation of these cells. Our results, therefore, suggest that PRP, which affects the differentiation/maturation processes of cells of monocyte/macrophage lineage, may regulate in this way the inflammatory processes in which these cells participate.     

Recursive partitioning models for linkage in COGA data

We have developed a recursive-partitioning (RP) algorithm for identifying phenotype and covariate groupings that interact with the evidence for linkage. This data-mining approach for detecting gene x environment interactions uses genotype and covariate data on affected relative pairs to find evidence for linkage heterogeneity across covariate-defined subgroups. We adapted a likelihood-ratio based test of linkage parameterized with relative risks to a recursive partitioning framework, including a cross-validation based deviance measurement for choosing optimal tree size and a bootstrap sampling procedure for choosing robust tree structure. ALDX2 category 5 individuals were considered affected, categories 1 and 3 unaffected, and all others unknown. We sampled non-overlapping affected relative pairs from each family; therefore, we used 144 affected pairs in the RP model. Twenty pair-level covariates were defined from smoking status, maximum drinks, ethnicity, sex, and age at onset. Using the all-pairs score in GENEHUNTER, the nonparametric linkage tests showed no regions with suggestive linkage evidence. However, using the RP model, several suggestive regions were found on chromosomes 2, 4, 6, 14, and 20, with detection of associated covariates such as sex and age at onset.     

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

Background

Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.

Methods

To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.

Results

Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between AKT3 rs2125230-PRKCQ rs571715 and disease aggressiveness using SEN-guided MDR (p = 0.011).

Conclusions

In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between AKT3-PRKCQ and aggressive PCA requires further validation using independent observational studies.     

Synthesis and binary QSAR study of antitubercular quinolylhydrazides

In continuation with our previous work in anti-TB research area, in the present study we have demonstrated the structural diversity of quinolylhydrazides as potent anti-tuberculars. The compound library was synthesized by molecular hybridization approach and tested in vitro against Mycobacterium tuberculosis H₃₇Rv strains. Among the designed conjugates, the most promising molecules were found to exhibit 100% Growth Inhibition (GI) at MIC <6.25 μg/mL. Moreover, several analogs in the designed series were also turned out as excellent anti-tuberculars. To probe the structural characteristics influencing on the SAR, the classification model was generated using a binary QSAR approach termed recursive partitioning (RP) analysis. The significant features outlined by the RP model act as a guide in order to design the ‘lead’ compound.     

Analysis of marital structure in Massachusetts using repeating pairs of surnames.

Analysis of surnames from marriages is now a well-established method in the study of marital and genetic structure. Traditional methods of partitioning inbreeding into random and nonrandom components rely on the total number of isonymous marriages. Because this number is often low, standard errors of inbreeding estimates tend to be high. Lasker and Kaplan (1985) devised a method that circumvents this problem by focusing on the total number of repeating pairs (RP) of surnames among marriages. The observed value of RP can be compared with the value expected at random (RPr) to assess patterns of subdivision within a population. The RP method is applied here to data from 3431 marriages that took place from 1800 to 1849 in 4 Massachusetts towns. The level of excess RP [(RP-RPr)/RPr] is positively associated with population size and exogamy rate. These results indicate a tendency for greater relative subdivision in larger, more exogamous populations. One possible reason for increased subdivision is preferential marriage by social class, although adequate data are not available for a test of this hypothesis.     

A human homolog of yeast pre-mRNA splicing gene, PRP31, underlies autosomal dominant retinitis pigmentosa on chromosome 19q13.4 (RP11).

We report mutations in a gene (PRPF31) homologous to Saccharomyces cerevisiae pre-mRNA splicing gene PRP31 in families with autosomal dominant retinitis pigmentosa linked to chromosome 19q13.4 (RP11; MIM 600138). A positional cloning approach supported by bioinformatics identified PRPF31 comprising 14 exons and encoding a protein of 499 amino acids. The level of sequence identity to the yeast PRP31 gene indicates that PRPF31 is also likely to be involved in pre-mRNA splicing. Mutations that include missense substitutions, deletions, and insertions have been identified in four RP11-linked families and three sporadic RP cases. The identification of mutations in a pre-mRNA splicing gene implicates defects in the splicing process as a novel mechanism of photoreceptor degeneration.     

Influence of IL-6 on MDR and MRP-mediated multidrug resistance in human hepatoma cells.

The objective of this study was to examine effects of interleukin-6 (IL-6) on the expression and activity of the drug resistance transporters (MDR1 and MRP) in human hepatoma cell lines. Expression and activity of MDR1 and MRP transporters were examined in IL-6-treated and control HuH 7 and HepG2 cells using semi-quantitative RT-PCR analysis and by rhodamine 123 and 5-carboxyfluorescin efflux assays. Results from RT-PCR demonstrated expression of MRP3, MRP6, and MDR1 in HuH 7 cells and expression of MRP1, MRP2, MRP3, MRP6, and MDR1 in HepG2 cells. Compared with controls, treatment of HuH 7 cells with IL-6 (10 ng/mL, 24 h) resulted in a 1.8-fold increase in MRP-mediated efflux of 5-CF with a corresponding 1.5-fold induction of MRP3 mRNA levels (p < 0.05). Similarly, in HepG2 cells, a 2-fold increase in MRP functional activity and a 1.8-fold induction of MRP1 mRNA levels were seen in the IL-6 treated cells (p < 0.05). Treatment of cells with IL-6 was also found to cause significant reductions in the expression and activity of MDR1 in HuH 7 cells, but not in HepG2 cells. Our data suggest that IL-6 induces MRP expression and activity in human hepatoma cell lines. Suppressive effects of IL-6 on MDR1 expression and activity were also observed in HuH 7 cells. This underscores the importance of examining the regulation of multiple drug resistance proteins as these proteins may have opposing regulatory mechanisms in malignant cells.     

Influence of propranolol on platelet aggregation and thromboxane B2 production from platelet-rich plasma and whole blood

The beta-adrenoceptor antagonist propranolol is used in the therapy of hypertension and ischemic heart disease. The aim of our study was to evaluate the effects of this drug on platelet aggregation and on synthesis of thromboxane B2 (the stable metabolite of Thromboxane A2) from platelet rich plasma (PRP), whole blood samples and during spontaneous clotting. The results indicate that propranolol at concentrations near the therapeutic range, significantly inhibit collagen and thrombin-induced platelet aggregation and TxB2 synthesis from PRP. Furthermore the drug demonstrates inhibitory activity on B-TG release and TxB2 production from whole blood samples and on spontaneous clotting. The results suggest that some benefits of propranolol in the treatment of patients with coronary artery disease or cardiovascular conditions associated with platelet hyperaggregability may also be related to interference with platelet activation "in vivo" and with TxA2 generation.     

Breeding phenology and winter activity predict subsequent breeding success in a trans-global migratory seabird

Inter-seasonal events are believed to connect and affect reproductive performance (RP) in animals. However, much remains unknown about such carry-over effects (COEs), in particular how behaviour patterns during highly mobile life-history stages, such as migration, affect RP. To address this question, we measured at-sea behaviour in a long-lived migratory seabird, the Manx shearwater (Puffinus puffinus) and obtained data for individual migration cycles over 5 years, by tracking with geolocator/immersion loggers, along with 6 years of RP data. We found that individual breeding and non-breeding phenology correlated with subsequent RP, with birds hyperactive during winter more likely to fail to reproduce. Furthermore, parental investment during one year influenced breeding success during the next, a COE reflecting the trade-off between current and future RP. Our results suggest that different life-history stages interact to influence RP in the next breeding season, so that behaviour patterns during winter may be important determinants of variation in subsequent fitness among individuals.