Bayesian Pathway Analysis of Cancer Microarray Data

High Throughput Biological Data (HTBD) requires detailed analysis methods and from a life science perspective, these analysis results make most sense when interpreted within the context of biological pathways. Bayesian Networks (BNs) capture both linear and nonlinear interactions and handle stochastic events in a probabilistic framework accounting for noise making them viable candidates for HTBD analysis. We have recently proposed an approach, called Bayesian Pathway Analysis (BPA), for analyzing HTBD using BNs in which known biological pathways are modeled as BNs and pathways that best explain the given HTBD are found. BPA uses the fold change information to obtain an input matrix to score each pathway modeled as a BN. Scoring is achieved using the Bayesian-Dirichlet Equivalent method and significance is assessed by randomization via bootstrapping of the columns of the input matrix. In this study, we improve on the BPA system by optimizing the steps involved in “Data Preprocessing and Discretization”, “Scoring”, “Significance Assessment”, and “Software and Web Application”. We tested the improved system on synthetic data sets and achieved over 98% accuracy in identifying the active pathways. The overall approach was applied on real cancer microarray data sets in order to investigate the pathways that are commonly active in different cancer types. We compared our findings on the real data sets with a relevant approach called the Signaling Pathway Impact Analysis (SPIA).     

Genotype-Imputation Accuracy across Worldwide Human Populations

A current approach to mapping complex-disease-susceptibility loci in genome-wide association (GWA) studies involves leveraging the information in a reference database of dense genotype data. By modeling the patterns of linkage disequilibrium in a reference panel, genotypes not directly measured in the study samples can be imputed and tested for disease association. This imputation strategy has been successful for GWA studies in populations well represented by existing reference panels. We used genotypes at 513,008 autosomal single-nucleotide polymorphism (SNP) loci in 443 unrelated individuals from 29 worldwide populations to evaluate the “portability” of the HapMap reference panels for imputation in studies of diverse populations. When a single HapMap panel was leveraged for imputation of randomly masked genotypes, European populations had the highest imputation accuracy, followed by populations from East Asia, Central and South Asia, the Americas, Oceania, the Middle East, and Africa. For each population, we identified “optimal” mixtures of reference panels that maximized imputation accuracy, and we found that in most populations, mixtures including individuals from at least two HapMap panels produced the highest imputation accuracy. From a separate survey of additional SNPs typed in the same samples, we evaluated imputation accuracy in the scenario in which all genotypes at a given SNP position were unobserved and were imputed on the basis of data from a commercial “SNP chip,” again finding that most populations benefited from the use of combinations of two or more HapMap reference panels. Our results can serve as a guide for selecting appropriate reference panels for imputation-based GWA analysis in diverse populations.     

Pathological States and Criminal Responsibility

The doctor is embarrassed in court when asked to testify to the effects of illness on a defendant''s capability “of appreciating the nature and quality of an act or omission or of knowing that an act or omission is wrong”. The source of his difficulty is traced to the legal concepts of “guilt”, “crime” and “punishment” which imply a legal view of man at variance with our modern biological view. To abolish this discrepancy we need not accept a medical model for criminal law where “crime” is analogous to “disease”, and “punishment” to “treatment”. A pragmatic approach to the handling of the criminal could exclude the notions of “guilt” and “punishment” and yet fulfil the rational goals of protecting society from the criminal and of compensating his victims.     

Genotype Imputation Reference Panel Selection Using Maximal Phylogenetic Diversity

The recent dramatic cost reduction of next-generation sequencing technology enables investigators to assess most variants in the human genome to identify risk variants for complex diseases. However, sequencing large samples remains very expensive. For a study sample with existing genotype data, such as array data from genome-wide association studies, a cost-effective approach is to sequence a subset of the study sample and then to impute the rest of the study sample, using the sequenced subset as a reference panel. The use of such an internal reference panel identifies population-specific variants and avoids the problem of a substantial mismatch in ancestry background between the study population and the reference population. To efficiently select an internal panel, we introduce an idea of phylogenetic diversity from mathematical phylogenetics and comparative genomics. We propose the “most diverse reference panel”, defined as the subset with the maximal “phylogenetic diversity”, thereby incorporating individuals that span a diverse range of genotypes within the sample. Using data both from simulations and from the 1000 Genomes Project, we show that the most diverse reference panel can substantially improve the imputation accuracy compared to randomly selected reference panels, especially for the imputation of rare variants. The improvement in imputation accuracy holds across different marker densities, reference panel sizes, and lengths for the imputed segments. We thus propose a novel strategy for planning sequencing studies on samples with existing genotype data.     

Using Linkage Analysis to Detect Gene-Gene Interactions. 2. Improved Reliability and Extension to More-Complex Models

Detecting gene-gene interaction in complex diseases has become an important priority for common disease genetics, but most current approaches to detecting interaction start with disease-marker associations. These approaches are based on population allele frequency correlations, not genetic inheritance, and therefore cannot exploit the rich information about inheritance contained within families. They are also hampered by issues of rigorous phenotype definition, multiple test correction, and allelic and locus heterogeneity. We recently developed, tested, and published a powerful gene-gene interaction detection strategy based on conditioning family data on a known disease-causing allele or a disease-associated marker allele⁴. We successfully applied the method to disease data and used computer simulation to exhaustively test the method for some epistatic models. We knew that the statistic we developed to indicate interaction was less reliable when applied to more-complex interaction models. Here, we improve the statistic and expand the testing procedure. We computer-simulated multipoint linkage data for a disease caused by two interacting loci. We examined epistatic as well as additive models and compared them with heterogeneity models. In all our models, the at-risk genotypes are “major” in the sense that among affected individuals, a substantial proportion has a disease-related genotype. One of the loci (A) has a known disease-related allele (as would have been determined from a previous analysis). We removed (pruned) family members who did not carry this allele; the resultant dataset is referred to as “stratified.” This elimination step has the effect of raising the “penetrance” and detectability at the second locus (B). We used the lod scores for the stratified and unstratified data sets to calculate a statistic that either indicated the presence of interaction or indicated that no interaction was detectable. We show that the new method is robust and reliable for a wide range of parameters. Our statistic performs well both with the epistatic models (false negative rates, i.e., failing to detect interaction, ranging from 0 to 2.5%) and with the heterogeneity models (false positive rates, i.e., falsely detecting interaction, ≤1%). It works well with the additive model except when allele frequencies at the two loci differ widely. We explore those features of the additive model that make detecting interaction more difficult. All testing of this method suggests that it provides a reliable approach to detecting gene-gene interaction.     

-Classifying prion and prion-like phenomena

The universe of prion and prion-like phenomena has expanded significantly in the past several years. Here, we overview the challenges in classifying this data informatically, given that terms such as “prion-like”, “prion-related” or “prion-forming” do not have a stable meaning in the scientific literature. We examine the spectrum of proteins that have been described in the literature as forming prions, and discuss how “prion” can have a range of meaning, with a strict definition being for demonstration of infection with in vitro-derived recombinant prions. We suggest that although prion/prion-like phenomena can largely be apportioned into a small number of broad groups dependent on the type of transmissibility evidence for them, as new phenomena are discovered in the coming years, a detailed ontological approach might be necessary that allows for subtle definition of different “flavors” of prion / prion-like phenomena.     

Analysis and Prediction of the Metabolic Stability of Proteins Based on Their Sequential Features,Subcellular Locations and Interaction Networks

The metabolic stability is a very important idiosyncracy of proteins that is related to their global flexibility, intramolecular fluctuations, various internal dynamic processes, as well as many marvelous biological functions. Determination of protein''s metabolic stability would provide us with useful information for in-depth understanding of the dynamic action mechanisms of proteins. Although several experimental methods have been developed to measure protein''s metabolic stability, they are time-consuming and more expensive. Reported in this paper is a computational method, which is featured by (1) integrating various properties of proteins, such as biochemical and physicochemical properties, subcellular locations, network properties and protein complex property, (2) using the mRMR (Maximum Relevance & Minimum Redundancy) principle and the IFS (Incremental Feature Selection) procedure to optimize the prediction engine, and (3) being able to identify proteins among the four types: “short”, “medium”, “long”, and “extra-long” half-life spans. It was revealed through our analysis that the following seven characters played major roles in determining the stability of proteins: (1) KEGG enrichment scores of the protein and its neighbors in network, (2) subcellular locations, (3) polarity, (4) amino acids composition, (5) hydrophobicity, (6) secondary structure propensity, and (7) the number of protein complexes the protein involved. It was observed that there was an intriguing correlation between the predicted metabolic stability of some proteins and the real half-life of the drugs designed to target them. These findings might provide useful insights for designing protein-stability-relevant drugs. The computational method can also be used as a large-scale tool for annotating the metabolic stability for the avalanche of protein sequences generated in the post-genomic age.     

Molecular Pathology of Rare Bleeding Disorders (RBDs) in India: A Systematic Review

Background

Though rare in occurrence, patients with rare bleeding disorders (RBDs) are highly heterogeneous and may manifest with severe bleeding diathesis. Due to the high rate of consanguinity in many caste groups, these autosomal recessive bleeding disorders which are of rare occurrence in populations across the world, may not be as rare in India.

Objectives

To comprehensively analyze the frequency and nature of mutations in Indian patients with RBDs.

Methods

Pubmed search was used (www.pubmed.com) to explore the published literature from India on RBDs using the key words “rare bleeding disorders”, “mutations”, “India”, “fibrinogen”, “afibrinogenemia”, “factor II deficiency”, “prothrombin” “factor VII deficiency”, “factor V deficiency”, “factor X deficiency”, “factor XI deficiency”, “combined factor V and VIII deficiency”, “factor XIII deficiency”, “Bernard Soulier syndrome” and “Glanzmanns thrombasthenia” in different combinations. A total of 60 relevant articles could be retrieved. The distribution of mutations from India was compared with that of the world literature by referring to the Human Gene Mutation Database (HGMD) (www.hgmd.org).

Results

Taken together, 181 mutations in 270 patients with different RBDs have been reported from India. Though the types of mutations reported from India and their percentage distribution with respect to the world data are largely similar, yet much higher percentage of small deletions, duplication mutations, insertions, indels were observed in this analysis. Besides the identification of novel mutations and polymorphisms, several common mutations have also been reported, which will allow to develop a strategy for mutation screening in Indian patients with RBDs.

Conclusion

There is a need for a consortium of Institutions working on the molecular pathology of RBDs in India. This will facilitate a quicker and cheaper diagnosis of RBDs besides its utility in first trimester prenatal diagnosis of the affected families.     

Missing value imputation in high-dimensional phenomic data: imputable or not,and how?

Background

In modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution. In high-throughput experiments such as microarray experiments, continuous intensities are measured and many mature missing value imputation methods have been developed and widely applied. Numerous methods for missing data imputation of microarray data have been developed. Large phenomic data, however, contain continuous, nominal, binary and ordinal data types, which void application of most methods. Though several methods have been developed in the past few years, not a single complete guideline is proposed with respect to phenomic missing data imputation.

Results

In this paper, we investigated existing imputation methods for phenomic data, proposed a self-training selection (STS) scheme to select the best imputation method and provide a practical guideline for general applications. We introduced a novel concept of “imputability measure” (IM) to identify missing values that are fundamentally inadequate to impute. In addition, we also developed four variations of K-nearest-neighbor (KNN) methods and compared with two existing methods, multivariate imputation by chained equations (MICE) and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). We performed simulations and applied different imputation methods and the STS scheme to three lung disease phenomic datasets to evaluate the methods. An R package “phenomeImpute” is made publicly available.

Conclusions

Simulations and applications to real datasets showed that MICE often did not perform well; KNN-A, KNN-H and random forest were among the top performers although no method universally performed the best. Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses. The STS scheme was accurate in selecting the optimal method by evaluating methods in a second layer of missingness simulation. All source files for the simulation and the real data analyses are available on the author’s publication website.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0346-6) contains supplementary material, which is available to authorized users.     

Caesarean Section: Could Different Transverse Abdominal Incision Techniques Influence Postpartum Pain and Subsequent Quality of Life? A Systematic Review

The choice of the type of abdominal incision performed in caesarean delivery is made chiefly on the basis of the individual surgeon’s experience and preference. A general consensus on the most appropriate surgical technique has not yet been reached. The aim of this systematic review of the literature is to compare the two most commonly used transverse abdominal incisions for caesarean delivery, the Pfannenstiel incision and the modified Joel-Cohen incision, in terms of acute and chronic post-surgical pain and their subsequent influence in terms of quality of life. Electronic database searches formed the basis of the literature search and the following databases were searched in the time frame between January 1997 and December 2013: MEDLINE, EMBASE Sciencedirect and the Cochrane Library. Key search terms included: “acute pain”, “chronic pain”, “Pfannenstiel incision”, “Misgav-Ladach”, “Joel Cohen incision”, in combination with “Caesarean Section”, “abdominal incision”, “numbness”, “neuropathic pain” and “nerve entrapment”. Data on 4771 patients who underwent caesarean section (CS) was collected with regards to the relation between surgical techniques and postoperative outcomes defined as acute or chronic pain and future pregnancy desire. The Misgav-Ladach incision was associated with a significant advantage in terms of reduction of post-surgical acute and chronic pain. It was indicated as the optimal technique in view of its characteristic of reducing lower pelvic discomfort and pain, thus improving quality of life and future fertility desire. Further studies which are not subject to important bias like pre-existing chronic pain, non-standardized analgesia administration, variable length of skin incision and previous abdominal surgery are required.     

Diversity of “Candidatus Liberibacter asiaticus,” Based on the omp Gene Sequence

Huanglongbing (yellow dragon disease) is a destructive disease of citrus. The etiological agent is a noncultured, phloem-restricted alpha-proteobacterium, “Candidatus Liberibacter africanus” in Africa and “Candidatus Liberibacter asiaticus” in Asia. In this study, we used an omp-based PCR-restriction fragment length polymorphism (RFLP) approach to analyze the genetic variability of “Ca. Liberibacter asiaticus” isolates. By using five different enzymes, each the 10 isolates tested could be associated with a specific combination of restriction profiles. The results indicate that the species “Ca. Liberibacter asiaticus,” even within a given region, may comprise several different variants. Thus, omp-based PCR-RFLP analysis is a simple method for detecting and differentiating “Ca. Liberibacter asiaticus” isolates.     

Evolution in a Changing Environment

We propose a simple model for genetic adaptation to a changing environment, describing a fitness landscape characterized by two maxima. One is associated with “specialist” individuals that are adapted to the environment; this maximum moves over time as the environment changes. The other maximum is static, and represents “generalist” individuals not affected by environmental changes. The rest of the landscape is occupied by “maladapted” individuals. Our analysis considers the evolution of these three subpopulations. Our main result is that, in presence of a sufficiently stable environmental feature, as in the case of an unchanging aspect of a physical habitat, specialists can dominate the population. By contrast, rapidly changing environmental features, such as language or cultural habits, are a moving target for the genes; here, generalists dominate, because the best evolutionary strategy is to adopt neutral alleles not specialized for any specific environment. The model we propose is based on simple assumptions about evolutionary dynamics and describes all possible scenarios in a non-trivial phase diagram. The approach provides a general framework to address such fundamental issues as the Baldwin effect, the biological basis for language, or the ecological consequences of a rapid climate change.     

Memory for Expectation-Violating Concepts: The Effects of Agents and Cultural Familiarity

Previous research has shown that ideas which violate our expectations, such as schema-inconsistent concepts, enjoy privileged status in terms of memorability. In our study, memory for concepts that violate cultural (cultural schema-level) expectations (e.g., “illiterate teacher”, “wooden bottle”, or “thorny grass”) versus domain-level (ontological) expectations (e.g., “speaking cat”, “jumping maple”, or “melting teacher”) was examined. Concepts that violate cultural expectations, or counter-schematic, were remembered to a greater extent compared with concepts that violate ontological expectations and with intuitive concepts (e.g., “galloping pony”, “drying orchid”, or “convertible car”), in both immediate recall, and delayed recognition tests. Importantly, concepts related to agents showed a memory advantage over concepts not pertaining to agents, but this was true only for expectation-violating concepts. Our results imply that intuitive, everyday concepts are equally attractive and memorable regardless of the presence or absence of agents. However, concepts that violate our expectations (cultural-schema or domain-level) are more memorable when pertaining to agents (humans and animals) than to non-agents (plants or objects/artifacts). We conclude that due to their evolutionary salience, cultural ideas which combine expectancy violations and the involvement of an agent are especially memorable and thus have an enhanced probability of being successfully propagated.     

Optimal design of genetic studies of gene expression with two-color microarrays in outbred crosses

Combining global gene-expression profiling and genetic analysis of natural allelic variation (genetical genomics) has great potential in dissecting the genetic pathways underlying complex phenotypes. Efficient use of microarrays is paramount in experimental design as the cost of conducting this type of study is high. For those organisms where recombinant inbred lines are available for mapping, the “distant pair design” maximizes the number of informative contrasts over all marker loci. Here, we describe an extension of this design, named the “optimal pair design,” for use with F₂ crosses between outbred lines. The performance of this design is investigated by simulation and compared to several other two-color microarray designs. We show that, for a given number of microarrays, the optimal pair design outperforms all other designs considered for detection of expression quantitative trait loci (eQTL) with additive effects by linkage analysis. We also discuss the suitability of this design for outbred crosses in organisms with large genomes and for detection of dominance.     

Psychological Aspects of Headache

Headache is considered as a non-specific syndrome illustrating the concept of pain as an emotion. Viewed in this way, its meaning looms larger than its site.Pain indicates dis-ease of the patient, sometimes with his body, but more often with his life. No pain is “imaginary”, nor can some pain be assigned to physiological and some to psychological pathways. Such a decision is often merely a judgmental one.Just as the “brain” cannot easily be separated from the “mind”, so to believe that some pain is “physical” and some “emotional” is a distortion. All painful syndromes are mixed and the problem is to decipher the meaning of the pain. Only rarely will headache respond to physical measures alone.     

Accelerated Wound Closure In Vitro by Fibroblasts from a Subgroup of Cleft Lip/Palate Patients: Role of Transforming Growth Factor-α

In a fraction of patients surgically treated for cleft lip/palate, excessive scarring disturbs maxillary growth and dento-alveolar development. Since certain genes are involved in craniofacial morphogenesis as well as tissue repair, a primary defect causing cleft lip/palate could lead to altered wound healing. We performed in vitro wound healing assays with primary lip fibroblasts from 16 cleft lip/palate patients. Nine foreskin fibroblast strains were included for comparison. Cells were grown to confluency and scratch wounds were applied; wound closure was monitored morphometrically over time. Wound closure rate showed highly significant differences between fibroblast strains. Statistically, fibroblast strains from the 25 individuals could be divided into three migratory groups, namely “fast”, “intermediate”, and “slow”. Most cleft lip/palate fibroblasts were distributed between the “fast” (5 strains) and the “intermediate” group (10 strains). These phenotypes were stable over different cell passages from the same individual. Expression of genes involved in cleft lip/palate and wound repair was determined by quantitative PCR. Transforming growth factor-α mRNA was significantly up-regulated in the “fast” group. 5 ng/ml transforming growth factor-α added to the culture medium increased the wound closure rate of cleft lip/palate strains from the “intermediate” migratory group to the level of the “fast”, but had no effect on the latter group. Conversely, antibody to transforming growth factor-α or a specific inhibitor of its receptor most effectively reduced the wound closure rate of “fast” cleft lip/palate strains. Thus, fibroblasts from a distinct subgroup of cleft lip/palate patients exhibit an increased migration rate into wounds in vitro, which is linked to higher transforming growth factor-α expression and attenuated by interfering with its signaling.