The evolution of signaling complexity suggests a mechanism for reducing the genomic search space in human association studies

Genome Rearrangements on Multigenomic Models: Applications of Graph Convexity Problems

Genome rearrangements are events where large blocks of DNA exchange pieces during evolution. The analysis of such events is a tool for understanding evolutionary genomics, in whose context many rearrangement distances have been proposed, based on finding the minimum number of rearrangements to transform one genome into another, using some predefined operation. However, when more than two genomes are considered, we have new challenging problems. Studying such problems from a combinatorial point of view has been shown to be a useful tool to approach such problems, for example, the reconstruction of phylogenetic trees. We focus on genome rearrangement problems related to graph convexity. Such an approach is in connection with some other well-known studies on multigenomic models, for example, those based on the median and on the closest string. We propose an association between graph convexities and genome rearrangements in such a way that graph convexity problems deal with input sets of vertices and try to answer questions concerning the closure of such inputs. The concept of closure is useful for studies on genome rearrangement by suggesting mechanisms to reduce the genomic search space. Regarding the computational complexity, and considering the Hamming distance on strings, we solve the following problems: decide if a given set is convex; compute the interval and the convex hull of a given set; and determine the convexity number, interval number, and hull number of a Hamming graph. All such problems are solved for three types of convexities: geodetic, monophonic, and P₃. Considering the Cayley distance on permutations, we solve the convexity number and interval determination problems for the geodetic convexity.

Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model

Scoring the correlation between two genes by their shared properties is a common and basic work in biological study. A prospective way to score this correlation is to quantify the overlap between the two sets of homogeneous properties of the two genes. However the proper model has not been decided, here we focused on studying the quantification of overlap and proposed a more effective model after theoretically compared 7 existing models. We defined three characteristic parameters (d, R, r) of an overlap, which highlight essential differences among the 7 models and grouped them into two classes. Then the pros and cons of the two groups of model were fully examined by their solution space in the (d, R, r) coordinate system. Finally we proposed a new model called OScal (Overlap Score calculator), which was modified on Poisson distribution (one of 7 models) to avoid its disadvantages. Tested in assessing gene relation using different data, OScal performs better than existing models. In addition, OScal is a basic mathematic model, with very low computation cost and few restrictive conditions, so it can be used in a wide-range of research areas to measure the overlap or similarity of two entities.

Approaches to understanding adaptations of skin color variation by detecting gene-environment interactions

Genetic and environmental factors are both part of an elaborate feedback mechanism whereby the human adaptive form reacts to environmental stimuli via internal adjustments. Human survival may ultimately depend on understanding two important components of future environmental adaptation. First, we must elucidate the dynamics of the human genome underpinning the complex human phenotype. Second, we must understand how the environment pressures and affects the genome, helping to determine human traits. This article reviews current approaches to detecting the natural selection of skin color variation in human populations. We include statistical methods for clarifying gene-environment interactions applicable to the interactions with UV radiation levels. We recommend spatial data mining as an efficient approach that applies environmental association rules, extending our knowledge of adaptation to the environment.

Interactions between genes involved in the antioxidant defence system and breast cancer risk

The aim of the study is to examine the association between multilocus genotypes across 10 genes encoding proteins in the antioxidant defence system and breast cancer. The 10 genes are SOD1, SOD2, GPX1, GPX4, GSR, CAT, TXN, TXN2, TXNRD1 and TXNRD2. In all, 2271 cases and 2280 controls were used to examine gene-gene interactions between 52 single nucleotide polymorphisms (SNPs) that are hypothesised to tag all common variants in the 10 genes. The statistical analysis is based on three methods: unconditional logistic regression, multifactor dimensionality reduction and hierarchical cluster analysis. We examined all two- and three-way combinations with unconditional logistic regression and multifactor dimensionality reduction, and used a global approach with all SNPs in the hierarchical cluster analysis. Single-locus studies of an association of genetic variants in the antioxidant defence genes and breast cancer have been contradictory and inconclusive. It is the first time, to our knowledge, the association between multilocus genotypes across genes coding for antioxidant defence enzymes and breast cancer is investigated. We found no evidence of an association with breast cancer with our multilocus approach. The search for two-way interactions gave experiment-wise significance levels of P=0.24 (TXN [t2715c] and TXNRD2 [g23524a]) and P=0.58 (GSR [c39396t] and TXNRD2 [a442g]), for the unconditional logistic regression and multifactor dimensionality reduction, respectively. The experiment-wise significance levels for the three-way interactions were P=0.94 (GPX4 [t2572c], TXN [t2715c] and TXNRD2 [g23524a]) and P=0.29 (GSR [c39396t], TXN [t2715c] and TXNRD2 [a442g]) for the unconditional logistic regression and multifactor dimensionality reduction, respectively. In the hierarchical cluster analysis neither the average across four rounds with replacement of missing values at random (P=0.12) nor a fifth round with more balanced proportion of missing values between cases and controls (P=0.17) was significant.

Transcription factor AP-2 and monoaminergic functions in the central nervous system

In the central nervous system, transcription factor AP-2 family is one of the critical regulatory factors for neural gene expression and neuronal development. Several genes in the monoaminergic systems display AP-2 binding sites in regulatory regions. In addition, brainstem levels of transcription factor AP-2alpha and AP-2beta are positively correlated to monoamine measures in rat forebrain, suggesting a regulatory role of AP-2 also in the adult brain. Great changes in psychiatric phenotypes due to genetic factors are seldom the result of a single gene polymorphism. Recently, identification of combinations of candidate genes that are all linked to one disease or psychiatric phenotype has been discussed. The expression of these candidate genes might be regulated by the same transcription factors, e.g. AP-2. Recent data on transcription factor AP-2 family in relation to monoaminergic functions are described in this paper. Transcription factor AP-2beta genotype has been studied in relation to personality, platelet monoamine oxidase (MAO) activity, CSF-levels of monoamine metabolites, binge-eating disorder, premenstrual dysphoric disorder, and schizophrenia. Furthermore, the involvement of AP-2 in the molecular mechanism of antidepressant drugs is discussed. Altogether, this paper discusses data supporting a notion that the transcription factor AP-2 family is involved in the regulation of the monoaminergic systems both pre- and postnatally, and, therefore, might be involved in the pathophysiology of neuropsychiatric disorders.

Techniques of SNP Genotyping and Remote Sensing Applied for Elucidation of the Contribution of Polygene and Environmental Factors to Inter-individual Variation in Skin Pigmentation Phenotype

We present a conceptual framework for applying techniques of SNP genotyping as a molecular biological approach and remote sensing as an ecological approach to elucidation of the contribution of polygene and environmental factors to inter-individual variation in skin pigmentation phenotype. Additionally, we discuss the obstacles that frustrate our efforts to identify how the human genome encodes the complex phenotype and suggest the use of computational methods designed for knowledge discovery within hereditary database.