Evolutionary conservation and disease gene association of the human genes composing pseudogenes

Pseudogenes, the 'genomic fossils' present portrayal of evolutionary history of human genome. The human genes configuring pseudogenes are also now coming forth as important resources in the study of human protein evolution. In this communication, we explored evolutionary conservation of the genes forming pseudogenes over the genes lacking any pseudogene and delving deeper, we probed an evolutionary rate difference between the disease genes in the two groups. We illustrated this differential evolutionary pattern by gene expressivity, number of regulatory miRNA targeting per gene, abundance of protein complex forming genes and lesser percentage of protein intrinsic disorderness. Furthermore, pseudogenes are observed to harbor sequence variations, over their entirety, those become degenerative disease-causing mutations though the disease involvement of their progenitors is still unexplored. Here, we unveiled an immense association of disease genes in the genes casting pseudogenes in human. We interpreted the issue by disease associated miRNA targeting, genes containing polymorphisms in miRNA target sites, abundance of genes having disease causing non-synonymous mutations, disease gene specific network properties, presence of genes having repeat regions, affluence of dosage sensitive genes and the presence of intrinsically unstructured protein regions.     

Polymorphism in environment responsive genes and association with Parkinson disease

Attempts were made in the present case-control study to investigate the association of polymorphism in the genes encoding proteins involved in toxication–detoxication and dopaminergic pathways and susceptibility to Parkinson’s disease (PD). Seventy patients suffering from PD and one hundred healthy controls belonging to the same geographical location and same ethnicity were included in the study. PCR-RFLP and allele-specific PCR-based methodology were used to identify the genotypes. Multivariate logistic regression analysis revealed that heterozygous genotypes of cytochrome P4502D6*4(CYP2D6*4), CYP2E1*5B (RsaI) polymorphism and homozygous mutant genotypes of CYP2E1*6 (Dra1) were found to be overrepresented in PD cases when compared to the controls. Risk was also found to be increased in patients carrying glutathione S-transferase T1 (GSTT1) null or homozygous variant genotypes of GSTP1. Significant association was observed for monoamine oxidase-B(MAO-B) variant allele G and PD, whereas no difference in genotype and allele frequencies was observed for manganese-superoxide dismutase (MnSOD), dopamine receptor-D2(DRD2), and dopamine transporter (DAT) genes between controls and PD cases. Genotype combinations characterized by the presence of two variant genotypes on their corresponding loci revealed that four combinations of GSTT1 null and MnSOD(-9Val) or GST null and MAOB-G or CYP2E1*5B and MAO-B-AG or CYP2E1*5B and DRD2 (Taq1A-het) genotypes in the patients exhibited severalfold higher and significant association with risk to PD. Our data suggest that polymorphism in the genes involved in detoxification and dopamine regulation may modulate the susceptibility to PD and could be important risk factors in the pathogenesis of PD.     

Combining association tests across multiple genetic markers in case-control studies

In the search to detect genetic associations between complex traits and DNA variants, a practice is to select a subset of Single Nucleotide Polymorphisms (tag SNPs) in a gene or chromosomal region of interest. This allows study of untyped polymorphisms in this region through the phenomenon of linkage disequilibrium (LD). However, it is crucial in the analysis to utilize such multiple SNP markers efficiently. In this study, we present a robust testing approach (T(C)) that combines single marker association test statistics or p values. This combination is based on the summation of single test statistics or p values, giving greater weight to those with lower p values. We compared the powers of T(C) in identifying common trait loci, using tag SNPs within the same haplotype block that the trait loci reside, with competing published tests, in case-control settings. These competing tests included the Bonferroni procedure (T(B)), the simple permutation procedure (T(P)), the permutation procedure proposed by Hoh et al. (T(P-H)) and its revised version using 'deflated' statistics (T(P-H_def)), the traditional chi(2) procedure (T(CHI)), the regression procedure (Hotelling T(2) test) (T(R)) and the haplotype-based test (T(H)). Results of these comparisons show that our proposed combining procedure (T(C)) is preferred in all scenarios examined. We also apply this new test to a data set from a previously reported association study on airway responsiveness to methacholine.     

A prioritization analysis of disease association by data-mining of functional annotation of human genes

Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.     

Finding disease candidate genes by liquid association

A novel approach to finding candidate genes by using gene expression data through liquid association is developed and used to identify multiple sclerosis susceptibility candidate genes.     

Nonparametric tests of association between survival time and continuously measured covariates: the logit-rank and associated procedures

O'Brien's logit-rank procedure (1978, Biometrics 34, 243-250) is shown to arise as a score test based on the partial likelihood for a proportional hazards model provided the covariate structure is suitably defined. Within this framework the asymptotic properties claimed by O'Brien can be readily deduced and can be seen to be valid under a more general model of censoring than that considered in his paper. More important, perhaps, it is now possible to make a more natural and interpretable generalization to the multiple regression problem than that suggested by O'Brien as a means of accounting for the effects of nuisance covariates. This can be achieved either by modelling or stratification. The proportional hazards framework is also helpful in that it enables us to recognize the logit-rank procedure as being one member of a class of contending procedures. One consequence of this is that the relative efficiencies of any two procedures can be readily evaluated using the results of Lagakos (1988, Biometrika 75, 156-160). Our own evaluations suggest that, for non-time-dependent covariates, a simplification of the logit-rank procedure, leading to considerable reduction in computational complexity, is to be preferred to the procedure originally outlined by O'Brien.     

Combining dependent tests for linkage or association across multiple phenotypic traits

A robust statistical method to detect linkage or association between a genetic marker and a set of distinct phenotypic traits is to combine univariate trait-specific test statistics for a more powerful overall test. This procedure does not need complex modeling assumptions, can easily handle the problem with partially missing trait values, and is applicable to the case with a mixture of qualitative and quantitative traits. In this note, we propose a simple test procedure along this line, and show its advantages over the standard combination tests for linkage or association in the literature through a data set from Genetic Analysis Workshop 12 (GAW12) and an extensive simulation study.     

Network properties of human disease genes with pleiotropic effects

Background  

The ability of a gene to cause a disease is known to be associated with the topological position of its protein product in the molecular interaction network. Pleiotropy, in human genetic diseases, refers to the ability of different mutations within the same gene to cause different pathological effects. Here, we hypothesized that the ability of human disease genes to cause pleiotropic effects would be associated with their network properties.     

Mapping disease genes: family-based association studies.

With recent rapid advances in mapping of the human genome, including highly polymorphic and closely linked markers, studies of marker associations with disease are increasingly relevant for mapping disease genes. The use of nuclear-family data in association studies was initially developed to avoid possible ethnic mismatching between patients and randomly ascertained controls. The parental marker alleles not transmitted to an affected child or never transmitted to an affected sib pair form the so-called AFBAC (affected family-based controls) population. In this paper, the theoretical foundation of the AFBAC method is proved for any single-locus model of disease and for any nuclear family-based ascertainment scheme. In a random-mating population, when there is a marker association with disease, the AFBAC population provides an unbiased estimate of the overall population (control) marker alleles when the recombination fraction (theta) between the marker and disease genes is sufficiently small that it can be taken as zero (theta = 0). With population stratification, only marker associations present in the subpopulations will be detected with family-based analyses. Of more importance, however, is the fact that, when theta not equal to 0, differences between transmitted parental (patient) marker allele frequencies and non- or never-transmitted parental marker allele frequencies (implying a marker association with disease) can only be observed for marker genes linked to a disease gene (theta < 1/2). Thus, associations of unlinked marker loci with disease at the population level, caused by population stratification, migration, or admixture, are eliminated. This validates the use of family-based association tests as an appropriate strategy for mapping disease genes.     

Significant association of multiple human cytomegalovirus genomic Loci with glioblastoma multiforme samples

Viruses are appreciated as etiological agents of certain human tumors, but the number of different cancer types induced or exacerbated by viral infections is unknown. Glioblastoma multiforme (GBM)/astrocytoma grade IV is a malignant and lethal brain cancer of unknown origin. Over the past decade, several studies have searched for the presence of a prominent herpesvirus, human cytomegalovirus (HCMV), in GBM samples. While some have detected HCMV DNA, RNA, and proteins in GBM tissues, others have not. Therefore, any purported association of HCMV with GBM remains controversial. In most of the previous studies, only one or a select few viral targets were analyzed. Thus, it remains unclear the extent to which the entire viral genome was present when detected. Here we report the results of a survey of GBM specimens for as many as 20 different regions of the HCMV genome. Our findings indicate that multiple HCMV loci are statistically more likely to be found in GBM samples than in other brain tumors or epileptic brain specimens and that the viral genome was more often detected in frozen samples than in paraffin-embedded archival tissue samples. Finally, our experimental results indicate that cellular genomes substantially outnumber viral genomes in HCMV-positive GBM specimens, likely indicating that only a minority of the cells found in such samples harbor viral DNA. These data argue for the association of HCMV with GBM, defining the virus as oncoaccessory. Furthermore, they imply that, were HCMV to enhance the growth or survival of a tumor (i.e., if it is oncomodulatory), it would likely do so through mechanisms distinct from classic tumor viruses that express transforming viral oncoproteins in the overwhelming majority of tumor cells.     

Pharmacogenomics of multiple sclerosis: association of immune response genes polymorphism with copaxone treatment efficacy

Complex association analysis of copaxone (glatiramer acetate) immunotherapy efficacy with allelic polymorphism in the number of immune response genes, which encode interferone beta (IFNB1), transforming growth factor beta1 (TGFB1), interferone gamma (IFNG), tumor necrosis factor (TNF), interferon alpha/beta receptor 1 (IFNAR1), CC chemokine receptor 5 (CCR5), interleukin 7 receptor alpha subunit (IL7RA), cytotoxic T-lymphocyte antigen 4 (CTLA4) and HLA class II histocompatibility antigen beta chain (DRB1) was performed with APSampler algorithm for 285 multiple sclerosis patients of Russian ethnicity. The results show evidence for the contribution of polymorphic variants in CCRS, DRB1, IFNG, TGFB1, IFNAR1, IL7RA and, probably, TNF and CTLA4 genes to copaxone treatment response. Single alleles of CCR5 and DRB1 genes are reliably associated with treatment efficacy. Carriage of allelic variants of other above mentioned genes contribute with reliable effect to copaxone treatment response as part of bi- and three-allelic combinations only. Present investigation may support basis toward the future possibility of prognostic test realization, which can provide a personal choice of immunomodulatory treatment for a patient with multiple sclerosis.