PseudoGA: cell pseudotime reconstruction based on genetic algorithm.

Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis can only detect average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq presents an unprecedented opportunity that helps in placing the cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory or 'pseudotime', may reveal the developmental pathway and provide us with information on dynamic transcriptomic changes and other biological processes. Existing approaches to build pseudotime heavily depend on reducing huge dimension to extremely low dimensional subspaces and may lead to loss of information. We propose PseudoGA, a genetic algorithm based approach to order cells assuming that gene expressions vary according to a smooth curve along the pseudotime trajectory. We observe superior accuracy of our method in simulated as well as benchmarking real datasets. Generality of the assumption behind PseudoGA and no dependence on dimensionality reduction technique make it a robust choice for pseudotime estimation from single cell transcriptome data. PseudoGA is also time efficient when applied to a large single cell RNA-seq data and adaptable to parallel computing. R code for PseudoGA is freely available at https://github.com/indranillab/pseudoga.

Do climatic and socioeconomic factors explain population vulnerability to malaria? Evidence from a national survey, India.

Background: Malaria remains a public health challenge across several African and South-East Asia Region countries, including India, despite making gains in malaria-related morbidity and mortality. Poor climatic and socioeconomic factors are known to increase population vulnerability to malaria. However, there is scant literature from India exploring this link using large population-based data. Objectives: This study aims to study the role of climatic and socioeconomic factors in determining population vulnerability to malaria in India. Materials and Methods: We used logistic regression models on a nationally representative sample of 91,207 households, obtained from the National Sample Survey Organization (69th round), to study the determinants of household vulnerability. Results: Households that resided in high (odds ratio [OR]: 1.876, P < 0.01) and moderately high (OR: 3.427, P < 0.01), compared to low climatically vulnerable states were at greater odds of suffering from malaria. Among households that faced the problem of mosquitoes/flies compared to the reference group, the urban households were at higher risk of suffering from malaria (OR: 8.318, P < 0.01) compared to rural households (OR: 2.951, P < 0.01). Households from the lower income quintiles, caste, poor physical condition of their houses, poor garbage management, and water stagnation around the source of drinking water, strongly predicted malaria vulnerability. Conclusion: Household's vulnerability to malaria differed according to state climatic vulnerability level and socioeconomic factors. More efforts by integrating local endemicity, epidemiological, and entomological information about malaria transmission must be considered while designing malaria mitigation strategies for better prevention and treatment outcomes.

Querying multiple sets of P-values through composed hypothesis testing.

MOTIVATION: Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as a set of P-values resulting from previous analyses, that need to be combined flexibly to explore complex hypotheses, while guaranteeing a low proportion of false discoveries. RESULTS: We introduce the generic concept of composed hypothesis, which corresponds to an arbitrary complex combination of simple hypotheses. We rephrase the problem of testing a composed hypothesis as a classification task and show that finding items for which the composed null hypothesis is rejected boils down to fitting a mixture model and classifying the items according to their posterior probabilities. We show that inference can be efficiently performed and provide a thorough classification rule to control for type I error. The performance and the usefulness of the approach are illustrated in simulations and on two different applications. The method is scalable, does not require any parameter tuning, and provided valuable biological insight on the considered application cases. AVAILABILITY AND IMPLEMENTATION: The QCH methodology is available in the qch package hosted on CRAN. Additionally, R codes to reproduce the Einkorn example are available on the personal webpage of the first author: https://www6.inrae.fr/mia-paris/Equipes/Membres/Tristan-Mary-Huard. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A powerful method to integrate genotype and gene expression data for dissecting the genetic architecture of a disease.

To decipher the genetic architecture of human disease, various types of omics data are generated. Two common omics data are genotypes and gene expression. Often genotype data for a large number of individuals and gene expression data for a few individuals are generated due to biological and technical reasons, leading to unequal sample sizes for different omics data. Unavailability of standard statistical procedure for integrating such datasets motivates us to propose a two-step multi-locus association method using latent variables. Our method is powerful than single/separate omics data analysis and it unravels comprehensively deep-seated signals through a single statistical model. Extensive simulation confirms that it is robust to various genetic models as its power increases with sample size and number of associated loci. It provides p-values very fast. Application to real dataset on psoriasis identifies 17 novel SNPs, functionally related to psoriasis-associated genes, at much smaller sample size than standard GWAS.

Effectiveness of government strategies for financial protection against costs of hospitalization Care in India.

BACKGROUND: In the past decade, India has seen the introduction of many 'publicly funded health insurance' schemes (PFHIs) that claim to cover approximately 300 million people and are essentially forms of purchasing care from both public and private providers to reduce out-of-pocket expenditure (OOPE) for hospitalization. METHODS: Data from a recent government-organized nationwide household survey, The National Sample Survey 71st Round, were used to analyse the effectiveness and equity of tax-funded public health services and PFHIs as distinct but overlapping approaches to financial protection for hospitalization across different socio-economic categories. Cross-tabulation analysis, multivariate logistic regression and propensity score matching were the main analytical methods used. RESULTS: Government hospitals provide access to 45.6% of all hospitalization needs. Although poorer quintiles use public hospitals more often, even in the poorest quintile, as many as 37.2% are utilizing private hospitals. The average OOPE that a household experiences for hospitalization in public hospitals is approximately only one-fifth of the OOPE for hospitalization in the private sector. PFHI schemes cover 12.8% of the population, and coverage is higher in upper quintiles and in urban areas. Hospitalization rates increase with PFHI coverage, and this occurs with both public and private providers. Propensity score matching shows that PFHI contributes to a marginal reduction (1%) in 'catastrophic health expenditure incidence at the 25% threshold' (CHE-25) for the bottom three quintiles. The reported coverage of PFHIs was greater in the upper income quintiles. Utilization of public services was greater in the poorer income quintiles and more marginalized social groups. CONCLUSIONS: Periodic surveys are essential to guide policy choices regarding the appropriate mix of strategies for financial protection in pluralistic systems. There is a need for caution regarding any shift in the role of governments from providing services to purchasing care, given the contexts and limitations of currently available PFHIs. Even with tax-funded public services, although the average OOPE is lower than the care purchased through PFHIs, there is still a modest level of CHE and impoverishment due to health care costs that persist. Both strategies need to be synergized for more effective financial protection.

Impact of genetic variations and transcriptional alterations of HLA class I genes on cervical cancer pathogenesis.

In a novel attempt to understand the variations in DNA sequences underlying HLA class I alleles associated with HPV16-related CaCx, we determined the alleles by reconstructing SNP-based haplotypes from resequencing of the most polymorphic exons 2 and 3 of HLA-A, HLA-B and HLA-C. We also determined the impact of SNPs and transcriptional alterations of the genes on CaCx. A high density of SNPs was identified from resequencing. HLA expression was determined by real-time PCR. We identified that even a single associated HLA allele had many underlying SNP-based haplotypes. Out of the most frequent (≥5%) HLA class I alleles, HLA-B*40:06 and HLA-B*15:02 respectively imparted significant risk towards and protection from CaCx as well as HPV16 infection. Employing median-joining networks to detect clusters of sequence-variations for specific HLA alleles, we found the protective SNP-based signature, GAATTTA, in all SNP-based haplotypes of HLA-B*15:02 allele. The signature was derived from seven SNPs within HLA-B which were newly associated with the disease. Contrarily, similarly derived risk-signature, TTGCGCC, mapped only to 52% of SNP-based haplotypes of HLA-B*40:06 allele. This indicated that all SNP-based haplotypes underlying a particular associated HLA allele might or might not have a single signature of risk/protection. HLA-A, HLA-B and HLA-C expressions were downregulated among CaCx cases compared to asymptomatic infections and HPV-negative controls. HLA-A and HLA-B were repressed in both cases harbouring episomal and integrated HPV16, whereas HLA-C in only the latter. Novel genetic variations and differential downregulation-patterns of HLA class I have a significant bearing on HPV16-related CaCx pathogenesis.

Genetic association of key Th1/Th2 pathway candidate genes, IRF2, IL6, IFNGR2, STAT4 and IL4RA, with atopic asthma in the Indian population.

Asthma is a complex, multifactorial disease resulting due to dysregulated immune responses. Genetic factors contribute significantly to asthma pathogenesis, and identification of these factors is one of the major goals in understanding the disease. Th1/Th2 helper differentiation has a critical role in modulating the phenotypes associated with atopic asthma. This study was aimed at identifying genetic modifiers of asthma in selected genes involved in T helper differentiation. A total of 354 single-nucleotide polymorphisms (SNPs) in 33 candidate genes were genotyped in a case-control cohort (cases=147, controls=199) and families (n=247) using Illumina's Golden Gate Assay. Five SNPs, rs3733475A/C (IRF2), rs2069832A/G (IL6), rs2012075G/A (IFNGR2) and rs1400656G/A (STAT4) and rs1805011C/A (IL4RA) were found to be associated with asthma in family based as well as in case-control analyses (P=0.002, P=0.001, P=0.004, P=0.003 and P=0.001, respectively). Interestingly, the minor alleles at these loci showed a protective effect. A five loci haplotype, TAACG, in IRF2 gene, was significantly associated with asthma in families (P=1.1 × 10(-6)) and in case-control cohort (P=0.01). In conclusion, our studies led to identification of some key candidate genes, namely IRF2, IL6, IFNGR2, STAT4 and IL4RA that modulate genetic susceptibility to asthma in the Indian population. Also, this is the first report of independent association of IL6 gene polymorphism with atopic asthma.

Association between risk of oral precancer and genetic variations in microRNA and related processing genes.

BACKGROUND: MicroRNAs have been implicated in cancer but studies on their role in precancer, such as leukoplakia, are limited. Sequence variations at eight miRNA and four miRNA processing genes were studied in 452 healthy controls and 299 leukoplakia patients to estimate risk of disease. RESULTS: Genotyping by TaqMan assay followed by statistical analyses showed that variant genotypes at Gemin3 and mir-34b reduced risk of disease [OR = 0.5(0.3-0.9) and OR = 0.7(0.5-0.9) respectively] in overall patients as well as in smokers [OR = 0.58(0.3-1) and OR = 0.68(0.5-0.9) respectively]. Among chewers, only mir29a significantly increased risk of disease [OR = 1.8(1-3)]. Gene-environment interactions using MDR-pt program revealed that mir29a, mir34b, mir423 and Xpo5 modulated risk of disease (p < 0.002) which may be related to change in expression of these genes as observed by Real-Time PCR assays. But association between polymorphisms and gene expressions was not found in our sample set as well as in larger datasets from open access platforms like Genevar and 1000 Genome database. CONCLUSION: Variations in microRNAs and their processing genes modulated risk of precancer but further in-depth study is needed to understand mechanism of disease process.

Synthesis and pharmacological evaluation of novel N-aryl-3,4-dihydro-1'H-spiro[chromene-2,4'-piperidine]-1'-carboxamides as TRPM8 antagonists.

A novel series of N-aryl-3,4-dihydro-1'H-spiro[chromene-2,4'-piperidine]-1'-carboxamides was identified as transient receptor potential melastatin 8 (TRPM8) channel blockers through analogue-based rational design, synthesis and screening. Details of the synthesis, effect of aryl groups and their substituents on in-vitro potency were studied. The effects of selected functional groups on the 4-position of the chromene ring were also studied, which showed interesting results. The 4-hydroxy derivatives showed excellent potency and selectivity. Optical resolution and screening of alcohols revealed that (R)-(-)-isomers were in general more potent than the corresponding (S)-(+)-isomers. The isomer (R)-(-)-10e (IC50: 8.9nM) showed a good pharmacokinetic profile upon oral dosing at 10mg/kg in Sprague-Dawley (SD) rats. The compound (R)-(-)-10e also showed excellent efficacy in relevant rodent models of neuropathic pain.

Predicting carrying capacity of a large carnivore from prey densities: a new approach.

Background: Large carnivores play a crucial role in maintaining the balance of the ecosystem. Successful conservation initiatives have often led to a huge increase in predators which has often led to negative interactions with humans. Without the knowledge of the carrying capacity of the top predator, such decisions become challenging. Here, we have derived a new equation to estimate the carrying capacity of tigers based on the individual prey species density. Methods: We used tiger densities and respective prey densities of different protected areas. Relative prey abundance was used instead of absolute prey density as this could be a better surrogate of the prey preference. We used a regression approach to derive the species-wise equation. We have also scaled these coefficients accordingly to control the variation in the standard error (heteroscedasticity) of the tiger density. Furthermore, we have extended this regression equation for different species to different weight classes for more generalized application of the method. Results: The new equations performed considerably better compared to the earlier existing carrying capacity equations. Incorporating the species-wise approach in the equation also reflected the preference of the prey species for the tiger. This is the first carrying capacity equation where the individual prey densities are used to estimate the carnivore population density. The coefficient estimates of the model with the comparison with prey-predator power laws also reflect the differential effect of tigers on different prey species. The carrying capacity estimates will aid in a better understanding of the predator-prey interaction and will advance better management of the top predator.

In-silico analysis of TCGA data showing multiple POLE-like favourable subgroups overlapping with TP53 mutated endometrial cancer: Implications for clinical practice in low and middle-income countries.

Introduction: The Cancer Genome Atlas cohort of endometrial carcinoma (TCGA-UCEC) includes almost 40% TP53-mutants encompassing missense and truncated variants. TCGA revealed 'POLE', characterized by POLE gene bearing exonuclease domain mutation (EDM), as the prognostically best molecular profile. The worst profile was characterized by TP53-mutated Type 2 cancer requiring adjuvant therapy having cost implications in low-resource settings. We aimed to find more 'POLE-like' favourable subgroups by searching TCGA cohort, especially within TP53 mutated risk group, that could eventually avoid adjuvant treatment in resource-poor settings. Method: Our study was an in-silico survival analysis performed on the TCGA-UCEC dataset using SPSS statistical package. TP53 and POLE mutations, microsatellite instability (MSI), time-to-event and clinicopathological parameters were compared among 512 endometrial cancer cases. Deleterious POLE-mutations were identified by Polyphen2. Progression free survival was studied using Kaplan-Meier plots keeping original 'POLE' as comparator. Result: In presence of wild type (WT)-TP53, other deleterious POLE-mutations behaved like POLE-EDM. Only truncated and not missense TP53 benefitted from POLE/MSI overlap. However, TP53 missense mutation, Y220C, was found to be as favourable as 'POLE'. Overlapping POLE, MSI and WT-TP53 also performed favourably. Truncated TP53 overlapped with POLE and/or MSI, TP53 Y220C alone and, WT-TP53 overlapped with POLE and MSI both, were named 'POLE-like' for prognostically behaving like the comparator 'POLE'. Conclusion: Obesity being a lesser frequent event in low and middle-income countries (LMICs), relative proportion of women with lower BMI and Type 2 endometrial cancers may be high. Identification of 'POLE-like' groups may facilitate therapeutic de-escalation in some TP53-mutated cases - a novel option. Instead of 5% (POLE-EDM), potential beneficiary would then comprise 10% (POLE-like) of TCGA-UCEC.

Beta-agonist-associated reduction in RGS5 expression promotes airway smooth muscle hyper-responsiveness.

Although short-acting and long-acting inhaled ß(2)-adrenergic receptor agonists (SABA and LABA, respectively) relieve asthma symptoms, use of either agent alone without concomitant anti-inflammatory drugs (corticosteroids) may increase the risk of disease exacerbation in some patients. We found previously that pretreatment of human precision-cut lung slices (PCLS) with SABA impaired subsequent ß(2)-agonist-induced bronchodilation, which occurred independently of changes in receptor quantities. Here we provide evidence that prolonged exposure of cultured human airway smooth muscle (HuASM) cells to ß(2)-agonists directly augments procontractile signaling pathways elicited by several compounds including thrombin, bradykinin, and histamine. Such treatment did not increase surface receptor amounts or expression of G proteins and downstream effectors (phospholipase Cß and myosin light chain). In contrast, ß-agonists decreased expression of regulator of G protein signaling 5 (RGS5), which is an inhibitor of G-protein-coupled receptor (GPCR) activity. RGS5 knockdown in HuASM increased agonist-evoked intracellular calcium flux and myosin light chain (MLC) phosphorylation, which are prerequisites for contraction. PCLS from Rgs5(-/-) mice contracted more to carbachol than those from WT mice, indicating that RGS5 negatively regulates bronchial smooth muscle contraction. Repetitive ß(2)-agonist use may not only lead to reduced bronchoprotection but also to sensitization of excitation-contraction signaling pathways as a result of reduced RGS5 expression.

What is the out-of-pocket expenditure on medicines in India? An empirical assessment using a novel methodology.

The share of expenditure on medicines as part of the total out-of-pocket (OOP) expenditure on healthcare services has been reported to be much higher in India than in other countries. This study was conducted to ascertain the extent of this share of medicine expenditure using a novel methodology. OOP expenditure data were collected through exit interviews with 5252 out-patient department patients in three states of India. Follow-up interviews were conducted after Days 1 and 15 of the baseline to identify any additional expenditure incurred. In addition, medicine prescription data were collected from the patients through prescription audits. Self-reported expenditure on medicines was compared with the amount imputed using local market prices based on prescription data. The results were also compared with the mean expenditure on medicines per spell of ailment among non-hospitalized cases from the National Sample Survey (NSS) 75th round for the corresponding states and districts, which is based on household survey methodology. The share of medicines in OOP expenditure did not change significantly for organized private hospitals using the patient-reported vs imputation-based methods (30.74-29.61%). Large reductions were observed for single-doctor clinics, especially in the case of 'Ayurvedic' (64.51-36.51%) and homeopathic (57.53-42.74%) practitioners. After adjustment for socio-demographic factors and types of ailments, we found that household data collection as per NSS methodology leads to an increase of 25% and 26% in the reported share of medicines for public- and private-sector out-patient consultations respectively, as compared with facility-based exit interviews with the imputation of expenditure for medicines as per actual quantity and price data. The nature of healthcare transactions at single-doctor clinics in rural India leads to an over-reporting of expenditure on medicines by patients. While household surveys are valid to provide total expenditure, these are less likely to correctly estimate the share of medicine expenditure.

Association tests using kernel-based measures of multi-locus genotype similarity between individuals.

In a genetic association study, it is often desirable to perform an overall test of whether any or all single-nucleotide polymorphisms (SNPs) in a gene are associated with a phenotype. Several such tests exist, but most of them are powerful only under very specific assumptions about the genetic effects of the individual SNPs. In addition, some of the existing tests assume that the direction of the effect of each SNP is known, which is a highly unlikely scenario. Here, we propose a new kernel-based association test of joint association of several SNPs. Our test is non-parametric and robust, and does not make any assumption about the directions of individual SNP effects. It can be used to test multiple correlated SNPs within a gene and can also be used to test independent SNPs or genes in a biological pathway. Our test uses an analysis of variance paradigm to compare variation between cases and controls to the variation within the groups. The variation is measured using kernel functions for each marker, and then a composite statistic is constructed to combine the markers into a single test. We present simulation results comparing our statistic to the U-statistic-based method by Schaid et al. ([2005] Am. J. Hum. Genet. 76:780-793) and another statistic by Wessel and Schork ([2006] Am. J. Hum. Genet. 79:792-806). We consider a variety of different disease models and assumptions about how many SNPs within the gene are actually associated with disease. Our results indicate that our statistic has higher power than other statistics under most realistic conditions.

Expression of functional TRPA1 receptor on human lung fibroblast and epithelial cells.

The transient receptor potential subfamily A member 1 (TRPA1) is a non-selective cation channel implicated in the pathogenesis of several airway diseases like asthma and chronic obstructive pulmonary disease (COPD). Most of the research on TRPA1 focuses on its expression and function in neuronal context; studies investigating non-neuronal expression of TRPA1 are lacking. In the present study, we show functional expression of TRPA1 in human lung fibroblast cells (CCD19-Lu) and human pulmonary alveolar epithelial cell line (A549). We demonstrate TRPA1 expression at both mRNA and protein levels in these cell types. TRPA1 selective agonists like allyl isothiocyanate (AITC), 4-hydroxynonenal (4-HNE), crotonaldehyde and zinc, induced a concentration-dependent increase in Ca+2 influx in CCD19-Lu and A549 cells. AITC-induced Ca+2 influx was inhibited by Ruthenium red (RR), a TRP channel pore blocker, and by GRC 17536, a TRPA1 specific antagonist. Furthermore, we also provide evidence that activation of the TRPA1 receptor by TRPA1 selective agonists promotes release of the chemokine IL-8 in CCD19-Lu and A549 cells. The IL-8 release in response to TRPA1 agonists was attenuated by TRPA1 selective antagonists. In conclusion, we demonstrate here for the first time that TRPA1 is functionally expressed in cultured human lung fibroblast cells (CCD19-Lu) and human alveolar epithelial cell line (A549) and may have a potential role in modulating release of this important chemokine in inflamed airways.

TiMEG: an integrative statistical method for partially missing multi-omics data.

Multi-omics data integration is widely used to understand the genetic architecture of disease. In multi-omics association analysis, data collected on multiple omics for the same set of individuals are immensely important for biomarker identification. But when the sample size of such data is limited, the presence of partially missing individual-level observations poses a major challenge in data integration. More often, genotype data are available for all individuals under study but gene expression and/or methylation information are missing for different subsets of those individuals. Here, we develop a statistical model TiMEG, for the identification of disease-associated biomarkers in a case-control paradigm by integrating the above-mentioned data types, especially, in presence of missing omics data. Based on a likelihood approach, TiMEG exploits the inter-relationship among multiple omics data to capture weaker signals, that remain unidentified in single-omic analysis or common imputation-based methods. Its application on a real tuberous sclerosis dataset identified functionally relevant genes in the disease pathway.

Determination of critical community size from an HIV/AIDS model.

After an epidemic outbreak, the infection persists in a community long enough to engulf the entire susceptible population. Local extinction of the disease could be possible if the susceptible population gets depleted. In large communities, the tendency of eventual damp down of recurrent epidemics is balanced by random variability. But, in small communities, the infection would die out when the number of susceptible falls below a certain threshold. Critical community size (CCS) is considered to be the mentioned threshold, at which the infection is as likely as not to die out after a major epidemic for small communities unless reintroduced from outside. The determination of CCS could aid in devising systematic control strategies to eradicate the infectious disease from small communities. In this article, we have come up with a simplified computation based approach to deduce the CCS of HIV disease dynamics. We consider a deterministic HIV model proposed by Silva and Torres, and following Nåsell, introduce stochasticity in the model through time-varying population sizes of different compartments. Besides, Metcalf's group observed that the relative risk of extinction of some infections on islands is almost double that in the mainlands i.e. infections cease to exist at a significantly higher rate in islands compared to the mainlands. They attributed this phenomenon to the greater recolonization in the mainlands. Interestingly, the application of our method on demographic facts and figures of countries in the AIDS belt of Africa led us to expect that existing control measures and isolated locations would assist in temporary eradication of HIV infection much faster. For example, our method suggests that through systematic control strategies, after 7.36 years HIV epidemics will temporarily be eradicated from different communes of island nation Madagascar, where the population size falls below its CCS value, unless the disease is reintroduced from outside.

Association of IL1A and IL1B loci with primary open angle glaucoma.

BACKGROUND: Recent studies suggest that glaucoma is a neurodegenerative disease in which secondary degenerative losses occur after primary insult by raised Intraocular pressure (IOP) or by other associated factors. It has been reported that polymorphisms in the IL1A and IL1B genes are associated with Primary Open Angle Glaucoma (POAG). The purpose of our study was to investigate the role of these polymorphisms in eastern Indian POAG patients. METHODS: The study involved 315 unrelated POAG patients, consisting of 116 High Tension Glaucoma (HTG) patients with intra ocular pressure (IOP) > 21 mmHg and 199 non-HTG patients (presenting IOP < 20 mmHg), and 301 healthy controls from eastern India. Genotypes were determined by polymerase chain reaction and restriction digestion for three single nucleotide polymorphisms (SNPs): IL1A (-889C/T; rs1800587), IL1B (-511C/T; rs16944) and IL1B (3953C/T; rs1143634). Haplotype frequency was determined by Haploview 4.1 software. The association of individual SNPs and major haplotypes was evaluated using chi-square statistics. The p-value was corrected for multiple tests by Bonferroni method. RESULTS: No significant difference was observed in the allele and genotype frequencies for IL1A and IL1B SNPs between total pool of POAG patients and controls. However, on segregating the patient pool to HTG and non-HTG groups, weak association was observed for IL1A polymorphism (-889C/T) where -889C allele was found to portray risk (OR = 1.380; 95% CI = 1.041-1.830; p = 0.025) for non-HTG patients. Similarly, 3953T allele of IL1B polymorphism (+3953C/T) was observed to confer risk to HTG group (OR = 1.561; 95% CI = 1.022-2.385; p = 0.039). On haplotype analysis it was observed that TTC was significantly underrepresented in non-HTG patients (OR = 0.538; 95% CI = 0.356- 0.815; p = 0.003) while TCT haplotype was overrepresented in HTG patients (OR = 1.784; 95% CI = 1.084- 2.937; p = 0.022) compared to control pool. However, after correction for multiple tests by Bonferroni method, an association of only TTC haplotype with non-HTG cases sustained (pcorrected = 0.015) and expected to confer protection. CONCLUSION: The study suggests that the genomic region containing the IL1 gene cluster influences the POAG pathogenesis mostly in non-HTG patients in eastern India. A similar study in additional and larger cohorts of patients in other population groups is necessary to further substantiate the observation.

Tight clustering for large datasets with an application to gene expression data.

This article proposes a practical and scalable version of the tight clustering algorithm. The tight clustering algorithm provides tight and stable relevant clusters as output while leaving a set of points as noise or scattered points, that would not go into any cluster. However, the computational limitation to achieve this precise target of tight clusters prohibits it from being used for large microarray gene expression data or any other large data set, which are common nowadays. We propose a pragmatic and scalable version of the tight clustering method that is applicable to data sets of very large size and deduce the properties of the proposed algorithm. We validate our algorithm with extensive simulation study and multiple real data analyses including analysis of real data on gene expression.

Dopaminergic mutations: within-family association and linkage in multiplex alcohol dependence families.

Animal and human studies of addiction indicate that the D2 dopamine receptor (DRD2) plays a critical role in the mechanism of drug reward. D2 receptor density in the brains of alcoholics has been shown to be reduced relative to controls. Previous studies of DRD2 in association with alcohol dependence using variation in the TaqI A locus were highly controversial. Recently, a synonymous mutation, C957T, in the coding region of the human DRD2 gene has been identified which appears to have functional effects including alteration in receptor availability. In order to determine if susceptibility to alcohol dependence (AD) within multiplex alcohol dependence families would be altered by the C957T in the coding region of the D2 gene, within-family association was studied in members of Caucasian multiplex alcohol dependence families. Members of control families with no personal alcohol or substance dependence history were included for case/control comparisons. Analyses performed to detect within-family association showed evidence favoring an association for the C957T polymorphism (P = 0.038). Linkage analyses of polymorphisms in this region showed that only the C957T locus remained of interest (P = 0.015). Evidence for the C957T T allele having a role in AD susceptibility at the population level using a case/control comparison was statistically marginal (P = 0.062), but was consistent with the family data results. These results support a role for DRD2 as a susceptibility gene for alcohol dependence within multiplex families at high risk for developing alcohol dependence.