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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Sub-national health accounts: Experience from Punjab State in India.

INTRODUCTION: Public health spending in India has been traditionally one of the lowest globally. Punjab is one of the states with highest proportion of out-of-pocket expenditures for healthcare in India. We undertook this study to produce the sub-national health accounts (SNHA) for Punjab state in India. METHODOLOGY: We used System of Health Accounts (SHA) 2011 framework for preparing health accounts for Punjab state. Data on health spending by government was obtained from concerned public sector departments both at state and central level. Estimates on Out-of-Pocket Expenditures (OOPE) expenditure were derived from National Sample Survey (NSS) 71st round data, Consumer Expenditure Survey (CES) data and Pharmatrac. Primary surveys were done for assessing health expenditure data by firms and non-governmental organizations. All estimates of healthcare expenditures reported in our paper pertain to 2013-14, and are reported in both Indian National Rupee (INR) and United States Dollar (US $),using average conversion rate of INR 60.50 per US $. RESULTS: In 2013-14, the current health expenditures (CE) in Punjab was INR 134,680million (US $ 2245 million) which was 4.02% of its gross state domestic product (GSDP).However, public spending on health was 0.95% of GSDP i.e. 21% of the total health expenditure (THE), while 79% was private expenditure. In per capita terms, THE in Punjab was INR 4963 (US $ 82.03). In terms of functions, medical goods (41.6%) and curative care (37%) consumed larger share of expenditure in the Punjab state. Households spent 52% of expenditures for medicines and other pharmaceutical goods. Risk pooling mechanisms are being adopted to a lesser extent in the state. CONCLUSION: The healthcare in Punjab is largely financed through private OOPE. Currently, public health spending in Punjab is inadequate to meet the healthcare demands of population, which is less than 1% of state's GSDP. Monitoring public resources is very important for better resource allocations. Health Accounts production is useful in order to assess future trends and impact of health financing policies on goals of universal health coverage and should be made a part of routine monitoring system both at national and sub-national level.

Transmission-based association mapping of triglyceride levels in a longitudinal framework using quasi-likelihood.

Complex genetic traits are often characterized by multiple quantitative phenotypes. Because values of such phenotypes vary over time, it is thought that analyses of longitudinal data on the phenotypes may lead to increased power in detecting genetic association. In this paper, we extend a transmission-based association test applying quasi-likelihood that has been developed by us to the longitudinal framework and to carry out a genome-wide association analysis of triglyceride levels based on the data provided in GAW20. We consider different phenotype definitions based on administration of fenofibrate and obtain significant association findings within genes involved in heart diseases.

Family-based genome-wide association of inflammation biomarkers and fenofibrate treatment response in the GOLDN study.

In this paper we analyzed whole-genome genetic information provided by GAW20 from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study for family data. Lipid levels such as triglycerides (TGs) and high-density lipoprotein (HDL) are measured at different time points before and after administration of an anti-inflammatory drug fenofibrate. Apart from that, the data contain some covariates and whole-genome genotype information. We propose 2 novel approaches based on Henderson's iterative mixed model to identify associated loci corresponding to (a) inflammatory biomarkers like TGs and HDLs together over time, and (b) the response to fenofibrate treatment. We developed a mixed-model approach using both TG and HDL phenotypes at all 4 time points for a genetic association study whereas we used TGs only to study genetic association with response to the drug. We expect that use of complete family data in a longitudinal framework under a single model involving the appropriate correlation structures would be able to exploit the maximum possible information contained in the sample. Our analysis of whole-genome single nucleotide polymorphisms (SNPs) and genomic regions corresponding to drug treatment finds no significant locus after multiple correction. Arguably, the moderately small sample size of the data set, as compared to the sample size usually used in genome-wide association studies (GWAS), could be a reason for such a result. Nevertheless, we report the top 20 SNPs associated with the phenotypes, and the top 20 SNPs and genomic regions associated with a response to fenofibrate treatment. Application of our methods to larger GWAS and further functional validation of the reported top SNPs and genomic regions might provide important biological insight into the genetic constitution of the trait.

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.

Blocking TRPA1 in Respiratory Disorders: Does It Hold a Promise?

Transient Receptor Potential Ankyrin 1 (TRPA1) ion channel is expressed abundantly on the C fibers that innervate almost entire respiratory tract starting from oral cavity and oropharynx, conducting airways in the trachea, bronchi, terminal bronchioles, respiratory bronchioles and upto alveolar ducts and alveoli. Functional presence of TRPA1 on non-neuronal cells got recognized recently. TRPA1 plays a well-recognized role of "chemosensor", detecting presence of exogenous irritants and endogenous pro-inflammatory mediators that are implicated in airway inflammation and sensory symptoms like chronic cough, asthma, chronic obstructive pulmonary disease (COPD), allergic rhinitis and cystic fibrosis. TRPA1 can remain activated chronically due to elevated levels and continued presence of such endogenous ligands and pro-inflammatory mediators. Several selective TRPA1 antagonists have been tested in animal models of respiratory disease and their performance is very promising. Although there is no TRPA1 antagonist in advanced clinical trials or approved on market yet to treat respiratory diseases, however, limited but promising evidences available so far indicate likelihood that targeting TRPA1 may present a new therapy in treatment of respiratory diseases in near future. This review will focus on in vitro, animal and human evidences that strengthen the proposed role of TRPA1 in modulation of specific airway sensory responses and also on preclinical and clinical progress of selected TRPA1 antagonists.

Evaluation of the IL1 Gene Cluster Single Nucleotide Polymorphisms in Primary Open-Angle Glaucoma Pathogenesis.

AIMS: Dysregulation of the immune system has previously been implicated in glaucoma pathogenesis. In this study, we investigated the potential association of SNPs in the IL1 gene cluster, consisting of nine genes, with primary open-angle glaucoma (POAG) cases. These cases presented with low to normal intraocular pressures (<20 mmHg), and are referred to as non-high tension glaucoma (non-HTG) cases. MATERIALS AND METHODS: In this biphasic study, the discovery phase was conducted with 198 non-HTG cases and 112 controls from eastern India. A total of 68 single nucleotide polymorphisms (SNPs) spanning the IL1 nine-gene cluster region were genotyped using the MALDI-TOF based Sequenom platform. SNPs, which were found to be significantly associated with non-HTG cases in the first phase of the study, were further genotyped by Sanger sequencing in a replication cohort consisting of 194 non-HTG cases and 242 controls. RESULTS: In the discovery phase, two nonsynonymous SNPs (rs3811046 and rs3811047), located in the IL1F7 gene and in an intergenic region, respectively were found to be weakly associated with non-HTG cases. However, the association was not sustained in the replication cohort. CONCLUSION: Our study did not reveal any reproducible association of SNPs in the IL1 gene cluster with POAG.

Increased Risk of Psoriasis due to combined effect of HLA-Cw6 and LCE3 risk alleles in Indian population.

HLA-Cw6 is one of the most associated alleles in psoriasis. Recently, Late Cornified Envelop 3 (LCE3) genes were identified as a susceptibility factor for psoriasis. Some population showed epistatic interaction of LCE3 risk variants with HLA-Cw6, while some population failed to show any association. We determined the associations of a 32.2 kb deletion comprising LCE3C-3B genes and three SNPs (rs1886734, rs4112788; rs7516108) at the LCE3 gene cluster among the psoriasis patients in India. All three SNPs at the LCE3 gene cluster failed to show any association. In contrary, for patients with HLA-Cw6 allele, all three SNPs and the LCE3C-3B deletion showed significant associations. While, all five LCE3 genes were upregulated in psoriatic skin, only LCE3A showed significant overexpression with homozygous risk genotype compared to the non-risk genotype. LCE3B also showed significant overexpression in patients with HLA-Cw6 allele. Moreover, LCE3A showed significantly higher expression in patients bearing homozygous risk genotype in presence of HLA-Cw6 allele but not in those having non-risk genotype, demonstrating the combined effect of HLA-Cw6 allele and risk associated genotype near LCE3A gene. Integration of genetic and gene expression data thus allowed us to identify the actual disease variants at the LCE3 cluster among the psoriasis patients in India.

Genetic Association and Gene-Gene Interaction Reveal Genetic Variations in ADH1B, GSTM1 and MnSOD Independently Confer Risk to Alcoholic Liver Diseases in India.

Genetic susceptibility is an important modifier of clinical outcome and natural history of progression in Alcoholic liver disease (ALD). While the significance of ethnicity in this evolution is very clear, subtle inter-individual genetic variant(s) might be important and thus we investigated those in an Indian population. Fourteen markers were genotyped within two alcohol metabolism genes [Alcohol dehydrogenase (ADH) gene clusters (ADH1B and ADH1C) and Aldehyde dehydrogenase (ALDH2)], one microsomal ethanol oxidizing enzyme cytochrome p450 (CYP2E1) and three oxidative stress response (OSR) genes (MnSOD, GSTT1 and GSTM1) among 490 Bengali individuals (322 ALD and 168 control) from Eastern and North-Eastern India and validation was performed in a new cohort of 150 Bengali patients including 100 ALD and 50 advanced non-alcoholic steatohepatitis (NASH). Out of 14 genetic variants, carriage of 5 genotypes (rs2066701CC in ADH1B, rs1693425TT in ADH1C, rs4880TT in MnSOD and GSTT1/GSTM1 null, p-value <0.05) were noted significantly higher among ALD patients while inter or intra group gene-gene interaction analysis revealed that addition of risk genotype of any OSR gene enhanced the possibility of ALD synergistically. Multiple logistic regression analysis showed independent association of rs2066701CC, rs4880TT and GSTM1 null genotype with ALD while lower frequencies of those genotypes in advanced NASH patients further confirmed their causal relation to ALD. Thus these findings suggest that the three variants of ADH1C, MnSOD and GSTM1 can be used to identify individuals who are at high risk to develop ALD and may be helpful in proper management of Indian alcoholics.