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1.  Multiple Pathway-Based Genetic Variations Associated with Tobacco Related Multiple Primary Neoplasms 
PLoS ONE  2012;7(1):e30013.
Background
In order to elucidate a combination of genetic alterations that drive tobacco carcinogenesis we have explored a unique model system and analytical method for an unbiased qualitative and quantitative assessment of gene-gene and gene-environment interactions. The objective of this case control study was to assess genetic predisposition in a biologically enriched clinical model system of tobacco related cancers (TRC), occurring as Multiple Primary Neoplasms (MPN).
Methods
Genotyping of 21 candidate Single Nucleotide Polymorphisms (SNP) from major metabolic pathways was performed in a cohort of 151 MPN cases and 210 cancer-free controls. Statistical analysis using logistic regression and Multifactor Dimensionality Reduction (MDR) analysis was performed for studying higher order interactions among various SNPs and tobacco habit.
Results
Increased risk association was observed for patients with at least one TRC in the upper aero digestive tract (UADT) for variations in SULT1A1 Arg213His, mEH Tyr113His, hOGG1 Ser326Cys, XRCC1 Arg280His and BRCA2 Asn372His. Gene - environment interactions were assessed using MDR analysis. The overall best model by MDR was tobacco habit/p53(Arg/Arg)/XRCC1(Arg399His)/mEH(Tyr113His) that had highest Cross Validation Consistency (8.3) and test accuracy (0.69). This model also showed significant association using logistic regression analysis.
Conclusion
This is the first Indian study on a multipathway based approach to study genetic susceptibility to cancer in tobacco associated MPN. This approach could assist in planning additional studies for comprehensive understanding of tobacco carcinogenesis.
doi:10.1371/journal.pone.0030013
PMCID: PMC3256192  PMID: 22253860
2.  Analysis of Exome Sequences With and Without Incorporating Prior Biological Knowledge 
Genetic epidemiology  2011;35(Suppl 1):S48-S55.
Next-generation sequencing technology provides new opportunities and challenges in the search for genetic variants that underlie complex traits. It will also presumably uncover many new rare variants, but exactly how these variants should be incorporated into the data analysis remains a question. Several papers in our group from Genetic Analysis Workshop 17 evaluated different methods of rare variant analysis, including single-variant, gene-based, and pathway-based analyses and analyses that incorporated biological information. Although the performance of some of these methods strongly depends on the underlying disease model, integration of known biological information is helpful in detecting causal genes. Two work groups demonstrated that use of a Bayesian network and a collapsing receiver operating characteristic curve approach improves risk prediction when a disease is caused by many rare variants. Another work group suggested that modeling local rather than global ancestry may be beneficial when controlling the effect of population structure in rare variant association analysis.
doi:10.1002/gepi.20649
PMCID: PMC3250084  PMID: 22128058
rare variant; association analysis; risk prediction model; population structure; biological information; receiver operating characteristic; Bayesian network
3.  Estimating heritability using family and unrelated individuals data 
BMC Proceedings  2011;5(Suppl 9):S34.
For the family data from Genetic Analysis Workshop 17, we obtained heritability estimates of quantitative traits Q1 and Q4 using the ASSOC program in the S.A.G.E. software package. ASSOC is a family-based method that estimates heritability through the estimation of variance components. The covariate-adjusted mean heritability was 0.650 for Q1 and 0.745 for Q4. For the unrelated individuals data, we estimated the heritability of Q1 as the proportion of total variance that can be accounted for by all single-nucleotide polymorphisms under an additive model. We examined a novel ordinary least-squares method, a naïve restricted maximum-likelihood method, and a calibrated restricted maximum-likelihood method. We applied the different methods to all 200 replicates for Q1. We observed that the ordinary least-squares method yielded many estimates outside the interval [0, 1]. The restricted maximum-likelihood estimates were more stable than the ordinary least-squares estimates. The naïve restricted maximum-likelihood method yielded an average estimate of 0.462 ± 0.1, and the calibrated restricted maximum-likelihood method yielded an average of 0.535 ± 0.121. Our results demonstrate discrepancies in heritability estimates using the family data and the unrelated individuals data.
doi:10.1186/1753-6561-5-S9-S34
PMCID: PMC3287870  PMID: 22373039
4.  A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis 
BMC Proceedings  2011;5(Suppl 9):S84.
We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach.
doi:10.1186/1753-6561-5-S9-S84
PMCID: PMC3287925  PMID: 22373405
5.  Capability of common SNPs to tag rare variants 
BMC Proceedings  2011;5(Suppl 9):S88.
Genome-wide association studies are based on the linkage disequilibrium pattern between common tagging single-nucleotide polymorphisms (SNPs) (i.e., SNPs having only common alleles) and true causal variants, and association studies with rare SNP alleles aim to detect rare causal variants. To better understand and explain the findings from both types of studies and to provide clues to improve the power of an association study with only common SNPs genotyped, we study the correlation between common SNPs and the presence of rare alleles within a region in the genome and look at the capability of common SNPs in strong linkage disequilibrium with each other to capture single rare alleles. Our results indicate that common SNPs can, to some extent, tag the presence of rare alleles and that including SNPs in strong linkage disequilibrium with each other among the tagging SNPs helps to detect rare alleles.
doi:10.1186/1753-6561-5-S9-S88
PMCID: PMC3287929  PMID: 22373521
6.  Identification of Gene-Gene Interactions in the Presence of Missing Data using the Multifactor Dimensionality Reduction Method 
Genetic epidemiology  2009;33(7):646-656.
Gene-gene interaction is believed to play an important role in understanding complex traits. Multifactor dimensionality reduction (MDR) was proposed by Ritchie, et al. [2001] to identify multiple loci that simultaneously affect disease susceptibility. Although the MDR method has been widely used to detect gene-gene interactions, few studies have been reported on MDR analysis when there are missing data. Currently, there are four approaches available in MDR analysis to handle missing data. The first approach uses only complete observations that have no missing data, which can cause a severe loss of data. The second approach is to treat missing values as an additional genotype category, but interpretation of the results may then be not clear and the conclusions may be misleading. Furthermore, it performs poorly when the missing rates are unbalanced between the case and control groups. The third approach is a simple imputation method that imputes missing genotypes as the most frequent genotype, which also may produce biased results. The fourth approach, Available, uses all data available for the given loci, to increase power. In any real data analysis, it is not clear which MDR approach one should use when there are missing data. In this paper, we consider a new EM Impute approach, to handle missing data more appropriately. Through simulation studies, we compared the performance of the proposed EM Impute approach with the current approaches. Our results showed that Available and EM Impute approaches perform better than the three other current approaches in terms of power and precision.
doi:10.1002/gepi.20416
PMCID: PMC2766017  PMID: 19241410
Gene-gene interaction; Multifactor Dimensionality Reduction; Missing genotypes; Association study
7.  Genome-wide analysis of haplotype interaction for the data from the North American Rheumatoid Arthritis Consortium 
BMC Proceedings  2009;3(Suppl 7):S34.
Recent genome-wide association studies on several complex diseases have focused on individual single-nucleotide polymorphism (SNP) analysis; however, not many studies have reported interactions among genes perhaps because the gene-gene and gene-environment interaction analysis could be infeasible due to heavy computing requirements. In this study we propose a new strategy for exploring the interactions among haplotypes. The proposed method consists of two steps. Step 1 tests the single-SNP association of whole genome with multiple testing corrections and finds the haplotype blocks of the significant SNPs. Step 2 performs interaction analysis of haplotypes within blocks. Our proposed method is applied to the rheumatoid arthritis data for Genetic Analysis Workshop 16.
PMCID: PMC2795932  PMID: 20018025
8.  Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm 
BMC Proceedings  2007;1(Suppl 1):S69.
Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.
PMCID: PMC2367540  PMID: 18466570
9.  Whole-genome association studies of alcoholism with loci linked to schizophrenia susceptibility 
BMC Genetics  2005;6(Suppl 1):S9.
Background
Alcoholism is a complex disease. There have been many reports on significant comorbidity between alcoholism and schizophrenia. For the genetic study of complex diseases, association analysis has been recommended because of its higher power than that of the linkage analysis for detecting genes with modest effects on disease.
Results
To identify alcoholism susceptibility loci, we performed genome-wide single-nucleotide polymorphisms (SNP) association tests, which yielded 489 significant SNPs at the 1% significance level. The association tests showed that tsc0593964 (P-value 0.000013) on chromosome 7 was most significantly associated with alcoholism. From 489 SNPs, 74 genes were identified. Among these genes, GABRA1 is a member of the same gene family with GABRA2 that was recently reported as alcoholism susceptibility gene.
Conclusion
By comparing 74 genes to the published results of various linkage studies of schizophrenia, we identified 13 alcoholism associated genes that were located in the regions reported to be linked to schizophrenia. These 13 identified genes can be important candidate genes to study the genetic mechanism of co-occurrence of both diseases.
doi:10.1186/1471-2156-6-S1-S9
PMCID: PMC1866690  PMID: 16451705

Results 1-9 (9)