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Moumita Sinha

Publications and source records attributed to Moumita Sinha.

5 recordsLinked to original sources
PubMed2006

Prediction of empirical p values from asymptotic p values for conditional logistic affected relative pair linkage analysis.

OBJECTIVE: p Values are inaccurate for model-free linkage analysis using the conditional logistic model if we assume that the LOD score is asymptotically distributed as a simple mixture of chi-square distributions. When analyzing affected relative pairs alone, permuting the allele sharing of relative pairs does not lead to a useful permutation distribution. As an alternative, we have developed regression prediction models that provide more accurate p values. METHODS: Let E(alpha) be the empirical p value, which is the proportion of statistical tests whose LOD score under the null hypothesis exceeds a threshold determined by alpha, the nominal single test significance value. We used simulated data to obtain values of E(alpha) and compared them with alpha. We also developed a regression model, based on sample size, number of covariates in the model, alpha and marker density, to derive predicted p values for both single-point and multipoint analyses. To evaluate our predictions we used another set of simulated data, comparing the Ealpha for these data with those obtained by using the prediction model, referred to as predicted p values (P(alpha)). RESULTS: Under almost all circumstances the values of P(alpha) were closer to the E(alpha) than were the values of alpha. CONCLUSION: The regression models suggested by our analysis provide more accurate alternative p values for model-free linkage analysis when using the conditional logistic model.

Animals↗
PubMed2005

Local false discovery rate and minimum total error rate approaches to identifying interesting chromosomal regions.

The simultaneous testing of a large number of hypotheses in a genome scan, using individual thresholds for significance, inherently leads to inflated genome-wide false positive rates. There exist various approaches to approximating the correct genomewide p-values under various assumptions, either by way of asymptotics or simulations. We explore a philosophically different criterion, recently proposed in the literature, which controls the false discovery rate. The test statistics are assumed to arise from a mixture of distributions under the null and non-null hypotheses. We fit the mixture distribution using both a nonparametric approach and commingling analysis, and then apply the local false discovery rate to select cut-off points for regions to be declared interesting. Another criterion, the minimum total error, is also explored. Both criteria seem to be sensible alternatives to controlling the classical type I and type II error rates.

Chromosomes, Human↗
PubMed2005

Linkage analysis of alcohol dependence using both affected and discordant sib pairs.

The basic idea of affected-sib-pair (ASP) linkage analysis is to test whether the inheritance pattern of a marker deviates from Mendelian expectation in a sample of ASPs. The test depends on an assumed Mendelian control distribution of the number of marker alleles shared identical by descent (IBD), i.e., 1/4, 1/2, and 1/4 for 2, 1, and 0 allele(s) IBD, respectively. However, Mendelian transmission may not always hold, for example because of inbreeding or meiotic drive at the marker or a nearby locus. A more robust and valid approach is to incorporate discordant-sib-pairs (DSPs) as controls to avoid possible false-positive results. To be robust to deviation from Mendelian transmission, here we analyzed Collaborative Study on the Genetics of Alcoholism data by modifying the ASP LOD score method to contrast the estimated distribution of the number of allele(s) shared IBD by ASPs with that by DSPs, instead of with the expected distribution under the Mendelian assumption. This strategy assesses the difference in IBD sharing between ASPs and the IBD sharing between DSPs. Further, it works better than the conventional LOD score ASP linkage method in these data in the sense of avoiding false-positive linkage evidence.

Alcoholism↗
PubMed2005

Genetic analysis of polymorphisms in biologically relevant candidate genes in patients with abdominal aortic aneurysms.

BACKGROUND: Abdominal aortic aneurysms (AAAs) are characterized by histologic signs of chronic inflammation, destructive remodeling of extracellular matrix, and depletion of vascular smooth muscle cells. We investigated the process of extracellular matrix remodeling by performing a genetic association study with polymorphisms in the genes for matrix metalloproteinases (MMPs), tissue inhibitors of metalloproteinases (TIMPs), and structural extracellular matrix molecules in AAA. Our hypothesis was that genetic variations in one or more of these genes contribute to greater or lesser activity of these gene products, and thereby contribute to susceptibility for developing AAAs. METHODS: DNA samples from 812 unrelated white subject (AAA, n = 387; controls, n = 425) were genotyped for 14 polymorphisms in 13 different candidate genes: MMP1(nt-1607), MMP2(nt-955), MMP3(nt-1612), MMP9(nt-1562), MMP10(nt+180), MMP12(nt-82), MMP13(nt-77), TIMP1(nt+434), TIMP1(rs2070584), TIMP2(rs2009196), TIMP3(nt-1296), TGFB1(nt-509), ELN(nt+422), and COL3A1(nt+581). Odds ratios and P values adjusted for gender and country of origin using logistic regression and stratified by family history of AAA were calculated to test for association between genotype and disease status. Haplotype analysis was carried out for the two TIMP1 polymorphisms in male subjects. RESULTS: Analyses with one polymorphism per test without interactions showed an association with the two TIMP1 gene polymorphisms (nt+434, P = .0047; rs2070584, P = .015) in male subjects without a family history of AAA. The association remained significant when analyzing TIMP1 haplotypes (chi 2 P = .014 and empirical P = .009). In addition, we found a significant interaction between the polymorphism and gender for MMP10 ( P = .037) in cases without a family history of AAA, as well as between the polymorphism and country of origin for ELN ( P = .0169) and TIMP3 ( P = .0023) in cases with a family history of AAA. CONCLUSIONS: These findings suggest that genetic variations in TIMP1, TIMP3, MMP10, and ELN genes may contribute to the pathogenesis of AAAs. Further work is needed to confirm the findings in an independent set of samples and to study the functional role of these variants in AAA. It is noteworthy that contrary to a previous study, we did not find an association between the MMP9 (nt-1562) polymorphism and AAA, suggesting genetic heterogeneity of the disease. CLINICAL RELEVANCE: Abdominal aortic aneurysms (AAAs) are an important cardiovascular disease, but the genetic and environmental risk factors, which contribute to individual's risk to develop an aneurysm, are poorly understood. Histologically, AAAs are characterized by signs of chronic inflammation, destructive remodeling of the extracellular matrix, and depletion of vascular smooth muscle cells. We hypothesized that genes involved in these events could harbor changes that make individuals more susceptible to developing aneurysms. This study identified significant genetic associations between DNA sequence changes in tissue inhibitor of metalloproteinase 1 (TIMP1), TIMP3, matrix metalloproteinase 10 (MMP10) and elastin (ELN) genes, and AAA. The results will require confirmation using an independent set of samples. After replication it is possible that these sequence changes in combination with other risk factors could be used in the future to identify individuals who are at increased risk for developing an AAA.

Aortic Aneurysm, Abdominal↗