Articles written in Journal of Genetics
Volume 79 Issue 2 August 2000 pp 47-53
An improved procedure of mapping a quantitative trait locus via the EM algorithm using posterior probabilities
Saurabh Ghosh Partha P. Majumder
Mapping a locus controlling a quantitative genetic trait (e.g. blood pressure) to a specific genomic region is of considerable contemporary interest. Data on the quantitative trait under consideration and several codominant genetic markers with known genomic locations are collected from members of families and statistically analysed to estimate the recombination fraction, θ, between the putative quantitative trait locus and a genetic marker. One of the major complications in estimating θ for a quantitative trait in humans is the lack of haplotype information on members of families. We have devised a computationally simple two-stage method of estimation of θ in the absence of haplotypic information using the expectation-maximization (EM) algorithm. In the first stage, parameters of the quantitative trait locus (QTL) are estimated on the basis of data of a sample of unrelated individuals and a Bayes’s rule is used to classify each parent into a QTL genotypic class. In the second stage, we have proposed an EM algorithm for obtaining the maximum-likelihood estimate of θ based on data of informative families (which are identified upon inferring parental QTL genotypes performed in the first stage). The purpose of this paper is to investigate whether, instead of using genotypically ‘classified’ data of parents, the use of posterior probabilities of QT genotypes of parents at the second stage yields better estimators. We show, using simulated data, that the proposed procedure using posterior probabilities is statistically more efficient than our earlier classification procedure, although it is computationally heavier.
Volume 84 Issue 2 August 2005 pp 143-146 Research Article
Dissecting the correlation structure of a bivariate phenotype: Common genes or shared environment?
High correlations between two quantitative traits may be either due to common genetic factors or common environmental factors or a combination of both. In this study, we develop statistical methods to extract the genetic contribution to the total correlation between the components of a bivariate phenotype. Using data on bivariate phenotypes and marker genotypes for sib-pairs, we propose a test for linkage between a common QTL and a marker locus based on the conditional cross-sib trait correlations (trait 1 of sib 1—trait 2 of sib 2 and conversely) given the identity-by-descent (i.b.d.) sharing at the marker locus. We use Monte-Carlo simulations to evaluate the performance of the proposed test under different trait parameters and quantitative trait distributions. An application of the method is illustrated using data on two alcohol-related phenotypes from a project on the collaborative study on the genetics of alcoholism.
Volume 90 Issue 2 August 2011 pp 323-326 Research Note
Lack of association of
Dhanasekaran Bodhini Venkatesan Radha Saurabh Ghosh Partha P. Majumder Viswanathan Mohan
Volume 94 Issue 4 December 2015 pp 619-628 Research Article
Statistical equivalent of the classical TDT for quantitative traits and multivariate phenotypes
Tanushree Haldar Saurabh Ghosh
Clinical end-point traits are usually governed by quantitative precursors. Hence, there is active research interest in developing statistical methods for association mapping of quantitative traits. Unlike population-based tests for association, family-based tests for transmission disequilibrium are protected against population stratification. In this study, we propose a logistic regression model to test the association for quantitative traits based on a trio design. We show that the method can be viewed as a direct extension of the classical transmission diequilibrium test for binary traits to quantitative traits. We evaluate the performance of our method using extensive simulations and compare it with an existing method, family-based association test. We found that the two methods yield comparable powers if all families are considered. However, unlike FBAT, which yields an inflated rate of false positives when noninformative trios with all three individuals’ heterozygous are removed, our method maintains the correct size without compromising too much on power. We show that our method can be easily modified to incorporate multivariate phenotypes. Here, we applied this method to analyse a quantitative endophenotype associated with alcoholism.
Volume 101 All articles Published: 25 January 2022 Article ID 0014 RESEARCH ARTICLE
Competing analytical strategies of combining associated SNPs for estimating genetic risks
ARUNABHA MAJUMDAR SAURABH GHOSH
In genomewide association study (GWAS) of a complex phenotype, a large number of variants, many with small effect sizes, are found to contribute to the variability of the phenotype. Subsequent to the identification of such variants in a GWAS, it is of interest to estimate the risk jointly conferred by the variants. We propose three different strategies of combining the risk SNPs to calculate an allele dosage score. Using simulations, we evaluate the different measures of allele dosage score with respect to the risk prediction accuracy of a binary trait and the proportion of variance explained for a quantitative trait. For a binary trait, an allele dosage score defined based on log odds ratio performs marginally better than the other two measures. For a quantitative trait, the measure based on the standardized slope coefficient in linear regression of the trait on single-nucleotide polymorphism (SNP) genotypes performs better than the measures using the weights proportional to log
Volume 102, 2023
Continuous Article Publishing mode
Click here for Editorial Note on CAP Mode