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Haldar, T.

Publications and source records attributed to Haldar, T..

5 recordsLinked to original sources

Single-molecule nanophotonic resolution of binding dynamics from apo to fully liganded for a cyclic nucleotide-gated ion channel in cell-derived vesicles

Many biological macromolecules are activated upon ligand binding at multiple specific binding domains. However, how these domains interact and the transient intermediate conformations that connect binding events to protein activity are typically unknown. Ensemble-averaged measures over stochastic binding events are challenged to resolve the underlying asynchronous dynamics. Single-molecule resolution of these dynamics offers an attractive approach to investigate the ligand-activation process. Optical methods using fluorescently labeled ligands enable observation of individual binding events that report on the energetics of early ligand-bound conformational changes. However, diffraction-limited microscopy limits these methods to low ligand concentrations, often below what is required for physiologically relevant activation. Here, we overcome this limitation using nanophotonic zero-mode waveguides to observe the sequential binding of a fluorescent cyclic nucleotide to each of four subunits in TAX-4 cyclic nucleotide-gated ion channels in cell-derived vesicles. Our observations suggest that binding at one domain positively promotes binding at other domains, and that binding induces an isomerization of the binding domain in individual subunits which we attribute to a sequence of pre-activated intermediate states. This approach provides a broadly applicable tool to dissect the energetic landscape of ligand-binding at macromolecules in native cell membranes.

biophysics↗

A unified Bayesian approach to transcriptome-wide association study

The transcriptome-wide association study (TWAS) has identified novel gene-trait associations, providing essential biological insights. TWAS combines reference panel transcriptome and genome-wide association study (GWAS) data. Traditional TWAS methods construct a prediction model for gene expression based on the transcriptome data, which is then employed to impute gene expression in the GWAS data. The complex trait in GWAS is regressed on the predicted expression to identify gene-trait associations. Such a two-step approach ignores the uncertainty of the imputed expression and can lead to reduced inference accuracy. We develop a unified Bayesian approach for TWAS that avoids the need for a two-step approach by modeling the two datasets simultaneously. We consider the horseshoe prior to model the relationship between gene expression and local SNPs, and the spike and slab prior to test for an association between the genetic component of expression and the trait to build an integrated Bayesian framework. We extend the method to conducting a multi-ancestry TWAS, focusing on discovering genes that affect the trait in either or both ancestries. Using extensive simulations, we demonstrate that the new approach performs better than existing methods in terms of correctly classifying non-null genes and the accuracy of effect size estimation. To demonstrate our approach, we perform a single and multi-ancestry TWAS for intraocular pressure (IOP), integrating the Geuvadis transcriptome and UK Biobank GWAS data. We find that the identified set of IOP-associated genes is enriched in relevant pathways.

genomics↗

Adjustment for the uncertainty of predicted expression in transcriptome-wide association study: a fusion of measurement error theory and bootstrapping

Transcriptome-wide association study (TWAS) is a powerful approach to identifying novel genes associated with complex phenotypes. Standard TWAS approaches build a prediction model for the genetic component of expression based on reference transcriptome data. Next, an outcome is regressed on the predicted expression in separate GWAS data. The traditional TWAS approach disregards the uncertainty of predicted expression, which can lead to unreliable inference on gene-phenotype associations. We propose a novel approach to adjust for the uncertainty of predicted expression in TWAS. We adapt techniques from measurement error theory and implement bootstrapping algorithms for penalized regression to explicitly obtain an adjustment factor that needs to be incorporated in the unadjusted TWAS. We base the framework on adaptive Lasso. We use extensive simulations to show that the traditional TWAS inflates the type I error rate, whereas the adjusted TWAS adequately controls it. At the expense of an inflated false positive rate, the unadjusted TWAS offers a limited increase in power than the adjusted TWAS, which is statistically unjustified. Our approach produces more accurate estimates of the genes effect size than a traditional approach. We demonstrate the merits of the adjusted approach by conducting TWAS for height and lipid phenotypes, LDL, HDL, and triglycerides while integrating the Geuvadis transcriptome and UK Biobank GWAS data.

genomics↗

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

Genetic predisposition for complex traits often acts through multiple tissues at different time points during development. As a simple example, the genetic predisposition for obesity could be manifested either through inherited variants that control metabolism through regulation of genes expressed in the brain, or that control fat storage through dysregulation of genes expressed in adipose tissue, or both. Here we describe a statistical approach that leverages tissue-specific expression quantitative trait loci (eQTLs) corresponding to tissue-specific genes to prioritize a relevant tissue underlying the genetic predisposition of a given individual for a complex trait. Unlike existing approaches that prioritize relevant tissues for the trait in the population, our approach probabilistically quantifies the tissue-wise genetic contribution to the trait for a given individual. We hypothesize that for a subgroup of individuals the genetic contribution to the trait can be mediated primarily through a specific tissue. Through simulations using the UK Biobank, we show that our approach can predict the relevant tissue accurately and can cluster individuals according to their tissue-specific genetic architecture. We analyze body mass index (BMI) and waist to hip ratio adjusted for BMI (WHRadjBMI) in the UK Biobank to identify subgroups of individuals whose genetic predisposition act primarily through brain versus adipose tissue, and adipose versus muscle tissue, respectively. Notably, we find that these individuals have specific phenotypic features beyond BMI and WHRadjBMI that distinguish them from random individuals in the data, suggesting biological effects of tissue-specific genetic contribution for these traits.Competing Interest StatementThe authors have declared no competing interest.View Full Text

genetics↗

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes produces a substantially better accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected nine independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis, and Peripheral Vascular Disease. The GERA cohort analysis suggests that CPBayes is more powerful than ASSET with respect to detecting independent pleiotropic variants. We provide an R-package CPBayes implementing the proposed method.\n\nAuthor SummaryGenome-wide association studies (GWASs) have highlighted shared genetic susceptibility to various human diseases (pleiotropy). We propose a Bayesian meta-analysis method CPBayes that simultaneously evaluates the evidence of aggregate-level pleiotropic association and selects an optimal subset of associated traits underlying a pleiotropic signal. CPBayes analyzes pleiotropy using summary-level data across a wide range of studies for two or more phenotypes - separate GWASs with or without shared subjects, cohort study for multiple traits. It performs a fully Bayesian analysis and offers various flexibilities in the inference. In addition to parameters of primary interest (e.g., the measures of overall pleiotropic association, the optimal subset of associated traits), it provides additional interesting insights into a pleiotropic signal (e.g., the trait-specific posterior probability of association, the credible interval of unknown true genetic effects). Using computer simulations and a real data application to the large Kaiser GERA cohort, we demonstrate that CPBayes offers substantially better accuracy while selecting the non-null traits compared to a well known subset-based meta analysis ASSET. In the GERA cohort analysis, CPBayes detected a larger number of independent pleiotropic variants than ASSET. We provide a user-friendly R-package CPBayes for general use.

genetics↗