Arunabha Majumdar

• PhD
• PostDoc Position at University of California, Los Angeles

Background: The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from twelve cancer genome-wide association studies (GWAS) to quantify pair-wise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. Methods: We collected GWAS summary...

Genome-wide association studies (GWAS) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 ca...

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 d...

Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expressio...

Motivation While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous...

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost....

p>Although cancer is a heterogeneous disease, there are shared hallmark mechanisms across multiple tumor types. Because of this, identifying genes associated with multiple cancer types has the potential to shed light on general oncogenic mechanisms. Conversely, integrating evidence for genetic association across multiple cancers could identify nove...

While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows...

Transcriptome-wide association studies (TWAS) test the association between traits and genetically predicted gene expression levels. The power of a TWAS depends in part on the strength of the correlation between a genetic predictor of gene expression and the causally relevant gene expression values. Consequently, TWAS power can be low when expressio...

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to simultaneously measure cellular composition and state which is impossible with bulk profiling approaches. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical powe...

SNP-heritability is a fundamental quantity in the study of complex traits. Recent studies have shown that existing methods to estimate genome-wide SNP-heritability can yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and linkage disequilibrium (LD)-dependent genetic architecture...

Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points in the development. As an example, the genetic predisposition for obesity could be manifested through inherited variants that control metabolism through regulation of genes expressed in the brain and/or through the control of...

Genetic predisposition for complex traits is often manifested through multiple tissues of interest at different time points during their development. For 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 through the con...

The proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various app...

Description: A Bayesian meta-analysis method for studying cross-phenotype genetic associations. It 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. CPBayes is based on a spike and slab prior an...

Estimated joint posterior probabilities of the association configurations obtained by CPBayes and GPA for the first 10 risk SNPs. Here 2% of 1000 SNPs are risk SNPs and associated only with the second trait, and 98% SNPs are null. (PDF)

Comparison of the accuracy of selection of associated traits by the continuous and Dirac spike for multiple overlapping case-control studies. (PDF)

Forest plot for pleiotropic signal at rs3957148 detected by CPBayes. (PDF)

Prior probabilities of various ranges of odds ratio (OR) under different choices of the slab variance in the continuous spike and slab prior. (PDF)

Comparison between main theoretical features of CPBayes and ASSET. (PDF)

Summary of measures of the evidence of overall pleiotropic association when a subset of traits are associated for 10 overlapping case-control studies. Here 6 and 8 among 10 traits are associated. (PDF)

Simulation study for 50 traits. Summary of measures for the evidence of the overall pleiotropic association for 50 non-overlapping case-control studies. Here 0, 5, and 10 among 50 traits are associated. (PDF)

Independent pleiotropic SNPs identified by CPBayes for which one phenotype was selected. (PDF)

Independent pleiotropic signals on chromosome 1-2 detected by ASSET. (PDF)

An example diagram of the continuous spike and slab prior used by CPBayes to model pleiotropy. In this diagram, the spike variance is chosen as 0.1. (PDF)

Estimated joint posterior probabilities of the association configurations obtained by CPBayes and GPA for the risk SNPs. Here 1% of 1000 SNPs are risk SNPs and associated only with the second trait, and 99% SNPs are null. (PDF)

Forest plot for pleiotropic signal at rs10455872 detected by CPBayes. (PDF)

Name of 22 phenotypes in the GERA cohort analyzed by CPBayes and ASSET. (PDF)

Selection accuracy of different methods for cohort study. The total number of phenotypes is denoted by K and m denotes the minor allele frequency at the risk SNP. K1+ and K1- denote the number of positively and negatively associated traits, respectively. Two different colors for each method present two scenarios: 1. all non-null effects are positiv...

Forest plot for pleiotropic signal at rs6025 detected by CPBayes. (PDF)

Forest plot for pleiotropic signal at rs687289 detected by CPBayes. (PDF)

Summary of measures of the overall pleiotropic association under the global null hypothesis of no association when multiple case-control studies with overlapping subjects are considered. (PDF)

Summary of measures of the evidence of overall pleiotropic association when a subset of traits are associated for 10 overlapping case-control studies. Here 2 and 4 among 10 traits are associated. (PDF)

Simulation study for 50 traits. Accuracy in selection of associated traits by CPBayes and BH0.01 for 50 case-control studies. (PDF)

Forest plot for pleiotropic signal at rs13211628 detected by CPBayes. (PDF)

Distance between estimated correlation matrices of effect estimates in the GERA cohort obtained by using different thresholds of the minimum of univariate association p-value across traits and r2 value between a pair of SNPs to select independent null SNPs. At the beginning of the table, the number of independent null SNPs obtained by using differe...

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...

Breast cancer is the most common solid organ malignancy and the most frequent cause of cancer death among women worldwide. Previous research has yielded insights into its genetic etiology, but there remains a gap in the understanding of genetic factors that contribute to risk, and particularly in the biological mechanisms by which genetic variation...

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...

Discovering pleiotropic loci is important to understand the biological basis of seemingly distinct phenotypes. Most methods for assessing pleiotropy only test for the overall association between genetic variants and multiple phenotypes. To determine which specific traits are pleiotropic, we evaluate via simulation and application three different st...

Binary phenotypes commonly arise due to multiple underlying quantitative precursors and genetic variants may impact multiple traits in a pleiotropic manner. Hence, simultaneously analyzing such correlated traits may be more powerful than analyzing individual traits. Various genotype-level methods, e.g., MultiPhen (O'Reilly et al. []), have been dev...

Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait. It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance c...

The Genome-wide association studies have been partially successful in identifying novel variants involved in complex disorders. However, correcting for multiple testing in such studies becomes inevitable to maintain the appropriate overall false positive error rate. In this article, we consider a block wise strategy MVNblock of multiple testing cor...

Summary While the population-based case-control approach is the popular study design for association mapping of complex genetic traits because of ease of data collection and statistical analyses, it suffers from the inherent problem of population stratification. There have been methodological developments for adjusting these studies for population...