Biology subjects

Ramdas, S.

Publications and source records attributed to Ramdas, S..

9 recordsLinked to original sources

bioRxiv2026

Population-Scale Landscape of Mobile Element Insertions Across 82 Diverse Indian Populations

Mobile element insertions (MEIs) are a major source of structural variation in human DNA that can affect gene regulation and disrupt coding sequences. Population-scale MEI catalogs remain disproportionately skewed toward European-ancestry cohorts, with limited representation from India despite comprising nearly one-sixth of the global population and encompassing thousands of genetically distinct, largely endogamous communities. We characterized non-reference Alu and LINE1 insertions in 7,478 whole genomes from the GenomeIndia project, representing 82 populations selected to capture Indias geographic, linguistic, and social diversity. We cataloged 26,977 Alu and 7,075 LINE1 insertions, identifying 18,982 previously unreported insertions absent from existing publicly available MEI databases. Insertions bore signatures of purifying selection, depleted in exons and active chromatin and enriched in quiescent regions. MEI variants recapitulated the population structure seen in SNP data. Regulatory variation associated with MEIs was strongly population-structured, with a substantial fraction of European-defined MEI-eQTLs absent in the GenomeIndia cohort or carried at sharply different frequencies across communities, and MEIs in high linkage disequilibrium with GWAS variants. Sixty-nine insertions fell within exons, including a truncating LINE1 in CYP2J2 carried across five populations and a community-specific Alu in CABYR with a founder-effect signature. Collectively, these results reveal a layer of population-specific mobile element variation in Indian populations that is not captured by existing European-anchored reference resources. These variants represent potentially novel sources of genetic variation contributing to lack of transferability of disease associations across populations.

genomics↗

bioRxiv2026

Beyond copy number: The regulatory architecture of mitochondrial DNA gene expression

Mitochondrial DNA (mtDNA) copy number is widely used as a biomarker for mitochondrial function and disease risk, yet its relationship to mtDNA gene expression - one important functional output - remains poorly understood. Prior studies examining this relationship have largely relied on heterogeneous tissue samples, where confounding by cell-type composition obscures the underlying biology. We rigorously tested this relationship in lymphoblastoid cell lines (LCLs), where we find no correlation between mtDNA copy number and gene expression across 731 individuals, and minimal association across 49 GTEx tissues except whole blood. Using population genetic modeling of heteroplasmy drift between DNA and RNA, we estimate that effectively {approx}50 out of 813 mtDNA templates are transcriptionally active in LCLs, indicating low mtDNA accessibility. This confirms, using an independent method and a different cell type, previous observations in HeLa cells, where mtDNA is largely compacted into nucleoids. Together, our results demonstrate that mtDNA copy number and expression are largely decoupled, and that above a certain rate-limiting threshold, mtDNA accessibility -- rather than absolute copy number -- is the more relevant quantity for explaining inter-individual differences in gene expression. This challenges the interpretation of mtDNA copy number as a proxy for mitochondrial transcriptional output and highlights the need for a more mechanistic understanding of mtDNA copy number associations with disease-relevant traits. Cis- and trans-eQTL mapping further reveals that genetic regulation of mtDNA gene expression operates primarily through post-transcriptional mechanisms rather than transcription initiation, yet despite the high inter-individual variance in mtDNA gene expression, genetic variation underlying mtDNA regulation appears to be under strong selective constraint.

genomics↗

bioRxiv2025

Frequency Chaos Game Representation - Singular Value Decomposition for Alignment-Free Phylogenetic Analysis

This paper introduces "SVD-FCGR," a scalable and efficient frame-work for phylogenetic analysis using Singular Value Decomposition (SVD) on Frequency Chaos Game Representation (FCGR). Unlike traditional MSA techniques, SVD-FCGR handles large datasets with lower computational complexity. It supports both genome-wide and gene-specific analyses, as demonstrated with datasets of Japanese Encephalitis Virus (JEV), Hepatitis B Virus (HBV), Human Immunodeficiency Virus(HIV-1), and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). For COVID-19, gene-level analysis of surface glycoprotein highlighted mutations affecting viral adaptability, while the envelope gene remained conserved. The method produced detailed phylogenetic trees, surpassing tools like MEGA in resolution and scalability. Validation from the AF Project ranked it 11th among alignment-free methods, confirming its reliability and adaptability.

bioinformatics↗

bioRxiv2025

ANOMALY: A Snakemake pipeline for identifying NuMTs from Long-Read Sequencing Data

MotivationNuclear mitochondrial DNA segments (NuMT) can significantly affect cellular processes, including cancer development and disease progression. Current methods to call NuMTs rely on short-read sequencing data but struggle to resolve complex NuMTs. These limitations can be overcome by employing long-read sequencing data. However, no such workflow exists to capture NuMTs from long-read sequencing data. ResultsHere, we introduce ANOMALY, a novel, easy-to-use workflow for detecting NuMTs from long-read sequencing data. The pipeline takes raw sequencing data or aligned data and calls and visualizes sample NuMTs. On 50 simulated datasets, the pipeline demonstrated high accuracy, with a precision of 1.000, a recall of 0.989, and an F1-score of 0.994. The pipeline underscores the limitations of short-read data in resolving and capturing complex NuMTs while demonstrating that long-read data enables their accurate identification. Availability and ImplementationThe Snakemake pipeline employs Python, Bash and R and is published under an open-source GNU GPL v3 license. Detailed information about setting up and running the pipeline and the source code can be accessed at https://github.com/Nirmal2310/ANOMALY.

bioinformatics↗

bioRxiv2022

The Genetic and Evolutionary Basis of Gene Expression Variation in East Africans

BackgroundMapping of quantitative trait loci (QTL) associated with molecular phenotypes is a powerful approach for identifying the genes and molecular mechanisms underlying human traits and diseases. How the genetic architecture of molecular traits varies across human populations, however, has been less explored. To better understand the genetics of gene regulation in East Africans, we perform expression and splicing QTL mapping in whole blood from a cohort of 162 diverse Africans from Ethiopia and Tanzania. We assess replication of these QTLs in cohorts of predominantly European ancestry and identify candidate genes under selection in human populations. ResultsWe find the gene regulatory architecture of African and non-African populations is broadly shared, though there is a considerable amount of variation at individual loci across populations. Comparing our analyses to an equivalently sized cohort of European Americans, we find that QTL mapping in Africans improves the detection of expression QTLs and fine mapping of causal variation. Integrating our QTL scans with signatures of selection, we find several genes related to immunity and metabolism that are highly differentiated between Africans and non-Africans, as well as a gene associated with pigmentation, TMEM216, with evidence of population-specific selection in Nilo-Saharan speaking pastoralists. ConclusionExtending QTL-mapping studies beyond groups of European ancestry, particularly to diverse indigenous populations, is vital for a complete understanding of the genetic architecture of human traits and can reveal novel functional variation underlying human traits and disease.

genomics↗

bioRxiv2021

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWAS) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations, and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels, and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. Two prioritized genes, CREBRF and RRBP1, show convergent evidence across functional datasets supporting their roles in lipid biology.

genomics↗

bioRxiv2019

Genomic architecture of Shh dependent cochlear morphogenesis

The mammalian cochlea develops from a ventral outgrowth of the otic vesicle in response to Shh signaling. Mouse embryos lacking Shh or its essential signal transduction components display cochlear agenesis, however, a detailed understanding of the transcriptional network mediating this process is unclear. Here, we describe an integrated genomic approach to identify Shh dependent genes and associated regulatory sequences that promote cochlear duct morphogenesis. A comparative transcriptome analysis of otic vesicles from mouse mutants exhibiting loss (Smoecko) and gain (Shh-P1) of Shh signaling revealed a set of Shh responsive genes partitioned into four expression categories in the ventral half of the otic vesicle. This target gene classification scheme provided novel insights into several unanticipated roles for Shh, including priming the cochlear epithelium for subsequent sensory development. We also mapped regions of open chromatin in the inner ear by ATAC-seq that, in combination with Gli2 ChIP-seq, identified inner ear enhancers in the vicinity of Shh responsive genes. These datasets are useful entry points for deciphering Shh dependent regulatory mechanisms involved in cochlear duct morphogenesis and establishment of its constituent cell types.\n\nSUMMARY STATEMENTAn integrated genomic approach identifies Shh responsive genes and associated regulatory sequences with known and previously uncharacterized roles in cochlear morphogenesis, including genes that prime the cochlea for sensory development.

developmental biology↗

bioRxiv2018

Coverage-based detection of copy number alterations in mixed samples using DNA sequencing data: a theoretical framework for evaluating statistical power

1DNA sequencing can discover not only single-base variants but also copy-number alterations (CNAs). In shotgun sequencing, regions of CNAs show step-wise changes in read depth when compared to adjacent \"normal\" regions, allowing their detection by parametric statistical tests that compare the mean coverage in suspected regions against that of a baseline distribution. Traditionally, the power of such a test depends on (1) the integer number of copy number change, (2) the overall sequencing depth, (3) the length of the CNA region, (4) the read length and (5) the variation of coverage along the genome, which depends on many experimental factors, including whether the chosen platform is whole-genome, whole-exome, or targeted-panel sequencing. In cases involving inadvertent sample mixing or genuine somatic mosaicism, power also depends on the mixing ratio. However, the analysis of statistical power that considers the interplay of all these factors has not been systematically developed. Here we present a general analytical framework and a series of simulations that explore situations from the simplest to the increasingly multifactorial. Specifically, we expand the expression of power to include not just the known factors but also one or both of two complications: (1) the dispersion of read depth around the mean beyond the independent sampling-by-sequencing assumption, and (2) the reduced fraction of the CNA-bearing sample (\"purity\") as seen in studies of intratumor heterogeneity or in clinical monitoring of minimal residual disease. We describe the analytical formula and their simplifications in special cases, and share the extendable scripts for others to perform customized power analysis using study-specific parameters. As study designs vary and technologies continue to evolve, the input data and the noise characteristics will change depending on the practical situation. We present two use cases commonly encountered in cancer research: ultra-shallow whole-genome sequencing for detecting large, chromosome-scale events, and targeted ultra-deep sequencing for surveillance of known CNAs in rare tumor clones in the task of sensitive detection of cancer relapse or metastasis. We also present an online calculator at https://shiny.med.umich.edu/apps/hanyou/CNV_Detection_Power_Calculator/.

bioinformatics↗

bioRxiv2018

Extended regions of suspected mis-assembly in the rat reference genome

We performed whole-genome sequencing for eight inbred rat strains commonly used in genetic mapping studies, and they are the founders of the NIH heterogeneous stock (HS) outbred colony. We provide their sequences and variant calls to the rat genomics community. When analyzing the variant calls we identified regions with unusually high heterozygosity. We show that these regions are consistent across the eight inbred strains, including the BN strain, which was the basis of the rat reference genome. These regions show significantly higher read depth than other regions in the genome. The evidence suggests that these regions may contain segmental duplications that are incorrectly overlaid in the reference genome. We provide masks for these suspected regions of mis-assembly as a resource for the community to flag potentially false interpretations of mapping results or functional data.

genomics↗