Publications by authors named "Narayanan Raghupathy"

The kinesin family member 18A () is an essential regulator of microtubule dynamics and chromosome alignment during mitosis. Functional dependency on KIF18A varies by cell type and genetic context but the heritable factors that influence this dependency remain unknown. To address this, we took advantage of the variable penetrance observed in different mouse strain backgrounds to screen for loci that modulate germ cell depletion in the absence of KIF18A.

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The Diversity Outbred (DO) mice and their inbred founders are widely used models of human disease. However, although the genetic diversity of these mice has been well documented, their epigenetic diversity has not. Epigenetic modifications, such as histone modifications and DNA methylation, are important regulators of gene expression and, as such, are a critical mechanistic link between genotype and phenotype.

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Some imprinted genes exhibit parental origin specific expression bias rather than being transcribed exclusively from one copy. The physiological relevance of this remains poorly understood. In an analysis of brain-specific allele-biased expression, we identified that Trappc9, a cellular trafficking factor, was expressed predominantly (~70%) from the maternally inherited allele.

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Mouse embryonic stem cells (mESCs) cultured in the presence of LIF occupy a ground state with highly active pluripotency-associated transcriptional and epigenetic circuitry. However, ground state pluripotency in some inbred strain backgrounds is unstable in the absence of ERK1/2 and GSK3 inhibition. Using an unbiased genetic approach, we dissect the basis of this divergent response to extracellular cues by profiling gene expression and chromatin accessibility in 170 genetically heterogeneous mESCs.

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Allele-specific expression (ASE) at single-cell resolution is a critical tool for understanding the stochastic and dynamic features of gene expression. However, low read coverage and high biological variability present challenges for analyzing ASE. We demonstrate that discarding multi-mapping reads leads to higher variability in estimates of allelic proportions, an increased frequency of sampling zeros, and can lead to spurious findings of dynamic and monoallelic gene expression.

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Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL.

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Isogenic laboratory mouse strains enhance reproducibility because individual animals are genetically identical. For the most widely used isogenic strain, C57BL/6, there exists a wealth of genetic, phenotypic, and genomic data, including a high-quality reference genome (GRCm38.p6).

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Motivation: Allele-specific expression (ASE) refers to the differential abundance of the allelic copies of a transcript. RNA sequencing (RNA-seq) can provide quantitative estimates of ASE for genes with transcribed polymorphisms. When short-read sequences are aligned to a diploid transcriptome, read-mapping ambiguities confound our ability to directly count reads.

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A variety of inherited animal models with different genetic causes and distinct genetic backgrounds are needed to help dissect the complex genetic etiology of glaucoma. The scarcity of such animal models has hampered progress in glaucoma research. Here, we introduce a new inherited glaucoma model: the inbred mouse strain YBR/EiJ (YBR).

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Genetic variation modulates protein expression through both transcriptional and post-transcriptional mechanisms. To characterize the consequences of natural genetic diversity on the proteome, here we combine a multiplexed, mass spectrometry-based method for protein quantification with an emerging outbred mouse model containing extensive genetic variation from eight inbred founder strains. By measuring genome-wide transcript and protein expression in livers from 192 Diversity outbred mice, we identify 2,866 protein quantitative trait loci (pQTL) with twice as many local as distant genetic variants.

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Article Synopsis
  • RNA editing involves changes to the RNA sequence after transcription, with many new targets being discovered recently.
  • The study focused on Diversity Outbred mice to explore how genetic diversity affects site-specific editing of RNA, revealing that different genetic factors influence C-to-U and A-to-I editing processes.
  • Findings suggest that while C-to-U editing is influenced by broader genetic control, A-to-I editing is more localized, highlighting the complexity and evolutionary significance of RNA editing across different types.
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Meiotic recombination generates new genetic variation and assures the proper segregation of chromosomes in gametes. PRDM9, a zinc finger protein with histone methyltransferase activity, initiates meiotic recombination by binding DNA at recombination hotspots and directing the position of DNA double-strand breaks (DSB). The DSB repair mechanism suggests that hotspots should eventually self-destruct, yet genome-wide recombination levels remain constant, a conundrum known as the hotspot paradox.

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Massively parallel RNA sequencing (RNA-seq) has yielded a wealth of new insights into transcriptional regulation. A first step in the analysis of RNA-seq data is the alignment of short sequence reads to a common reference genome or transcriptome. Genetic variants that distinguish individual genomes from the reference sequence can cause reads to be misaligned, resulting in biased estimates of transcript abundance.

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Identifying genomic regions that descended from a common ancestor is important for understanding the function and evolution of genomes. In distantly related genomes, clusters of homologous gene pairs are evidence of candidate homologous regions. Demonstrating the statistical significance of such "gene clusters" is an essential component of comparative genomic analyses.

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Gene clusters that span three or more chromosomal regions are of increasing importance, yet statistical tests to validate such clusters are in their infancy. Current approaches either conduct several pairwise comparisons or consider only the number of genes that occur in all of the regions. In this paper, we provide statistical tests for clusters spanning exactly three regions based on genome models of typical comparative genomics problems, including analysis of conserved linkage within multiple species and identification of large-scale duplications.

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