Biology subjects

Agarwal, I.

Publications and source records attributed to Agarwal, I..

8 recordsLinked to original sources

bioRxiv2026

A sibling study of variation in parental mutation rates

People are born with variable numbers of de novo germline mutations (DNMs), depending primarily on the ages of their parents. To explore additional causes, we developed an approach to call DNMs from nucleotide differences between siblings in genomic regions inherited identical by descent from both parents. Applying it to whole genome sequences from 28,985 sibling pairs of diverse genetic ancestries present in the UK Biobank and All of Us datasets, as well as 2,330 trios, we identified >800K autosomal DNMs and characterized mutation phenotypes in 27,645 sets of parents. We found subtle shifts in the mutation spectrum but no differences in total DNM rates among genetic ancestry groups, or between smokers and non-smokers. Testing for associations between parental mutation phenotypes and their burden of loss-of-function and deleterious missense variants in a set of 180 DNA repair and maintenance genes, we discovered that disruptions in REV1 and LIG1 increase germline mutation rates, and thus that rare mutator alleles segregate in population cohorts.

genomics↗

bioRxiv2025

Beyond deep versus superficial: true laminar inference with MEG

Neural dynamics at the laminar level are critical for cortical computation. However, in humans, non-invasive methods to probe such dynamics have been limited to coarse distinctions between deep and superficial layers. Here, we present a multilayer magnetoencephalography source reconstruction framework and evaluate the conditions under which depth-resolved laminar inference may be feasible. Using simulations, we systematically assess the limits of magnetoencephalography depth resolution, showing that laminar discrimination depends on sufficiently high signal-to-noise ratio, precise co-registration, and accurate specification of cortical column orientation. We demonstrate that regional variations in cortical anatomy influence reconstruction fidelity, with lead-field separability emerging as a key determinant. We then apply this framework to empirical data from three independent datasets and find laminar activation patterns that align with canonical feedforward and feedback motifs in visual and sensorimotor circuits, supporting the plausibility of laminar inference under favorable conditions and offering opportunities to bridge invasive electrophysiology and human neuroimaging.

neuroscience↗

bioRxiv2023

Refinement of Cryo-EM 3D Maps with Self-Supervised Denoising Model: crefDenoiser

Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. Despite extensive processing of large image sets collected in a cryo-EM experiment to amplify the signal-to-noise ratio, the reconstructed 3D protein density maps are often limited in quality due to residual noise, which in turn affects the accuracy of the macromolecular representation. In this paper, we introduce crefDenoiser, a denoising neural network model designed to enhance the signal in 3D cryo-EM maps produced with standard processing pipelines, beyond the current state of the art. crefDenoiser is trained without the need for clean, ground-truth target maps. Instead, we employ a custom dataset composed of real noisy protein half-maps sourced from the Electron Microscopy Data Bank repository. Strong model performance is achieved by optimizing for the theoretical noise-free map during self-supervised training. We demonstrate that our model successfully amplifies the signal across a wide variety of protein maps, outperforming a classical map denoiser and a network-based sharpening model. Without biasing the map, the proposed denoising method often leads to improved visibility of protein structural features, including protein domains, secondary structure elements, and amino-acid side chains.

bioinformatics↗

bioRxiv2023

Threat-dependent scaling of prelimbic dynamics to enhance fear representation

Promptly identifying threatening stimuli is crucial for survival. Freezing is a natural behavior displayed by rodents towards potential or actual threats. While it is known that the prelimbic cortex (PL) is involved in both risk evaluation and in fear and anxiety-like behavior expression, here we explored whether PL neuronal activity can dynamically represent different internal states of the same behavioral output. We found that freezing can always be decoded from PL activity at a population level. However, the sudden presentation of a fearful stimulus quickly reshaped PL to a new neuronal activity state, an effect not observed in other cortical or subcortical regions. This shift changed PL freezing representation and is necessary for fear memory expression. Our data reveal the unique role of PL in detecting threats and internally adjusting to distinguish between different freezing-related states. IN BRIEFThrough a comparative analysis across brain regions and risk situations, we demonstrate the distinctive role of the Prelimbic Cortex (PL) in fear-related behavior representation: the PL promptly detects threat stimuli and adjusts its neuronal configuration accordingly, resulting in a change in freezing representation, which is necessary for memory-associated fear expression. HIGHLIGHTS- PL population activity represents fear-related behavior (i.e., freezing) - Same behavioral outcome (freezing) can be represented as different PL neuronal states - Other brain regions do not show changes in dynamics to represent freezing - PL dynamics following threats are necessary for proper fear memory expression

neuroscience↗

bioRxiv2022

Relating pathogenic loss-of function mutations in humans to their evolutionary fitness costs

Causal loss-of-function (LOF) variants for Mendelian and severe complex diseases are enriched in "mutation intolerant" genes. We show how such observations can be interpreted in light of a model of mutation-selection balance, and use the model to relate the pathogenic consequences of LOF mutations at present-day to their evolutionary fitness effects. To this end, we first infer posterior distributions for the fitness costs of LOF mutations in 17,322 autosomal and 679 X-linked genes from exome sequences in 56,855 individuals. Estimated fitness costs for the loss of a gene copy are typically above 1%; they tend to be largest for X-linked genes, whether or not they have a Y homolog, followed by autosomal genes and genes in the pseudoautosomal region. We then compare inferred fitness effects for all possible de novo LOF mutations to those of de novo mutations identified in individuals diagnosed with one of six severe, complex diseases or developmental disorders. Probands carry an excess of mutations with estimated fitness effects above 10%; as we show by simulation, such highly deleterious mutations are typically only a couple of generations old when sampled in the population. Moreover, the proportion of highly deleterious mutations carried by probands reflects the typical age of onset of the disease. The study design also has a discernible influence: a greater proportion of highly deleterious mutations is detected in pedigree than case-control studies, and for autism, in simplex than multiplex families and in female versus male probands. Thus, anchoring observations in human genetics to a population genetic model allows us to learn about the fitness effects of mutations identified by different mapping strategies and for different traits.

genetics↗

bioRxiv2021

Mutation saturation for fitness effects at human CpG sites

Whole exome sequences have now been collected for millions of humans, with the related goals of identifying pathogenic mutations in patients and establishing reference repositories of data from unaffected individuals. As a result, we are approaching an important limit, in which datasets are large enough that, in the absence of natural selection, every highly mutable site will have experienced at least one mutation in the genealogical history of the sample. Here, we focus on putatively-neutral, synonymous CpG sites that are methylated in the germline and experience mutations to T at an elevated rate of ~10-7 per site per generation; in a sample of 390,000 individuals, ~99% of such CpG sites harbor a C/T polymorphism. These CpG sites provide a natural mutation saturation experiment for fitness effects: as we show, at current sample sizes, not seeing a polymorphism is indicative of strong selection against that mutation. We rely on this idea in order to directly identify a subset of highly deleterious CpG transitions, including ~27% of possible loss-of-function mutations, and up to 21% of possible missense mutations, depending on the type of site in which they occur. Unlike methylated CpGs, most mutation types, with rates on the order of 10-8 or 10-9, remain very far from saturation. We discuss what this contrast implies about interpreting the potential clinical relevance of mutations from their presence or absence in reference databases and for inferences about the fitness effects of new mutations.

evolutionary biology↗

bioRxiv2019

Signatures of replication, recombination and sex in the spectrum of rare variants on the human X chromosome and autosomes

The sources of human germline mutations are poorly understood. Part of the difficulty is that mutations occur very rarely, and so direct pedigree-based approaches remain limited in the numbers that they can examine. To address this problem, we consider the spectrum of low frequency variants in a dataset (gnomAD) of 13,860 human X chromosomes and autosomes. X-autosome differences are reflective of germline sex differences, and have been used extensively to learn about male versus female mutational processes; what is less appreciated is that they also reflect chromosome-level biochemical features that differ between the X and autosomes. We tease these components apart by comparing the mutation spectrum in multiple genomic compartments on the autosomes and between the X and autosomes. In so doing, we are able to ascribe specific mutation patterns to replication timing and recombination, and to identify differences in the types of mutations that accrue in males and females. In particular, we identify C>G as a mutagenic signature of male meiotic double strand breaks on the X, which may result from late repair. Our results show how biochemical processes of damage and repair in the germline interact with sex-specific life history traits to shape mutation patterns on both the X chromosome and autosomes.

genomics↗

bioRxiv2017

2-Way k-Means as a Model for Microbiome Samples

MotivationMicrobiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster-characterizing ones, and therefore lie in-between them in cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project.\n\nContactwjj2106@columbia.edu

bioinformatics↗