Biology subjects

Lawler, A. J.

Publications and source records attributed to Lawler, A. J..

11 recordsLinked to original sources

bioRxiv2026

Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomics

AbstractMeasures of nucleotide sequence conservation across species are useful for identifying functional genomic loci, but can fail when regulatory function is maintained, often in a cell type-specific manner, even when sequence is not. We introduce CTACIT, the Cell Type-Aware Conservation Inference Toolkit, to identify trait-associated regulatory variants. CTACIT integrates sequence conservation scores with cell type-specific open chromatin data collected from a few mammalian species to impute function for hundreds more. Applying CTACIT to neuropsychiatric trait loci identifies higher heritability enrichment and more fine-mapped variants than nucleotide conservation and human chromatin data alone. Our in vivo reporter assays validate predictions for enhancers with risk variants near the DRD2 schizophrenia risk locus. By integrating genome conservation and multi-species open chromatin data, CTACIT prioritizes variants within regions of conserved regulatory function for in vivo characterization and addresses a major challenge in translating disease associations to mechanistic understanding. One-Sentence SummaryRegulatory functions conserved across mammals underlying caudate cell type evolution reveal functions of human risk variants.

genomics↗

bioRxiv2025

Challenges in predicting chromatin accessibility differences between species

Enhancers are transcriptional regulatory elements that help drive phenotypic diversity, yet they often undergo rapid sequence evolution despite functional conservation, posing a challenge for predicting their function across species. Machine learning models that predict quantitative enhancer activity using DNA sequence have not previously been evaluated for their ability to predict quantitative differences across orthologous regions. Here, we trained convolutional neural networks (CNNs) on a regression task to predict chromatin accessibility, which is a proxy for enhancer activity, in the liver across five mammals, and we developed a novel framework to evaluate cross-species performance. We demonstrated that training on multiple species improves model generalization to both species used in training and held-out species. However, the models consistently achieved poor performance in predicting quantitative differences in accessibility between species at orthologous regions. Our study highlights the challenges in using regression models to predict chromatin accessibility changes between species.

genomics↗

bioRxiv2023

Title: Leveraging Base Pair Mammalian Constraint to Understand Genetic Variation and Human Disease

Although thousands of genomic regions have been associated with heritable human diseases, attempts to elucidate biological mechanisms are impeded by a general inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function that is agnostic to cell type or disease mechanism. Here, single base phyloP scores from the whole genome alignment of 240 placental mammals identified 3.5% of the human genome as significantly constrained, and likely functional. We compared these scores to large-scale genome annotation, genome-wide association studies (GWAS), copy number variation, clinical genetics findings, and cancer data sets. Evolutionarily constrained positions are enriched for variants explaining common disease heritability (more than any other functional annotation). Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

genomics↗

bioRxiv2023

Evolutionary constraint and innovation across hundreds of placental mammals

Evolutionary constraint and acceleration are powerful, cell-type agnostic measures of functional importance. Previous studies in mammals were limited by species number and reliance on human-referenced alignments. We explore the evolution of placental mammals, including humans, through reference-free whole-genome alignment of 240 species and protein-coding alignments for 428 species. We estimate 10.7% of the human genome is evolutionarily constrained. We resolve constraint to single nucleotides, pinpointing functional positions, and refine and expand by over seven-fold the catalog of ultraconserved elements. Overall, 48.5% of constrained bases are as yet unannotated, suggesting yet-to-be-discovered functional importance. Using species-level phenotypes and an updated phylogeny, we associate coding and regulatory variation with olfaction and hibernation. Focusing on biodiversity conservation, we identify genomic metrics that predict species at risk of extinction.

genomics↗

bioRxiv2022

Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements

Vocal learning, the ability to modify vocal behavior based on experience, is a convergently evolved trait in birds and mammals. To identify genomic elements associated with vocal learning, we integrated new experiments conducted in the brain of the Egyptian fruit bat with analyses of the genomes of 222 placental mammals. We first identified an anatomically specialized region of the bat motor cortex containing direct monosynaptic projections to laryngeal motoneurons. Using wireless neural recordings of this brain region in freely vocalizing bats, we verified that single neuron activity in this region relates to vocal production. We profiled the open chromatin of this vocal-motor region, which we used to train machine learning models to identify enhancers associated with vocal learning across mammals. We found 201 proteins and 45 candidate enhancers that display convergent evolution associated with vocal learning, many of which overlapped loci associated with human speech disability. One such locus contains the neurodevelopmental transcription factors TSHZ3 and ZNF536 and multiple candidate vocal learning-associated enhancers, suggesting the co-evolution of protein and regulatory sequences underlying vocal learning. One-Sentence SummaryAnalyses of bat neural activity and epigenomic data in a brain region involved in vocal behavior were used to identify proteins and regulatory elements associated with vocal learning in mammals.

neuroscience↗

bioRxiv2022

An in vivo massively parallel platform for deciphering tissue-specific regulatory function

Genetic studies are rapidly identifying non-protein-coding human disease-associated loci. Understanding the regulatory mechanisms underlying these loci remains a challenge because the causal variants and the tissues in which they act are often unclear. Massively parallel reporter assays (MPRAs) have the potential to link differences in genome sequence, including genetic variants, to tissue-specific regulatory function. Although MPRA and similar technologies have been widely adopted in cell culture, there have been several barriers to widespread use in animals. We overcome these challenges with a new whole-animal MPRA (WhAMPRA), where systemic intravenous AAV effectively transduces the plasmid MPRA library to mouse tissues. Our WhAMPRA approach revealed models of tissue-specific regulation that generally match machine learning model predictions. In addition, we measured the regulatory effects of disrupting MEF2C transcription factor binding sites and impacts of late onset Alzheimers disease-associated genetic variations. Overall, our WhAMPRA technology simultaneously determines the transcriptional functions of hundreds of enhancers in vivo across multiple tissues.

genomics↗

bioRxiv2022

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

Protein-coding differences between mammals often fail to explain phenotypic diversity, suggesting involvement of enhancers, often rapidly evolving regions that regulate gene expression. Identifying associations between enhancers and phenotypes is challenging because enhancer activity is context-dependent and may be conserved without much sequence conservation. We developed TACIT (Tissue-Aware Conservation Inference Toolkit) to associate open chromatin regions (OCRs) with phenotypes using predictions in hundreds of mammalian genomes from machine learning models trained to learn tissue-specific regulatory codes. Applying TACIT for motor cortex and parvalbumin-positive interneurons to neurological phenotypes revealed dozens of new OCR-phenotype associations. Many associated OCRs were near relevant genes, including brain size-associated OCRs near genes mutated in microcephaly or macrocephaly. Our work creates a forward genomics foundation for identifying candidate enhancers associated with phenotype evolution. One Sentence SummaryA new machine learning-based approach associates enhancers with the evolution of brain size and behavior across mammals.

genomics↗

bioRxiv2021

Machine learning sequence prioritization for cell type-specific enhancer design

Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations, enabling characterization of their roles in behavior and in disease states. Available approaches for engineering targeted technologies for new neuron subtypes are low-yield, involving intensive transgenic strain or virus screening. Here, we introduce SNAIL (Specific Nuclear-Anchored Independent Labeling), a new virus-based strategy for cell labeling and nuclear isolation from heterogeneous tissue. SNAIL works by leveraging machine learning and other computational approaches to identify DNA sequence features that confer cell type-specific gene activation and using them to make a probe that drives an affinity purification-compatible reporter gene. As a proof of concept, we designed and validated two novel SNAIL probes that target parvalbumin-expressing (PV) neurons. Furthermore, we show that nuclear isolation using SNAIL in wild type mice is sufficient to capture characteristic open chromatin features of PV neurons in the cortex, striatum, and external globus pallidus. Expansion of this technology has broad applications in cell type-specific observation, manipulation, and therapeutics across species and disease models.

genomics↗

bioRxiv2020

Predicting lineage-specific differences in open chromatin across dozens of mammalian genomes

BackgroundEvolutionary conservation is an invaluable tool for inferring functional significance in the genome, including regions that are crucial across many species and those that have undergone convergent evolution. Computational methods to test for sequence conservation are dominated by algorithms that examine the ability of one or more nucleotides to align across large evolutionary distances. While these nucleotide alignment-based approaches have proven powerful for protein-coding genes and some non-coding elements, they fail to capture conservation at many enhancers, distal regulatory elements that control spatio-temporal patterns of gene expression. The function of enhancers is governed by a complex, often tissue- and cell type-specific, code that links combinations of transcription factor binding sites and other regulation-related sequence patterns to regulatory activity. Thus, function of orthologous enhancer regions can be conserved across large evolutionary distances, even when nucleotide turnover is high. ResultsWe present a new machine learning-based approach for evaluating enhancer conservation that leverages the combinatorial sequence code of enhancer activity rather than relying on the alignment of individual nucleotides. We first train a convolutional neural network model that is able to predict tissue-specific open chromatin, a proxy for enhancer activity, across mammals. Then, we apply that model to distinguish instances where the genome sequence would predict conserved function versus a loss regulatory activity in that tissue. We present criteria for systematically evaluating model performance for this task and use them to demonstrate that our models accurately predict tissue-specific conservation and divergence in open chromatin between primate and rodent species, vastly out-performing leading nucleotide alignment-based approaches. We then apply our models to predict open chromatin at orthologs of brain and liver open chromatin regions across hundreds of mammals and find that brain enhancers associated with neuron activity and liver enhancers associated with liver regeneration have a stronger tendency than the general population to have predicted lineage-specific open chromatin. ConclusionThe framework presented here provides a mechanism to annotate tissue-specific regulatory function across hundreds of genomes and to study enhancer evolution using predicted regulatory differences rather than nucleotide-level conservation measurements.

genetics↗

bioRxiv2020

The Regulatory Evolution of the Primate Fine-Motor System

In mammals, fine motor control is essential for skilled behavior, and is subserved by specialized subdivisions of the primary motor cortex (M1) and other components of the brains motor circuitry. We profiled the epigenomic state of several components of the Rhesus macaque motor system, including subdivisions of M1 corresponding to hand and orofacial control. We compared this to open chromatin data from M1 in rat, mouse, and human. We found broad similarities as well as unique specializations in open chromatin regions (OCRs) between M1 subdivisions and other brain regions, as well as species- and lineage-specific differences reflecting their evolutionary histories. By distinguishing shared mammalian M1 OCRs from primate- and human-specific specializations, we highlight gene regulatory programs that could subserve the evolution of skilled motor behaviors such as speech and tool use.

neuroscience↗

bioRxiv2020

Addiction-associated genetic variants implicate brain cell type- and region-specific cis-regulatory elements in addiction neurobiology

Recent large genome-wide association studies (GWAS) have identified multiple confident risk loci linked to addiction-associated behavioral traits. Genetic variants linked to addiction-associated traits lie largely in non-coding regions of the genome, likely disrupting cis-regulatory element (CRE) function. CREs tend to be highly cell type-specific and may contribute to the functional development of the neural circuits underlying addiction. Yet, a systematic approach for predicting the impact of risk variants on the CREs of specific cell populations is lacking. To dissect the cell types and brain regions underlying addiction-associated traits, we applied LD score regression to compare GWAS to genomic regions collected from human and mouse assays for open chromatin, which is associated with CRE activity. We found enrichment of addiction-associated variants in putative CREs marked by open chromatin in neuronal (NeuN+) nuclei collected from multiple prefrontal cortical areas and striatal regions known to play major roles in reward and addiction. To further dissect the cell type-specific basis of addiction-associated traits, we also identified enrichments in human orthologs of open chromatin regions of mouse neuronal subtypes: cortical excitatory, D1, D2, and PV. Lastly, we developed machine learning models from mouse cell type-specific regions of open chromatin to further dissect human NeuN+ open chromatin regions into cortical excitatory or striatal D1 and D2 neurons and predict the functional impact of addiction-associated genetic variants. Our results suggest that different neuronal subtypes within the reward system play distinct roles in the variety of traits that contribute to addiction. Significance StatementWe combine statistical genetic and machine learning techniques to find that the predisposition to for nicotine, alcohol, and cannabis use behaviors can be partially explained by genetic variants in conserved regulatory elements within specific brain regions and neuronal subtypes of the reward system. This computational framework can flexibly integrate open chromatin data across species to screen for putative causal variants in a cell type-and tissue-specific manner across numerous complex traits.

genomics↗