Biology subjects

Courchesne, E.

Publications and source records attributed to Courchesne, E..

11 recordsLinked to original sources

bioRxiv2022

The molecular genetic landscape of human brain size variation

Human brain size increases dynamically through early development, peaks in adolescence, and varies up to two-fold among adults. Although previous studies have elucidated changes in brain size across evolution, development, traits, and diseases, the molecular underpinnings of interindividual variation in brain size remain unknown. Here, we leverage postmortem brain RNA sequencing and estimates of brain weight (BW) in 2,531 individuals across three independent datasets, to identify 928 genes that show genome-wide significant associations with either higher or lower BW (BW+, BW-, respectively). These BW gene sets showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto developmental, functional and cellular axes of brain organization. Expression differences among evolutionarily conserved BW genes were predictive of interspecies differences in brain size, and functional annotation of BW genes revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses of in vivo neuroimaging phenotypes confirmed that the genetic regulation of BW-transcripts influences cortical surface area and volume, as well as behavioral traits related to brain function and disease. Cumulatively, our study represents a major step towards the goal of delineating the causal mechanisms of human brain size variation in health and disease.

neuroscience↗

bioRxiv2021

Disrupted intrinsic connectivity links to language and social deficits in toddlers with autism

Social and language abilities are closely intertwined during early development. Yet, it is still unknown how neural features underlying early social and language deficits are linked in toddlers with autism spectrum disorders (ASD). We examined functional connectivity of left and right temporal language regions and its correlations with language and social abilities in a cohort of 1- 4 years old toddlers (52 ASD/34 non-ASD). Further, ASD toddlers were stratified into those who strongly prefer social visual stimuli (ASDSoc) vs. those who do not (ASDnonSoc) based on performance on an eye-tracking paradigm. In non-ASD toddlers, connectivity between temporal regions and other language- and social-related cortical regions was significantly correlated with language, communication, and social scores. Conversely, ASD toddlers showed atypical correlations between temporal-visual cortex (cuneus) connectivity and communication ability. This temporal-visual connectivity was also correlated with social visual attention in ASDnonSoc but not in ASDSoc toddlers. These findings suggest language- and social-related functional connectivity was not correlated with language and social functions in ASD toddlers. Abnormal engagement of temporal-visual cortex connectivity may be an early-age signature of ASD and may help explain why interventions targeting social skills and language are so challenging, particularly in those with poor social engagement.

neuroscience↗

bioRxiv2021

Optimal balancing of clinical factors in large scale clinical RNA-Seq studies

Omics technologies are ubiquitous in biomedical research. However, improper sample selection is an often-overlooked complication with large omics studies, resulting in confounding effects that can disrupt the internal validity of a study and lead to false conclusions. Here, we present a method called BalanceIT, which uses a genetic algorithm to identify an optimal set of samples with balanced clinical factors for large-scale omics experiments. We apply our approach to two large RNA-Seq studies in autism (1) to find a post-hoc balanced sample set among an imbalanced study, and (2) to design an optimal study that allows for efficient batch correction. Our approach leads to near-perfect estimates of differential gene expression, superior performance of pathway-level enrichment analysis, and consistent network dysregulation patterns of autism symptom severity. These results provide empirical support for the importance of balanced experimental design, and BalanceIT will be invaluable for large-scale study design and batch effect correction.

bioinformatics↗

bioRxiv2021

Transfer learning improves outcome predictions for ASD from gene expression in blood

BackgroundPredicting outcomes on human genetic studies is difficult because the number of variables (genes) is often much larger than the number of observations (human subject tissue samples). We investigated means for improving model performance on the types of under-constrained problems that are typical in human genetics, where the number of strongly correlated genes (features) may exceed 10,000, and the number of study participants (observations) may be limited to under 1,000. MethodsWe created train, validate and test datasets from 240 microarray observations from 127 subjects diagnosed with autism spectrum disorder (ASD) and 113 typically developing (TD) subjects. We trained a neural network model (a.k.a., the naive model) on 10,422 genes using the train dataset, composed of 70 ASD and 65 TD subjects, and we restricted the model to one, fully-connected hidden layer to minimize the number of trainable parameters, including a dropout layer to help prevent overfitting. We experimented with alternative network architectures and tuned the hyperparameters using the validate dataset, and performed a single, final evaluation using the holdout test dataset. Next, we trained a neural network model using the identical architecture and identical genes to predict tissue type in GTEx data. We transferred that learning by replacing the top layer of the GTEx model with a layer to predict ASD outcome and we retrained the new layer on the ASD dataset, again using the identical 10,422 genes. FindingsThe naive neural network model had AUROC=0.58 for the task of predicting ASD outcomes, which saw a statistically significant 7.8% improvement from transfer learning. InterpretationWe demonstrated that neural network learning could be transferred from models trained on large RNA-Seq gene expression to a model trained on a small, microarray gene expression dataset with clinical utility for mitigating over-training on small sample sizes. Incidentally, we built a highly accurate classifier of tissue type with which to perform the transfer learning. FundingThis work was supported in part by NIMH R01-MH110558 (E.C., N.E.L.) Author SummaryImage recognition and natural language processing have enjoyed great success in reusing the computational efforts and data sources to overcome the problem of over-training a neural network on a limited dataset. Other domains using deep learning, including genomics and clinical applications, have been slower to benefit from transfer learning. Here we demonstrate data preparation and modeling techniques that allow genomics researchers to take advantage of transfer learning in order to increase the utility of limited clinical datasets. We show that a non-pre-trained, naive model performance can be improved by 7.8% by transferring learning from a highly performant model trained on GTEx data to solve a similar problem.

bioinformatics↗

bioRxiv2021

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.

neuroscience↗

bioRxiv2020

Atypical genomic patterning of the cerebral cortex in autism with poor early language outcome

Cortical regional identities develop through anterior-posterior (A-P) and dorsal-ventral (D-V) prenatal genomic patterning gradients. Here we find that A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT) is intact in typically developing and autistic toddlers with good language outcome, but is absent in autistic toddlers with poor early language outcome. Genes driving this effect are prominent in midgestational A-P and D-V gene expression gradients and prenatal cell types driving SA and CT variation (e.g., progenitor cells versus excitatory neurons). These genes are also important for vocal learning, human-specific evolution, and prenatal co-expression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be linked to atypical genomic cortical patterning starting in prenatal periods and which impacts later development of regional functional specialization and circuit formation. One Sentence SummaryGenomic patterning of the cortex is atypical in autistic toddlers with poor early language outcome.

neuroscience↗

bioRxiv2020

Autism genetics perturb prenatal neurodevelopment through a hierarchy of broadly-expressed and brain-specific genes

Numerous genes are associated with autism spectrum disorder (ASD); however, it remains unclear how most ASD risk genes influence neurodevelopment and result in similar traits. Recent genetic models of complex traits suggest non-tissue-specific genes converge on core disease genes; so we analyzed ASD genetics in this context. We found ASD risk genes partition cleanly into broadly-expressed and brain-specific genes. The two groups show sequential roles during neurodevelopment with broadly-expressed genes modulating chromatin remodeling, proliferation, and cell fate, while brain-specific risk genes are involved in neural maturation and synapse functioning. Broadly-expressed risk genes converge onto brain-specific risk genes and core neurodevelopmental genes through regulatory networks including PI3K/AKT, RAS/ERK, and WNT/{beta}-catenin signaling pathways. Broadly-expressed and brain-specific risk genes show unique properties, wherein the broadly-expressed risk gene network is expressed prenatally and conserved in non-neuronal cells like microglia. However, the brain-specific gene network expression is limited to excitatory and inhibitory neurons, spanning prenatal to adulthood. Furthermore, the two groups are linked differently to comorbidities associated with ASD. Collectively, we describe here the organization of the genetic architecture of ASD as a hierarchy of broadly-expressed and brain-specific genes that disrupt successive stages of core neurodevelopmental processes.

systems biology↗

bioRxiv2020

Multiple freeze-thaw cycles lead to a loss of consistency in poly(A)-enriched RNA sequencing

RNA-Seq is ubiquitous, but depending on the study, sub-optimal sample handling may be required, resulting in repeated freeze-thaw cycles. However, little is known about how each cycle impacts downstream analyses, due to a lack of study and known limitations in common RNA quality metrics, e.g., RIN, at quantifying RNA degradation following repeated freeze-thaws. Here we quantify the impact of repeated freeze-thaw on the reliability of downstream RNA-Seq analysis. To do so, we developed a method to estimate the relative noise between technical replicates independently of RIN. Using this approach we inferred the effect of both RIN and the number of freeze-thaw cycles on sample noise. We find that RIN is unable to fully account for the change in sample noise due to freeze-thaw cycles. Additionally, freeze-thaw is detrimental to sample quality and differential expression (DE) reproducibility, approaching zero after three cycles for poly(A)-enriched samples, wherein the inherent 3 bias in read coverage is more exacerbated by freeze-thaw cycles, while ribosome-depleted samples are less affected by freeze-thaws. The use of poly(A)-enrichment for RNA sequencing is pervasive in library preparation of frozen tissue, and thus, it is important during experimental design and data analysis to consider the impact of repeated freeze-thaw cycles on reproducibility. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/020792v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@c2c756org.highwire.dtl.DTLVardef@1ad1c03org.highwire.dtl.DTLVardef@a4136org.highwire.dtl.DTLVardef@13f403f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2018

Transcriptional organization of autism spectrum disorder and its connection to ASD risk genes and phenotypic variation

Hundreds of genes are implicated in autism spectrum disorder (ASD) but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here, we analyzed leukocyte transcriptomics from 1-4 year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes genes that are highly expressed during fetal brain development and which is dysregulated in hiPSC-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS/ERK, PI3K/AKT, and WNT/{beta}-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.

systems biology↗

bioRxiv2017

Paternally inherited noncoding structural variants contribute to autism

The genetic architecture of autism spectrum disorder (ASD) is known to consist of contributions from gene-disrupting de novo mutations and common variants of modest effect. We hypothesize that the unexplained heritability of ASD also includes rare inherited variants with intermediate effects. We investigated the genome-wide distribution and functional impact of structural variants (SVs) through whole genome analysis ([≥]30X coverage) of 3,169 subjects from 829 families affected by ASD. Genes that are intolerant to inactivating variants in the exome aggregation consortium (ExAC) were depleted for SVs in parents, specifically within fetal-brain promoters, UTRs and exons. Rare paternally-inherited SVs that disrupt promoters or UTRs were over-transmitted to probands (P = 0.0013) and not to their typically-developing siblings. Recurrent functional noncoding deletions implicate the gene LEO1 in ASD. Protein-coding SVs were also associated with ASD (P = 0.0025). Our results establish that rare inherited SVs predispose children to ASD, with differing contributions from each parent.

genomics↗

bioRxiv2017

Toddlers later diagnosed with autism exhibit multiple structural abnormalities in temporal corpus callosum fibers

Interhemispheric functional connectivity abnormalities are often reported in autism and it is thus not surprising that structural defects of the corpus callosum (CC) are consistently found using both traditional MRI and DTI techniques. Past DTI studies however, have subdivided the CC into 2 or 3 segments without regard for where fibers may project to within the cortex, thus placing limitations on our ability to understand the nature, timing and neurobehavioral impact of early CC abnormalities in autism. Leveraging a unique cohort of 97 toddlers (68 autism; 29 typical) we utilized a novel technique that identified seven CC tracts according to their cortical projections. Results revealed that younger (<2.5 years old), but not older toddlers with autism exhibited abnormally low mean, radial, and axial diffusivity values in the CC tracts connecting the occipital lobes and the temporal lobes. Fractional anisotropy and the cross sectional area of the temporal CC tract were significantly larger in young toddlers with autism. These findings indicate that water diffusion is more restricted and unidirectional in the temporal CC tract of young toddlers who develop autism. Such results may be explained by a potential overabundance of small caliber axons generated by excessive prenatal neural proliferation as proposed by previous genetic, animal model, and postmortem studies of autism. Furthermore, early diffusion measures in the temporal CC tract of the young toddlers were correlated with outcome measures of autism severity at later ages. These findings regarding the potential nature, timing, and location of early CC abnormalities in autism add to accumulating evidence, which suggests that altered inter-hemispheric connectivity, particularly across the temporal lobes, is a hallmark of the disorder.

neuroscience↗