Autism Spectrum Disorder Genetics and the Search for Pathological Mechanisms DOI
Devanand S. Manoli, Matthew W. State

American Journal of Psychiatry, Journal Year: 2021, Volume and Issue: 178(1), P. 30 - 38

Published: Jan. 1, 2021

Recent progress in the identification of genes and genomic regions contributing to autism spectrum disorder (ASD) has had a broad impact on our understanding nature genetic risk for range psychiatric disorders, ASD biology, defining key challenges now facing field efforts translate gene discovery into an actionable pathology. While these advances have not yet transformative clinical practice, there is nonetheless cause real optimism: reliable lists are large growing rapidly; identified encoded proteins already begun point relatively small number areas where parallel neuroscience functional genomics yielding profound insights; strong evidence pointing mid-fetal prefrontal cortical development as one nexus vulnerability some largest-effect genes; multiple plausible paths forward toward rational therapeutics that, while admittedly challenging, constitute fundamental departures from what was possible prior era successful discovery.

Language: Английский

Human Gut Microbiota from Autism Spectrum Disorder Promote Behavioral Symptoms in Mice DOI Creative Commons
Gil Sharon,

Nikki Jamie Cruz,

Dae‐Wook Kang

et al.

Cell, Journal Year: 2019, Volume and Issue: 177(6), P. 1600 - 1618.e17

Published: May 1, 2019

Language: Английский

Citations

915
A genomic mutational constraint map using variation in 76,156 human genomes DOI
Siwei Chen, Laurent C. Francioli, Julia K. Goodrich

et al.

Nature, Journal Year: 2023, Volume and Issue: 625(7993), P. 92 - 100

Published: Dec. 6, 2023

Language: Английский

Citations

518
Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks DOI Creative Commons
Elizabeth K. Ruzzo, Laura Pérez‐Cano, Jae-Yoon Jung

et al.

Cell, Journal Year: 2019, Volume and Issue: 178(4), P. 850 - 866.e26

Published: Aug. 1, 2019

Language: Английский

Citations

396
Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution DOI Creative Commons
Alexandro E. Trevino, Fabian Müller, Jimena Andersen

et al.

Cell, Journal Year: 2021, Volume and Issue: 184(19), P. 5053 - 5069.e23

Published: Aug. 13, 2021

Language: Английский

Citations

329
A genome-wide mutational constraint map quantified from variation in 76,156 human genomes DOI Creative Commons
Siwei Chen, Laurent C. Francioli, Julia K. Goodrich

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: March 21, 2022

Abstract The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders, but attempts assess constraint for non-protein-coding regions have proven more difficult. Here we aggregate, process, and release a dataset 76,156 genomes from the Genome Aggregation Database (gnomAD), largest public open-access genome reference dataset, use this build mutational map whole genome. We present refined model that incorporates local sequence context regional genomic features detect depletions across As expected, proteincoding sequences overall are under stronger than non-coding regions. Within genome, constrained enriched known regulatory elements variants implicated in complex diseases traits, facilitating triangulation biological annotation, disease association, DNA analysis. More tend regulate genes, while captures additional functional information underrecognized gene metrics. demonstrate genome-wide provides an effective approach characterizing improving identification interpretation genetic variation.

Language: Английский

Citations

279
Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk DOI
Jian Zhou, Christopher Y. Park, Chandra L. Theesfeld

et al.

Nature Genetics, Journal Year: 2019, Volume and Issue: 51(6), P. 973 - 980

Published: May 27, 2019

Language: Английский

Citations

272
Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes DOI Creative Commons
Xueya Zhou, Pamela Feliciano, Chang Shu

et al.

Nature Genetics, Journal Year: 2022, Volume and Issue: 54(9), P. 1305 - 1319

Published: Aug. 18, 2022

Abstract To capture the full spectrum of genetic risk for autism, we performed a two-stage analysis rare de novo and inherited coding variants in 42,607 autism cases, including 35,130 new cases recruited online by SPARK. We identified 60 genes with exome-wide significance ( P < 2.5 × 10 −6 ), five NAV3 , ITSN1 MARK2 SCAF1 HNRNPUL2 ). The association is primarily driven loss-of-function (LoF) variants, an estimated relative 4, consistent moderate effect. Autistic individuals LoF four moderate-risk ; n = 95) have less cognitive impairment than 129 autistic highly penetrant CHD8, SCN2A, ADNP, FOXP1 SHANK3 ) (59% vs 88%, 1.9 Power calculations suggest that much larger numbers are needed to identify additional genes.

Language: Английский

Citations

261
Genome-wide detection of tandem DNA repeats that are expanded in autism DOI
Brett Trost, Worrawat Engchuan,

Charlotte Nguyen

et al.

Nature, Journal Year: 2020, Volume and Issue: 586(7827), P. 80 - 86

Published: July 27, 2020

Language: Английский

Citations

196
Rare-variant collapsing analyses for complex traits: guidelines and applications DOI
Gundula Povysil, Slavé Petrovski, Joseph Hostyk

et al.

Nature Reviews Genetics, Journal Year: 2019, Volume and Issue: 20(12), P. 747 - 759

Published: Oct. 11, 2019

Language: Английский

Citations

183
Cross-species regulatory sequence activity prediction DOI Creative Commons
David R. Kelley

PLoS Computational Biology, Journal Year: 2020, Volume and Issue: 16(7), P. e1008050 - e1008050

Published: July 20, 2020

Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles gene regulation and guided genetic variation analysis. While human genome has been extensively annotated studied, model organisms less explored. Model organism genomes offer both additional training unique annotations describing tissue cell states unavailable in humans. Here, we develop a strategy train deep convolutional neural networks simultaneously on multiple apply it learn sequence predictors for large compendia mouse data. Training improves expression prediction accuracy held out variant sequences. We further demonstrate novel powerful approach models analyze variants associated with molecular phenotypes disease. Together these techniques unleash thousands non-human epigenetic transcriptional profiles toward more effective investigation how affects

Language: Английский

Citations

179