ArticleAmerican journal of human genetics2018
A Statistical Framework for Mapping Risk Genes from De Novo Mutations in Whole-Genome-Sequencing Studies.
Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Loss of Zmiz1 in Mice Leads to Impaired Cortical Development and Autistic-Like Behaviors.Biological psychiatry · 2026Article
- MIRAGE: A Bayesian statistical method for gene-level rare-variant analysis incorporating functional annotations.American journal of human genetics · 2026Article
- Identification of Transdiagnostic Childhood Externalizing Pathology Within an Electronic Medical Records Database and Application to the Analysis of Rare Copy Number Variation.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Article
- Association of genetic variants, protein domains, and phenotypes in the ZMIZ1 syndromic neurodevelopmental disorder.Frontiers in neuroscience · 2025Article
- CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.Briefings in bioinformatics · 2024Article
- CWAS-Plus: Estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data.medRxiv : the preprint server for health sciences · 2024Article
- Statistical methods for assessing the effects of de novo variants on birth defects.Human genomics · 2024Review
- Zmiz1 is a novel regulator of brain development associated with autism and intellectual disability.Frontiers in psychiatry · 2024Article
- Zmiz1 is a novel regulator of lymphatic endothelial cell gene expression and function.PloS one · 2024Article
- Single-cell long-read sequencing in human cerebral organoids uncovers cell-type-specific and autism-associated exons.Cell reports · 2023Article
- Zmiz1 is a novel regulator of lymphatic endothelial cell gene expression and function.bioRxiv : the preprint server for biology · 2023Article
- Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder.Nature communications · 2022Article
- Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease.PLoS genetics · 2022Article
- Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy.Journal of personalized medicine · 2022Review
- M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.PLoS genetics · 2021Article
- Identification of putative causal loci in whole-genome sequencing data via knockoff statistics.Nature communications · 2021Article
- Tanc2-mediated mTOR inhibition balances mTORC1/2 signaling in the developing mouse brain and human neurons.Nature communications · 2021Article
- An increased burden of rare exonic variants in NRXN1 microdeletion carriers is likely to enhance the penetrance for autism spectrum disorder.Journal of cellular and molecular medicine · 2021Article
- Rare variant association testing in the non-coding genome.Human genetics · 2020Review
- Modeling neuronal consequences of autism-associated gene regulatory variants with human induced pluripotent stem cells.Molecular autism · 2020Review
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
Analysis of de novo mutations (DNMs) from sequencing data of nuclear families has identified risk genes for many complex diseases, including multiple neurodevelopmental and psychiatric disorders. Most of these efforts have focused on mutations in protein-coding sequences. Evidence from genome-wide association studies (GWASs) strongly suggests that variants important to human diseases often lie in non-coding regions. Extending DNM-based approaches to non-coding sequences is challenging, however, because the functional significance of non-coding mutations is difficult to predict. We propose a statistical framework for analyzing DNMs from whole-genome sequencing (WGS) data. This method, TADA-Annotations (TADA-A), is a major advance of the TADA method we developed earlier for DNM analysis in coding regions. TADA-A is able to incorporate many functional annotations such as conservation and enhancer marks, to learn from data which annotations are informative of pathogenic mutations, and to combine both coding and non-coding mutations at the gene level to detect risk genes. It also supports meta-analysis of multiple DNM studies, while adjusting for study-specific technical effects. We applied TADA-A to WGS data of ∼300 autism-affected family trios across five studies and discovered several autism risk genes. The software is freely available for all research uses.
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