ArticleNature communications2020
mTADA is a framework for identifying risk genes from de novo mutations in multiple traits.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Joint analysis ofNAR genomics and bioinformatics · 2025Article
- Statistical methods for assessing the effects of de novo variants on birth defects.Human genomics · 2024Review
- VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants.Communications biology · 2023Article
- DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders.Patterns (New York, N.Y.) · 2022Article
- Article
- 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
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Authors and funding
10 authors.
Funding
Abstract
Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. Studies of neuropsychiatric disorders and congenital heart disease (CHD) which use de novo mutations (DNMs) from parent-offspring trios have reported multiple putatively causal genes. However, a joint analysis method designed to integrate DNMs from multiple studies has yet to be implemented. We here introduce multiple-trait TADA (mTADA) which jointly analyzes two traits using DNMs from non-overlapping family samples. We first demonstrate that mTADA is able to leverage genetic overlaps to increase the statistical power of risk-gene identification. We then apply mTADA to large datasets of >13,000 trios for five neuropsychiatric disorders and CHD. We report additional risk genes for schizophrenia, epileptic encephalopathies and CHD. We outline some shared and specific biological information of intellectual disability and CHD by conducting systems biology analyses of genes prioritized by mTADA.
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