mTADA is a framework for identifying risk genes from de novo mutations in multiple traits

Tan-Hoang Nguyen (), Amanda Dobbyn, Ruth C. Brown, Brien P. Riley, Joseph D. Buxbaum, Dalila Pinto, Shaun M. Purcell, Patrick F. Sullivan, Xin He () and Eli A. Stahl ()
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Tan-Hoang Nguyen: Icahn School of Medicine at Mount Sinai
Amanda Dobbyn: Icahn School of Medicine at Mount Sinai
Ruth C. Brown: Virginia Commonwealth University
Brien P. Riley: Virginia Commonwealth University
Joseph D. Buxbaum: Icahn School of Medicine at Mount Sinai
Dalila Pinto: Icahn School of Medicine at Mount Sinai
Shaun M. Purcell: Harvard Medical School
Patrick F. Sullivan: University of North Carolina
Xin He: University of Chicago
Eli A. Stahl: Icahn School of Medicine at Mount Sinai

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-16487-z

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