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.
Highlights
Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation
We compared gene numbers identified by multiple-trait TADA (mTADA) and our previous single-trait method, extTADA, using the same threshold posterior probabilities (PP) > 0.8
We propose a method to jointly analyze two traits using de novo exome sequencing data
Summary
Joint analysis of multiple traits can result in the identification of associations not found through the analysis of each trait in isolation. We utilize simulation data and demonstrate that, compared with a single-trait method, mTADA substantially improves the power of risk-gene identification when genetic overlaps increase, especially for traits with smaller sample sizes or smaller relative risks. To illustrate the advantage of the new pipeline over its previous single-trait version, we apply the method to large data sets of different NPDs and CHD (>13,000 parent-offspring trios) and identify shared genes between each pair of these disorders. We demonstrate that mTADA’s results could be used to better understand the shared and specific biological information for two tested disorders by using multiple systems biology approaches to test the top prioritized risk genes of the CHD-ID pair.
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