MTADA is a Framework for Identifying Risk Genes from De Novo Mutations in Multiple Traits

Overview

Journal Nat Commun

Specialty Biology

Date 2020 Jun 12

PMID 32522981

Citations 7

Authors

Tan-Hoang Nguyen

Amanda Dobbyn

Ruth C Brown

Brien P Riley

Joseph D Buxbaum

Dalila Pinto

Shaun M Purcell

Patrick F Sullivan

Xin He

Eli A Stahl

Affiliations

Soon will be listed here.

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.

Citing Articles

Statistical methods for assessing the effects of de novo variants on birth defects.

Xie Y, Wu R, Li H, Dong W, Zhou G, Zhao H Hum Genomics. 2024; 18(1):25.

PMID: 38486307 PMC: 10938830. DOI: 10.1186/s40246-024-00590-z.

VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants.

Zhong G, Choi Y, Shen Y Commun Biol. 2023; 6(1):774.

PMID: 37491581 PMC: 10368729. DOI: 10.1038/s42003-023-05155-9.

DeepND: Deep multitask learning of gene risk for comorbid neurodevelopmental disorders.

Beyreli I, Karakahya O, Cicek A Patterns (N Y). 2022; 3(7):100524.

PMID: 35845835 PMC: 9278518. DOI: 10.1016/j.patter.2022.100524.

Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease.

Xie Y, Jiang W, Dong W, Li H, Jin S, Brueckner M PLoS Genet. 2022; 18(6):e1010252.

PMID: 35671298 PMC: 9205499. DOI: 10.1371/journal.pgen.1010252.

Quantifying concordant genetic effects of de novo mutations on multiple disorders.

Guo H, Hou L, Shi Y, Jin S, Zeng X, Li B Elife. 2022; 11.

PMID: 35666111 PMC: 9217133. DOI: 10.7554/eLife.75551.

References

Wang S, Mandell J, Kumar Y, Sun N, Morris M, Arbelaez J . De Novo Sequence and Copy Number Variants Are Strongly Associated with Tourette Disorder and Implicate Cell Polarity in Pathogenesis. Cell Rep. 2018; 24(13):3441-3454.e12. PMC: 6475626. DOI: 10.1016/j.celrep.2018.08.082. View

Galesloot T, Van Steen K, Kiemeney L, Janss L, Vermeulen S . A comparison of multivariate genome-wide association methods. PLoS One. 2014; 9(4):e95923. PMC: 3999149. DOI: 10.1371/journal.pone.0095923. View

Gentleman R, Carey V, Bates D, Bolstad B, Dettling M, Dudoit S . Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004; 5(10):R80. PMC: 545600. DOI: 10.1186/gb-2004-5-10-r80. View

De Rubeis S, He X, Goldberg A, Poultney C, Samocha K, Cicek A . Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014; 515(7526):209-15. PMC: 4402723. DOI: 10.1038/nature13772. View

Genovese G, Fromer M, Stahl E, Ruderfer D, Chambert K, Landen M . Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia. Nat Neurosci. 2016; 19(11):1433-1441. PMC: 5104192. DOI: 10.1038/nn.4402. View

Willsey A, Fernandez T, Yu D, King R, Dietrich A, Xing J . De Novo Coding Variants Are Strongly Associated with Tourette Disorder. Neuron. 2017; 94(3):486-499.e9. PMC: 5769876. DOI: 10.1016/j.neuron.2017.04.024. View

Fromer M, Moran J, Chambert K, Banks E, Bergen S, Ruderfer D . Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am J Hum Genet. 2012; 91(4):597-607. PMC: 3484655. DOI: 10.1016/j.ajhg.2012.08.005. View

Allen A, Cossette P, Delanty N, Eichler E, Goldstein D, Han Y . De novo mutations in epileptic encephalopathies. Nature. 2013; 501(7466):217-21. PMC: 3773011. DOI: 10.1038/nature12439. View

Maier R, Zhu Z, Lee S, Trzaskowski M, Ruderfer D, Stahl E . Improving genetic prediction by leveraging genetic correlations among human diseases and traits. Nat Commun. 2018; 9(1):989. PMC: 5841449. DOI: 10.1038/s41467-017-02769-6. View

10.

White J, Beck C, Harel T, Posey J, Jhangiani S, Tang S . POGZ truncating alleles cause syndromic intellectual disability. Genome Med. 2016; 8(1):3. PMC: 4702300. DOI: 10.1186/s13073-015-0253-0. View

11.

Fromer M, Pocklington A, Kavanagh D, Williams H, Dwyer S, Gormley P . De novo mutations in schizophrenia implicate synaptic networks. Nature. 2014; 506(7487):179-84. PMC: 4237002. DOI: 10.1038/nature12929. View

12.

Lee S, Ripke S, Neale B, Faraone S, Purcell S, Perlis R . Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013; 45(9):984-94. PMC: 3800159. DOI: 10.1038/ng.2711. View

13.

Skene N, Bryois J, Bakken T, Breen G, Crowley J, Gaspar H . Genetic identification of brain cell types underlying schizophrenia. Nat Genet. 2018; 50(6):825-833. PMC: 6477180. DOI: 10.1038/s41588-018-0129-5. View

14.

Volkmar F, Cohen D . Comorbid association of autism and schizophrenia. Am J Psychiatry. 1991; 148(12):1705-7. DOI: 10.1176/ajp.148.12.1705. View

15.

Guo B, Wu B . Integrate multiple traits to detect novel trait-gene association using GWAS summary data with an adaptive test approach. Bioinformatics. 2018; 35(13):2251-2257. PMC: 6596889. DOI: 10.1093/bioinformatics/bty961. View

16.

Lutz S, Fingerlin T, Hokanson J, Lange C . A general approach to testing for pleiotropy with rare and common variants. Genet Epidemiol. 2016; 41(2):163-170. PMC: 5472207. DOI: 10.1002/gepi.22011. View

17.

Ware J, Samocha K, Homsy J, Daly M . Interpreting de novo Variation in Human Disease Using denovolyzeR. Curr Protoc Hum Genet. 2015; 87:7.25.1-7.25.15. PMC: 4606471. DOI: 10.1002/0471142905.hg0725s87. View

18.

Takata A, Ionita-Laza I, Gogos J, Xu B, Karayiorgou M . De Novo Synonymous Mutations in Regulatory Elements Contribute to the Genetic Etiology of Autism and Schizophrenia. Neuron. 2016; 89(5):940-7. PMC: 4793939. DOI: 10.1016/j.neuron.2016.02.024. View

19.

Hamdan F, Myers C, Cossette P, Lemay P, Spiegelman D, Laporte A . High Rate of Recurrent De Novo Mutations in Developmental and Epileptic Encephalopathies. Am J Hum Genet. 2017; 101(5):664-685. PMC: 5673604. DOI: 10.1016/j.ajhg.2017.09.008. View

20.

Scrucca L, Fop M, Murphy T, Raftery A . mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models. R J. 2016; 8(1):289-317. PMC: 5096736. View