Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits

Andrew D. Grotzinger; Javier de la Fuente; Gail Davies; Michel G. Nivard; Elliot M. Tucker-Drob

doi:10.1038/s41467-022-33724-9

Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits

Andrew D. Grotzinger^*, Javier de la Fuente, Gail Davies, Michel G. Nivard, Elliot M. Tucker-Drob

^*Corresponding author for this work

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

Functional genomic methods are needed that consider multiple genetically correlated traits. Here we develop and validate Transcriptome-wide Structural Equation Modeling (T-SEM), a multivariate method for studying the effects of tissue-specific gene expression across genetically overlapping traits. T-SEM allows for modeling effects on broad dimensions spanning constellations of traits, while safeguarding against false positives that can arise when effects of gene expression are specific to a subset of traits. We apply T-SEM to investigate the biological mechanisms shared across seven distinct cognitive traits (N = 11,263–331,679), as indexed by a general dimension of genetic sharing (g). We identify 184 genes whose tissue-specific expression is associated with g, including 10 genes not identified in univariate analysis for the individual cognitive traits for any tissue type, and three genes whose expression explained a significant portion of the genetic sharing across g and different subclusters of psychiatric disorders. We go on to apply Stratified Genomic SEM to identify enrichment for g within 28 functional categories. This includes categories indexing the intersection of protein-truncating variant intolerant (PI) genes and specific neuronal cell types, which we also find to be enriched for the genetic covariance between g and a psychotic disorders factor.

Original language	English
Article number	6280
Pages (from-to)	1-16
Number of pages	16
Journal	Nature Communications
Volume	13
DOIs	https://doi.org/10.1038/s41467-022-33724-9
Publication status	Published - 21 Oct 2022

Bibliographical note

Funding Information:
This work presented here would not have been possible without the enormous efforts put forth by the investigators and participants from Psychiatric Genetics Consortium and UK Biobank. The work from these contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donations. Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH120219 and the National Institute of Aging under Award Number RF1AG073593. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. A.D.G. was additionally supported by NIH Grant R01HD083613. E.M.T.-D. was additionally supported by NIH grants R01AG054628 and R01HD083613 and the Jacobs Foundation. J.F. and E.M.T-D. are members of the Population Research Center (PRC) and Center on Aging and Population Sciences (CAPS) at The University of Texas at Austin, which are supported by National Institutes of Health (NIH) grants P2CHD042849 and P30AG066614, respectively. M.G.N. is additionally supported by ZonMW grants 849200011 and 531003014 from The Netherlands Organization for Health Research and Development, a VENI grant awarded by NWO (VI.Veni.191 G.030) and is a Jacobs Foundation Fellow.

Publisher Copyright:
© 2022, The Author(s).

Funding

This work presented here would not have been possible without the enormous efforts put forth by the investigators and participants from Psychiatric Genetics Consortium and UK Biobank. The work from these contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donations. Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH120219 and the National Institute of Aging under Award Number RF1AG073593. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. A.D.G. was additionally supported by NIH Grant R01HD083613. E.M.T.-D. was additionally supported by NIH grants R01AG054628 and R01HD083613 and the Jacobs Foundation. J.F. and E.M.T-D. are members of the Population Research Center (PRC) and Center on Aging and Population Sciences (CAPS) at The University of Texas at Austin, which are supported by National Institutes of Health (NIH) grants P2CHD042849 and P30AG066614, respectively. M.G.N. is additionally supported by ZonMW grants 849200011 and 531003014 from The Netherlands Organization for Health Research and Development, a VENI grant awarded by NWO (VI.Veni.191 G.030) and is a Jacobs Foundation Fellow.

Funders	Funder number
Center on Aging and Population Sciences	P2CHD042849
Population Research Center
National Institutes of Health
National Institute of Mental Health	R01MH120219
National Institute on Aging	P30AG066614, R01AG054628, RF1AG073593
National Institute of Child Health and Human Development	R01HD083613
ZonMw	531003014, 849200011
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	VI.Veni.191 G.030
Jacobs Foundation

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title = "Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits",

abstract = "Functional genomic methods are needed that consider multiple genetically correlated traits. Here we develop and validate Transcriptome-wide Structural Equation Modeling (T-SEM), a multivariate method for studying the effects of tissue-specific gene expression across genetically overlapping traits. T-SEM allows for modeling effects on broad dimensions spanning constellations of traits, while safeguarding against false positives that can arise when effects of gene expression are specific to a subset of traits. We apply T-SEM to investigate the biological mechanisms shared across seven distinct cognitive traits (N = 11,263–331,679), as indexed by a general dimension of genetic sharing (g). We identify 184 genes whose tissue-specific expression is associated with g, including 10 genes not identified in univariate analysis for the individual cognitive traits for any tissue type, and three genes whose expression explained a significant portion of the genetic sharing across g and different subclusters of psychiatric disorders. We go on to apply Stratified Genomic SEM to identify enrichment for g within 28 functional categories. This includes categories indexing the intersection of protein-truncating variant intolerant (PI) genes and specific neuronal cell types, which we also find to be enriched for the genetic covariance between g and a psychotic disorders factor.",

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note = "Funding Information: This work presented here would not have been possible without the enormous efforts put forth by the investigators and participants from Psychiatric Genetics Consortium and UK Biobank. The work from these contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donations. Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number R01MH120219 and the National Institute of Aging under Award Number RF1AG073593. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. A.D.G. was additionally supported by NIH Grant R01HD083613. E.M.T.-D. was additionally supported by NIH grants R01AG054628 and R01HD083613 and the Jacobs Foundation. J.F. and E.M.T-D. are members of the Population Research Center (PRC) and Center on Aging and Population Sciences (CAPS) at The University of Texas at Austin, which are supported by National Institutes of Health (NIH) grants P2CHD042849 and P30AG066614, respectively. M.G.N. is additionally supported by ZonMW grants 849200011 and 531003014 from The Netherlands Organization for Health Research and Development, a VENI grant awarded by NWO (VI.Veni.191 G.030) and is a Jacobs Foundation Fellow. Publisher Copyright: {\textcopyright} 2022, The Author(s).",

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AU - Tucker-Drob, Elliot M.

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N2 - Functional genomic methods are needed that consider multiple genetically correlated traits. Here we develop and validate Transcriptome-wide Structural Equation Modeling (T-SEM), a multivariate method for studying the effects of tissue-specific gene expression across genetically overlapping traits. T-SEM allows for modeling effects on broad dimensions spanning constellations of traits, while safeguarding against false positives that can arise when effects of gene expression are specific to a subset of traits. We apply T-SEM to investigate the biological mechanisms shared across seven distinct cognitive traits (N = 11,263–331,679), as indexed by a general dimension of genetic sharing (g). We identify 184 genes whose tissue-specific expression is associated with g, including 10 genes not identified in univariate analysis for the individual cognitive traits for any tissue type, and three genes whose expression explained a significant portion of the genetic sharing across g and different subclusters of psychiatric disorders. We go on to apply Stratified Genomic SEM to identify enrichment for g within 28 functional categories. This includes categories indexing the intersection of protein-truncating variant intolerant (PI) genes and specific neuronal cell types, which we also find to be enriched for the genetic covariance between g and a psychotic disorders factor.

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Transcriptome-wide and stratified genomic structural equation modeling identify neurobiological pathways shared across diverse cognitive traits

Abstract

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