Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Fang Chen; Xingyan Wang; Seon-Kyeong Jang; Bryan C. Quach; J. Dylan Weissenkampen; Chachrit Khunsriraksakul; Lina Yang; Renan Sauteraud; Christine M. Albert; Nicholette D.D. Allred; Donna K. Arnett; Allison E. Ashley-Koch; Kathleen C. Barnes; R. Graham Barr; Diane M. Becker; Lawrence F. Bielak; Joshua C. Bis; John Blangero; Meher Preethi Boorgula; Daniel I. Chasman; Sameer Chavan; Yii Der I. Chen; Lee Ming Chuang; Adolfo Correa; Joanne E. Curran; Sean P. David; Lisa de las Fuentes; Ranjan Deka; Ravindranath Duggirala; Jessica D. Faul; Melanie E. Garrett; Sina A. Gharib; Xiuqing Guo; Michael E. Hall; Nicola L. Hawley; Jiang He; Brian D. Hobbs; John E. Hokanson; Chao A. Hsiung; Shih Jen Hwang; Thomas M. Hyde; Marguerite R. Irvin; Andrew E. Jaffe; Eric O. Johnson; Robert Kaplan; Sharon L.R. Kardia; Joel D. Kaufman; Tanika N. Kelly; Joel E. Kleinman; Charles Kooperberg; I. Te Lee; Daniel Levy; Sharon M. Lutz; Ani W. Manichaikul; Lisa W. Martin; Olivia Marx; Stephen T. McGarvey; Ryan L. Minster; Matthew Moll; Karine A. Moussa; Take Naseri; Kari E. North; Elizabeth C. Oelsner; Juan M. Peralta; Patricia A. Peyser; Bruce M. Psaty; Nicholas Rafaels; Laura M. Raffield; Muagututi’a Sefuiva Reupena; Stephen S. Rich; Jerome I. Rotter; David A. Schwartz; Aladdin H. Shadyab; Wayne H.H. Sheu; Mario Sims; Jennifer A. Smith; Xiao Sun; Kent D. Taylor; Marilyn J. Telen; Harold Watson; Daniel E. Weeks; David R. Weir; Lisa R. Yanek; Kendra A. Young; Kristin L. Young; Wei Zhao; Dana B. Hancock; Bibo Jiang; Scott Vrieze; Dajiang J. Liu

doi:10.1038/s41588-022-01282-x

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Fang Chen, Xingyan Wang, Seon-Kyeong Jang, Bryan C. Quach, J. Dylan Weissenkampen, Chachrit Khunsriraksakul, Lina Yang, Renan Sauteraud, Christine M. Albert, Nicholette D.D. Allred, Donna K. Arnett, Allison E. Ashley-Koch, Kathleen C. Barnes, R. Graham Barr, Diane M. Becker, Lawrence F. Bielak, Joshua C. Bis, John Blangero, Meher Preethi Boorgula, Daniel I. ChasmanSameer Chavan, Yii Der I. Chen, Lee Ming Chuang, Adolfo Correa, Joanne E. Curran, Sean P. David, Lisa de las Fuentes, Ranjan Deka, Ravindranath Duggirala, Jessica D. Faul, Melanie E. Garrett, Sina A. Gharib, Xiuqing Guo, Michael E. Hall, Nicola L. Hawley, Jiang He, Brian D. Hobbs, John E. Hokanson, Chao A. Hsiung, Shih Jen Hwang, Thomas M. Hyde, Marguerite R. Irvin, Andrew E. Jaffe, Eric O. Johnson, Robert Kaplan, Sharon L.R. Kardia, Joel D. Kaufman, Tanika N. Kelly, Joel E. Kleinman, Charles Kooperberg, I. Te Lee, Daniel Levy, Sharon M. Lutz, Ani W. Manichaikul, Lisa W. Martin, Olivia Marx, Stephen T. McGarvey, Ryan L. Minster, Matthew Moll, Karine A. Moussa, Take Naseri, Kari E. North, Elizabeth C. Oelsner, Juan M. Peralta, Patricia A. Peyser, Bruce M. Psaty, Nicholas Rafaels, Laura M. Raffield, Muagututi’a Sefuiva Reupena, Stephen S. Rich, Jerome I. Rotter, David A. Schwartz, Aladdin H. Shadyab, Wayne H.H. Sheu, Mario Sims, Jennifer A. Smith, Xiao Sun, Kent D. Taylor, Marilyn J. Telen, Harold Watson, Daniel E. Weeks, David R. Weir, Lisa R. Yanek, Kendra A. Young, Kristin L. Young, Wei Zhao, Dana B. Hancock, Bibo Jiang, Scott Vrieze, Dajiang J. Liu

Psychology (Twin Cities)

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19 Scopus citations

Abstract

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Original language	English (US)
Pages (from-to)	291-300
Number of pages	10
Journal	Nature Genetics
Volume	55
Issue number	2
DOIs	https://doi.org/10.1038/s41588-022-01282-x
State	Published - Feb 2023

Bibliographical note

Funding Information:
Methodology development and meta-analyses were supported by the National Institutes of Health (NIH) grants (nos. R01HG008983 to D.J.L., R56HG011035 to D.J.L., B.J. and S.V., R01HG011035 to F.C., D.J.L., S.V. and X.W., R56HG012358 to D.J.L., R01GM126479 to D.J.L., R21AI160138 to D.J.L. and R03OD032630 to D.J.L. and B.J.). D.J.L. and X.W. and were in part supported by the Penn State College of Medicine’s Biomedical Informatics and Artificial Intelligence Program in the Strategic Plan. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the NIH or the US Department of Health and Human Services. Funding acknowledgement for participating cohorts is in Supplementary Text.

Funding Information:
Methodology development and meta-analyses were supported by the National Institutes of Health (NIH) grants (nos. R01HG008983 to D.J.L., R56HG011035 to D.J.L., B.J. and S.V., R01HG011035 to F.C., D.J.L., S.V. and X.W., R56HG012358 to D.J.L., R01GM126479 to D.J.L., R21AI160138 to D.J.L. and R03OD032630 to D.J.L. and B.J.). D.J.L. and X.W. and were in part supported by the Penn State College of Medicine’s Biomedical Informatics and Artificial Intelligence Program in the Strategic Plan. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the NIH or the US Department of Health and Human Services. Funding acknowledgement for participating cohorts is in .

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© 2023, The Author(s).

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Chen, F., Wang, X., Jang, S.-K., Quach, B. C., Weissenkampen, J. D., Khunsriraksakul, C., Yang, L., Sauteraud, R., Albert, C. M., Allred, N. D. D., Arnett, D. K., Ashley-Koch, A. E., Barnes, K. C., Barr, R. G., Becker, D. M., Bielak, L. F., Bis, J. C., Blangero, J., Boorgula, M. P., ... Liu, D. J. (2023). Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing. Nature Genetics, 55(2), 291-300. https://doi.org/10.1038/s41588-022-01282-x

Chen, F, Wang, X, Jang, S-K, Quach, BC, Weissenkampen, JD, Khunsriraksakul, C, Yang, L, Sauteraud, R, Albert, CM, Allred, NDD, Arnett, DK, Ashley-Koch, AE, Barnes, KC, Barr, RG, Becker, DM, Bielak, LF, Bis, JC, Blangero, J, Boorgula, MP, Chasman, DI, Chavan, S, Chen, YDI, Chuang, LM, Correa, A, Curran, JE, David, SP, Fuentes, LDL, Deka, R, Duggirala, R, Faul, JD, Garrett, ME, Gharib, SA, Guo, X, Hall, ME, Hawley, NL, He, J, Hobbs, BD, Hokanson, JE, Hsiung, CA, Hwang, SJ, Hyde, TM, Irvin, MR, Jaffe, AE, Johnson, EO, Kaplan, R, Kardia, SLR, Kaufman, JD, Kelly, TN, Kleinman, JE, Kooperberg, C, Lee, IT, Levy, D, Lutz, SM, Manichaikul, AW, Martin, LW, Marx, O, McGarvey, ST, Minster, RL, Moll, M, Moussa, KA, Naseri, T, North, KE, Oelsner, EC, Peralta, JM, Peyser, PA, Psaty, BM, Rafaels, N, Raffield, LM, Reupena, MS, Rich, SS, Rotter, JI, Schwartz, DA, Shadyab, AH, Sheu, WHH, Sims, M, Smith, JA, Sun, X, Taylor, KD, Telen, MJ, Watson, H, Weeks, DE, Weir, DR, Yanek, LR, Young, KA, Young, KL, Zhao, W, Hancock, DB, Jiang, B, Vrieze, S & Liu, DJ 2023, 'Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing', Nature Genetics, vol. 55, no. 2, pp. 291-300. https://doi.org/10.1038/s41588-022-01282-x

@article{daffb288c19f45638f823236d2fdf69e,

title = "Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing",

abstract = "Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.",

author = "Fang Chen and Xingyan Wang and Seon-Kyeong Jang and Quach, {Bryan C.} and Weissenkampen, {J. Dylan} and Chachrit Khunsriraksakul and Lina Yang and Renan Sauteraud and Albert, {Christine M.} and Allred, {Nicholette D.D.} and Arnett, {Donna K.} and Ashley-Koch, {Allison E.} and Barnes, {Kathleen C.} and Barr, {R. Graham} and Becker, {Diane M.} and Bielak, {Lawrence F.} and Bis, {Joshua C.} and John Blangero and Boorgula, {Meher Preethi} and Chasman, {Daniel I.} and Sameer Chavan and Chen, {Yii Der I.} and Chuang, {Lee Ming} and Adolfo Correa and Curran, {Joanne E.} and David, {Sean P.} and Fuentes, {Lisa de las} and Ranjan Deka and Ravindranath Duggirala and Faul, {Jessica D.} and Garrett, {Melanie E.} and Gharib, {Sina A.} and Xiuqing Guo and Hall, {Michael E.} and Hawley, {Nicola L.} and Jiang He and Hobbs, {Brian D.} and Hokanson, {John E.} and Hsiung, {Chao A.} and Hwang, {Shih Jen} and Hyde, {Thomas M.} and Irvin, {Marguerite R.} and Jaffe, {Andrew E.} and Johnson, {Eric O.} and Robert Kaplan and Kardia, {Sharon L.R.} and Kaufman, {Joel D.} and Kelly, {Tanika N.} and Kleinman, {Joel E.} and Charles Kooperberg and Lee, {I. Te} and Daniel Levy and Lutz, {Sharon M.} and Manichaikul, {Ani W.} and Martin, {Lisa W.} and Olivia Marx and McGarvey, {Stephen T.} and Minster, {Ryan L.} and Matthew Moll and Moussa, {Karine A.} and Take Naseri and North, {Kari E.} and Oelsner, {Elizabeth C.} and Peralta, {Juan M.} and Peyser, {Patricia A.} and Psaty, {Bruce M.} and Nicholas Rafaels and Raffield, {Laura M.} and Reupena, {Muagututi{\textquoteright}a Sefuiva} and Rich, {Stephen S.} and Rotter, {Jerome I.} and Schwartz, {David A.} and Shadyab, {Aladdin H.} and Sheu, {Wayne H.H.} and Mario Sims and Smith, {Jennifer A.} and Xiao Sun and Taylor, {Kent D.} and Telen, {Marilyn J.} and Harold Watson and Weeks, {Daniel E.} and Weir, {David R.} and Yanek, {Lisa R.} and Young, {Kendra A.} and Young, {Kristin L.} and Wei Zhao and Hancock, {Dana B.} and Bibo Jiang and Scott Vrieze and Liu, {Dajiang J.}",

note = "Funding Information: Methodology development and meta-analyses were supported by the National Institutes of Health (NIH) grants (nos. R01HG008983 to D.J.L., R56HG011035 to D.J.L., B.J. and S.V., R01HG011035 to F.C., D.J.L., S.V. and X.W., R56HG012358 to D.J.L., R01GM126479 to D.J.L., R21AI160138 to D.J.L. and R03OD032630 to D.J.L. and B.J.). D.J.L. and X.W. and were in part supported by the Penn State College of Medicine{\textquoteright}s Biomedical Informatics and Artificial Intelligence Program in the Strategic Plan. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the NIH or the US Department of Health and Human Services. Funding acknowledgement for participating cohorts is in Supplementary Text. Funding Information: Methodology development and meta-analyses were supported by the National Institutes of Health (NIH) grants (nos. R01HG008983 to D.J.L., R56HG011035 to D.J.L., B.J. and S.V., R01HG011035 to F.C., D.J.L., S.V. and X.W., R56HG012358 to D.J.L., R01GM126479 to D.J.L., R21AI160138 to D.J.L. and R03OD032630 to D.J.L. and B.J.). D.J.L. and X.W. and were in part supported by the Penn State College of Medicine{\textquoteright}s Biomedical Informatics and Artificial Intelligence Program in the Strategic Plan. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the NIH or the US Department of Health and Human Services. Funding acknowledgement for participating cohorts is in . Publisher Copyright: {\textcopyright} 2023, The Author(s).",

year = "2023",

month = feb,

doi = "10.1038/s41588-022-01282-x",

language = "English (US)",

volume = "55",

pages = "291--300",

journal = "Nature Genetics",

issn = "1061-4036",

publisher = "Nature Publishing Group",

number = "2",

}

TY - JOUR

T1 - Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

AU - Chen, Fang

AU - Wang, Xingyan

AU - Jang, Seon-Kyeong

AU - Quach, Bryan C.

AU - Weissenkampen, J. Dylan

AU - Khunsriraksakul, Chachrit

AU - Yang, Lina

AU - Sauteraud, Renan

AU - Albert, Christine M.

AU - Allred, Nicholette D.D.

AU - Arnett, Donna K.

AU - Ashley-Koch, Allison E.

AU - Barnes, Kathleen C.

AU - Barr, R. Graham

AU - Becker, Diane M.

AU - Bielak, Lawrence F.

AU - Bis, Joshua C.

AU - Blangero, John

AU - Boorgula, Meher Preethi

AU - Chasman, Daniel I.

AU - Chavan, Sameer

AU - Chen, Yii Der I.

AU - Chuang, Lee Ming

AU - Correa, Adolfo

AU - Curran, Joanne E.

AU - David, Sean P.

AU - Fuentes, Lisa de las

AU - Deka, Ranjan

AU - Duggirala, Ravindranath

AU - Faul, Jessica D.

AU - Garrett, Melanie E.

AU - Gharib, Sina A.

AU - Guo, Xiuqing

AU - Hall, Michael E.

AU - Hawley, Nicola L.

AU - He, Jiang

AU - Hobbs, Brian D.

AU - Hokanson, John E.

AU - Hsiung, Chao A.

AU - Hwang, Shih Jen

AU - Hyde, Thomas M.

AU - Irvin, Marguerite R.

AU - Jaffe, Andrew E.

AU - Johnson, Eric O.

AU - Kaplan, Robert

AU - Kardia, Sharon L.R.

AU - Kaufman, Joel D.

AU - Kelly, Tanika N.

AU - Kleinman, Joel E.

AU - Kooperberg, Charles

AU - Lee, I. Te

AU - Levy, Daniel

AU - Lutz, Sharon M.

AU - Manichaikul, Ani W.

AU - Martin, Lisa W.

AU - Marx, Olivia

AU - McGarvey, Stephen T.

AU - Minster, Ryan L.

AU - Moll, Matthew

AU - Moussa, Karine A.

AU - Naseri, Take

AU - North, Kari E.

AU - Oelsner, Elizabeth C.

AU - Peralta, Juan M.

AU - Peyser, Patricia A.

AU - Psaty, Bruce M.

AU - Rafaels, Nicholas

AU - Raffield, Laura M.

AU - Reupena, Muagututi’a Sefuiva

AU - Rich, Stephen S.

AU - Rotter, Jerome I.

AU - Schwartz, David A.

AU - Shadyab, Aladdin H.

AU - Sheu, Wayne H.H.

AU - Sims, Mario

AU - Smith, Jennifer A.

AU - Sun, Xiao

AU - Taylor, Kent D.

AU - Telen, Marilyn J.

AU - Watson, Harold

AU - Weeks, Daniel E.

AU - Weir, David R.

AU - Yanek, Lisa R.

AU - Young, Kendra A.

AU - Young, Kristin L.

AU - Zhao, Wei

AU - Hancock, Dana B.

AU - Jiang, Bibo

AU - Vrieze, Scott

AU - Liu, Dajiang J.

N1 - Funding Information: Methodology development and meta-analyses were supported by the National Institutes of Health (NIH) grants (nos. R01HG008983 to D.J.L., R56HG011035 to D.J.L., B.J. and S.V., R01HG011035 to F.C., D.J.L., S.V. and X.W., R56HG012358 to D.J.L., R01GM126479 to D.J.L., R21AI160138 to D.J.L. and R03OD032630 to D.J.L. and B.J.). D.J.L. and X.W. and were in part supported by the Penn State College of Medicine’s Biomedical Informatics and Artificial Intelligence Program in the Strategic Plan. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the NIH or the US Department of Health and Human Services. Funding acknowledgement for participating cohorts is in Supplementary Text. Funding Information: Methodology development and meta-analyses were supported by the National Institutes of Health (NIH) grants (nos. R01HG008983 to D.J.L., R56HG011035 to D.J.L., B.J. and S.V., R01HG011035 to F.C., D.J.L., S.V. and X.W., R56HG012358 to D.J.L., R01GM126479 to D.J.L., R21AI160138 to D.J.L. and R03OD032630 to D.J.L. and B.J.). D.J.L. and X.W. and were in part supported by the Penn State College of Medicine’s Biomedical Informatics and Artificial Intelligence Program in the Strategic Plan. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute, the NIH or the US Department of Health and Human Services. Funding acknowledgement for participating cohorts is in . Publisher Copyright: © 2023, The Author(s).

PY - 2023/2

Y1 - 2023/2

N2 - Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

AB - Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

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VL - 55

SP - 291

EP - 300

JO - Nature Genetics

JF - Nature Genetics

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ER -

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing

Abstract

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