Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis

Vivian K. Kawai; Mingjian Shi; Ge Liu; Qi Ping Feng; Wei Qi Wei; Cecilia P. Chung; Theresa L. Walunas; Adam S. Gordon; James G. Linneman; Scott J. Hebbring; John B. Harley; Nancy J. Cox; Dan M. Roden; C. Michael Stein; Jonathan D. Mosley

doi:10.1177/09612033211014952

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis

Vivian K. Kawai^*, Mingjian Shi, Ge Liu, Qi Ping Feng, Wei Qi Wei, Cecilia P. Chung, Theresa L. Walunas, Adam S. Gordon, James G. Linneman, Scott J. Hebbring, John B. Harley, Nancy J. Cox, Dan M. Roden, C. Michael Stein, Jonathan D. Mosley

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Objectives: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. Methods: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. Results: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10⁻⁵). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. Conclusion: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

Original language	English (US)
Pages (from-to)	1264-1272
Number of pages	9
Journal	Lupus
Volume	30
Issue number	8
DOIs	https://doi.org/10.1177/09612033211014952
State	Published - Jul 2021

Funding

The study was supported by American Heart Association (AHA) grant 18SFRN34230089. The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU which is supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH funded Shared Instrumentation Grant S10RR025141; and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vanderbilt.edu/pub/biovu/ . VKK was supported by K23GM117395, R01AR076516, and the Arthritis National Research Foundation – All Arthritis Grant Program Award. JDM is funded by the AHA 16FTF30130005 and R01GM130791. QF is funded by R01GM120523, and CPC by R01AR073764. The eMERGE Network was initiated and funded by NHGRI through the following grants for phase II: U01HG006828 (Cincinnati Children’s Hospital Medical Center/Boston Children’s Hospital); U01HG006830 (Children’s Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. For phase I: U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and Vanderbilt University Medical Center); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-04603 (Vanderbilt University Medical Center, also serving as the Administrative Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. We acknowledge the following Consortiums/studies for summary statistics: () on SLE from Bentham et al, in Nature Genetics (2015) publication and available in the GWAS catalog; () on AF contributed by AFGen Consortium investigators and available in the GWAS catalog; () on IS contributed by MEGASTROKE Consortium project, which received funding from sources specified at http://www.megastroke.org/acknowledgements.html and a list of MEGASTROKE Consortium investigators are available at http://www.megastroke.org/authors.html ; () on CAD contributed by CARDIoGRAMplusC4D investigators and available at www.CARDIOGRAMPLUSC4D.ORG ; () on CKD contributed by CKDGEN Consortium and available at https://ckdgen.imbi.uni-freiburg.de/ ; () for LDL cholesterol contributed by the GLGC and available at http://csg.sph.umich.edu/willer/public/lipids2013/ ; () for waist circumference contributed by the GIANT consortium and available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files ; () for SBP from Evangelou et al, in Nat Genet (2018) publication and available in the GWAS catalog; () for T1D contributed by the T1D Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF); () for T2D contributed by the DIAGRAM Consortium and available at http://diagram-consortium.org/downloads.html ; () for C-reactive protein from Ligthart et al, in Am J Hum Genetics (2018) publication and available by contacting the investigator ( [email protected] ); () for interleukin 6 from Ahola-Olli et al, in Am J Hum Genetics (2017) publication and available at http://computationalmedicine.fi/data#Cytokine_GWAS .

Keywords

Systemic lupus erythematosus
genetic risk score
pleiotropy

ASJC Scopus subject areas

Rheumatology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1177/09612033211014952

Cite this

Kawai, V. K., Shi, M., Liu, G., Feng, Q. P., Wei, W. Q., Chung, C. P., Walunas, T. L., Gordon, A. S., Linneman, J. G., Hebbring, S. J., Harley, J. B., Cox, N. J., Roden, D. M., Stein, C. M., & Mosley, J. D. (2021). Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis. Lupus, 30(8), 1264-1272. https://doi.org/10.1177/09612033211014952

@article{c6903439e2be4bf7831784710ff936ee,

title = "Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis",

abstract = "Objectives: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. Methods: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. Results: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10−5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. Conclusion: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.",

keywords = "Systemic lupus erythematosus, genetic risk score, pleiotropy",

author = "Kawai, {Vivian K.} and Mingjian Shi and Ge Liu and Feng, {Qi Ping} and Wei, {Wei Qi} and Chung, {Cecilia P.} and Walunas, {Theresa L.} and Gordon, {Adam S.} and Linneman, {James G.} and Hebbring, {Scott J.} and Harley, {John B.} and Cox, {Nancy J.} and Roden, {Dan M.} and Stein, {C. Michael} and Mosley, {Jonathan D.}",

note = "Funding Information: The study was supported by American Heart Association (AHA) grant 18SFRN34230089. The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center{\textquoteright}s BioVU which is supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH funded Shared Instrumentation Grant S10RR025141; and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vanderbilt.edu/pub/biovu/ . VKK was supported by K23GM117395, R01AR076516, and the Arthritis National Research Foundation – All Arthritis Grant Program Award. JDM is funded by the AHA 16FTF30130005 and R01GM130791. QF is funded by R01GM120523, and CPC by R01AR073764. The eMERGE Network was initiated and funded by NHGRI through the following grants for phase II: U01HG006828 (Cincinnati Children{\textquoteright}s Hospital Medical Center/Boston Children{\textquoteright}s Hospital); U01HG006830 (Children{\textquoteright}s Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. For phase I: U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and Vanderbilt University Medical Center); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-04603 (Vanderbilt University Medical Center, also serving as the Administrative Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. Funding Information: We acknowledge the following Consortiums/studies for summary statistics: () on SLE from Bentham et al, in Nature Genetics (2015) publication and available in the GWAS catalog; () on AF contributed by AFGen Consortium investigators and available in the GWAS catalog; () on IS contributed by MEGASTROKE Consortium project, which received funding from sources specified at http://www.megastroke.org/acknowledgements.html and a list of MEGASTROKE Consortium investigators are available at http://www.megastroke.org/authors.html ; () on CAD contributed by CARDIoGRAMplusC4D investigators and available at www.CARDIOGRAMPLUSC4D.ORG ; () on CKD contributed by CKDGEN Consortium and available at https://ckdgen.imbi.uni-freiburg.de/ ; () for LDL cholesterol contributed by the GLGC and available at http://csg.sph.umich.edu/willer/public/lipids2013/ ; () for waist circumference contributed by the GIANT consortium and available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files ; () for SBP from Evangelou et al, in Nat Genet (2018) publication and available in the GWAS catalog; () for T1D contributed by the T1D Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF); () for T2D contributed by the DIAGRAM Consortium and available at http://diagram-consortium.org/downloads.html ; () for C-reactive protein from Ligthart et al, in Am J Hum Genetics (2018) publication and available by contacting the investigator ( [email protected] ); () for interleukin 6 from Ahola-Olli et al, in Am J Hum Genetics (2017) publication and available at http://computationalmedicine.fi/data#Cytokine_GWAS . Publisher Copyright: {\textcopyright} The Author(s) 2021.",

year = "2021",

month = jul,

doi = "10.1177/09612033211014952",

language = "English (US)",

volume = "30",

pages = "1264--1272",

journal = "Lupus",

issn = "0961-2033",

publisher = "SAGE Publications Ltd",

number = "8",

}

Kawai, VK, Shi, M, Liu, G, Feng, QP, Wei, WQ, Chung, CP, Walunas, TL , Gordon, AS, Linneman, JG, Hebbring, SJ, Harley, JB, Cox, NJ, Roden, DM, Stein, CM & Mosley, JD 2021, 'Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis', Lupus, vol. 30, no. 8, pp. 1264-1272. https://doi.org/10.1177/09612033211014952

TY - JOUR

T1 - Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders

T2 - A phenome-wide association study and inverse-variance weighted meta-analysis

AU - Kawai, Vivian K.

AU - Shi, Mingjian

AU - Liu, Ge

AU - Feng, Qi Ping

AU - Wei, Wei Qi

AU - Chung, Cecilia P.

AU - Walunas, Theresa L.

AU - Gordon, Adam S.

AU - Linneman, James G.

AU - Hebbring, Scott J.

AU - Harley, John B.

AU - Cox, Nancy J.

AU - Roden, Dan M.

AU - Stein, C. Michael

AU - Mosley, Jonathan D.

N1 - Funding Information: The study was supported by American Heart Association (AHA) grant 18SFRN34230089. The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center’s BioVU which is supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH funded Shared Instrumentation Grant S10RR025141; and CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vanderbilt.edu/pub/biovu/ . VKK was supported by K23GM117395, R01AR076516, and the Arthritis National Research Foundation – All Arthritis Grant Program Award. JDM is funded by the AHA 16FTF30130005 and R01GM130791. QF is funded by R01GM120523, and CPC by R01AR073764. The eMERGE Network was initiated and funded by NHGRI through the following grants for phase II: U01HG006828 (Cincinnati Children’s Hospital Medical Center/Boston Children’s Hospital); U01HG006830 (Children’s Hospital of Philadelphia); U01HG006389 (Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University); U01HG006382 (Geisinger Clinic); U01HG006375 (Group Health Cooperative/University of Washington); U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); U01HG006385 (Vanderbilt University Medical Center serving as the Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. For phase I: U01-HG-004610 (Group Health Cooperative/University of Washington); U01-HG-004608 (Marshfield Clinic Research Foundation and Vanderbilt University Medical Center); U01-HG-04599 (Mayo Clinic); U01HG004609 (Northwestern University); U01-HG-04603 (Vanderbilt University Medical Center, also serving as the Administrative Coordinating Center); U01HG004438 (CIDR) and U01HG004424 (the Broad Institute) serving as Genotyping Centers. Funding Information: We acknowledge the following Consortiums/studies for summary statistics: () on SLE from Bentham et al, in Nature Genetics (2015) publication and available in the GWAS catalog; () on AF contributed by AFGen Consortium investigators and available in the GWAS catalog; () on IS contributed by MEGASTROKE Consortium project, which received funding from sources specified at http://www.megastroke.org/acknowledgements.html and a list of MEGASTROKE Consortium investigators are available at http://www.megastroke.org/authors.html ; () on CAD contributed by CARDIoGRAMplusC4D investigators and available at www.CARDIOGRAMPLUSC4D.ORG ; () on CKD contributed by CKDGEN Consortium and available at https://ckdgen.imbi.uni-freiburg.de/ ; () for LDL cholesterol contributed by the GLGC and available at http://csg.sph.umich.edu/willer/public/lipids2013/ ; () for waist circumference contributed by the GIANT consortium and available at https://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium_data_files ; () for SBP from Evangelou et al, in Nat Genet (2018) publication and available in the GWAS catalog; () for T1D contributed by the T1D Genetics Consortium, a collaborative clinical study sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Allergy and Infectious Diseases (NIAID), National Human Genome Research Institute (NHGRI), National Institute of Child Health and Human Development (NICHD), and Juvenile Diabetes Research Foundation International (JDRF); () for T2D contributed by the DIAGRAM Consortium and available at http://diagram-consortium.org/downloads.html ; () for C-reactive protein from Ligthart et al, in Am J Hum Genetics (2018) publication and available by contacting the investigator ( [email protected] ); () for interleukin 6 from Ahola-Olli et al, in Am J Hum Genetics (2017) publication and available at http://computationalmedicine.fi/data#Cytokine_GWAS . Publisher Copyright: © The Author(s) 2021.

PY - 2021/7

Y1 - 2021/7

N2 - Objectives: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. Methods: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. Results: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10−5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. Conclusion: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

AB - Objectives: To test the hypothesis that genetic predisposition to systemic lupus erythematosus (SLE) increases the risk of cardiometabolic disorders. Methods: Using 41 single nucleotide polymorphisms (SNPs) associated with SLE, we calculated a weighted genetic risk score (wGRS) for SLE. In a large biobank we tested the association between this wGRS and 9 cardiometabolic phenotypes previously associated with SLE: atrial fibrillation, ischemic stroke, coronary artery disease, type 1 and type 2 diabetes, obesity, chronic kidney disease, hypertension, and hypercholesterolemia. Additionally, we performed a phenome-wide association analysis (pheWAS) to discover novel clinical associations with a genetic predisposition to SLE. Findings were replicated in the Electronic Medical Records and Genomics (eMERGE) Network. To further define the association between SLE-related risk alleles and the selected cardiometabolic phenotypes, we performed an inverse variance weighted regression (IVWR) meta-analysis. Results: The wGRS for SLE was calculated in 74,759 individuals of European ancestry. Among the pre-selected phenotypes, the wGRS was significantly associated with type 1 diabetes (OR [95%CI] =1.11 [1.06, 1.17], P-value = 1.05x10−5). In the PheWAS, the wGRS was associated with several autoimmune phenotypes, kidney disorders, and skin neoplasm; but only the associations with autoimmune phenotypes were replicated. In the IVWR meta-analysis, SLE-related risk alleles were nominally associated with type 1 diabetes (P = 0.048) but the associations were heterogeneous and did not meet the adjusted significance threshold. Conclusion: A weighted GRS for SLE was associated with an increased risk of several autoimmune-related phenotypes including type I diabetes but not with cardiometabolic disorders.

KW - Systemic lupus erythematosus

KW - genetic risk score

KW - pleiotropy

UR - http://www.scopus.com/inward/record.url?scp=85105910055&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85105910055&partnerID=8YFLogxK

U2 - 10.1177/09612033211014952

DO - 10.1177/09612033211014952

M3 - Article

C2 - 33977795

AN - SCOPUS:85105910055

SN - 0961-2033

VL - 30

SP - 1264

EP - 1272

JO - Lupus

JF - Lupus

IS - 8

ER -

Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis

Abstract

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

sj-xlsx-1-lup-10.1177_09612033211014952 - Supplemental material for Pleiotropy of systemic lupus erythematosus risk alleles and cardiometabolic disorders: A phenome-wide association study and inverse-variance weighted meta-analysis