TY - JOUR
T1 - Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits
AU - Highland, Heather M.
AU - Wojcik, Genevieve L.
AU - Graff, Mariaelisa
AU - Nishimura, Katherine K.
AU - Hodonsky, Chani J.
AU - Baldassari, Antoine R.
AU - Cote, Alanna C.
AU - Cheng, Iona
AU - Gignoux, Christopher R.
AU - Tao, Ran
AU - Li, Yuqing
AU - Boerwinkle, Eric
AU - Fornage, Myriam
AU - Haessler, Jeffrey
AU - Hindorff, Lucia A.
AU - Hu, Yao
AU - Justice, Anne E.
AU - Lin, Bridget M.
AU - Lin, Danyu
AU - Stram, Daniel O.
AU - Haiman, Christopher A.
AU - Kooperberg, Charles
AU - Le Marchand, Loic
AU - Matise, Tara C.
AU - Kenny, Eimear E.
AU - Carlson, Christopher S.
AU - Stahl, Eli A.
AU - Avery, Christy L.
AU - North, Kari E.
AU - Ambite, Jose Luis
AU - Buyske, Steven
AU - Loos, Ruth J.
AU - Peters, Ulrike
AU - Young, Kristin L.
AU - Bien, Stephanie A.
AU - Huckins, Laura M.
N1 - Funding Information:
The Population Architecture using Genomics and Epidemiology (PAGE) program is funded by the National Human Genome Research Institute ( NHGRI ) with co-funding from the National Institute on Minority Health and Health Disparities ( NIMHD ), supported by U01HG007416 (CALiCo), U01HG007417 (ISMMS), U01HG007397 (MEC), U01HG007376 (WHI), and U01HG007419 (Coordinating Center). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The PAGE consortium thanks the staff and participants of all PAGE studies for their important contributions. The complete list of PAGE members can be found in the web resources.
Funding Information:
The Population Architecture using Genomics and Epidemiology (PAGE) program is funded by the National Human Genome Research Institute (NHGRI) with co-funding from the National Institute on Minority Health and Health Disparities (NIMHD), supported by U01HG007416 (CALiCo), U01HG007417 (ISMMS), U01HG007397 (MEC), U01HG007376 (WHI), and U01HG007419 (Coordinating Center). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. The PAGE consortium thanks the staff and participants of all PAGE studies for their important contributions. The complete list of PAGE members can be found in the web resources. Data management, integration, and dissemination; genotype imputation; ancestry deconvolution; population genetics; analysis pipelines; and study coordination were provided by the PAGE Coordinating Center (under NHGRI grants U01HG007419 and U01HG004801). Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is fully funded through a federal contract from the NIH to The Johns Hopkins University, contract number HHSN268201200008I. Genotype data quality control and quality assurance services were provided by the Genetic Analysis Center in the Biostatistics Department of the University of Washington, through support provided by the CIDR contract. H.M.H. received support from NHLBI training grants T32 HL007055 and T32 HL129982, American Diabetes Association Grant #1-19-PDF-045, and R01HL142825. C.J.H. received support from NHLBI training grants T32 HL129982 and T32 HL007824. C.R.G. received support from R56HG010297 and R01HG010297. A.E.J. was supported by K99 HL130580. C.K. received support from 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, 75N92021D00005, S10OD028685, and U01HG007376. K.E.N. is supported by R01HL142302, R01HL151152, R01DK122503, R01HD057194, R01HG010297, and R01HL143885. K.L.Y. received support from R01HL149683 and R21HL140419. L.M.H. received support from R01MH118278. E.E.K. has received speaker honoraria from Illumina, 23andMe, and Regeneron Pharmaceuticals and serves as a scientific board member for Galateo Bio.
Funding Information:
H.M.H. received support from NHLBI training grants T32 HL007055 and T32 HL129982 , American Diabetes Association Grant # 1-19-PDF-045 , and R01HL142825 . C.J.H. received support from NHLBI training grants T32 HL129982 and T32 HL007824 . C.R.G. received support from R56HG010297 and R01HG010297 . A.E.J. was supported by K99 HL130580 . C.K. received support from 75N92021D00001 , 75N92021D00002 , 75N92021D00003 , 75N92021D00004 , 75N92021D00005 , S10OD028685 , and U01HG007376 . K.E.N. is supported by R01HL142302 , R01HL151152 , R01DK122503 , R01HD057194 , R01HG010297 , and R01HL143885 . K.L.Y. received support from R01HL149683 and R21HL140419 . L.M.H. received support from R01MH118278 .
Funding Information:
Genotyping services were provided by the Center for Inherited Disease Research ( CIDR ). CIDR is fully funded through a federal contract from the NIH to The Johns Hopkins University, contract number HHSN268201200008I . Genotype data quality control and quality assurance services were provided by the Genetic Analysis Center in the Biostatistics Department of the University of Washington, through support provided by the CIDR contract.
Publisher Copyright:
© 2022 American Society of Human Genetics
PY - 2022/4/7
Y1 - 2022/4/7
N2 - One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
AB - One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
KW - PrediXcan, TWAS, ancestrally diverse, gene expression, cardiometabolic traits, PAGE
UR - http://www.scopus.com/inward/record.url?scp=85127327919&partnerID=8YFLogxK
U2 - 10.1016/j.ajhg.2022.02.013
DO - 10.1016/j.ajhg.2022.02.013
M3 - Article
C2 - 35263625
AN - SCOPUS:85127327919
VL - 109
SP - 669
EP - 679
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
SN - 0002-9297
IS - 4
ER -