Abstract
Aims/hypothesis: Elevated levels of fasting glucose and fasting insulin in non-diabetic individuals are markers of dysregulation of glucose metabolism and are strong risk factors for type 2 diabetes. Genome-wide association studies have discovered over 50 SNPs associated with these traits. Most of these loci were discovered in European populations and have not been tested in a well-powered multi-ethnic study. We hypothesised that a large, ancestrally diverse, fine-mapping genetic study of glycaemic traits would identify novel and population-specific associations that were previously undetectable by European-centric studies. Methods: A multiethnic study of up to 26,760 unrelated individuals without diabetes, of predominantly Hispanic/Latino and African ancestries, were genotyped using the Metabochip. Transethnic meta-analysis of racial/ethnic-specific linear regression analyses were performed for fasting glucose and fasting insulin. We attempted to replicate 39 fasting glucose and 17 fasting insulin loci. Genetic fine-mapping was performed through sequential conditional analyses in 15 regions that included both the initially reported SNP association(s) and denser coverage of SNP markers. In addition, Metabochip-wide analyses were performed to discover novel fasting glucose and fasting insulin loci. The most significant SNP associations were further examined using bioinformatic functional annotation. Results: Previously reported SNP associations were significantly replicated (p ≤ 0.05) in 31/39 fasting glucose loci and 14/17 fasting insulin loci. Eleven glycaemic trait loci were refined to a smaller list of potentially causal variants through transethnic meta-analysis. Stepwise conditional analysis identified two loci with independent secondary signals (G6PC2-rs477224 and GCK-rs2908290), which had not previously been reported. Population-specific conditional analyses identified an independent signal in G6PC2 tagged by the rare variant rs77719485 in African ancestry. Further Metabochip-wide analysis uncovered one novel fasting insulin locus at SLC17A2-rs75862513. Conclusions/interpretation: These findings suggest that while glycaemic trait loci often have generalisable effects across the studied populations, transethnic genetic studies help to prioritise likely functional SNPs, identify novel associations that may be population-specific and in turn have the potential to influence screening efforts or therapeutic discoveries. Data availability: The summary statistics from each of the ancestry-specific and transethnic (combined ancestry) results can be found under the PAGE study on dbGaP here: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v1.p1.
Original language | English (US) |
---|---|
Pages (from-to) | 2384-2398 |
Number of pages | 15 |
Journal | Diabetologia |
Volume | 60 |
Issue number | 12 |
DOIs | |
State | Published - Dec 1 2017 |
Keywords
- Fine-mapping
- Genetic
- Glucose
- Glycaemic
- Insulin
- Multiethnic
- Page
- Transethnic
- Type 2 diabetes
ASJC Scopus subject areas
- Internal Medicine
- Endocrinology, Diabetes and Metabolism
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In: Diabetologia, Vol. 60, No. 12, 01.12.2017, p. 2384-2398.
Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Transethnic insight into the genetics of glycaemic traits
T2 - fine-mapping results from the Population Architecture using Genomics and Epidemiology (PAGE) consortium
AU - Bien, Stephanie A.
AU - Pankow, James S.
AU - Haessler, Jeffrey
AU - Lu, Yinchang N.
AU - Pankratz, Nathan
AU - Rohde, Rebecca R.
AU - Tamuno, Alfred
AU - Carlson, Christopher S.
AU - Schumacher, Fredrick R.
AU - Bůžková, Petra
AU - Daviglus, Martha L.
AU - Lim, Unhee
AU - Fornage, Myriam
AU - Fernandez-Rhodes, Lindsay
AU - Avilés-Santa, Larissa
AU - Buyske, Steven
AU - Gross, Myron D.
AU - Graff, Mariaelisa
AU - Isasi, Carmen R.
AU - Kuller, Lewis H.
AU - Manson, Jo Ann E.
AU - Matise, Tara C.
AU - Prentice, Ross L.
AU - Wilkens, Lynne R.
AU - Yoneyama, Sachiko
AU - Loos, Ruth J.F.
AU - Hindorff, Lucia A.
AU - Le Marchand, Loic
AU - North, Kari E.
AU - Haiman, Christopher A.
AU - Peters, Ulrike
AU - Kooperberg, Charles
N1 - Funding Information: Funding support for the ‘Epidemiology of putative genetic variants: The Women’s Health Initiative’ study is provided through the NHGRI PAGE programme (U01HG004790 and its NHGRI ARRA supplement). The WHI programme is funded by the National Heart, Lung, and Blood Institute (NHLBI), the National Institutes of Health (NIH) and the US Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32 and 44221. Funding Information: Funding The PAGE programme is funded by the NHGRI, supported by U01HG004803 (CALiCo), U01HG004802 (MEC), U01HG004790 (WHI) and U01HG004801 (Coordinating Center), and their respective NHGRI ARRA supplements. Funding Information: The MEC characterisation of epidemiological architecture is funded through the NHGRI PAGE programme (U01HG004802 and its NHGRI ARRA supplement). The MEC study is funded by the National Cancer Institute (R37CA54281, R01 CA63, P01CA33619, U01CA136792 and U01CA98758). Funding Information: Funding support for the CALiCo programme was provided by the NHGRI PAGE programme (U01HG004803 and its NHGRI ARRA supplement). The following studies are funded as follows: The ARIC study is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by grant no. UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. The CARDIA study is supported by contracts HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C and HHSN268200900041C from the NHLBI, the Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI (AG0005). Funding Information: Acknowledgements The PAGE programme is supported by Genetic Epidemiology of Causal Variants Across the Life Course (CALiCo), MEC, WHI and the 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 complete list of PAGE members can be found at www.pagestudy.org, accessed 29 April 2016. The data and materials included in this report result from a collaboration between the following studies: (1) The MEC characterisation of epidemiological architecture; (2) The Mount Sinai BioMe Biobank; (3) ‘Epidemiology of putative genetic variants: The Women’s Health Initiative’ study. The authors thank the WHI investigators and staff for their dedication and the study participants for making the program possible. Full listing of WHI investigators can be found at www.whi.org/researchers/Documents% 20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List. pdf, accessed 2 June 2016; (4) The CALiCo programme and the ARIC, CARDIA and HCHS/SOL studies contributed to this manuscript. The authors thank the staff and participants of the ARIC study for their important contributions. Funding Information: The HCHS/SOL was carried out as a collaborative study supported by contracts from the NHLBI to the University of North Carolina (N01- HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236) and San Diego State University (N01-HC65237). Additional support was provided by 1R01DK101855-01 and 13GRNT16490017. The following Institutes/Centres/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Center on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke and the Office of Dietary Supplements. Funding Information: The PAGE programme is supported by Genetic Epidemiology of Causal Variants Across the Life Course (CALiCo), MEC, WHI and the 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 complete list of PAGE members can be found at www.pagestudy.org , accessed 29 April 2016. The data and materials included in this report result from a collaboration between the following studies: (1) The MEC characterisation of epidemiological architecture; (2) The Mount Sinai BioMe Biobank; (3) ?Epidemiology of putative genetic variants: The Women?s Health Initiative? study. The authors thank the WHI investigators and staff for their dedication and the study participants for making the program possible. Full listing of WHI investigators can be found at www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short%20List.pdf , accessed 2 June 2016; (4) The CALiCo programme and the ARIC, CARDIA and HCHS/SOL studies contributed to this manuscript. The authors thank the staff and participants of the ARIC study for their important contributions. Assistance with phenotype harmonisation, SNP selection and annotation, data cleaning, data management, integration and dissemination and general study coordination was provided by the PAGE Coordinating Center. The authors gratefully acknowledge B. Voight for sharing the Metabochip SNP linkage disequilibrium and MAF statistics estimated in the Malm? Diet and Cancer Study. The PAGE consortium thanks the staff and participants of all PAGE studies for their important contributions. A correction to this article is available online at https://doi.org/10.1007/s00125-017-4476-z. The PAGE programme is funded by the NHGRI, supported by U01HG004803 (CALiCo), U01HG004802 (MEC), U01HG004790 (WHI) and U01HG004801 (Coordinating Center), and their respective NHGRI ARRA supplements. The MEC characterisation of epidemiological architecture is funded through the NHGRI PAGE programme (U01HG004802 and its NHGRI ARRA supplement). The MEC study is funded by the National Cancer Institute (R37CA54281, R01 CA63, P01CA33619, U01CA136792 and U01CA98758). Funding support for the ?Epidemiology of putative genetic variants: The Women?s Health Initiative? study is provided through the NHGRI PAGE programme (U01HG004790 and its NHGRI ARRA supplement). The WHI programme is funded by the National Heart, Lung, and Blood Institute (NHLBI), the National Institutes of Health (NIH) and the US Department of Health and Human Services through contracts N01WH22110, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32 and 44221. Funding support for the CALiCo programme was provided by the NHGRI PAGE programme (U01HG004803 and its NHGRI ARRA supplement). The following studies are funded as follows: The ARIC study is carried out as a collaborative study supported by NHLBI contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HSN268201100009C, HHSN268201100010C, HHSN268201100011C and HHSN268201100012C), R01HL087641, R01HL59367 and R01HL086694; National Human Genome Research Institute contract U01HG004402; and NIH contract HHSN268200625226C. Infrastructure was partly supported by grant no. UL1RR025005, a component of the NIH and NIH Roadmap for Medical Research. The CARDIA study is supported by contracts HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C and HHSN268200900041C from the NHLBI, the Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI (AG0005). The HCHS/SOL was carried out as a collaborative study supported by contracts from the NHLBI to the University of North Carolina (N01-HC65233), University of Miami (N01-HC65234), Albert Einstein College of Medicine (N01-HC65235), Northwestern University (N01-HC65236) and San Diego State University (N01-HC65237). Additional support was provided by 1R01DK101855-01 and 13GRNT16490017. The following Institutes/Centres/Offices contribute to the HCHS/SOL through a transfer of funds to the NHLBI: National Center on Minority Health and Health Disparities, the National Institute of Deafness and Other Communications Disorders, the National Institute of Dental and Craniofacial Research, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute of Neurological Disorders and Stroke and the Office of Dietary Supplements. The Mount Sinai BioMe Biobank is supported by The Andrea and Charles Bronfman Philanthropies. Funding support for the PAGE Coordinating Center is provided through the NHGRI PAGE programme (U01HG004801-01 and its NHGRI ARRA supplement). The National Institute of Mental Health also contributes to the support for the Coordinating Center. Publisher Copyright: © 2017, The Author(s).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Aims/hypothesis: Elevated levels of fasting glucose and fasting insulin in non-diabetic individuals are markers of dysregulation of glucose metabolism and are strong risk factors for type 2 diabetes. Genome-wide association studies have discovered over 50 SNPs associated with these traits. Most of these loci were discovered in European populations and have not been tested in a well-powered multi-ethnic study. We hypothesised that a large, ancestrally diverse, fine-mapping genetic study of glycaemic traits would identify novel and population-specific associations that were previously undetectable by European-centric studies. Methods: A multiethnic study of up to 26,760 unrelated individuals without diabetes, of predominantly Hispanic/Latino and African ancestries, were genotyped using the Metabochip. Transethnic meta-analysis of racial/ethnic-specific linear regression analyses were performed for fasting glucose and fasting insulin. We attempted to replicate 39 fasting glucose and 17 fasting insulin loci. Genetic fine-mapping was performed through sequential conditional analyses in 15 regions that included both the initially reported SNP association(s) and denser coverage of SNP markers. In addition, Metabochip-wide analyses were performed to discover novel fasting glucose and fasting insulin loci. The most significant SNP associations were further examined using bioinformatic functional annotation. Results: Previously reported SNP associations were significantly replicated (p ≤ 0.05) in 31/39 fasting glucose loci and 14/17 fasting insulin loci. Eleven glycaemic trait loci were refined to a smaller list of potentially causal variants through transethnic meta-analysis. Stepwise conditional analysis identified two loci with independent secondary signals (G6PC2-rs477224 and GCK-rs2908290), which had not previously been reported. Population-specific conditional analyses identified an independent signal in G6PC2 tagged by the rare variant rs77719485 in African ancestry. Further Metabochip-wide analysis uncovered one novel fasting insulin locus at SLC17A2-rs75862513. Conclusions/interpretation: These findings suggest that while glycaemic trait loci often have generalisable effects across the studied populations, transethnic genetic studies help to prioritise likely functional SNPs, identify novel associations that may be population-specific and in turn have the potential to influence screening efforts or therapeutic discoveries. Data availability: The summary statistics from each of the ancestry-specific and transethnic (combined ancestry) results can be found under the PAGE study on dbGaP here: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v1.p1.
AB - Aims/hypothesis: Elevated levels of fasting glucose and fasting insulin in non-diabetic individuals are markers of dysregulation of glucose metabolism and are strong risk factors for type 2 diabetes. Genome-wide association studies have discovered over 50 SNPs associated with these traits. Most of these loci were discovered in European populations and have not been tested in a well-powered multi-ethnic study. We hypothesised that a large, ancestrally diverse, fine-mapping genetic study of glycaemic traits would identify novel and population-specific associations that were previously undetectable by European-centric studies. Methods: A multiethnic study of up to 26,760 unrelated individuals without diabetes, of predominantly Hispanic/Latino and African ancestries, were genotyped using the Metabochip. Transethnic meta-analysis of racial/ethnic-specific linear regression analyses were performed for fasting glucose and fasting insulin. We attempted to replicate 39 fasting glucose and 17 fasting insulin loci. Genetic fine-mapping was performed through sequential conditional analyses in 15 regions that included both the initially reported SNP association(s) and denser coverage of SNP markers. In addition, Metabochip-wide analyses were performed to discover novel fasting glucose and fasting insulin loci. The most significant SNP associations were further examined using bioinformatic functional annotation. Results: Previously reported SNP associations were significantly replicated (p ≤ 0.05) in 31/39 fasting glucose loci and 14/17 fasting insulin loci. Eleven glycaemic trait loci were refined to a smaller list of potentially causal variants through transethnic meta-analysis. Stepwise conditional analysis identified two loci with independent secondary signals (G6PC2-rs477224 and GCK-rs2908290), which had not previously been reported. Population-specific conditional analyses identified an independent signal in G6PC2 tagged by the rare variant rs77719485 in African ancestry. Further Metabochip-wide analysis uncovered one novel fasting insulin locus at SLC17A2-rs75862513. Conclusions/interpretation: These findings suggest that while glycaemic trait loci often have generalisable effects across the studied populations, transethnic genetic studies help to prioritise likely functional SNPs, identify novel associations that may be population-specific and in turn have the potential to influence screening efforts or therapeutic discoveries. Data availability: The summary statistics from each of the ancestry-specific and transethnic (combined ancestry) results can be found under the PAGE study on dbGaP here: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000356.v1.p1.
KW - Fine-mapping
KW - Genetic
KW - Glucose
KW - Glycaemic
KW - Insulin
KW - Multiethnic
KW - Page
KW - Transethnic
KW - Type 2 diabetes
UR - http://www.scopus.com/inward/record.url?scp=85029429063&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029429063&partnerID=8YFLogxK
U2 - 10.1007/s00125-017-4405-1
DO - 10.1007/s00125-017-4405-1
M3 - Article
C2 - 28905132
AN - SCOPUS:85029429063
SN - 0012-186X
VL - 60
SP - 2384
EP - 2398
JO - Diabetologia
JF - Diabetologia
IS - 12
ER -