Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

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Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes. / O'Connor, Mark J; Schroeder, Philip; Huerta-Chagoya, Alicia; Cortés-Sánchez, Paula; Bonàs-Guarch, Silvía; Guindo-Martínez, Marta; Cole, Joanne B; Kaur, Varinderpal; Torrents, David; Veerapen, Kumar; Grarup, Niels; Kurki, Mitja; Rundsten, Carsten F; Pedersen, Oluf; Brandslund, Ivan; Linneberg, Allan; Hansen, Torben; Leong, Aaron; Florez, Jose C; Mercader, Josep M.

In: Diabetes, Vol. 71, No. 3, 2022, p. 554–565.

Research output: Contribution to journalJournal articlepeer-review

Harvard

O'Connor, MJ, Schroeder, P, Huerta-Chagoya, A, Cortés-Sánchez, P, Bonàs-Guarch, S, Guindo-Martínez, M, Cole, JB, Kaur, V, Torrents, D, Veerapen, K, Grarup, N, Kurki, M, Rundsten, CF, Pedersen, O, Brandslund, I, Linneberg, A, Hansen, T, Leong, A, Florez, JC & Mercader, JM 2022, 'Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes', Diabetes, vol. 71, no. 3, pp. 554–565. https://doi.org/10.2337/db21-0545

APA

O'Connor, M. J., Schroeder, P., Huerta-Chagoya, A., Cortés-Sánchez, P., Bonàs-Guarch, S., Guindo-Martínez, M., Cole, J. B., Kaur, V., Torrents, D., Veerapen, K., Grarup, N., Kurki, M., Rundsten, C. F., Pedersen, O., Brandslund, I., Linneberg, A., Hansen, T., Leong, A., Florez, J. C., & Mercader, J. M. (2022). Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes. Diabetes, 71(3), 554–565. https://doi.org/10.2337/db21-0545

Vancouver

O'Connor MJ, Schroeder P, Huerta-Chagoya A, Cortés-Sánchez P, Bonàs-Guarch S, Guindo-Martínez M et al. Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes. Diabetes. 2022;71(3):554–565. https://doi.org/10.2337/db21-0545

Author

O'Connor, Mark J ; Schroeder, Philip ; Huerta-Chagoya, Alicia ; Cortés-Sánchez, Paula ; Bonàs-Guarch, Silvía ; Guindo-Martínez, Marta ; Cole, Joanne B ; Kaur, Varinderpal ; Torrents, David ; Veerapen, Kumar ; Grarup, Niels ; Kurki, Mitja ; Rundsten, Carsten F ; Pedersen, Oluf ; Brandslund, Ivan ; Linneberg, Allan ; Hansen, Torben ; Leong, Aaron ; Florez, Jose C ; Mercader, Josep M. / Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes. In: Diabetes. 2022 ; Vol. 71, No. 3. pp. 554–565.

Bibtex

@article{f9519728704b45f39b8797045012377e,
title = "Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes",
abstract = "Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, P=1×10-16) and a stronger effect in men than in women (interaction P=7×10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.",
author = "O'Connor, {Mark J} and Philip Schroeder and Alicia Huerta-Chagoya and Paula Cort{\'e}s-S{\'a}nchez and Silv{\'i}a Bon{\`a}s-Guarch and Marta Guindo-Mart{\'i}nez and Cole, {Joanne B} and Varinderpal Kaur and David Torrents and Kumar Veerapen and Niels Grarup and Mitja Kurki and Rundsten, {Carsten F} and Oluf Pedersen and Ivan Brandslund and Allan Linneberg and Torben Hansen and Aaron Leong and Florez, {Jose C} and Mercader, {Josep M}",
note = "{\textcopyright} 2021 by the American Diabetes Association.",
year = "2022",
doi = "10.2337/db21-0545",
language = "English",
volume = "71",
pages = "554–565",
journal = "Diabetes",
issn = "0012-1797",
publisher = "American Diabetes Association",
number = "3",

}

RIS

TY - JOUR

T1 - Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

AU - O'Connor, Mark J

AU - Schroeder, Philip

AU - Huerta-Chagoya, Alicia

AU - Cortés-Sánchez, Paula

AU - Bonàs-Guarch, Silvía

AU - Guindo-Martínez, Marta

AU - Cole, Joanne B

AU - Kaur, Varinderpal

AU - Torrents, David

AU - Veerapen, Kumar

AU - Grarup, Niels

AU - Kurki, Mitja

AU - Rundsten, Carsten F

AU - Pedersen, Oluf

AU - Brandslund, Ivan

AU - Linneberg, Allan

AU - Hansen, Torben

AU - Leong, Aaron

AU - Florez, Jose C

AU - Mercader, Josep M

N1 - © 2021 by the American Diabetes Association.

PY - 2022

Y1 - 2022

N2 - Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, P=1×10-16) and a stronger effect in men than in women (interaction P=7×10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

AB - Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, P=1×10-16) and a stronger effect in men than in women (interaction P=7×10-7). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby PELO gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

U2 - 10.2337/db21-0545

DO - 10.2337/db21-0545

M3 - Journal article

C2 - 34862199

VL - 71

SP - 554

EP - 565

JO - Diabetes

JF - Diabetes

SN - 0012-1797

IS - 3

ER -

ID: 290602908