Meta-Analysis

. 2023 Jul;66(7):1273-1288.

doi: 10.1007/s00125-023-05912-9. Epub 2023 May 6.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

Alicia Huerta-Chagoya^{1

2

3

4}, Philip Schroeder^{5

6

7}, Ravi Mandla^{5

6

7}, Aaron J Deutsch^{5

6

7

8}, Wanying Zhu⁹, Lauren Petty⁹, Xiaoyan Yi¹⁰, Joanne B Cole^{5

6

11

12

13}, Miriam S Udler⁶, Peter Dornbos⁵, Bianca Porneala¹⁴, Daniel DiCorpo¹⁵, Ching-Ti Liu¹⁵, Josephine H Li^{5

6

7

8}, Lukasz Szczerbiński^{5

6

7

16

17}, Varinderpal Kaur⁷, Joohyun Kim¹⁸, Yingchang Lu¹⁸, Alicia Martin^{5

19}, Decio L Eizirik^{10

20}, Piero Marchetti²¹, Lorella Marselli²¹, Ling Chen⁶, Shylaja Srinivasan²², Jennifer Todd²³, Jason Flannick^{5

24}, Rose Gubitosi-Klug²⁵, Lynne Levitsky²⁶, Rachana Shah²⁷, Megan Kelsey²⁸, Brian Burke²⁹, Dana M Dabelea³⁰, Jasmin Divers³¹, Santica Marcovina³², Lauren Stalbow³³, Ruth J F Loos^{33

34}, Burcu F Darst³⁵, Charles Kooperberg³⁵, Laura M Raffield³⁶, Christopher Haiman^{37

38}, Quan Sun³⁹, Joseph B McCormick⁴⁰, Susan P Fisher-Hoch⁴⁰, Maria L Ordoñez⁴¹, James Meigs^{5

8

14}, Leslie J Baier⁴², Clicerio González-Villalpando⁴³, Maria Elena González-Villalpando⁴³, Lorena Orozco⁴⁴, Lourdes García-García⁴⁵, Andrés Moreno-Estrada⁴⁶; Mexican Biobank; Carlos A Aguilar-Salinas⁴⁷, Teresa Tusié^{48

41}, Josée Dupuis¹⁵, Maggie C Y Ng¹⁸, Alisa Manning⁴⁹, Heather M Highland⁵⁰, Miriam Cnop^{10

51}, Robert Hanson⁵², Jennifer Below⁹, Jose C Florez^{5

6

7

8

11

53}, Aaron Leong^{5

6

7

8

14

53}, Josep M Mercader^{54

55

56

57}

Affiliations

¹ Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. ahuerta@broadinstitute.org.
² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. ahuerta@broadinstitute.org.
³ Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico. ahuerta@broadinstitute.org.
⁴ Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico. ahuerta@broadinstitute.org.
⁵ Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁶ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁷ Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.
⁸ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁹ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁰ ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium.
¹¹ Department of Medicine, Harvard Medical School, Boston, MA, USA.
¹² Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
¹³ Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
¹⁴ Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁵ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
¹⁶ Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland.
¹⁷ Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland.
¹⁸ Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁹ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
²⁰ WELBIO, Université Libre de Bruxelles, Brussels, Belgium.
²¹ Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy.
²² Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
²³ Department of Pediatrics, University of Vermont, Burlington, VT, USA.
²⁴ Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
²⁵ Pediatric Endocrinology, Diabetes, and Metabolism, Case Western Reserve University and Rainbow Babies and Children's Hospital, Cleveland, OH, USA.
²⁶ Department of Pediatrics, Division of Pediatric Endocrinology and Pediatric Diabetes Center, Massachusetts General Hospital, Boston, MA, USA.
²⁷ Pediatric Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
²⁸ Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA.
²⁹ Biostatistics Center, The George Washington University, Rockville, MD, USA.
³⁰ Department of Epidemiology, University of Colorado School of Medicine, Aurora, CO, USA.
³¹ NYU Langone Health, New York, NY, USA.
³² Medpace Reference Laboratories, Cincinnati, OH, USA.
³³ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³⁴ Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.
³⁵ Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA.
³⁶ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
³⁷ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
³⁸ Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
³⁹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁴⁰ School of Public Health, The University of Texas Health Science Center at Houston, Brownsville, TX, USA.
⁴¹ Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico.
⁴² Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA.
⁴³ Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico.
⁴⁴ Laboratorio Inmunogénomica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.
⁴⁵ Instituto Nacional de Salud Pública, Cuernavaca, Mexico.
⁴⁶ Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Unidad de Genómica Avanzada (UGA), CINVESTAV, Irapuato, Mexico.
⁴⁷ Unidad de Investigación de Enfermedades Metabólicas y Dirección de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
⁴⁸ Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico.
⁴⁹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
⁵⁰ Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁵¹ Division of Endocrinology, Erasmus Hospital, Université Libre de Bruxelles, Brussels, Belgium.
⁵² Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA.
⁵³ Endocrine Division, Massachusetts General Hospital, Boston, MA, USA.
⁵⁴ Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. mercader@broadinstitute.org.
⁵⁵ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. mercader@broadinstitute.org.
⁵⁶ Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. mercader@broadinstitute.org.
⁵⁷ Department of Medicine, Harvard Medical School, Boston, MA, USA. mercader@broadinstitute.org.

PMID: 37148359
PMCID: PMC10244266
DOI: 10.1007/s00125-023-05912-9

Meta-Analysis

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

Alicia Huerta-Chagoya et al. Diabetologia. 2023 Jul.

. 2023 Jul;66(7):1273-1288.

doi: 10.1007/s00125-023-05912-9. Epub 2023 May 6.

Authors

Affiliations

¹ Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. ahuerta@broadinstitute.org.
² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. ahuerta@broadinstitute.org.
³ Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico. ahuerta@broadinstitute.org.
⁴ Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico. ahuerta@broadinstitute.org.
⁵ Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
⁶ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁷ Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.
⁸ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁹ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁰ ULB Center for Diabetes Research, Medical Faculty, Université Libre de Bruxelles, Brussels, Belgium.
¹¹ Department of Medicine, Harvard Medical School, Boston, MA, USA.
¹² Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA.
¹³ Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
¹⁴ Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA.
¹⁵ Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
¹⁶ Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland.
¹⁷ Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland.
¹⁸ Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
¹⁹ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
²⁰ WELBIO, Université Libre de Bruxelles, Brussels, Belgium.
²¹ Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy.
²² Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA.
²³ Department of Pediatrics, University of Vermont, Burlington, VT, USA.
²⁴ Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
²⁵ Pediatric Endocrinology, Diabetes, and Metabolism, Case Western Reserve University and Rainbow Babies and Children's Hospital, Cleveland, OH, USA.
²⁶ Department of Pediatrics, Division of Pediatric Endocrinology and Pediatric Diabetes Center, Massachusetts General Hospital, Boston, MA, USA.
²⁷ Pediatric Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
²⁸ Pediatric Endocrinology, University of Colorado School of Medicine, Aurora, CO, USA.
²⁹ Biostatistics Center, The George Washington University, Rockville, MD, USA.
³⁰ Department of Epidemiology, University of Colorado School of Medicine, Aurora, CO, USA.
³¹ NYU Langone Health, New York, NY, USA.
³² Medpace Reference Laboratories, Cincinnati, OH, USA.
³³ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
³⁴ Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.
³⁵ Division of Public Health Science, Fred Hutchinson Cancer Center, Seattle, WA, USA.
³⁶ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
³⁷ Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
³⁸ Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA.
³⁹ Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁴⁰ School of Public Health, The University of Texas Health Science Center at Houston, Brownsville, TX, USA.
⁴¹ Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición, Mexico City, Mexico.
⁴² Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA.
⁴³ Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Pública, Mexico City, Mexico.
⁴⁴ Laboratorio Inmunogénomica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, Mexico City, Mexico.
⁴⁵ Instituto Nacional de Salud Pública, Cuernavaca, Mexico.
⁴⁶ Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Unidad de Genómica Avanzada (UGA), CINVESTAV, Irapuato, Mexico.
⁴⁷ Unidad de Investigación de Enfermedades Metabólicas y Dirección de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico.
⁴⁸ Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City, Mexico.
⁴⁹ Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
⁵⁰ Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁵¹ Division of Endocrinology, Erasmus Hospital, Université Libre de Bruxelles, Brussels, Belgium.
⁵² Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ, USA.
⁵³ Endocrine Division, Massachusetts General Hospital, Boston, MA, USA.
⁵⁴ Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA. mercader@broadinstitute.org.
⁵⁵ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. mercader@broadinstitute.org.
⁵⁶ Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA. mercader@broadinstitute.org.
⁵⁷ Department of Medicine, Harvard Medical School, Boston, MA, USA. mercader@broadinstitute.org.

PMID: 37148359
PMCID: PMC10244266
DOI: 10.1007/s00125-023-05912-9

Abstract

Aims/hypothesis: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population.

Methods: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort.

Results: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10^-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.

Conclusions/interpretation: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.

Data availability: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Keywords: GWAS meta-analysis; Latino population; Polygenic score; TOPMed imputation; Type 2 diabetes.

PubMed Disclaimer

Figures

**Fig. 1**
General overview of the study. Six cohorts of admixed Latino ancestry, representing a total of 8150 type 2 diabetes cases and 10,735 controls, were imputed with the TOPMed and 1000G Phase 3 panels (grey box). A type 2 diabetes GWAS meta-analysis of the imputed variants resulted in the identification of two novel loci, which were tested for replication in six additional Latino cohorts (green box). They were also interrogated for association with a collection of phenotypes in eight Latino cohorts (blue box) and for functional evidence in multiple available resources (purple box). The generated Latino type 2 diabetes GWAS data were used, in combination with GWAS from other ancestries, to construct ancestry-specific and multi-ancestry type 2 diabetes PSs (brown box). CMDK, Common Metabolic Disease Knowledge; sum stats, summary statistics

**Fig. 2**
Performance of the TOPMed reference panel for the imputation of Latino samples. (a) Number of chromosome-wide well-imputed variants (imputation r²≥0.8) by AF for each analysed cohort when using the 1000GP3 (blue) or the TOPMed (black) reference panels. (b) Average chromosome-wide imputation quality by AF for each analysed cohort when using the 1000GP3 (blue) or the TOPMed (black) reference panels. (c) Effective sample size required for reaching 80% statistical power to detect genome-wide significant signals at different effect sizes (OR). The dotted lines show the discovery effective sample size of this study. (d) Percentage of the exome sequenced variants in chromosome 22 that could be imputed when using the 1000GP3 (blue) or the TOPMed (black) reference panels. (e) Percentage of the exome sequenced LoF and deleterious predicted variants based on CADD score in chromosome 22 that could be imputed when using the 1000GP3 (blue) or the TOPMed (black) reference panels

**Fig. 3**
Type 2 diabetes GWAS meta-analysis in the Latino population. (a) Manhattan plot of the meta-analysis association statistics, highlighting the loci with genome-wide significance (red) or sub-genome-wide significance (orange) for type 2 diabetes. (b) Regional association plot of the novel *ORC5/LHFPL3* locus associated with type 2 diabetes risk. (c) Forest plot of the GWAS association statistics for the novel *ORC5/LHFPL3* locus in the discovery (black), the replication (blue) and overall (red) cohorts

**Fig. 4**
Sub-genome-wide significant *HDAC2* novel type 2 diabetes loci. (a) Scatter plot of the effect allele frequencies (EAFs) from the sub-genome-wide significant variants in Latino (LAT) vs European (EUR) populations, highlighting those that are distinct from the known lead type 2 diabetes-associated variants (purple) and those that are in novel loci (yellow). (b) Regional association plot of the novel *HDAC2* locus associated with type 2 diabetes risk. (c) Forest plot of the association statistics in the discovery (black) and the replication (blue) cohorts. (d) Violin plots of serum 3-hydroxybutyrate levels in non-carriers (blue) and carriers (yellow) of the rs1016378028 variant. Whiskers range from upper and lower fences (1.5 × IQR); points represent outliers. (e) *HDAC2* gene expression in human islets from donors with type 1 and type 2 diabetes and control islets treated (brown) or not (green) with different cytokines or other stressor compounds. **p<0.01, ***p<0.001 vs no treatment (adjusted p values, Benjamini–Hochberg correction). Whiskers range from upper and lower fences (1.5 × IQR); points represent outliers. (f) *HDAC2* gene expression in multiple tissues from GTEx and TIGER portals. Each box plot shows expression in a different tissue or cell line Whiskers range from upper and lower fences (1.5 × IQR); points represent outliers

**Fig. 5**
PS for the risk of type 2 diabetes in Latino population. (a) Variance explained by a PS using these Latino GWAS association statistics (LAT, green), the AGEN East Asian GWAS association statistics (EAS, grey), the DIAMANTE European GWAS association statistics (EUR, red), a combination of DIAMANTE European and AGEN East Asian GWAS association statistics (yellow) and a combination of DIAMANTE European, AGEN East Asian and these Latino GWAS association statistics (blue). METSB was used as the testing cohort. (b) Receiver operating characteristic curves for the type 2 diabetes risk prediction explained by a model including sex, age and ten PCs of ancestry (black), a model including covariates and a PS constructed using DIAMANTE European (red) and a model including covariates and a PS constructed using a combination of DIAMANTE European, AGEN East Asian and these Latino GWAS association statistics (blue). (c) Distribution of a multi-ancestry PS using a combination of DIAMANTE European, AGEN East Asian and these Latino GWAS association statistics in type 2 diabetes cases (blue) and controls (black). The table shows the OR per SD attributed to the multi-ancestry PS, as well as the OR for high-risk individuals

See this image and copyright information in PMC

References

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1. The SIGMA Type 2 Diabetes Consortium. Estrada K, Aukrust I, et al. Association of a low-frequency variant in HNF1A with type 2 diabetes in a Latino population. JAMA. 2014;311(22):2305–2314. doi: 10.1001/jama.2014.6511. - DOI - PMC - PubMed
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The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

Affiliations

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

Authors

Affiliations

Abstract

Figures

References

Publication types

MeSH terms

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LinkOut - more resources

Full Text Sources

Medical