ABSTRACT Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though

disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this

race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS)

study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two

into the genetic basis of T2D in AAs for ongoing genetic discovery efforts. SIMILAR CONTENT BEING VIEWED BY OTHERS MULTI-ANCESTRY GENETIC STUDY OF TYPE 2 DIABETES HIGHLIGHTS THE POWER OF

DIVERSE POPULATIONS FOR DISCOVERY AND TRANSLATION Article 12 May 2022 VALIDITY OF EUROPEAN-CENTRIC CARDIOMETABOLIC POLYGENIC SCORES IN MULTI-ANCESTRY POPULATIONS Article Open access 05

January 2024 POPULATION-SPECIFIC AND TRANS-ANCESTRY GENOME-WIDE ANALYSES IDENTIFY DISTINCT AND SHARED GENETIC RISK LOCI FOR CORONARY ARTERY DISEASE Article 05 October 2020 INTRODUCTION

Diabetes mellitus is a heterogeneous group of metabolic and health conditions characterized by glucose dysregulation and defects in insulin secretion and/or insulin action1. Chronic

hyperglycemia has been associated with long-term damage and dysfunction of the kidneys, heart, and blood vessels2. Diabetes is a major risk factor for cardiovascular disease (CVD),

particularly coronary heart disease and stroke3. In the United States, type 2 diabetes (T2D) is the most common form of diabetes in adults, constituting > 90% of cases4 and is growing in

prevalence among adolescents5. T2D is more often associated with increased age6; however, the T2D epidemic can largely be attributed to a worldwide increase in obesity7. Since lifestyle

intervention focused on obesogenic behaviors is effective at preventing T2D8, identifying populations with increased genetic susceptibility could lower disease morbidity. It is

well-established that T2D is a complex disease, and the risk of developing T2D depends on environmental and genetic factors. Further supporting the genetic background of the disease is the

high concordance in monozygotic twins compared to dizygotic twins in familial studies9,10,11,12. While traditional twin studies have been considered the “gold standard” for measuring the

heritability of a trait, more recent literature has suggested that twin studies may overestimate heritability due to shared environment and non-additive genetic effects creating “phantom

heritability”13. While broad sense heritability of a trait is the proportion of phenotypic variation attributed to genetics, the narrow-sense heritability (h2) is the proportion attributable

to the additive gene effects14. These additive effects of variants underlying a trait is of particular importance because they constitute the genetic variation component transferred from

parent to offspring 15. With advances in genotyping technologies, the generation of genome-wide common genetic variant data has enabled approaches that provide an alternative to twin or

family heritability studies16,17. These methods estimate heritability in unrelated individuals via correlation between genetic and phenotypic sharing, similar to family-based studies.

However, instead of using the theoretic estimates of genetic sharing (i.e., Mendel’s Laws), single nucleotide polymorphism (SNP)-based heritability analyses allow for empirical estimates of

genetic sharing to be directly obtained from the genotype data17. Thus, by extending the models to utilize the contributions from all common genetic variants, these methods can detect

considerable shares of the additive genetic effect13. While previous h2 estimates of T2D and related clinical traits (e.g., fasting glucose, fasting insulin) have varied between 25% and 80%,

they have either excluded or underrepresented individuals of non-European ancestry, particularly African Americans (AAs)2,18,19,20,21,22. Importantly, known disparities exist in T2D, where

AAs have a higher prevalence of T2D, increased mortality rates, and an increased risk of T2D complications compared to individuals of European descent23,24. Further, there is a concern that

AAs and African ancestry groups may benefit less from genetic research, potentially exacerbating health disparities of common chronic conditions, such as T2D25. Given these trends,

additional heritability studies are warranted for these populations. For example, accurate heritability estimates are needed to understand the utility of polygenic risk scores (PRS) in

multi-ancestral populations with regard to the maximum trait variance expected to be explained by a PRS. In the present study, we seek to capture the T2D additive genetic variation (i.e.,

h2) from ~ 19,300 AA participants in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, the Genetics of Hypertension Associated Treatments (GenHAT) study, and the

Electronic Medical Records and Genomics (eMERGE) network. Each study used an overlapping subset of 8.2 million imputed genetic variants to estimate the h2 using two common approaches,

Genome-Wide Complex Trait Analysis (GCTA) and Linkage-Disequilibrium Adjusted Kinships (LDAK), making this one of the most extensive studies to estimate T2D heritability in AAs. RESULTS

Descriptive statistics for 7957 AA T2D cases and 11,378 AA controls are presented in Table 1. On average, cases were slightly older (65 years versus 63 years for controls, 66 years versus 66

 years for controls, and 68 years versus 67 years in controls for REGARDS, GenHAT, and eMERGE, respectively). Furthermore, T2D cases were more likely to be men in REGARDS (59%), but women in

GenHAT (61%) and eMERGE (65%). Study-level heritability estimates are provided in Table 2. The base model (Model 1) h2 estimates using LDAK were 19% in REGARDS, 19% in GenHAT, and 33% in

eMERGE. Similar trends were observed when estimates were calculated using GCTA, ranging from 21% in GenHAT to 31% in eMERGE. Upon age, sex, and genetic ancestry adjustment (Model 3), h2

estimates from LDAK were similar to Model 1 (18% in GenHAT, 18% in REGARDS, and 33% for eMERGE). In the fully-adjusted (Model 3) using GCTA, h2 estimates for REGARDS, GenHAT, and eMERGE were

24%, 21%, and 32%, respectively. In a sensitivity analysis, the study site was included as a covariate for the eMERGE cohort and results remained similar with and without adjustment.

Further, when adjusting for the AA-specific population prevalence of T2D (heritability liability, h2liab), estimates increased marginally across all models and methods (Supplemental Table

1). DISCUSSION African American populations continue to be underrepresented in genomic research. In the era of genomic medicine, this could unintentionally create new disparities in

prevention and prediction of T2D. By leveraging genome-wide SNP array data from unrelated participants belonging to well-characterized cohort studies the current study provides insight into

the additive genetic factors that contribute to the phenotypic variance of T2D in this non-European population. This type of common genetic variant heritability study is less prone to the

confounding effects of shared environment observed in family studies. Therefore, the results presented may give a more precise estimation of the genetic contribution to T2D which may inform

how genome-wide data is used in the future for the treatment and prevention of the condition. We estimated the h2 of T2D using two well-characterized estimation approaches, LDAK and GCTA.

Upon covariate adjustment, we observed h2 estimates from GCTA ranging from 21 to 32%. LDAK provided more conservative fully-adjusted estimates, ranging from 18 to 33%. This is most likely

due to the inclusion of LD and allele frequency patterns into the estimation, correcting uneven LD patterns across the genome26. Subsequent h2liab estimates, aimed to measure heritability

independent of disease prevalence, ranged from 19% to 34%. As described, we observed variable estimates across studies. This leads us to believe that age and sex were confounders, justifying

the need for a fully adjusted model and age-matching cases and controls in eMERGE to be representative of what was present in GenHAT and REGARDS. The h2liab estimates also showed slight

inflation in REGARDS across both methods and we speculate that could be a result of the smaller case proportion (~ 30%) compared to eMERGE and GenHAT (~ 50%). It is also important to note

that h2liab could potentially be biased, as previously reported in Golan et al., as it is a function of the prevalence in a given sample population27. Prior reports of heritability of T2D

and T2D-related traits (ranging 25–80%) have been described largely in populations or families of European descent. A 2005 familial aggregation study in ~ 2400 non-diabetic AA participants,

described heritability of 29% ± 9% for fasting glucose and 28% ± 8% for fasting insulin measures19, similar to our findings. Importantly, over 700 unique genetic loci associated with T2D

have been reported by GWAS28,29,30,31,32, explaining almost 20% of T2D heritability in a multi-ancestry sample31. The majority of these loci were identified in European or Asian

populations33, though > 25 loci were discovered in AAs31,34,35,36. Given the wide-range of reported heritability estimates it’s difficult to decipher how much missing heritability remains

after accounting for discovered significant loci. Therefore, more precise estimates of heritability such as that presented in our study are needed to inform future GWAS discovery and

precision medicine applications of GWAS data. Our study is subjected to several limitations. First, heterogeneity in the estimates could result from heterogeneity in the T2D phenotype,

heterogeneity in study designs, and population admixture. To account for these limitations, we adjusted for genetic principal components in each study, as well as the study site in

sensitivity models in eMERGE. We followed the same validated T2D phenotyping as a previously published report37, attempting to harmonize the phenotype between the population-based

longitudinal study (REGARDS), randomized clinical trial (GenHAT/ALLHAT), and electronic medical records (EMR) from eMERGE. While attempting to harmonize the genetic variants, it is important

to note the differences in the imputation reference panel between REGARDS and GenHAT (TOPMed release 2) versus eMERGE (HRC), which did result in the exclusion of nearly 7 million variants

from REGARDS and GenHAT. However, this number of variants is still compatible with other publications that have used these approaches38. Lastly, while the use of the contemporary arrays and

methodologies (e.g., Illumina MEGA array and TOPMed reference panel) allows for interrogation of more African-specific variants, the potential genetic contribution of rare minor allele

frequency (MAF) < 1% or structural variants was not investigated in this study. This is important since rare variants are thought to harbor more deleterious effects than common variants,

as well as playing an important role in accurately calculating the heritability of complex diseases39. In conclusion, we conducted one of the largest heritability estimates of T2D to date

utilizing genetic array data and to the best of our knowledge, the largest study of h2 in AAs, comprising three independent studies with sizable AA populations and T2D prevalence. All three

studies have well-described and extensive phenotyping, allowing for appropriate covariate adjustment and previously validated T2D case and control definitions. The use of imputed data allows

for more consistency across studies, with the inclusion of more than 8.2 million overlapping genetic variants. Though we observed similar trends using LDAK and GCTA, the considerations of

LD resulted in a more conservative h2 range across studies using LDAK. Our h2 estimates are lower with smaller confidence intervals than most familial studies on T2D that have been

predominately described in European populations; however, they are similar to a family study on fasting insulin and glucose levels in AAs. Future work in determining the heritability of

complex diseases, including T2D, is warranted in order to advance the availability of genomic resources for non-European populations. METHODS STUDY POPULATIONS Three cohorts, REGARDS,

GenHAT, and eMERGE contributed genetic data for this study, comprising 7,957 T2D cases and 11,378 controls of African descent (Fig. 1). Descriptive statistics for each study are described in

Table 1. Signed informed consent was collected from all participants in each study. All studies were reviewed and approved by the institutional review boards of the participating

institutions and study sites. REASONS FOR GEOGRAPHIC AND RACIAL DIFFERENCES IN STROKE (REGARDS) STUDY REGARDS is a national, longitudinal study of incident stroke and associated risk

factors, enrolling over 30,000 Black and white adults aged 45 years or older from all 48 contiguous US states and the District of Columbia40. Participants completed a computer-assisted

telephone interview (CATI) and an in-home visit where blood and urine were collected, as well as blood pressure measurements and a medicine review. Participants are contacted at 6 month

intervals to obtain information regarding incident stroke or secondary outcomes. A subset of 8916 Black REGARDS participants underwent genotyping using Illumina Infinium AMR/AFR (MEGA)

BeadChip arrays. Quality control procedures have been previously described41, but briefly, participants were excluded based on sex mismatches, internal duplicates, or HapMap controls.

Variants were excluded if they were located on sex chromosomes, had ambiguous strands, were not bi-allelic, were in violation of Hardy Weinberg (p < 1.00e-12 for REGARDS), had MAF < 

5%, and/or had a missing rate > 10%. Imputation was performed using the Trans-omics for Precision Medicine (TOPMed) release 2 (Freeze 8) reference panel42. GENETICS OF HYPERTENSION

ASSOCIATED TREATMENTS (GENHAT) STUDY GenHAT is an ancillary study to the Antihypertensive and Lipid Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). ALLHAT was a randomized,

double-blind multicenter clinical trial that enrolled over 42,000 high-risk individuals 55 years or older with hypertension and at least one additional risk factor for cardiovascular

disease43,44. The GenHAT study (N = 39,114) evaluated the interaction between candidate hypertensive genetic variants and antihypertensive treatments to modify the risk of CVD outcomes. A

subset of 7711 Black adults with hypertension was genotyped on Illumina MEGA BeadChip arrays. Similar to GenHAT, QC procedures have been previously described41. Participants were excluded

based on sex mismatches, internal duplicates, or HapMap controls, while variants were excluded if they were located on sex chromosomes, had ambiguous strands, were not bi-allelic, were in

violation of Hardy Weinberg (p < 1.00e-05), had MAF < 5%, and/or had a missing rate > 10%. Imputation was performed using the Trans-omics for Precision Medicine (TOPMed) release 2

(Freeze 8) reference panel42. ELECTRONIC MEDICAL RECORDS AND GENOMICS (EMERGE) NETWORK The eMERGE network combines DNA biorepositories with electronic medical records (EMR) for the purpose

of research focused on advancing efforts in genomic medicine. The eMERGE III cohort was initiated in 2015 and culminated in over 100,000 GWAS samples across the network, while eMERGE IV

aimed to develop and disseminate methodologies for genetic risk assessment, integrate genetics into routine medical practice to identify individuals at high risk for common disease, and

recommend interventions37,45. For the current study, genetic data from eight sites (Cincinnati Children’s Hospital Medical Center, Children’s Hospital of Philadelphia, Columbia University,

Mass General Brigham, Mayo Clinic, Icahn School of Medicine at Mount Sinai, Northwestern University, and Vanderbilt University Medical Center)46 was imputed against the Haplotype Reference

Consortium (HRC) panel47. To age-match cases and controls from eMERGE to the REGARDS and GenHAT studies, samples were selected from the age range 30–100. Individuals were divided into

deciles and numbers of cases and controls were matched in each decile (Supplemental Fig. 1). DEFINITION OF TYPE-2 DIABETES STATUS T2D status was defined independently across all three

studies. In the REGARDS study, T2D cases were classified based on fasting glucose ≥ 126 mg/dL (7 mmol/L), non-fasting glucose ≥ 200 mg/dL (11.1 mmol/L), or the use of diabetes medications

(e.g., oral hypoglycemic pills or insulin). The ALLHAT/GenHAT definition of T2D was described as a fasting glucose ≥ 140 mg/dL or use of diabetes medication43,44. Therefore, in the current

study we excluded controls that had baseline fasting glucose ≥ 126 mg/dL or missing a fasting glucose measure. In eMERGE, a revised EMR-based phenotyping algorithm based on ICD9/ICD10 codes

was applied across the participants37. Further information regarding all T2D definitions has been previously described in detail37. STATISTICAL ANALYSIS To estimate the h2 for T2D, we

employed two widely used methodologies, GCTA and LDAK, using an overlapping subset of 8,240,835 imputed genetic variants (Fig. 1). Three statistical models were fit: a base model without

covariates (Model 1), a model adjusting for genetic ancestry through principal component analysis48 (Model 2), and a model adjusting for genetic ancestry, age, and sex (Model 3). In addition

to the stated Model 3, a sensitivity analysis further adjusting for the study site to account for potential heterogeneity in sample characteristics across sites in the eMERGE cohort was

performed, and the results remained consistent. GENOME-WIDE COMPLEX TRAIT ANALYSIS (GCTA) METHOD Both genotypes and imputed variants that passed quality control (QC) filters were used to

construct a genomic relationship matrix (GRM) using the GCTA tool, as previously described using the genome-based restricted maximum likelihood (GREML)49,50. The GRM reflects allele sharing

(\({A}_{ij}\)) between two individuals (_i_ and _j_) across variants with entries $$A_{ij} = \frac{1}{m}\mathop \sum \limits_{k = 1}^{k = m} \frac{{\left( {x_{ik} - 2p_{k} } \right)\left(

{x_{jk} - 2p_{k} } \right)}}{{2p_{k} \left( {1 - p_{k} } \right)}},$$ (1) where _m_ is the number of variants, _x__ik_ and _x__jk_ are the genotypes coded as 0, 1, or 2 of individuals i and

_j,_ respectively, at the _k__th_ locus, and _p__k_ is the MAF of the _k__th_ locus. The variance of T2D was calculated as $$var\left( {T2D} \right) = A\sigma_{v}^{2} + I\sigma_{e}^{2} ,$$

(2) where the variance explained by the genetic variants (\({\sigma }_{v}^{2}\)) corresponding to GRM and residual error variance (\({\sigma }_{e}^{2}\)) were estimated using restricted

maximum likelihood (REML), _A_ is an _n_ × _n_ matrix with elements _A__ij,_ and I is an _n_ × _n_ identity matrix. The proportion of the variance of T2D explained by all the genetic

variants (h2) on the observed scale was then calculated as: $$h^{2} = \frac{{\sigma_{v}^{2} }}{{\left( {\sigma_{v}^{2} + \sigma_{e}^{2} } \right)}}$$ (3) We removed one individual from

relative pairs with estimated genetic relatedness greater than 0.025 to ensure no closely-related individuals were included in heritability estimates (e.g., parent-offspring, siblings,

cousins). LINKAGE-DISEQUILIBRIUM ADJUSTED KINSHIPS (LDAK) METHOD Knowing that African ancestral populations have greater haplotype diversity and, in turn, shorter segments of linked

alleles51, we utilized LDAK, which incorporates linkage disequilibrium (LD), as an additional approach. LDAK can be used as an alternative method of generating a GRM by weighting genetic

variants based on local LD patterns52. As previously described, the genetic variance of variants in high LD with a causal variant is typically overestimated in GCTA, while the genetic

variance is underestimated in lower LD regions52, thus demonstrating the importance of accounting for LD in the construction of the LD-weighted GRM. Therefore LD-weighting eliminates the

overestimation of heritability in high LD regions and underestimation of heritability in low LD regions by giving smaller weights to markers in the high-LD regions and large weights to

markers in low LD regions53. The GRM for LDAK is constructed as follows: $$GRM_{LDAK} = \frac{XWX^{\prime}}{m}$$ (4) where W is the diagonal matrix with elements representing the LD-weight

for each variant, N is the total number of variants, and X is a matrix with the general term $$x_{ij} = \left( {m_{ij} - 2_{pj} } \right) / \left( {\sqrt {2_{pj} \left( {1 - p_{j} } \right)}

} \right)$$ (5) with \({p}_{j}\) being the frequency of a given allele at variant _j_ and \({m}_{ij}\) being the genotype for the _j-th_ variant in the _i-th_ individual (represented by 0,

1, or 2). When estimating heritability, LDAK assumes: $$E\left[ {h_{j}^{2} } \right] \propto \left[ {f_{i} \left( {1 - f_{i} } \right)} \right]^{1 + \alpha } \times \varpi_{j} \times r_{j}$$

(6) where \(E\left[{h}_{j}^{2}\right]\) is the expected heritability contribution of genetic variant _j_ and \({f}_{i}\) is its observed MAF. The parameter α determines the assumed

relationship between heritability and MAF. The genetic variant weighs ( \({\varpi }_{j}\)) are based on the local level of LD and tend to be higher for variants in low-LD regions; thus, LDAK

assumes that these variants contribute more than those in the high-LD areas. \({r}_{j}\) \(\epsilon \left[\text{0,1}\right]\) is an information score measuring genotype certainty, where

LDAK assumes higher-quality variants contribute more than lower-quality ones54. LIABILITY SCALE In order to account for the inflated proportion of cases in case–control designs, the

heritability estimation on the observed scale was transformed to that on the liability as $$h_{liab}^{2} = h^{2} \frac{{K\left( {1 - K} \right)}}{{z^{2} }} \frac{{K\left( {1 - K}

\right)}}{{P\left( {1 - P} \right)}},$$ (7) where \(K\) is the population prevalence of the T2D in AAs,_ P_ is the sample prevalence of T2D, and z is the height of the standard normal

probability density function at the truncation threshold t, as previously described55. The AA-specific T2D prevalence of 12.5% was extracted from recent literature56. DATA AVAILABILITY The

REGARDS (Study Accession: phs002719.v1.p1), GenHAT (Study Accession: phs002716.v1.p1) and eMERGE (Study Accession: phs001584.v2.p2) phenotypic and genetic data are available on dbGaP.

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  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS The authors thank all the eMERGE sites for providing genomic and health information. Additionally, the authors thank

the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be

found at: https://www.uab.edu/soph/regardsstudy/. FUNDING The eMERGE Network was funded by the National Human Genome Research Institute (NHGRI) through the following grants: U01HG006828

(Cincinnati Children's Hospital Medical Center and 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 and the University of Washington);

U01HG006379 (Mayo Clinic); U01HG006380 (Icahn School of Medicine at Mount Sinai); U01HG006388 (Northwestern University); U01HG006378 (Vanderbilt University Medical Center); and U01HG006385

(Vanderbilt University Medical Center serving as the Coordinating Center). The eMERGE IV Mass General Brigham site was funded by the NHGRI through U01HG008685, the Columbia University site

was funded through U01HG008680, and the University of Alabama at Birmingham site was funded through U01HG011167. The REGARDS (R01HL136666, MRI, LAL) and GenHAT (R01HL123782, MRI) genetic

studies were supported by the National Heart, Lung, and Blood Institute (NHLBI). The parent REGARDS study was supported by cooperative agreement U01 NS041588, co-funded by the National

Institute of Neurological Disorders and Stroke (NINDS) and the National Institute on Aging (NIA).The content is solely the responsibility of the authors and does not necessarily represent

the official views of the NINDS or the NIA. Representatives of the NINDS were involved in the review of the manuscript but were not directly involved in the collection, management, analysis,

or interpretation of the data. Other funding sources include NHLBI T32HL072757 (N.D.A.), UM1 DK078616 (J.B.M.) R01HL151855 (J.B.M.), R01HL092173 (N.A.L.), and R00AG054573 (T.G.). AUTHOR

INFORMATION AUTHORS AND AFFILIATIONS * Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA Nicole D. Armstrong & Marguerite R. Irvin * Department of

Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA Amit Patki, Vinodh Srinivasasainagendra & Hemant K. Tiwari * Center for Genomic Medicine, Massachusetts General

Hospital, Boston, MA, USA Tian Ge * Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA Tian Ge * Division of Biomedical Informatics and Personalized Medicine,

Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA Leslie A. Lange * Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical

Center, Cincinnati, OH, USA Leah Kottyan & Bahram Namjou * Department of Pediatrics, Cincinnati Children’s Hospital Medical Center &, The University of Cincinnati, Cincinnati, OH,

USA Amy S. Shah * Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA Laura J. Rasmussen-Torvik * Division of Medical Genetics,

Department of Medicine, University of Washington, Seattle, WA, USA Gail P. Jarvik * Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA,

USA James B. Meigs * Department of Medicine, Harvard Medical School, Boston, MA, USA James B. Meigs * Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA James

B. Meigs * Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA Elizabeth W. Karlson * Mass General Brigham Personalized Medicine, Boston, MA, USA Elizabeth W. Karlson *

Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA Nita A. Limdi Authors * Nicole D. Armstrong View author publications You can also search for this author

inPubMed Google Scholar * Amit Patki View author publications You can also search for this author inPubMed Google Scholar * Vinodh Srinivasasainagendra View author publications You can also

search for this author inPubMed Google Scholar * Tian Ge View author publications You can also search for this author inPubMed Google Scholar * Leslie A. Lange View author publications You

can also search for this author inPubMed Google Scholar * Leah Kottyan View author publications You can also search for this author inPubMed Google Scholar * Bahram Namjou View author

publications You can also search for this author inPubMed Google Scholar * Amy S. Shah View author publications You can also search for this author inPubMed Google Scholar * Laura J.

Rasmussen-Torvik View author publications You can also search for this author inPubMed Google Scholar * Gail P. Jarvik View author publications You can also search for this author inPubMed 

Google Scholar * James B. Meigs View author publications You can also search for this author inPubMed Google Scholar * Elizabeth W. Karlson View author publications You can also search for

this author inPubMed Google Scholar * Nita A. Limdi View author publications You can also search for this author inPubMed Google Scholar * Marguerite R. Irvin View author publications You

can also search for this author inPubMed Google Scholar * Hemant K. Tiwari View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS NDA, NAL, MRI,

and HKT contributed to the concept and design of the study. AP and VS performed quality control of the genomics data. AP performed the heritability analysis. TG, EWK, MRI, and HKT provided

insight into methodology. LAL, LK, BN, ASS, LJR-T, GPJ, JBM, provided interpretation of the results. MRI and HKT supervised the study. NDA, MRI, and HKW drafted the manuscript. All authors

provided critical edits on subsequent versions of the manuscript and approved the final version. CORRESPONDING AUTHOR Correspondence to Nicole D. Armstrong. ETHICS DECLARATIONS COMPETING

INTERESTS JBM is an Academic Associate with Quest Diagnostics R&D. The remaining authors declare that they have no competing interests. ADDITIONAL INFORMATION PUBLISHER'S NOTE

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Armstrong, N.D., Patki, A., Srinivasasainagendra, V. _et al._ Variant level heritability estimates of type 2 diabetes in African Americans. _Sci Rep_ 14, 14009 (2024).

https://doi.org/10.1038/s41598-024-64711-3 Download citation * Received: 13 July 2023 * Accepted: 12 June 2024 * Published: 18 June 2024 * DOI: https://doi.org/10.1038/s41598-024-64711-3

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clipboard Provided by the Springer Nature SharedIt content-sharing initiative KEYWORDS * Heritable quantitative trait * Type 2 diabetes mellitus * Genomics * Genetic polymorphisms *

Disparities