Genetic underpinnings of regional adiposity distribution in African Americans: Assessments from the Jackson Heart Study.

BACKGROUND: African ancestry individuals with comparable overall anthropometric measures to Europeans have lower abdominal adiposity. To explore the genetic underpinning of different adiposity patterns, we investigated whether genetic risk scores for well-studied adiposity phenotypes like body mass index (BMI) and waist circumference (WC) also predict other, less commonly measured adiposity measures in 2420 African American individuals from the Jackson Heart Study.
METHODS: Polygenic risk scores (PRS) were calculated using GWAS-significant variants extracted from published studies mostly representing European ancestry populations for BMI, waist-hip ratio (WHR) adjusted for BMI (WHRBMIadj), waist circumference adjusted for BMI (WCBMIadj), and body fat percentage (BF%). Associations between each PRS and adiposity measures including BF%, subcutaneous adiposity tissue (SAT), visceral adiposity tissue (VAT) and VAT:SAT ratio (VSR) were examined using multivariable linear regression, with or without BMI adjustment.
RESULTS: In non-BMI adjusted models, all phenotype-PRS were found to be positive predictors of BF%, SAT and VAT. WHR-PRS was a positive predictor of VSR, but BF% and BMI-PRS were negative predictors of VSR. After adjusting for BMI, WHR-PRS remained a positive predictor of BF%, VAT and VSR but not SAT. WC-PRS was a positive predictor of SAT and VAT; BF%-PRS was a positive predictor of BF% and SAT only.
CONCLUSION: These analyses suggest that genetically driven increases in BF% strongly associate with subcutaneous rather than visceral adiposity and BF% is strongly associated with BMI but not central adiposity-associated genetic variants. How common genetic variants may contribute to observed differences in adiposity patterns between African and European ancestry individuals requires further study.

Introduction

Despite the wide adoption of body mass index (BMI), a measure that correlates well with numerous health risk factors [1], there are limitations to this metric [2]; notably, it does not differentiate between variation in fat and lean mass. This can lead to an imprecise categorization of obesity [3], and hence misleading inferences for cardiometabolic outcomes [4]. This is a major drawback given that obesity-health outcome associations are differentially mediated by bodily composition, with adipose tissues more prominently linked with adverse outcomes [5]. Total adiposity [6], regional fat distribution [7], and especially visceral adiposity [8] are significant risk factors for cardiovascular diseases.

Since body fat is linearly associated with BMI in sedentary populations [9], a proportion of genetic variants associated with overall body mass expectedly overlap with loci linked to body fat percentage [10]. However, this BMI-body fat mass link is known to exhibit phenotypic variability across ethnicities [11], which extends to regional distribution of fat tissue as well: in African ancestry (AA) individuals with comparable BMI metrics to those with European ancestry (EA), the proportion of visceral adiposity is lower [12, 13]. Paradoxically, the prevalence of cardiometabolic diseases in AA, compared to EA with similar BMI, is higher [14]; genetics may also play a significant role in this differential adiposity distribution and merits a thorough examination.

Despite some evidence for AA-specific variants associated with BMI [15], most reported BMI-associated SNPs in AAs are variants first reported in EA individuals [16], and European-derived genetic risk scores have been found to be predictive of BMI variation in AA populations as well [17]. But evidence for generalization of variants associated with other adiposity traits to AAs, including measures of central obesity, is limited [18], and no study has examined replicability of body fat percentage-associated variants identified in EA populations to AA. Since genomic loci associated with body fat percentage are suggested to be more closely aligned with multiple cardiometabolic disease risks than BMI-associated variants [19], assessing if genetic variants associated with adiposity patterns in EA—particularly body fat percentage (BF%)—can be extrapolated to AA individuals is an important question.

The objective of this this study was to assess the utility of known variants associated with anthropometric and adiposity measures discovered in predominantly EA populations to predict BF%, SAT, VAT, and VSR among AA individuals from Jackson Heart Study (JHS). We also estimated the association of variants with evidence of directional replicability and nominal significance in JHS for their originally reported adiposity measure to other adiposity measures, allowing us to examine the relationships between adiposity measures.

Methods

Study population

The JHS recruited 5306 African American residents living in the Jackson, Mississippi, metropolitan area of Hinds, Madison, and Rankin Counties. Participants were recruited to participate in the study from four pools: random sampling (17% of participants), volunteers (30%), participants in the Atherosclerosis Risk in Communities (ARIC) study (31%), and secondary family members (22%). The age at enrollment for the unrelated cohort was 35–84 years; the family cohort included related individuals >21 years old. Extensive phenotypic data was collected during a baseline examination (September 2000 –March 2004), and two follow-up examinations (October 2005 –December 2008, and February 2009 –January 2013). A third follow-up examination is in progress. Annual follow-up interviews and cohort surveillance for cardiovascular events and mortality are also ongoing.

For BMI and WC genome-wide association tests, we used phenotypic observations from visit 1 to maximize sample size (N = 3020); for other traits, we used phenotypic measures from visit 2 for the same purpose (N = 2554). However, for polygenic risk score regression analyses, we exclusively used phenotypic observations from visit 2. In our analyses, 10 participants were excluded due to pregnancy during the visit 2 examination, and 124 were excluded for missing or biologically implausible values. The total sample size used was n = 2420 individuals.

The study protocol was approved by the participating JHS institutions including the Tougaloo College, the Jackson State University, and the University of Mississippi Medical Center IRB, under IRB number- UMMC IRB File#1998–6004. All JHS participants gave written informed consent. The IRB registration for UMMC is #00000061, DHHS FWA #00003630.

Genotyping

Genotyping for single nucleotide polymophisms (SNPs) was performed with the Affymetrix 6.0 SNP Array (Affymetrix, Santa Clara, Calif). Outliers based on principal components, sample swaps, duplicates, and one of each pair of monozygotic twins were excluded. Samples with a mismatch between pedigree vs genetic sex were also removed. Variants with a minor allele frequency ≥ 1%, a call rate ≥ 90%, and a Hardy Weinberg equilibrium (HWE) p-value >10–6 (n = 832,508 variants) were used for imputation to 1000 Genomes Project population SNP reference panel (Phase 3, Version 5), using Minimac3 on the Michigan Imputation Server. Only SNPs with an imputation quality r2>0.9 were selected for polygenic risk score analyses.

Polygenic risk scores

The use of polygenic risk scores (PRS) to predict complex traits [20], closely related phenotypes [21], and the same phenotype across different populations [22] has been validated. Both weighted and unweighted methods have been employed for estimation of PRS [23]. Weighted risk scores account for each variant’s effect size on the phenotype. Unweighted PRS assume that all variants have equal effect on the trait; this assumption is almost always violated, as in genome-wide association studies (GWAS), some variants have much larger effect sizes than others. In contrast, use of a weighted PRS method in a non-EA population may also introduce bias because nearly all known variants were identified in predominantly EA populations, and studies suggest dissimilar effect sizes for the same SNPs across ancestries, likely due to differential LD with true causal variants [24]. Although the weighted method can lead to reduced mean square error for prediction in some cases [25], the main applications for polygenic scores, namely association testing and prediction, do not appear to differ substantially between two methods. In addition, an unweighted score is more robust against error in estimating the effect sizes arising from limited samples, “winner’s curse bias” [26], and confounding by demographic structure [27]. Therefore, we used an unweighted PRS approach in the AA population studied here.

At each locus (SNP), participants were assigned a dosage value between 0 and 2 inclusive, based on the estimated number and frequency of phenotype-increasing alleles under an additive genetic effect model. The PRS value for each individual reflects the summation of risk alleles across all selected loci.

SNP risk sets

To construct SNP sets used for PRS calculation, we utilized both the European Bioinformatic Institute GWAS repository (ebi.ac.uk/gwas, accessed December 2020), and PubMed for extraction of SNPs linked to anthropometric measures including body mass index (BMI), waist to hip ratio adjusted for BMI (WHRBMIadj), waist circumference adjusted for BMI (WCBMIadj), and body fat percentage (BF%). Only SNPs reported at GWAS significance level (p≤5.00 ×10−8) were selected. Most variants were discovered in EA-only studies, but some are from large multi-ethnic GWAS meta-analyses or AA specific analyses. PRS derived from only AA-specific variants for target phenotypes were judged to be underpowered due to the low number of available variants, as well as PRS calculated from SNPs associated with VAT, SAT and VSR.

We conducted linkage disequilibrium (LD) analysis using LDlink (ldlink.nci.nih.gov) with multi-ethnic populations. If a pair or a group of SNPs were in LD with one another (R2≥0.1), we prioritized sentinel variants from larger and more recent studies with lower p-values and selected a single SNP to ensure independence of variants and avoid double counting the same functional locus (site). This set of LD-pruned variants was then used to calculate the PRS.

Anthropometry measures

WC was measured at the umbilicus level using a non-elastic tape measurer and rounded to the nearest centimeter; hip circumference (HC) was measured at the level of the widest circumference over the greater trochanter. WHR was obtained by dividing WC over HC.

Adiposity measures

A variety of different techniques for assessment of body composition exist [28]. For overall BF%, bioimpedance is a widely adopted method [29]. Under this technique, BF% is calculated based on the measured resistance of the adipose tissue as the person lays supine with electrodes placed on the arm and/or leg; bare foot-to-foot bioimpedance was conducted using the Tanita Body Fat Monitor (Tanita Corp, Tokyo). BF% was estimated using a programmed algorithm that incorporates bioimpedance readings with a height, weight, age and sex-specific equation and additional adjustment for physical activity levels.

To estimate visceral and subcutaneous adipose volumes, the study employed computed tomography (CT) technique at visit 2, where the heart and lower abdomen regions were scanned with 16-channel mutidetector CT machine (Lightspeed 16 Pro, GE Healthcare, Milwaukee, WI). Abdominal imaging slices covering the lower abdomen from L3 to S1 were used to quantify both VAT and SAT [29], such that 24 adjacent 2-mm thick slices centered on the lumbar disk space at L4 to L5 were used for quantification of both types of adiposity; 12 images before the center of L4 to L5 disk space and 12 images after that space [30].

Statistical analysis

To facilitate comparison across different phenotypes, we performed inverse-normal transformations prior to analysis. Genome-wide association analyses were completed for target traits. We used EPACTS 3.2.6 [31] to perform GWAS analyses, adjusting for age, sex, and a genetic relationship matrix using the EMMAX test; additionally, BMI was incorporated as covariate in WHR and WC analyses.

We calculated PRS under three separate but complementary scenarios using the following configurations: (a) set of all known loci (LD-pruned) reported at genome-wide significance (5×10−8) in multi-ethnic or European studies regardless of replicability in JHS (principle approach) (S1 Table); (b) the subset of risk loci with evidence of directional replication in the JHS-GWAS results (approach 2); and (c) a more restricted subset of risk loci with evidence for both directional replication as well as nominal statistical significance (p<5×10−2) in JHS results (approach 3, S2 Table). PRS for BMI, WHRBMIadj,WCBMIadj, and BF% were first tested against their respective phenotypes in JHS to ensure the validity of constructed predictors (S3 Table).

Phenotype-specific PRS obtained under various approaches were then tested for cross-sectional associations with phenotypic measures. Results for PRS obtained under the principle approach (e.g. all known loci) were reported in the manuscript, whereas models that were obtained with PRS under complementary approaches were provided as supplementary material. Both multivariable linear regression and mixed models [32] were employed to investigate the associations between PRS and adiposity measures, with age, sex, and the top 10 ancestry principal components as covariates; additionally, family ID was utilized as random component in mixed models. Both offered similar results; linear results were chosen for simplicity.

Some loci associated with obesity traits and/or fat distributions [33] are known to be sex-specific. Although we performed gender-stratified analysis, given the sex imbalance in this sample (36.9% males), results for the females largely mirrored the combined findings, while the male-only analysis lacked precision; therefore we chose to report the sex-combined analysis.

Finally, to characterize the association between EA-established variants for anthropometric traits with evidence of transferrability to AA (i.e. statistically significant in JHS-GWAS), and type of adiposity (BF%, SAT, VAT, VSR), we constructed heatmap plots of SNPs’ effect sizes (betas*100) to investigate if genetic variations underpinning obesity traits are closely correlated with overall body fat change or aligned to specific adiposity patterns. For phenotype and SNP clustering, Ward’s minimum variance method was used which aims at finding compact, spherical clusters [34].

Statistical analyses including PRS generation and regression models were performed using RStudio (V 1.1.463), and heatmap plots were obtained using the R package “pheatmap” [35].

Results

Population characteristics and adiposity measures

Table 1 provides a descriptive distribution of demographic, anthropometric and adiposity traits in the JHS population. Using the standard BMI cutpoints of ≥25 and ≥30 for overweight and obesity respectively, the majority of participants were either overweight (31.5%) or obese (54.2%). The mean WC and BF% also indicate a high prevalence of excess adiposity.

Table 1

Baseline characteristics of the study participants.

Variable	Total (N = 2420)
Male, N. (%)	892 (36.9%)
AGE (years), Mean (SD)	60.0 (12.4)
BMI (kg/m2), Mean (SD), Unit	32.2 (7.2)
BMI Categories
Underweight, N. (%)	26 (1.1%)
Normal Weight, N. (%)	244 (10.1%)
Overweight, N. (%)	762 (31.5%)
Obese, N. (%)	1312 (54.2%)
Missing, N. (%)	76 (3.1%)
Hip Circumference (cm), Mean (SD)	114.7 (14.9)
Waist Circumference (cm), Mean (SD)	102.9(16.2)
Waist/Hip (ratio*100), Mean (SD)	89.8(8.8)
Adiposity Composition
Fat Mass (kg), Mean (SD)	76.3(33.5)
VAT (cm3), Mean (SD), Unit	839.4(383.1)
SAT (cm3), Mean (SD), Unit	2335.8(1014.7)
Body Fat Mass (%), Mean (SD)	38.2(9.9)

VAT: Visceral Adipose Tissue, SAT: Subcutaneous Adipose Tissue.

Spearman correlation coefficients for anthropometric and adiposity measures (S4 Table) illustrated a high degree of correlation of BMI with WC (r = 0.82), but much weaker correlation with WHR (r = 0.16). BMI was also highly correlated with SAT (r = 0.83) and BF% (r = 0.70), but not as strongly with VAT (r = 0.49).

Association with polygenicc risk scores

In non-BMI adjusted models, all phenotype-specific PRS were found to be positive predictors of BF%; β = 2.4 (p = 2.1 ×10−10) for BF%-PRS; β = 1.2 (p = 3.3 ×10−51) for BMI-PRS; β = 1.1 (p = 2.2 ×10−19) for WC-PRS; and β = 0.7 (p = 1.4×10−8) for WHR-PRS respectively, where β represents % change in phenotype z-score per increase of 1 trait-increasing allele (Table 2). For the SAT phenotype, like BF%, all phenotype-specific PRS were positive predictors: β = 1.9 (p = 1.9 ×10−4) for BF%-PRS; β = 1.4 (p = 1.4 ×10−37) for BMI-PRS; β = 1.3 (p = 9.8 ×10−15) for WC-PRS; and β = 0.6 (p = 5.8×10−5) for WHR-PRS respectively. For the VAT phenotype, BMI-PRS and WC-PRS were positively associated with similarly close coefficients (e.g. slope) as the SAT phenotype (Table 2), but for WHR-PRS, the coefficient was notably larger (β = 1.2 (p = 3.5×10−9)); BF%-PRS was positive but not strong predictor of VAT (β = 0.8 (p = 1.4×10−1)). For the VSR, BF%-PRS was a significant negative predictor (β = -0.9 (p = 5.2×10−2)) and WHR-PRS was a positive predictor (β = 0.4 (p = 3.8×10−3)).

Table 2

Associations between phenotype-PRS (columns), and measures of adiposity (rows).

Betas are reported for standardized inverse normalized values, followed by their respective p-values. Nominally statistically significant results (p<5.00×10–2) are in bold font.

Phenotype-PRS/Adiposity trait	BF% β (p-value) (95%CI)	SAT β (p-value) (95%CI)	VAT β (p-value) (95%CI)	VAT: SAT R. β (p-value) (95%CI)	BMI Adjusted
WHR	0.7 (1.4×10−8) (0.4, 0.9)	0.6 (5.8×10−5) (0.3, 1.0)	1.2 (3.5×10−9) (0.8, 1.5)	0.4 (3.8×10−3) (0.1, 0.7)	No
WC	1.1 (2.2×10−19) (0.9, 1.3)	1.3 (9.8×10−15) (0.9, 1.6)	1.2 (6.9×10−11) (0.8, 1.5)	-0.0 (6.9×10−1) (-0.3, 0.3)
BMI	1.2 (3.3×10−51) (1.1, 1.4)	1.4 (1.0×10−37) (1.2, 1.6)	1.1 (2.0×10−20) (0.9, 1.4)	-0.2 (2.2×10−2) (-0.4, 0.0)
BF%	2.4 (2.1×10−10) (1.7, 3.2)	1.9 (1.9×10−4) (0.9, 2.9)	0.8 (1.4×10−1) (-0.3, 2.0)	-0.9 (5.2×10−2) (-1.9, 0.0)
WHR	0.2 (2.6×10−3) (0.1, 04)	0.1 (5.5×10−1) (-0.1, 0.2)	0.7 (6.7×10−7) (0.4, 1.0)	0.5 (3.3×10−4) (0.2, 0.8)	Yes
WC	0.1 (3.2×10−1) (-0.1, 0.2)	0.2 (3.0×10−2) (0.0, 0.4)	0.3 (2.8×10−2) (0.0, 0.6)	0.1 (3.8×10−1) (-0.2, 0.4)
BMI
BF%	1.3 (2.1×10−7) (0.8, 1.8)	1.0 (2.7×10−4) (0.4, 1.6)	0.1 (9.1×10−1) (-0.9, 1.0)	-0.8 (8.2×10−2) (-1.8, 0.1)

WHR: Waist to Hip Ratio, WC: Waist Circumference, BF%: Body Fat Percentage, SAT: Subcutaneous Adipose Tissue, VAT: Visceral Adipose Tissue, VAT/SAT R.: VAT to SAT Ratio, β: effect size (% change in z-score per increase in number of risk alleles).

* Associations adjusted for age, sex, first 10 ancestry principle components and BMI. Associations estimated under approach 1 (using all variants in the study).

In contrast, after adjusting for BMI in the models, only BF% and WHR were found to be positive predictors of BF%, with attenuated effect sizes (β = 1.3 (p = 2.1 ×10−7) for BF%-PRS and β = 0.2 (p = 2.6 ×10−3) for WHR-PRS respectively) (Table 2). For the SAT phenotype, BF% and WC-PRS remained positive predictors, but not WHR-PRS. For the VAT phenotype, both WHR and WC-PRS were positively associated, but not BF%-PRS. For the VSR, BF%-PRS was consistent negative predictor and WHR-PRS was positive predictor. Generally, the PRS of BMI and BF% phenotypes explained higher proportions of variance for BF% and SAT than VAT (S4 Table).

Results for PRS obtained under approach 2 and 3 are provided in S5 Table, which were largely reflective of estimates under the principle approach but less precise due to reduced number of SNPs used for the PRS calculation. Associations between WHR and WC-PRS were less consistent in both BMI and non-BMI adjusted models. BF%-PRS, in contrast, was consistently associated with BF% and SAT under both approach 2 and 3 and with/without BMI adjustment.

Association with individual SNPs

Since PRS were summary statistics of risk alleles and do not illustrate the scale of association of each risk allele, which may vary when compared to other variants, we performed separate multivariate regression tests with individual variants. We used 33, 8, 12, and 4 risk allele SNPs which were nominally significant in BMI, WCBMIadj, WHRBMIadj, and BF% GWAS results in JHS, to characterize individual variants’ association with BF%, SAT, VAT, and VSR ratio, respectively; each SNP represents an independent locus within the genome (based on linkage disequilibrium).

On the heatmap (S1 Fig), the primary clustering of adiposity pattern variables on the x-axis separate different types of fat. The primary clustering of the SNPs on the y-axis separates group of SNPs that are associated with increase in BF%, BMI, WCBMIadj, and WHRBMIadj. A clear majority of risk alleles associated with increased BMI and BF% are positively associated with SAT and overall BF%. Among WHRBMIadj and WCBMIadj-increasing alleles, no discernible or consistent pattern can be observed (S1 Fig).

Discussion

In this study we characterized the associations between genetic risk scores for BMI, WCBMIadj, WHRBMIadj, and BF% with body fat composition phenotypes, including overall BF%, VAT, SAT, and VSR among AA individuals in the JHS cohort.

Phenotypically, pairwise correlations among BMI, WC, BF% and SAT were strong, but considerably weaker correlations with VAT were observed (S4 Table). Although the pairwise correlation between BMI and WHR was somewhat weaker (ρ = 0.16) than observed in most studies (which often report a correlation of ~0.4 or more) [36-38], similarly weak correlations have been previously reported [39, 40], including in studies of African Americans individuals [41]. The BMI-WHR correlation is known to vary across age, sex, and ethnic groups [38].

All PRS were significant and positive predictors of BF% in AA; this illustrates that similar to most BMI-linked SNPs [16], most known BF%-associated loci, initially observed in EA individuals [19],, are likely transferrable to AA populations (e.g. associatons may be replicable across populations). Furthermore, BMI-associated loci have cross-phenotypic effects on body fat [19]; the significant association between BMI-PRS and BF% measures in our analysis may also be indicative of transferrability of similar biological mechanisms for these GWAS identified variants from EA to AA populations, though the causal SNPs at most of these loci are unknown.

Both WCBMIadj and WHRBMIadj-PRS were also found to be positive predictors of BF% when associations were not adjusted for BMI; only WHRBMIadj-PRS remained positively associated with BF% after adjusting for BMI, albeit at an attenuated scale (Table 2). Similarly, both WCBMIadj and WHRBMIadj-PRS were positive predictors of SAT, but only WCBMIadj-PRS remained associated after adjustment for BMI. Similarly inconsistent patterns were also observed at the individual SNP level.

Examination of individual SNPs that exhibited nominally significant associations to BF%, BMI, WCBMIadj, and WHRBMIadj in this cohort shows that larger proportions of BMI- and BF%- associated SNPs cluster with BF% and subcutaneous adiposity measures. WCBMIadj- associated SNPs, in contrast, do not appreciably cluster with any of the adiposity traits.

Collectively, results from both PRS models and supplementary cluster analyses suggest that BF% and BMI SNPs seem to better predict SAT, whereas the more deleterious VAT (and VSR) are more likely represented by WHR-associated variants, of the more commonly measured adiposity indices. Difference in patterns of visceral adiposity in AA [42], with significantly lower levels of VAT in BMI-adjusted analysis noted previously [13], that suggests a favorable visceral adiposity profile compared to EA [43]. Results highlighted the importance of population-specific GWAS studies on central adiposity measures in addition to overall adiposity phenotypes like BMI. Due to the limited availability of GWAS on VAT or VSR phenotypes [44], with none in a well-powered AA cohort, we are unable to asssess whether these observed differences are primarily genetically driven. Larger studies in AA populations are needed to assess the genetics of these traits across ancestries.

This is the first study to examine the relationship between adiposity PRS, including BF%, WCBMIadj and WHRBMIadj, and adiposity traits in AA. Utilization of several PRS with different risk set configurations made it possible to examine associations under comparative scenarios, minimizing potential sources of bias in estimates.

Relatively few African American cohorts with comprehensive assessment of adiposity metrics (including fat mass and CT based adiposity metrics) and genetic data exist, limiting our statistical power for these analyses. However, JHS is to our knowledge the largest such cohort and consistency of results under multiple approaches suggested a measure of robustness for estimates.

Results of this assessment pertain only to adults. Many genetic variants associated with obesity traits are age-dependent [45]. For example, in one prior study, there was no association between BMI-PRS and BF% in <5 years olds [46]; thus, extrapolation of results from this study to younger age categories in AA individuals may be unwarranted. This limitation is likely to be extended to WCBMIadj- and WHRBMIadj- associated SNPs since they also exhibit interaction with age [47]. We could not account for differential associations of dimorphic or sex-specific variants because of smaller numbers of male participants. Bioimpedance methods also have limitations [48], where the method slightly underestimates BF% in highly obese males [49]. However, we do not expect this limitation to substantially bias our results since only 6% of males had BMI measures >40 in this dataset.

Due to limited sample size for deriving adequate training and testing datasets, we did not perform extensive comparisons of different PRS derivation methods in JHS (for example comparisons of LDpred and pruning [22] and thresholding methods). Our unweighted risk score method, including an LD-pruned list of previously reported genome-wide significant variants in PRS derivation for each trait, may not provide ideal predictive power. However, this simple unweighted method is also likely to be less strongly influenced by differential LD and variant effect sizes across ancestral populations. Additionally, this approach allowed us to utilize SNPs both from large European focused GWAS for these traits and smaller studies in African American populations. PRS derivation and weighting by previously reported effect sizes is complex in admixed African American populations [50, 51], especially given the lack of representation of participants of diverse genetic ancestry in prior GWAS [52, 53]. Comparisons of different methods and use of ancestry-specific effect size estimates for PRS should be explored in future work for adiposity traits. Finally, the cross-sectional nature of the assessment restricts causal inferences.

In conclusion, these analyses illustrate that anthropometric phenotype-associated loci, initially explored in predominantly EA populations, are generally transferrable to AAs. Our results suggest that total gain in fat mass in AA, at least for gains in fat mass mediated by genetic factors, may be mostly through subcutaneous rather than visceral adiposity, but a comparable assessment in other populations, including Europeans, would be required to make firm conclusions about any population differences. Absence of association between anthropometric PRS, particularly WCBMIadj and WHRBMIadj, and adiposity traits like BF% may imply that the latter phenotypic measure are likely driven by genetic variants which influence overall adiposity versus central obesity.

SNPs used for configuration of polygenic risk scores.

(XLSX)

Click here for additional data file.

Number of SNPs configurations used for calculation of polygenic risk scores under complementary approaches.

(DOCX)

Click here for additional data file.

Polygenic risk score validation.

Estimates for all phenotypes other than percentage body fat (%BF).

(DOCX)

Click here for additional data file.

Matrix of correlation between observed phenotypic measures for Jackson Heart Study.

Spearman correlation coefficients were calculated.

(DOCX)

Click here for additional data file.

Coefficients of determination (R2) for PRS.

They exhibit the proportion of adiposity traits’(BF%, SAT, VAT, VSR) variances were predicted by each individual PRS.

(DOCX)

Click here for additional data file.

(DOCX)

Click here for additional data file.

Heatmap plot of association between top phenotype-linked SNPs and adiposity traits.

These SNPs were used for PRS calculation under approach 3 and represent polymorphisms with evidence of directionally consistent and statistically significant associations with their respective traits in JHS-genome wide assessment. Ward’s method used for SNP and trait clustering.

(TIF)

Click here for additional data file.

19 Apr 2021

PONE-D-21-03239

Genetic Underpinnings of Regional Adiposity Distribution in African Americans: Assessments from the Jackson Heart Study

PLOS ONE

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7)  In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

8) Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Anwar et al use genetic variants associated with anthropometric traits in Europeans and explore their associations with several adiposity measures in African Americans using genetic risk scores.

Comments:

The correlation between WHR and BMI seems weaker than what is expected, is there an explanation from the authors to that regards? Is that specific to this population?

PRS calculation:

The authors need to provide more details about the PRS calculation in the “Methods” section. For example, what tool was used to generate it? How many variants were included? What was the explained variance for each PRS-trait. Was the JHS data split into training and target data, or not?

Clustering and Supplementary figure 1:

What did the authors use to cluster the SNPs and the traits? Did they use Betas? I don’t think that was mentioned in the methods or results sections.

The numbers in the boxes are not in agreement with the color key. For example, some red boxes are positive instead of being negative as expected based on the key. Which ones are the correct values, the color of the box or the numbers written on them? These need to be cross-checked and updated.

The authors state that: “The primary clustering of the SNPs on the y-axis separates group of SNPs…”, however, the heatmap shows only dendograms pertaining to the rows, i.e to the traits. Did the clustering algorithm cluster both SNPs and traits, or only traits? If the SNPs were also clustered (which I believe is what the authors want), could the dendograms for the columns be shown?

Supplementary Table 6:

Is there any reason why more than one GWAS was used to use its summary stats for the PRS calculation? For example, 4 studies were listed to be used for BFP. Typically, one chooses the largest GWAS to date to use in the PRS calculation. Also, is it correctly stated that this study was used, “Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank” . Same question regarding this study: “Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank” which was used for both WHR and body fat percentage PRS creation according to Supplementary table 6 and so on.

It is surprising to use these specific studies. Also, this study (30239722) was used to generate the BMI PRS but is a study of WHR... It is not clear how and why these studies were chosen, and whether there is a specific reason behind choosing more than one for each trait, which is not typical. The PRSs are better constructed each from one study.

Minor comments:

-The last sentence in the introduction: “We also characterized … adiposity measures” this statement is not clear.

-Table 1 : Values for WHR and Body fat percentage are not clear in the table.

-I do not think Supplementary tables 2 and 6 were mentioned in the text.

-The paper needs to be revised for minor English errors, for example: line 280 : “Taken together…”; line 284: “Results highlight importance ...BMI” and several others.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

18 Jun 2021

Note :

First reply to editor

Then reply to reviewer’s comments

Reply to Editorial:

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1) Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Answer:

The revised manuscript has been reformatted to accord with guidelines referred above.

2) Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information

Answer:

We refer you to our reply to comment #1.

3) Thank you for including your ethics statement: "The study protocol was approved by the participating JHS institutions_–including the Tougaloo College, the Jackson State University and the University of Mississippi Medical Center."

a. Please amend your current ethics statement to include the full name of the ethics committee/institutional review board(s) that approved your specific study.

b. Once you have amended this/these statement(s) in the Methods section of the manuscript, please add the same text to the “Ethics Statement” field of the submission form (via “Edit Submission”).

For additional information about PLOS ONE ethical requirements for human subjects research, please refer to http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research.

Answer:

The method section was amended accordingly (see lines 129-132).

4) Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified whether consent was informed. If your study included minors, state whether you obtained consent from parents or guardians.

Answer:

We refer you to our reply to comment #3. All JHS participants were >=21 years of age. Consent was informed.

5) In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as: a) a table of relevant demographic details, b) a description of how participants were recruited, and c) descriptions of where participants were recruited and where the research took place.

Answer:

In the method section, study population sub-section was revised and expanded to include enlisted details. Please see lines 113-122.

6) Please provide a sample size and power calculation in the Methods, or discuss the reasons for not performing one before study initiation.

Answer:

Given the paucity of research on fat mass and CT based adiposity traits in African Americans, we felt study was highly important and warranted to be performed even as the power was limited by availability of the small number of African American cohorts with needed genetic and phenotypic observations. We therefore did not include power calculation. However, to minimize bias in results, we performed analyses under comparative approaches, and results suggested robustness of estimates. The discussion section was amended to highlight this issue (lines 344-347).

You may also see our answer to comment#2 (under title PRS calculation) to reviewer #1, and see lines 358-370.

7) In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Answer:

A new “Data Availability” part was added before reference list, and detailed information was included under the section (see lines 396-402).

8) Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables (should remain/ be uploaded) as separate "supporting information" files.

Answer:

Tables were duly embedded in manuscript as per guidelines, and supplementary materials have been submitted separately. Thank you for providing directions.

Reply to reviewer’s comments:

Reviewer #1: Anwar et al use genetic variants associated with anthropometric traits in Europeans and explore their associations with several adiposity measures in African Americans using genetic risk scores.

Comments:

The correlation between WHR and BMI seems weaker than what is expected, is there an explanation from the authors to that regards? Is that specific to this population?

Answer:

Although the correlation in JHS is weaker than reported in most studies, similar or even lower values were previously reported in some studies, including in studies of African American individuals (perhaps as low as r=0.05 (PMID: 21487399).

We added a paragraph (lines 306-310) to the discussion section to highlight the findings.

PRS calculation:

The authors need to provide more details about the PRS calculation in the “Methods” section. For example, what tool was used to generate it? How many variants were included? What was the explained variance for each PRS-trait. Was the JHS data split into training and target data, or not?

Answer:

Due to limited sample size for deriving adequate training and testing datasets, the study lacked adequate power to perform extensive comparisons of different PRS derivation methods, for example comparisons of LDpred and pruning [PMID: 26430803] and thresholding methods. We therefore opted to use a simpler unweighted risk score method, including an LD pruned list of previously reported genome-wide significant variants in PRS derivation for each trait. We acknowledge that this approach may not provide ideal predictive power; however, the unweighted method is also likely to be less strongly influenced by differential LD and variant effect sizes across ancestral populations. This approach allowed us to utilize SNPs both from large European focused GWAS for these traits (e.g. PMID: 31669095, 28448500 etc.) and smaller studies in African American populations (e.g. PMID:28430825). PRS derivation and weighting by previously reported effect sizes is complex in admixed African American populations, especially given the lack of representation of participants of diverse genetic ancestry in prior GWAS. Use of ancestry specific effect sizes for variants included in PRS should be explored in future for adiposity traits.

We added a paragraph to the discussion section to highlight this methodological limitation and highlight the strength of our approach which harbors an inherently lower probability of bias (lines 358-370).

We also added an additional supplementary table for coefficient of determination of estimates (as new supplementary Table 5) to exhibit the proportion of variance in adiposity traits explained by each phenotype-PRS.

And finally, a short paragraph was added at the end of Methods section to correctly cite platforms used for PRS generation and heatmap plotting.

Clustering and Supplementary figure 1:

What did the authors use to cluster the SNPs and the traits? Did they use Betas? I don’t think that was mentioned in the methods or results sections.

The numbers in the boxes are not in agreement with the color key. For example, some red boxes are positive instead of being negative as expected based on the key. Which ones are the correct values, the color of the box or the numbers written on them? These need to be cross-checked and updated.

The authors state that: “The primary clustering of the SNPs on the y-axis separates group of SNPs…”, however, the heatmap shows only dendograms pertaining to the rows, i.e to the traits. Did the clustering algorithm cluster both SNPs and traits, or only traits? If the SNPs were also clustered (which I believe is what the authors want), could the dendograms for the columns be shown?

Answer:

Heatmaps were reconstructed to accommodate both traits and SNPs clustering with row and column dendograms added. We also slightly revised the method section and the caption of the supplementary figure to reflect the choice of betas for heatmap plotting. Thank you for suggestions.

Supplementary Table 6:

Is there any reason why more than one GWAS was used to use its summary stats for the PRS calculation? For example, 4 studies were listed to be used for BFP. Typically, one chooses the largest GWAS to date to use in the PRS calculation. Also, is it correctly stated that this study was used, “Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank” . Same question regarding this study: “Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank” which was used for both WHR and body fat percentage PRS creation according to Supplementary table 6 and so on.

It is surprising to use these specific studies. Also, this study (30239722) was used to generate the BMI PRS but is a study of WHR... It is not clear how and why these studies were chosen, and whether there is a specific reason behind choosing more than one for each trait, which is not typical. The PRSs are better constructed each from one study.

Answer:

The reviewer has raised an important methodological consideration. While it is certainly possible to generate PRS using a single large-scale meta-analysis containing hundreds of GWAS-significant SNPs for frequently studied phenotypes like BMI, that approach would have left us underpowered for generating body fat mass PRS. The number of GWAS studies about body fat mass is limited, and none report a sizeable number of SNPs. Using multiple studies allowed us to capture a larger number of loci, and we confirmed that variants included in our final PRS scores were not in strong LD in relevant 1000 Genomes populations. We also thought it was important to accommodate lead findings from African American specific GWAS studies (e.g. PMID: 23966867 or PMID: 28430825), which are likely not well-captured by the largest existing GWAS for adiposity traits, all of which are predominantly in European ancestry populations.

Therefore, we opted for a database approach where we extracted all GWAS-significant SNPs associated with each target trait from relevant sources, primarily GWAS-catalogue. Given that several studies, including those referred to by the reviewer, included GWAS results for several traits, it is expected that they contribute SNPs to multiple PRS.

Additionally, we re-evaluated the list of SNPs used for PRS generation to ensure they were indeed reported by purported studies, particularly those referred above. This was done by checking GWAS-catalogue, dbSNP, and going back to original studies. The rigorous process found handful number of SNPs mis-allocated to different phenotype in the original list. Estimates in tables were subsequently updated to reflect corrections made in polygenic risk sets.

Supplementary Table 1 was also amended, and now includes all the SNPs used for polygenic risk scores calculations under approach 1 (e.g. the principle approach used in the study).

Minor comments:

-The last sentence in the introduction: “We also characterized … adiposity measures” this statement is not clear.

Answer:

Changed to: “We also estimated the associations….”.

-Table 1 : Values for WHR and Body fat percentage are not clear in the table.

Answer:

Reporting units were amended.

-I do not think Supplementary tables 2 and 6 were mentioned in the text.

Answer:

Supplementary table 2 and 6 were revised and re-arranged as S3 and S1 tables, respectively.

-The paper needs to be revised for minor English errors, for example: line 280 : “Taken together…”; line 284: “Results highlight importance ...BMI” and several others.

Answer:

Manuscript reviewed again for grammatical mistakes.

________________________________________

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

21 Jul 2021

Genetic underpinnings of regional adiposity distribution in African Americans: Assessments from the Jackson Heart Study

PONE-D-21-03239R1

Dear Dr. Anwar,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Heming Wang, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have improved the manuscript compared to their original submission and have addressed all comments. No further comments.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

26 Jul 2021

PONE-D-21-03239R1

Genetic underpinnings of regional adiposity distribution in African Americans: Assessments from the Jackson Heart Study

Dear Dr. Anwar:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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