ARTICLE

Genetic associations with carotid intima-media
thickness link to atherosclerosis with sex-specific
effects in sub-Saharan Africans
Palwende Romuald Boua 1,2,3✉, Jean-Tristan Brandenburg 2, Ananyo Choudhury 2, Hermann Sorgho1,

Engelbert A. Nonterah 4,5, Godfred Agongo4,6, Gershim Asiki 7, Lisa Micklesfield 8, Solomon Choma9,

Francesc Xavier Gómez-Olivé 10, Scott Hazelhurst 11, Halidou Tinto1, Nigel J. Crowther12,

Christopher G. Mathew 2,3,13, Michèle Ramsay 2,3✉, AWI-Gen Study* & the H3Africa Consortium*

Atherosclerosis precedes the onset of clinical manifestations of cardiovascular diseases

(CVDs). We used carotid intima-media thickness (cIMT) to investigate genetic susceptibility

to atherosclerosis in 7894 unrelated adults (3963 women, 3931 men; 40 to 60 years)

resident in four sub-Saharan African countries. cIMT was measured by ultrasound and

genotyping was performed on the H3Africa SNP Array. Two new African-specific genome-

wide significant loci for mean-max cIMT, SIRPA (p= 4.7E-08), and FBXL17 (p= 2.5E-08),

were identified. Sex-stratified analysis revealed associations with one male-specific locus,

SNX29 (p= 6.3E-09), and two female-specific loci, LARP6 (p= 2.4E-09) and PROK1

(p= 1.0E-08). We replicate previous cIMT associations with different lead SNPs in linkage

disequilibrium with SNPs primarily identified in European populations. Our study find sig-

nificant enrichment for genes involved in oestrogen response from female-specific signals.

The genes identified show biological relevance to atherosclerosis and/or CVDs,

sex-differences and transferability of signals from non-African studies.

https://doi.org/10.1038/s41467-022-28276-x OPEN

1 Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Centre national de la Recherche scientifique et technologique (CNRST),
Nanoro, Burkina Faso. 2 Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South
Africa. 3 Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the
Witwatersrand, Johannesburg, South Africa. 4 Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana. 5 Julius Global Health, Julius
Centre for Health Sciences and Primary Care, University Medical Centre, Utrecht University, Utrecht, The Netherlands. 6 C.K. Tedam University of Technology
and Applied Sciences, Navrongo, Ghana. 7 African Population and Health Research Center, Nairobi, Kenya. 8MRC/Wits Developmental Pathways for Health
Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 9 Department of Pathology and Medical Sciences,
University of Limpopo, Polokwane, South Africa. 10MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health,
Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa. 11 School of Electrical and Information Engineering, University of the
Witwatersrand, Johannesburg, South Africa. 12 Department of Chemical Pathology, National Health Laboratory Service, Faculty of Health Sciences, University
of the Witwatersrand, Johannesburg, South Africa. 13 Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King’s College
London, London, UK. *Lists of authors and their affiliations appear at the end of the paper. ✉email: romyboua@gmail.com; michele.ramsay@wits.ac.za

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mailto:michele.ramsay@wits.ac.za
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Atherosclerosis is a complex multifactorial trait with an
enigmatic genetic aetiology. Despite discoveries from
genome-wide association studies (GWAS), little is known

about the genetic contributions to atherosclerosis. Meanwhile, the
worldwide epidemic of cardiovascular diseases (CVDs), including
clinical manifestation of atherosclerosis, is growing and has
become the leading cause of deaths worldwide1,2. Moreover, the
health and demographic transition in sub-Saharan Africa (SSA)
has shifted the major causes of death from communicable to non-
communicable diseases (NCDs).

Atherosclerosis results from injury to the arterial endothelium,
resulting in an inflammatory response in the vessel wall. The
location and morphology of the atherosclerotic lesions predict the
nature of the resulting vascular disease. Whereas family and twins
studies provided evidence of high heritability of common carotid
intima-media thickness (cIMT) (20-65%)3–6, the GWAS studies
reported associations that account for only 1.1% of the variance of
cIMT7. cIMT is a widely accepted surrogate marker for the risk of
generalized atherosclerosis and is a measurement used in large-
scale research studies on genetic associations with future cardi-
ovascular events8,9.

The genetic diversity of African populations and their deep
evolutionary roots represent opportunities for novel genetic dis-
coveries. Haplotypes blocks are shorter in Africans compared to
other populations (average haplotype block ~8.8 kb in Africans,
~20.7 kb in Europeans, and ~25.2 kb in Han Chinese), and
therefore identification of causal variants is facilitated10,11. The
role of ancestry in atherosclerosis risk has been established from
studies in multi-ethnic settings and admixture studies12,13. Afri-
can ancestry was reported to be associated with a higher risk of
atherosclerosis compared to Europeans, Hispanics and Asians.

Since phenotypic differences between men and women are a
pervasive feature of several quantitative traits, studies of sex inter-
actions for complex human traits may shed light on the molecular
mechanisms that lead to biological differences between men and
women. Sex has been found to play a role in variations between
gene expression and genotype across a range of human complex
traits14. Sex differences in the transcriptomes of cells involved in the
atherosclerotic process have been previously reported15 and are
supported by sex-stratified GWAS analyses16,17. Sex provides two
different environmental contexts determined by the hormonal
milieu and differential gene expression between the sexes. A recent
GWA study of cIMT reported sex-specific loci from analyses of
women and men from the United Kingdom BioBank (UKBB)
data18. Despite the success of GWAS efforts, men and women have
typically been analyzed together in sex-combined analyses, with sex
used as a covariate in the model to account for marginal differences
in traits between them. Sex-combined analyses assume homo-
geneity of the allelic effects in men and women, and therefore are
suboptimal in the presence of heterogeneity in genetic effects by sex,
i.e. sex-dimorphic effects19,20.

Several genetic association studies of cIMT have been per-
formed in the major world populations and provided insights into
genes and tissue-specific regulatory mechanisms linking athero-
sclerosis both to its functional genomic origins and its clinical
consequences in humans. To date, 136 SNPs from 98 indepen-
dent loci have been found to be associated with cIMT (GWAS
Catalog) regardless of ancestry21. The loci were reported for
cIMT, in the presence or absence of gene-environment interac-
tions (Gene × HIV, Gene × Smoking, Gene × Sex, Gene ×
Rheumatoid arthritis). The studies that are reported in the GWAS
Catalog are primarily from European-ancestry populations, with
small numbers of Hispanic, African-American and Chinese par-
ticipants. There was only one study on sub-Saharan African
populations resident in Africa22.

The Africa Wits-INDEPTH Partnership for Genomic Studies
cohort (AWI-Gen) was developed to examine genetic and
environmental contributions to cardiometabolic diseases in
Africans. It has over 12,000 participants from four sub-Saharan
African countries, Burkina Faso, Ghana, Kenya and South Africa,
and the distributions and associated risk factors for cIMT have
been described23–26. This study aimed to investigate genetic
susceptibility to atherosclerosis in sub-Saharan Africans in the
AWI-Gen cohort. cIMT was used as an endophenotype, with
further investigation of sex differences.

In this study, we identify five novel loci associated with cIMT
in sub-Saharan African which relates to atherosclerosis’ biology
and showed the potential role of menopause in the pathobiology
of atherosclerosis. Our study report sex-specific effects and
highlights the need for more genome-wide studies in under-
represented populations.

Results
Genetic association with cIMT. Analyses were performed using
the imputed dataset of 13.9 M SNPs in 7894 participants from the
AWI-Gen study (characteristics for each study site are shown in
Supplementary Data 1) and tested for association with mean-
max-cIMT. Despite the population sub-structure demonstrated
by principal component analysis in the study sample (Supple-
mentary Fig. 1), our results did not show evidence of genomic
inflation (λ= 0.997). The genome-wide association results for the
combined dataset are illustrated in the Manhattan plot and the
genomic inflation by the QQ-plot (Fig. 1a, b). In the combined
dataset, we identified two new genome-wide significant loci in
Signal regulatory protein alpha (SIRPA) on chromosome 20
(rs6045318, p= 4.7E-08, Info Score= 0.88) and F-box and
leucine-rich repeat protein 17 (FBXL17) on chromosome 5
(rs552690895, p= 2.5E-08, Info Score= 0.97) (Table 1). These
two SNPs are African-specific and the variant alleles have not
been observed in European or Asian populations. The effect allele
frequencies were similar in East, West and Southern Africa
(respectively, 0.98, 0.96 and 0.97 for rs6045318 and 0.99, 0.98 and
0.99 for rs552690895) (Supplementary Data 2). The regional plots
of the identified associated variants show the distribution of
additional variants around the lead SNPs (Fig. 2A, B). Genotyped
variants were distributed around the imputed lead SNPs with
higher p-values. Suggestive association signals (p < 1-06) had lead
variants located in an intergenic region on chromosome 8
(rs11781274, p= 1.8E-07), an intronic region in sortilin related
VPS10 domain-containing receptor 1 (SORCS1) (rs11193156,
p= 2.1E-07), an intronic region in ankyrin repeat and kinase
domain-containing 1 (ANKK1) (rs11214599, p= 5.4E-07), an
exonic region in C-terminal binding protein 2 (CTBP2)
(rs3781409, p= 6.6E-07) and an intronic region in SWI/SNF
related, matrix associated, actin-dependent regulator of chroma-
tin, subfamily a, member 2 (SMARCA2) (rs1324201, p= 8.6E-07)
(Supplementary Data 3).

Sex-specific analyses revealed three genome-wide significant loci
(as illustrated in the Miami plots, Fig. 3): one male-specific locus led
by an intronic variant in sorting nexin 29 (SNX29) (rs190770959,
p= 6.3E-9) and a near significant male-specific association at
mitogen-activated protein kinase kinase kinase 7 (MAP3K7)
(rs284509, p= 5.3E-8), and two female-specific loci one located in
an intergenic region between uveal autoantigen with coiled-coil
domains and ankyrin repeats (UACA) and LDL receptor-related
protein 6 (LRP6) (a downstream variant located in the promoter
flanking region) (rs150840489, p= 2.4E-09) and a variant in
a transcription factor binding site near prokineticin 1/ chymosin,
pseudogene (PROK1/CYMP) (rs115473055, p= 1.0E-08) (Fig. 2C–F).

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Loci with suggestive sex-specific associations are shown in
Supplementary Data 3).

Sex differences that were limited to men or women were
assessed (Supplementary Fig. 2). We found suggestive signals in
the intergenic regions of insulin-like growth factor binding
protein-like 1/ family with sequence similarity 95 member C
(IGFBPL/FAM95C) (rs12350396, p= 3.4E-07), UACA/LARP6
(rs150840489, p= 4.4E-07), myogenic differentiation 1/ potassium
voltage-gated channel subfamily C member 1 (MYOD1/KCNC1)
(rs150481830, p= 4.9E-07), and in an intronic region of ciliary

rootlet coiled-coil, rootletin (CROCC) (rs11585710, p= 4.8E-07).
This sex-difference are shown in Supplementary Data 4. Regional
plots of significant loci are shown in Fig. 3.

Analysis of sex-dimorphism requires both a significant (or
suggestive p < 1E-04 as applied in our study) SNP association
with cIMT in at least one sex and a nominally significant
association in the other sex; as well as a significant sex-difference
for the SNP association (P-value testing for difference in sex-
specific effect estimates <1E-06). Several scenarios can describe
sexual dimorphism for SNP associations: (i) concordant effect

Fig. 1 Genetic association with cIMT in sub-Saharan Africans (7894 participants). A QQ-plot for the combined dataset GIF= 0.997. B Manhattan plot
showing the −log10-transformed two-tailed P-value of each SNP from the GWAS for Mean-Max cIMT on the Y-axis and base-pair positions along the
chromosomes on the X-axis. Adjustment was made for age, sex and 8 PCs. The red line indicates Bonferroni-corrected genome-wide significance (p < 5E-
08); the blue line indicates the threshold for suggestive association (p < 1E-05).

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direction (CED); (ii) single sex effect (SSE); or (iii) opposite effect
direction (OED)27. In our study, we identified all three types of
sexual dimorphism: the LARP6 locus was a case of a single sex
effect (rs150840489: p-female= 2.4E-09, beta-female=−0.051;
p-male= 0.17, beta-male= 0.012); the CROCC locus showed
opposite effect direction (rs11585710: p-female= 4.9E-03,
beta-female= 0.007; p-male= 2.7E-05, beta-male=−0.012),
and the FBXL17 variant showed a concordant effect direction
(rs547840497, p-female= 0.037, beta-female=−0.022;
p-male= 1.8E-07, beta-male=−0.062). In total 177 SNPs
showed CED, 89 SNPs had OED and 3213 SNPs showed SSE.

Replication of previous associations with cIMT. We investi-
gated the replication of 47 previous cIMT associations in our
GWAS study (Supplementary Data 5). Although, we did not
observe an exact replication, one variant (rs561732; p= 0.0012;
beta= 0.006) was nearly significant after correction for multiple
testing (Bonferroni correction for 47 SNPs; p= 0.05). This variant
in the CBFA2/RUNX1 partner transcriptional co-repressor 3
(CBFA2T3) region has been reported for association with cIMT in
a British ancestry population (UKBB)18. The association of the
CBFA2T3 locus with cIMT, based on a different variant, was first
reported in a European-ancestry study28.

In addition, four of the previously reported signals were found to
be replicated in the local replication analysis which considered, for
each signal, all the SNPs that were within 250 kb of the index SNP
and showed a LD > 0.7 (Supplementary Data 6). This included five
variants on chromosome10 (bp= 56620608–56625539) close to a
variant (rs975809) previously associated with cIMT progression in
a Chinese population29. Similarly, variants on chromosome 16
(bp= 88966667–88978850) replicated previous findings in Eur-
opean and British ancestry populations. For another signal on
chromosome 16 detected by Strawbridge and colleagues18, variants
in close proximity (bp= 88968540–89016494) also showed local
replication. Finally, we found evidence of regional replication for a
variant on chromosome 8 (rs6601530), as first reported for cIMT
GWAS in a European study (bp= 10584288–10722058) study7

(Supplementary Data 6).

Look-up for cardiovascular traits in the GWAS Catalog. Var-
iants with suggestive associations in our study were located in loci
previously reported for traits such as plaque, coronary artery
calcification (CAC), coronary artery disease (CAC), coronary
heart disease (CHD), coronary aneurysm and coronary athero-
sclerosis. Previously reported locus for association with a carotid
plaque in European populations30 at GEM (rs72672639, p= 4.0E-
06) to be suggestively associated in our female-specific subset with
two SNPs (rs78571209, rs76489670, p= 7.8E-05) located
approximatively 2200 bp from the SNP reported for plaque in
Europeans. Similarly, the association with the mitochondrial
ribosomal protein L37 (MRPL37) locus (rs11206301, p= 8.00E-
06) for plaque in European populations was suggestively asso-
ciated in our male-specific analysis for cIMT (rs13374450,
p= 3.0E-05). The two SNPs in the MRPL37 locus were not in LD
despite their proximity (201 bp). The suggestive variant in our
study rs4773141 (p= 4.7E-05, in the combined dataset), located
in collagen type IV alpha 1 chain (COL4A1), was previously
reported for CAD (p= 4.0E-17) in European populations31.

In our combined analysis, a total of 10 suggestively associated
SNPs (p < 1E-04) were previously described for association with
CAC (another surrogate marker of atherosclerosis) 32,33 and for
CAC in African patients with type 2 diabetes34. Fourteen SNPs
reported for coronary heart disease and coronary artery disease
were suggestive in our dataset (Supplementary Data 7).T

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Functional annotation. Annotation of the genic positions of the
467, 515 and 581 SNPs respectively from the combined, female-
specific and male-specific analyses with significant and suggestive
associations (p < 1E-05) showed that these were mostly intronic
or intergenic. Fifty SNPs displayed a CADD score above
12.37 suggesting being potentially deleterious (19 in the com-
bined; 18 in female-specific; 13 in male-specific datasets) (Sup-
plementary Data 8a, b, c). In the female-specific sample, the lead
SNP in CYMP (rs115473055) had a Regulome DB score of 2a
suggesting the variant was likely affecting a transcription binding
site (CTCF). Positional mapping, eQTL mapping (matched cis-
eQTL SNPs) and chromatin interaction mapping (on the basis of
3D DNA–DNA interactions) is reported (Supplementary Data 9a,

b, c). We found that rs78172571, in high LD with rs150840489
(the top SNP associated in our female-specific analysis), was
involved in HiC type chromatin interactions in multiple tissues
including aorta, in which the variant acts as an enhancer of
THAP domain-containing 10 (THAP10) (FDR= 2.03E-17).

Gene-based and gene-set analysis. In a gene-based analysis (using
MAGMA threshold of p < 2.6E-06) of the combined dataset analysis
there was a significant association with CALD1 (p= 5.9E-07)
(Supplementary Fig. 3A) with mean-max cIMT, whereas in female-
specific analysis FLT4 (p= 4.3E-07) was significantly associated
(Supplementary Fig. 3B). The results from gene-set analysis in the

Fig. 2 Regional association plots for selected top SNPs showing genetic associations with mean-max cIMT. A Regional association plot of the FBXL17
region in the combined dataset. B Plot of the SIRPA region in the combined dataset. C Regional association plot of the PROK1 region in the female-specific
dataset. D Regional association plot of the LARP6/UACA region female-specific dataset. E Regional association plot of the MAP3K7 region in the male-
specific dataset. F Regional association plot of the SNX29 region in the male-specific dataset. For each locus, the plots show the −log10-transformed
p-value of each SNP on the y-axis and base-pair positions along the chromosomes on the X-axis. Genes overlapping the locus are displayed below the plot.
SNPs are coloured by their LD value r2 (generated from the study population) with the lead SNP in the region shown as a purple diamond.

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combined dataset showed significant enrichment for “Chemical and
Genetic perturbation” gene-set (adjP= 3.9E-05). The female-
specific analysis revealed significant enrichment of gene-sets (Sup-
plementary Data 10a, b, c), with among them “Hallmark gene-sets
for Oestrogen response”, with “Early Oestrogen response” and “Late
Oestrogen response” both being significant (2.2E-6). In order to
further investigate the effect of menopause in our sample, we tested
the impact of menopause on female-specific significant variants
(rs115473055, rs150840489) in two strata (pre-menopausal and
post-menopausal women). We found that for rs115473055, the
carriers of the T allele displayed higher cIMT values than those with
the C allele in post-menopausal participants. There was no differ-
ence in pre-menopausal women (Fig. 4).

Discussion
The outcome variable for our GWAS study in African populations
was cIMT, often used as a proxy for the risk of developing
atherosclerosis. We identified two new loci associated with cIMT
in the full dataset, two new loci specific to the female-only analysis
and one locus associated with the male-only analysis (p < 5E-08).
We demonstrated that some signals observed in European
populations replicate in Africans, in spite of differences in the lead
associated variants, allele frequencies and effect sizes. The modest
sample size of our study limited our capacity to confirm previous
low-effect associated loci detected in Europeans.

Measurements of cIMT are used clinically to assess vascular
pathophysiology and to reflect the risk of developing athero-
sclerosis. Our study identified cIMT-associated loci relevant to
genes related to macrophage activity and polarization (SIRPA), to
vascular smooth muscle cells (MAP3K7, CALD1), to vascular
endothelial growth (PROK1, FLT4), to collagen synthesis and pla-
que stability (LARP6) and a pathway of blood vessel occlusion
(SNX29). The loci for the genome-wide significant associations and
their potential role in atherosclerosis are briefly described below.

FBXL17 (lead SNP:rs552690895; p= 2.5E-08) was associated
with cIMT in the combined dataset analysis and is linked to

cardiovascular physiology through its involvement in protein
degradation where it plays a central role in cardiovascular phy-
siology and disease: from endothelial function, the cell cycle,
atherosclerosis, myocardial ischaemia, cardiac hypertrophy,
inherited cardiomyopathies and heart failure. A GWAS in
Lithuanian families found that variants in FBXL17 were asso-
ciated with coronary heart diseases35. Signal regulatory protein
alpha (SIRPA) (lead SNP:rs6045318; p= 4.7E-08 in the combined
analysis) has a role in the mediation of phagocytosis and polar-
ization of macrophages which is important in the pathophysiol-
ogy of atherosclerosis36. There is evidence that SIRPA is involved
in discrete stages of cardiovascular cell lineage differentiation37

and that defects in the gene (knock out) reduce atherosclerosis in
mice38. SIRPA expression has been found as a signature of
inflamed atherosclerotic plaque39.

In the sex-specific analysis, the top cIMT-associated variant in
men was in the SNX29 gene (rs147978408; p= 6.3E-09). The
sorting nexin (SNX) family of genes are associated with CVDs,
and dysfunction of the SNX pathway is involved in several forms
of cardiovascular disease (CVD)40. In a study of genes that reg-
ulate smooth muscle cell differentiation and disease risk, SNX29
was involved in pathways for occlusion of blood vessels and
atherosclerosis41. Ito and colleagues identified sex-dependent
differentially methylated regions close to SNX29 in mouse liver
and found that methylation status was influenced by testosterone
and contributed to sex-dimorphic chromatin decondensation42.
This might explain the sex-specific effect observed in our study.
In view of the previous link between SNX29 and hypertension, we
ran further GWAS analyses stratified by hypertensive status and
found that the association was driven by the hypertensive group
(the effect was three times higher in hypertensives compared to
non-hypertensives), thereby demonstrating that the association of
SNX29 with cIMT might be mediated by the vascular remodelling
caused by hypertension.

Another implicated gene, LARP6 (La-related protein 6), is a
ribonucleoprotein domain family member 6 with a role in collagen
regulation by targeting mRNA encoding Type I collagen43,44.

Fig. 3 Miami plot showing female and male-specific associated p-values for mean-max cIMT. The −log10-transformed two-tailed P-value of each SNP
from the GWAS for Mean-Max cIMT on the Y-axis (Adjusted for age and 5 PCs for each sex) and base-pair positions along the chromosomes on the
X-axis. On top are the results for female-specific analysis (n= 3963) and on the bottom the results for the male-specific analysis (n= 3931). The red line
indicates Bonferroni-corrected genome-wide significance (p < 5 × 10−8).

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Collagen is a hallmark of atherosclerotic plaque stability, thus
alteration of the collagen balance may lead to instability of
atherosclerotic lesions, and therefore promote plaque formation
and rupture39,45. In the Taiwanese population, the LARP6 locus
was found to be associated with coronary artery disease46. Myo-
cardial gene expression in non-ischemic human heart failure
found that LARP6 was differentially expressed between men and
women47. The female-specific effect of this locus in our study may
be explained by the enhancer function of rs78172571. This SNP is
in high LD with rs150840489 (the top SNP associated in our
female-specific analysis), on the THAP10 gene (FDR= 2.03E-17),
known to be regulated by oestrogen48. Our study is the first to
report prokineticin 1 (PROK1) for any trait in a GWAS. It was
associated with cIMT in the female-specific analysis (lead
SNP:rs115473055, p= 1.00E-08). PROK1 is a specific placental
angiogenic factor that plays a role in the control of normal (e.g.
endometrial decidualization) and pathological placental
angiogenesis49. The gene is known to be predominantly expressed
in the steroidogenic glands, such as the ovary, testis and adrenal
cortex, and is often complementary to the expression of vascular
endothelial growth factor (VEGF), suggesting that these molecules
function in a coordinated manner. The function and a particular
pattern of this gene’s activity might explain why we identified the
locus only in our female-specific analysis. Our study revealed that
rs115473055, situated in a transcription binding site and likely to
affect gene expression, was subject to different allelic effects
depending on the menopausal status of women. This is a novel
finding, and more discussion on potential biological relevance is
presented in Supplementary Note 1.

Interestingly, our study identified loci with suggestive asso-
ciation (P < 1E-04) which had opposite effects on cIMT between
men and women (led by signals in the CROCC locus). In contrast,

the sex-stratified analysis with participants from the UKBB
reported a single discordant sex effect and the remainder of the
effect directions were concordant. It is possible that sex differ-
ences are amplified in African populations and are therefore
noteworthy. Although statistically significant (Supplementary
Fig. 2 of sex-difference test Manhattan plot), these findings need
to be explored further through replication in additional studies
because our power to detect the associations observed was low,
substantially increasing the likelihood that the associations we
observed are false positives.

When analysing sex-specific or gene-sex interactions, it is
important to keep in mind that they also reflect the influences of
non-genetic factors such as behaviour, as evidenced by the pre-
viously reported gene-smoking interactions22. Hence, environ-
mental exposure, anatomical differences and sex hormone
environment, which create systemic differences between males
and females for trait expression, affect disease risk and
heritability50.

Our study identified significant enrichment of oestrogen
pathway genes in our female-specific analysis. Oestrogen-
dependent regulation of vascular gene expression and vascular
physiology encompasses complex processes involving both
nuclear and membrane-associated oestrogen signalling pathways.
In recent years we have witnessed major progress in under-
standing how these regulatory processes contribute to the athero-
protective effects exerted by oestrogens. Animal models of
atherosclerosis provided compelling evidence that physiological
oestrogen levels potently attenuate both early and advanced stages
of atherosclerosis lesion development in females and suggested
similar protective effects in males. The effect of oestrogens
on atherosclerosis may be mediated by effects on metabolism
(lipid, glucose), macrophage function or smooth muscle cells.

Fig. 4 Genotype plots for genetic associations of rs115473055 stratified by menopausal status. A Pre-menopausal women (n= 1245), B Post-
menopausal women (n= 1108) with cIMT. Bounds of boxes represent 1st and 3rd quintiles, bars represent 95th percentiles, mean.

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Nonetheless, hormone replacement therapy during menopause
has not been shown to conclusively reduce atherosclerosis risk,
suggesting that more studies are needed to fully decipher the
biological mechanisms.

The two novel genome-wide significant associations with cIMT
in our African study were not reported in previous studies. SIRPA
and FBXL17 are both biologically plausible candidates for cardi-
ovascular diseases and there are several possible explanations for
why these associations have not been identified in European and
Asian populations. Firstly, the associated SNPs are monomorphic
in these populations and were therefore not present in the
replication datasets, secondly regional replication is affected by
linkage disequilibrium and thirdly there may be unique gene-
environment interactions that are at play in the African context.
We found evidence of gene-set enrichment for biological pro-
cesses. Our study is the first GWAS to report significant
enrichment of genes in the oestrogen pathway for cIMT in our
female-specific analysis. The findings from our study support the
notion that genomics studies in Africa are likely to contribute to
the understanding of complex traits, such as atherosclerosis.

Strengths and limitations. Our study is the first population-
based study to investigate the genetic architecture of cIMT in sub-
Saharan African populations. We used an analysis framework
allowing us to identify genetic effects that point in opposite
directions in men and women and to detect genetic effects that
are only present or more pronounced in one stratum, a method
that has been shown to have better power to identify qualitative
gene-sex interactions51. The use of a new SNP genotyping array
with a better representation of common African variants and
imputation reference panels from African participants has
improved the SNP coverage in ethnically diverse African popu-
lations. However, many of these additional SNPs are not present
on Eurocentric GWAS arrays, and if they are monomorphic or
have very low allele frequencies they will not be accurately
imputed, if at all. SNP level replication would therefore be limited
for such loci.

Although the threshold for genome-wide significance that would
be considered appropriate for GWAS conducted in African
populations is under debate52–54, in the absence of a clear guideline
we have employed the traditional genome-wide significance thresh-
old of 5E-08. Post-hoc power calculation showed that the power to
discover the six genome-wide significant variants was modest
(Supplementary Data 11), suggesting the need for validation.

The absence of an ethnically matched replication cohort is a
limitation in our study, and it will be important to replicate these
findings in additional suitable cohorts. We identified African-
specific variants in new loci and replicated previously reported
loci, revealing opportunities for trans-ancestry fine-mapping.

Methods
Study population and phenotype assessments. This is a cross-sectional study
that investigated populations from six sub-Saharan African sites in West Africa
(Burkina Faso (Nanoro) and Ghana (Navrongo)), East Africa (Kenya (Nairobi))
and South Africa (Agincourt, Dikgale and Soweto) as part of the AWI-Gen
study23,24,55–60. The participants for this study include 10,703 black African men
and women from two urban settings (Nairobi and Soweto) and four rural settings
(Agincourt, Dikgale, Nanoro and Navrongo), aged 40–60 years. Participants
completed a questionnaire requesting information on demography, health history
and behaviour. Anthropometric measurements were taken and blood collected for
genotyping (H3Africa SNP array) and phenotyping (biomarkers)24. Ultrasound
scans were performed to assess cIMT of the right and left carotid arteries. No cIMT
data was collected for female participants from Soweto because they were drawn
from the Study of Women Entering and Endocrine Transition (SWEET) study for
whom no cIMT data were collected, and they were therefore not included in the
subsequent GWAS. This study received approval from the Human Research Ethics
Committee (Medical), University of the Witwatersrand, South Africa (M121029,
M1706110). All the participants provided written informed consent prior to

enrolment and participation in the study. Menopause was defined as the absence of
a menstrual period for more than 12 months.

cIMT measurement. cIMT was measured using Dual B-mode ultrasound images
of the carotid tree showing a typical double line for the arterial wall. Details of the
method for measurement are provided in Ali et al.24. The cIMT values were QCed
according to the Mannheim Consensus guidelines defining the use of cIMT in
population-based studies. The mean-max cIMT was generated as the average of the
maximum cIMT from the left and right carotid arteries, and this value was used for
the GWAS analyses. The association of cIMT with non-genetic risk factors in our
cohort have been documented26,61.

Genotyping and imputation. The H3Africa genotyping array (https://
chipinfo.h3abionet.org), designed as an African-common-variant-enriched GWAS
array (Illumina) with ~2.3 million SNPs, was used to genotype genomic DNA using
the Illumina FastTrack Sequencing Service (https://www.illumina.com/services/
sequencing-services.html). The following pre-imputation QC steps were applied to
the entire AWI-Gen genotype dataset. Individuals with a missing SNP calling rate
greater than 0.05 were removed. SNPs with genotype missingness greater than 0.05,
MAF less than 0.01 and Hardy-Weinberg equilibrium (HWE) P-value less than
0.0001 were removed. Non-autosomal and mitochondrial SNPs, and ambiguous
SNPs that did not match the GRCh37 reference alleles or strands were also
removed using the H3ABioNet pipeline62. Imputation was performed on the
cleaned dataset (with 1,729,661 SNPs and 10,903 individuals) using the Sanger
Imputation Server and the African Genome Resources as reference panel. We
selected EAGLE263 for pre-phasing and the default PBWT algorithm was used for
imputation. After imputation, poorly imputed SNPs with info scores less than 0.6,
MAF less than 0.01, and HWE P-value less than 0.00001 were excluded. The final
QC-ed imputed dataset had 13.98M SNPs, and only participants with both good
quality cIMT and genotyping data (n= 7894) were used for the GWAS analyses.

Genome-wide association analysis. Linear regression of Mean-Max cIMT was
performed with covariates in R (https://www.R-project.org/). Residuals were
extracted from the linear regression analyses and used for the GWAS analysis. We
used as covariates age, sex and 8 principal components (PCs) computed on genetic
data. In our sex-stratified analysis (3963 women, 3931 men), the covariates were
age and 5 PCs. The number of PCs to include in each model was determined using
stepwise regression and applied the Kaiser criteria as a stopping rule, which
recommends stopping when the addition of PCs no longer increase the variance
explained. We performed all association testing with the residuals in BOLT-LMM,
which implements testing using a Linear Mixed Model (LMM). To run efficiently,
BOLT-LMM required three components: the (imputed) genotypic data for asso-
ciation testing; a reference panel of LD scores per SNP, calculated using 1000
Genomes Project African samples; and genotype data used to approximate a
genetic relationship matrix (GRM) (using a subset of the SNP array genotypes
following LD filtering). This method is expected to account for all forms of
relatedness, ancestral heterogeneity in the samples and other (potentially hidden)
structure in the data. The analyses were run on the automated workflow developed
by H3ABioNet (H3agwas) (http://github.com/h3abionet/h3agwas/)62. We screened
the output for a genome-wide significance threshold (p-values < 5.E-08). To assess
genomic inflation, we compared our observed distribution of −log10(P) values to
that expected in the absence of association (Lambda) and illustrated the results in
QQ plots. The same process was applied for sex-stratified analyses.

We used EasyStrata64 to test for the joint effect calculated from sex strata
results65 and to test for the difference between the results from the two strata as a
means to test for sex effects27. The joint and stratified frameworks were found to be
the most efficient way to test for gene-environment interactions66. Power
calculations were performed for the combined dataset, not the sex-specific analyses,
using Quanto (Version 1.2.4) (http://biostats.usc.edu/Quanto.html), based on a
range of previously reported effect sizes and different allele frequencies. We showed
that a model that assesses the cIMT in independent individuals with an additive
genetic inheritance and an allele frequency of 0.04 will be >93% powered (α= 5E-
08) to detect a βG (genetic effect) of 0.0147 mm. Likewise, an allele frequency of
0.48 will have 98% power to detect even a very small genetic effect (β= 0.0067 mm)
(Supplementary Fig. 4). We have performed post-hoc power calculation based on
the gwas significant results, using power add (https://rpubs.com/maffleur/post-hoc-
power).

Replication from the GWAS catalog. The GWAS Catalog database was down-
loaded (https://www.ebi.ac.uk/gwas/, accessed on 20 May 2021) and a subset of the
data generated using the following keywords relevant to our study: coronary artery
disease, carotid atherosclerosis, cIMT, coronary artery calcification and abdominal
artery aneurism. In order to examine replication of previous findings, a two-stage
approach was followed: 1. exact replication of lead SNPs and 2. local regional
replication. For exact replication 53 variants present in the GWAS Catalog were
extracted from our database and the p-values in our study reported. For local
regional replications, only the SNP showing LD > 0.7 (in 1000 Genomes European
populations) with the index (lead) SNP (in the discovery GWAS) and occurring

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https://chipinfo.h3abionet.org
https://chipinfo.h3abionet.org
https://www.illumina.com/services/sequencing-services.html
https://www.illumina.com/services/sequencing-services.html
https://www.R-project.org/
http://github.com/h3abionet/h3agwas/
http://biostats.usc.edu/Quanto.html
https://rpubs.com/maffleur/post-hoc-power
https://rpubs.com/maffleur/post-hoc-power
https://www.ebi.ac.uk/gwas/
www.nature.com/naturecommunications


within 250 kb with it were tested. We searched for markers in our dataset that were
from the output after Bonferroni correction was applied to determine significance.

Functional analysis. The FUMA online platform (http://fuma.ctglab.nl/)67 was
used to annotate, prioritize, visualize and interpret GWAS results. GWAS summary
statistics (p < 1E-05) from out study was used as the input. FUMA provided
extensive functional annotation for all SNPs in genomic areas identified by lead
SNPs. From the list of gene IDs (as identified by SNP2GENE option in FUMA)
FUMA annotated genes in a biological context67. We selected all candidate SNPs in
the associated genomic region having r2 ≥ 0.6 (with 1000 Genome Project African
references) with one of the independently significant SNPs, with a suggestive P-
value (p < 1E-05) and MAF > 0.01 for annotation. Predicted functional con-
sequences for these SNPs were obtained by matching the SNP’s chromosome base-
pair position, and reference and alternate alleles, to databases containing known
functional annotations, including ANNOVAR68, combined annotation-dependent
depletion (CADD) scores69, and Regulome DB (RDB)70 scores. Additionally,
eQTLs scans71 were performed.

Functional annotation of mapped genes. Genes implicated by mapping of sig-
nificant GWAS SNPs were further investigated using the GENE2FUNC option in
FUMA67, which provides hypergeometric tests of enrichment of the list of mapped
genes in 53 GTEx tissue-specific gene expression sets71, 7,246 MSigDB gene-sets
(http://software.broadinstitute.org/gsea/msigdb), and chromatin states72.

MAGMA Gene-based and gene-sets analysis. Multi-marker analysis of genomic
annotation (MAGMA, v1.6) gene analysis was performed using summary statistics
of our association results as input in the FUMA online platform using 1000
Genomes Project Africans LD. Gene-based analysis enabled summarizing SNP
associations at the gene level and association of the set of genes to biological
pathways. MAGMA employs multiple linear regression to obtain gene-based p-
values67,73. The window for gene annotation was set for 25 kb and genome-wide
significance was set at 0.05/number of tested genes. MAGMA gene-set analysis
used a competitive testing framework, with gene-sets from MsigDB (v6.2, 10678
gene-sets (curated gene-sets: 4761, GO terms: 5917))74. MAGMA analysis was
implemented within FUMA.

Reporting summary. Further information on research design is available in the Nature
Research Reporting Summary linked to this article.

Data availability
The processed data generated in this study are provided in the Supplementary
Information. The AWI-Gen data used in this study are available to interested researchers
through EGA, subject to controlled access review by the Data and Biospecimen Access
Committee of the H3Africa Consortium. AWI-Gen (EGA00001002482) phenotype
dataset is available at study number EGAD00001006425. AWI-Gen genotype dataset
accession number: EGAD00010001996. GWAS Catalog (https://www.ebi.ac.uk/gwas/).
Summary statistics reported in the paper are accessible on GWAS Catalog at the
accession numbers: GCST90092502, GCST90092503, GCST90092504, GCST90092505.

Received: 14 January 2021; Accepted: 13 January 2022;

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Acknowledgements
This study would not have been possible without the generosity of the participants who
spent many hours responding to questionnaires, being measured and having samples
taken. We wish to acknowledge the sterling contributions of our field workers, phlebo-
tomists, laboratory scientists, administrators, data personnel, and all other staff who
contributed to the data and sample collections, processing, storage and shipping.
Investigators responsible for the conception and design of the AWI-Gen study include
the following: MR (PI, Wits), Osman Sankoh (co-PI, INDEPTH), Stephen Tollman and
Kathleen Kahn (Agincourt PI), Marianne Alberts (Dikgale PI), Catherine Kyobutungi
(Nairobi PI), HT (Nanoro PI), Abraham Oduro (NavrongoPI), Shane Norris (Soweto PI)
and SH, Nigel Crowther, Himla Soodyall and Zane Lombard (Wits). We would like to
acknowledge each of the following investigators for their significant contributions to this
research, mentioned according to affiliation: Wits AWI-Gen Collaborative Centre: Stuart
Ali, AC, SH, Freedom Mukomana, Cassandra Soo; Soweto (DPHRU): Nomses Baloyi,
Yusuf Guman. This study was funded by the National Institutes of Health (NIH) through
the H3Africa AWI-Gen project (NIH grant number U54HG006938) and the Wits Non-
Communicable Disease Research Leadership Programme (NIH Fogarty International
Centre grant number D43TW008330). AWI-Gen is supported by the National Human
Genome Research Institute (NHGRI), Eunice Kennedy Shriver National Institute of
Child Health & Human Development (NICHD), Office of the Director (OD) at the
National Institutes of Health. P.R.B. was funded by the National Research Foundation/
The World Academy of Sciences “African Renaissance Doctoral Fellowship” (Grant no.
100004).

Author contributions
P.R.B., H.S., H.T., A.C., C.M. and M.R. designed the study. P.R.B. and J.-T.B. performed
the analysis. P.R.B. wrote the manuscript. P.R.B., J.T.B., H.S., H.T., A.C., C.M., M.R.,
G.Ag., G.As., E.A.N., L.M., S.C., F.X.G., S.H. and N.J.C. critically reviewed and approved
the manuscript.

Competing interests
The authors declare no competing interests.

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Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41467-022-28276-x.

Correspondence and requests for materials should be addressed to Palwende Romuald
Boua or Michèle Ramsay.

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© The Author(s) 2022

AWI-Gen Study

Palwende Romuald Boua 1,2,3✉, Jean-Tristan Brandenburg 2, Ananyo Choudhury 2, Hermann Sorgho1,

Engelbert A. Nonterah 4,5, Godfred Agongo4,6, Gershim Asiki 7, Lisa Micklesfield 8, Solomon Choma9,

Francesc Xavier Gómez-Olivé 10, Scott Hazelhurst 11, Halidou Tinto1, Nigel J. Crowther12 &

Michèle Ramsay 2,3✉

the H3Africa Consortium

Palwende Romuald Boua 1,2,3✉, Jean-Tristan Brandenburg 2, Ananyo Choudhury 2, Hermann Sorgho1,

Engelbert A. Nonterah 4,5, Godfred Agongo4,6, Gershim Asiki 7, Lisa Micklesfield 8, Solomon Choma9,

Francesc Xavier Gómez-Olivé 10, Scott Hazelhurst 11, Halidou Tinto1, Nigel J. Crowther12 & Michèle Ramsay
2,3✉

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	Genetic associations with carotid intima-media thickness link to atherosclerosis with sex-specific effects in sub-Saharan Africans
	Results
	Genetic association with cIMT
	Replication of previous associations with cIMT
	Look-up for cardiovascular traits in the GWAS Catalog
	Functional annotation
	Gene-based and gene-set analysis

	Discussion
	Strengths and limitations

	Methods
	Study population and phenotype assessments
	cIMT measurement
	Genotyping and imputation
	Genome-wide association analysis
	Replication from the GWAS catalog
	Functional analysis
	Functional annotation of mapped genes
	MAGMA Gene-based and gene-sets analysis

	Reporting summary
	Data availability
	References
	Acknowledgements
	Author contributions
	Competing interests
	Additional information