African-specific genetic loci determine iron status and risk of severe malaria and bacteremia in African children

Domain: neuroscience
Published: 2026-04-07
Editorial outcome: minor_revision

Abstract
Iron is essential for both humans and pathogens, yet its genetic regulation remains understudied in African populations. Here, we report genome-wide association studies of six iron-related biomarkers in 3,928 children from five sites across Africa, with replication in 2,868 African American adults and investigate associations with severe malaria and bacteremia. We identify previously unreported loci at genome-wide significance, for transferrin at GTF3C5, and for hepcidin at CHCHD7/SDR16C5. Variants tagging the DUP4 haplotype, encoding the Dantu blood group (rs552439837) are associated with soluble transferrin receptor levels. Variants at GTF3C5 (rs2905094) and DUP4 confer protection against severe malaria and bacteremia. The CHCHD7/SDR16C5 variant (rs73596248) increases hepcidin levels and is associated with reduced risk of Klebsiella pneumoniae and Staphylococcus aureus bacteremia. Polygenic risk scores derived from European data show limited transferability to African populations. In this work, we demonstrate new genetic insights into iron regulation and highlight iron’s role in host-pathogen interactions.

Introduction
Iron is an essential nutrient required for critical biological functions in both humans and pathogens. The evolutionary balance between maintaining adequate iron for physiological functions and restricting its availability to pathogens is especially delicate in African populations, due to the high burden of iron deficiency (ID) and infectious disease. ID affects over half of young children living in sub-Saharan Africa (SSA)1 accounting for up to 50% of anemia cases.2 In contrast to other WHO regions, the prevalence of ID has been rising in SSA,3 while malaria and bacterial infections remain as major causes of morbidity and mortality. In 2023, 95% of the estimated 263 million cases and 597,000 deaths from malaria globally occurred in the WHO African region.4 Similarly, age-standardized mortality rates due to invasive bacterial infections were highest in sub-Saharan Africa in 2019.5

Iron plays a critical role in the relationship between host and pathogen6 and we hypothesize that infectious diseases, including malaria and bacterial infections, may have exerted powerful selective pressure on iron-regulating genes in Africa. Iron homeostasis is tightly regulated in humans since both iron deficiency and overload alter the risk of infection.7 For instance, iron supplementation is associated with increased risk of malaria infection,8 and ID protects against malaria infection in observational studies.9 Iron is tightly sequestered by transferrin to reduce its availability to systemic organisms, while cellular iron uptake through transferrin receptor 1 is critical for the development of host immunity.10 During inflammation and infection iron availability is further reduced by the actions of hepcidin which inhibits enteric absorption and sequesters plasma iron into macrophages6,11 thus inhibiting the growth of malaria parasites and bacteria.12,13

The genetic architecture of iron status in African ancestry populations remains understudied, in contrast to the many large-scale genome-wide association studies (GWAS) of iron status conducted in European ancestry populations.14,15 Studies conducted in Africa are largely limited to a few candidate gene studies16,17 and to our knowledge, this is the first GWAS of iron biomarkers undertaken in continental African populations. A single GWAS of iron status has been conducted in middle-aged African-American adults,18 however transferability to continental African populations might be complicated by admixture,19 limited representation of the greater genetic diversity found among continental Africans,20,21 and by different environmental exposures in terms of nutrition and infectious disease.22 Even within Africa, divergent patterns in linkage disequilibrium might yield region-specific patterns of association.23 Given the greater genetic diversity20 and selective pressure from malaria and other infections in continental African populations, an iron GWAS may reveal differential effects compared to other populations due to genotype-environment interactions.

In this work, to enhance discovery of genetic determinants of iron status in an understudied population, we conducted a GWAS of six iron biomarkers (transferrin, hepcidin, soluble transferrin receptor (sTfR), ferritin, serum iron, and transferrin saturation (TSAT)) in 3,928 children recruited from Kenya, Uganda, Burkina Faso, South Africa, and The Gambia with replication in 2,868 African American adults. To evaluate the relationship between iron-associated genetic variants and infection risk, we examined the lead loci identified in the iron GWAS for association with severe malaria and bacteremia outcomes using the largest African case-control GWAS datasets (7,957 malaria cases and 7,746 controls; 1,970 bacteremia cases and 4,013 controls).24,25 We identify previously unreported African-specific genome-wide significant loci that are associated with iron homeostasis and risk of severe malaria and invasive bacterial infections.

Results
Characteristics of discovery sample
To assess the genetic contribution to iron status in continental Africans, we performed a discovery GWAS analysis using data from five independent sites in Africa (Fig. 1a). A total of 3928 children (1059 Kenyan, 1360 Ugandan, 348 Burkinabe, 611 South African, and 550 Gambian), aged between birth and eight years were included. ID, inflammation, malaria parasitemia, stunting, and underweight were highly prevalent among study participants (Supplementary Table 1). Prevalence of ID and geometric mean concentrations of measured iron biomarkers (transferrin, ferritin, sTfR, hepcidin, serum iron, and TSAT) varied by study site (Fig. 1b). Principal component plots of the genotypes showed clustering of the study participants by geographical region and ethnolinguistic group (Fig. 1c). A summary of data quality control steps for each population is provided in Supplementary Tables 2 - 3.

Discovery genetic association analyses
To identify variants associated with iron biomarkers, we performed individual GWAS within each of the African discovery sites (Supplementary Fig. 1) followed by meta-analysis. Fig. 2 shows the distribution of association statistics for the GWAS of each of the six iron biomarkers after fixed-effects meta-analyses of all five African discovery sites. We applied a linear mixed model including a genetic relatedness matrix (GRM) to account for relatedness in the sample, and age and sex as covariates, and corrected any population stratification. There was minimal inflation of the association statistics (lambda ranged from 0.98 to 1.05, Supplementary Fig. 2). All lead SNPs were either genotyped or imputed with high quality (INFO>0.95; Supplementary Data 1). Additional adjustment for inflammatory markers yielded similar results (Supplementary Table 4). Meta-analysis of GWAS results from all the African cohorts replicated a previously reported genome-wide significant locus for transferrin on chromosome 3 at the TF gene (Fig. 2a) and a previously unreported African-specific locus associated with hepcidin levels (Fig. 2b). We did not observe any loci at genome-wide significance (set at P<5x10-8) after meta-analysis of GWAS results from all African discovery sites for ferritin, sTfR, serum iron, and TSAT (Figs. 2c, 2d, 2e and 2f).

The TF locus
The TF locus is well characterized across ancestries. The lead SNP in our continental African transferrin GWAS was rs6762719 (P=1.59x10-27) which is in strong LD (r2=0.9) with the lead common SNP (rs3811647) associated with transferrin levels in European ancestry transferrin GWAS.26 The rs3811647 SNP (P= 3.45x10-16) was among the top SNPs in our African transferrin GWAS. After conditional analyses adjusting for rs6762719, we found another independent locus (lead SNP rs4854748) indicating at least two independent loci for transferrin (Table 1) as previously reported in African American populations.18 This SNP (rs4854748) was in strong LD (r2=0.9) with the lead SNP, rs9872999, in the conditional analysis in African-Americans.18 Notably, rs4854748 has been strongly associated with reduced total iron binding capacity (TIBC), a proxy for transferrin levels, in the largest European ancestry TIBC GWAS (P=3.05x10-578).15 Genome-wide significant SNPs associated with transferrin levels in our discovery meta-analysis replicated in the Jackson Heart Study (JHS) and the largest European-ancestry TIBC GWAS15 (except for rs113656013 which is rare in Europeans; Supplementary Data 2), thus extending the transferability of known transferrin loci in Africa.

GTF3C5, an East African-specific locus for transferrin
Since genetic associations can be highly population-specific across Africa,27 we performed meta-analysis by African region (i.e., East, West, and South Africa; Supplementary Fig. 3). For transferrin levels, we observed a previously unreported locus, GTF3C5 (general transcription factor IIIC, polypeptide 5) on chromosome 9, with evidence in East Africa only (Kenya and Uganda; Fig. 3a). Three SNPs reached genome-wide significance (Supplementary Table 5). The lead SNP, rs2905094, was in high LD with the other two SNPs (Fig. 3b) and explained 1.42% of variation in transferrin levels. The T allele frequency of rs2905094 in East Africa was 16%, with allele frequencies of 14-23% across continental Africa and African American cohorts, and 33% in European populations (Fig. 3c). The rs2905094 SNP was associated with increased levels of ferritin, TSAT and serum iron, decreased levels of sTfR (Fig. 3d), and a 19% reduction in ID (OR=0.81 (95% CI 0.68, 0.97); P=0.02) in East Africa.

We then used external reference data to annotate and evaluate the functional or regulatory impact of genome-wide significant variants and nearby gene(s). The three genome-wide significant SNPs (Supplementary Table 5) mapped to the GTF3C5 (general transcription factor IIIC, polypeptide 5) gene. GTF3C5 encodes transcription factor IIIC63 (TFIIIC63) which binds DNA to recruit transcription factor IIIB and RNA polymerase III to mediate the transcription of small noncoding RNAs, such as tRNAs. The lead SNP rs2905094 is located upstream of the transcription start site of GTF3C5 and is a significant eQTL for GTF3C5 (Beta = 0.07, SE =0.03, P=0.04, n=593) in immortalized lymphoblastoid cell lines in the African Functional Genomics Resources (AFGR, https://github.com/smontgomlab/AFGR).

Given the above observations, we next performed haplotype analyses to investigate whether the rs2905094 variant might reside within distinct haplotypes in East Africa. We found two dominant haplotypes from chromosomes containing the rs2905094 mutant (T) allele (Supplementary Fig. 4). Haplotype 1 predominated in West Africa (The Gambia 62%, Burkina Faso 52%) but was less frequent in East and Southern Africa (Kenya 32%, Uganda 23%, South Africa 31%), whereas Haplotype 2 was absent in The Gambia, present at 9% in Burkina Faso and 15% in South Africa, and observed at moderate frequencies in East Africa (Kenya 33%, Uganda 20%; Supplementary Table 6). However, in haplotype-level analyses in East Africans, both Haplotypes 1 and 2 were associated with lower transferrin levels, and these effects disappeared after conditioning on rs2905094, indicating that the signal is driven by rs2905094 or an unidentified variant in strong LD with rs2905094 rather than by a haplotype-specific effect (Supplementary Table 7). Through fine-mapping, we identified a single credible set centred on rs2905094 which had the highest posterior inclusion probability (PIP = 0.57), with rs2905095 and rs2905089 contributing minor probabilities (Supplementary Fig. 5), consistent with a single causal signal at GTF3C5. Although rs2905094 is more common in Europeans, analysis of 1000 Genomes EUR data revealed two predominant haplotypes (Haplotype 3 and Haplotype 4; Supplementary Fig. 6) that could differ from African haplotypes, suggesting that the rs2905094-T allele may segregate on a different haplotype background in Europeans, potentially explaining the absence of an association in European iron GWAS.

Since GTF3C5 knockdown has been shown to increase endocytosis and internalization of transferrin,28 and cellular iron uptake influences risk of infection,29 we investigated the association between rs2905094 and susceptibility to infection using large case-control GWAS of severe malaria24 and bacteremia25 (Supplementary Table 8). Since the effect of rs2905094 on transferrin levels was restricted to East Africa, we compared fixed effects meta-analyses of severe malaria GWAS data from East Africa (i.e. Kenya, Malawi, and Tanzania) and West Africa (The Gambia, Mali, Burkina Faso, Ghana, Nigeria, and Cameroon; Supplementary Fig. 7). We found that rs2905094 was associated with 12% (OR=0.88, 95% CI 0.78, 0.98) protection against severe malaria in East Africa where it altered transferrin levels but was not associated with protection in West Africa where it did not alter transferrin levels (OR=0.95; 95% CI 0.87, 1.03; Fig. 3e and Supplementary Fig. 7). rs2905094 was also associated with 15% (OR=0.85, 95% CI 0.75, 0.97; P=0.02) protection against bacteremia in Kenyan children (Fig. 3e).

CHCHD7/SDR16C5, an African-specific locus for hepcidin
Meta-analysis of hepcidin GWAS results from all five African discovery sites yielded a previously unreported locus at genome-wide significance, found on chromosome 8 near the CHCHD7 (Coiled-Coil-Helix-Coiled-Coil-Helix Domain Containing 7) and SDR16C5 (Short Chain Dehydrogenase/Reductase Family 16C Member 5) genes (Fig. 2b). The regional plot suggests that this association is within an extended haplotype (Fig. 4a) and we observed elevated normalized integrated haplotype score (iHS) values across all African cohorts (Supplementary Fig. 8 and Supplementary Data 3), indicative of extended haplotype homozygosity. The A allele of the lead SNP, rs73596248, occurred at a frequency of 6% in African populations with consistently increased hepcidin levels across all the African sites (Fig. 4b). rs73596248 explained 0.82% of variation in hepcidin levels. In contrast, the rs73596248 A allele was very rare in European and Latin American populations and was monomorphic in Asian populations (Supplementary Table 9). Associations between rs73596248 and other individual iron biomarkers and red cell indices did not reach statistical significance (Supplementary Fig. 9a).

The rs73596248 SNP lies downstream of CHCHD7 and is an intergenic variant between the CHCHD7 and SDR16C5 genes. rs73596248 is a significant eQTL for CHCHD7 (Beta = -0.60, SE =0.16, P=1.50x10-4, n=593) in the AFGR immortalized lymphoblastoid cell lines. CHCHD7 is a protein coding gene involved in mitochondrial function and is ubiquitously expressed but mainly in peripheral blood mononuclear cells and implicated in pleomorphic adenoma carcinoma and myoepithelial carcinoma through CHCHD7-PLAG1 gene fusions that promote tumorigenesis.30 The SDR16C5 gene encodes short chain dehydrogenase reductase that oxidizes retinol to retinoic acid, which is known to suppress hepcidin production.31 However, we did not find any rs73596248 eQTL data for SDR16C5 in the eQTL databases.

Since hepcidin is implicated in the pathophysiology of malaria and bacterial infections in mouse models,12,13,32 we determined the association between rs73596248 and these infections in the case-control GWAS of severe malaria and bacteremia described above.24,25 We found that rs73596248 was associated with protection against bacteremia due to Klebsiella pneumoniae (OR=0.22; 95% CI 0.05, 0.92; P=0.04) and Staphylococcus aureus (OR=0.56; 95% CI 0.33, 0.92; P=0.02). Protection against Escherichia coli and overall bacteremia did not reach statistical significance, while severe malaria was not associated with the rs73596248 SNP (Fig. 4c).

Dantu blood type is associated with sTfR levels in Kenya
We observed a genome-wide significant locus at the SMARCA5 gene associated with increased sTfR levels in Kenya (Fig. 5a). SMARCA5 lies just outside a region of segmental duplication that contains the genes encoding the erythrocyte surface antigens glycophorin A and B. A complex structural variant at this locus, termed DUP4, encoding the Dantu blood group phenotype, has previously been shown to be under strong selective pressure due to its protective effect against malaria along the Kenyan coast.27,33 The lead sTfR SNP in our GWAS, rs552439837, was in strong LD with DUP4 (e.g. r2>0.9 with the previously identified tag SNP, rs186873296),33 which was also associated with increased sTfR levels (P=1.3x10-6). The alternate ‘G’ allele of rs552439837 occurred at a frequency of 9% in Kenya (Table 1) but as expected was rare outside Kenya (Supplementary Table 10). The regional plot suggests an extended haplotype underlying the association (Fig. 5b). The rs552439837 variant explained 3.8% of variation in sTfR levels and was associated with increased TSAT and serum iron concentrations (Supplementary Fig. 9b). Consistent with the East African-specific nature of the Dantu variant, we did not find any reported eQTL data for the top sTfR GWAS SNPs in available eQTL databases.

The Dantu blood group locus, marked by variants associated with soluble transferrin receptor (sTfR) levels, showed strong and concordant protective effects against both bacteremia and severe malaria. The lead sTfR GWAS SNP, rs552439837, was 20% protective against bacteremia (OR=0.80; 95% CI 0.68, 0.95; P=0.01; Fig. 5c), as previously shown using rs186873296.34 Since rs552439837 was missing in the case-control study of severe malaria, we identified a tag SNP, rs141274959, from among the genome-wide significant sTfR GWAS SNPs (Supplementary Table 10). rs141274959 is in strong LD with rs552439837 (r2=0.9) and was highly protective against severe malaria (OR=0.56; 95% CI 0.46, 0.69; P=3.3x10-9). rs141274959 exhibited a 99.97% probability of colocalization between severe malaria and sTfR suggesting that this locus drives both traits.

Transferability of established European iron GWAS SNPs
To investigate the transferability of European ancestry iron GWAS findings to African populations, we identified the estimates of European ancestry iron GWAS index SNPs14,15 in our meta-analyzed GWAS results of African children. Of the 138 index SNPs available in our GWAS, 69 (25 for ferritin, 20 transferrin, 9 serum iron, 6 hepcidin, 6 sTfR, and 4 TSAT) had the same direction of effect as the estimate in the European iron GWAS but only the TF locus was genome-wide significant (Supplementary Data 4 and 5). We observed some correlation in effect estimates for variants predicting transferrin and serum iron concentrations and TSAT between African and European populations, but little evidence of correlation for other iron markers (Fig. 6a). Additionally, most of the variant allele frequencies differed substantially between European and continental African populations. Notably, the HFE variants (rs1800562 and rs1799945) were very rare or monomorphic in African populations and the frequency of the effect allele (A) for the TMPRSS6 rs855791 variant, was lower in African (7.7%) compared to European (43.1%) populations, although effect estimates were similar across the populations (Supplementary Data 4 and 5). We then calculated polygenic risk scores (PRS) for each iron biomarker using the European iron biomarkers GWAS index SNPs and similarly found that only the transferrin PRS replicated in African children (Fig. 6b).

Methods
Ethical approvals
Individual study site ethical approvals were obtained. For Kilifi, Kenya (by the Scientific Ethics Review Unit of the Kenya Medical Research Institute (KEMRI/SERU/CGMR-C/046/3257/2983)); Entebbe, Uganda (locally by the Uganda Virus Research Institute (GC/127/12/07/32) and Uganda National Council for Science and Technology (MV625), and in the UK by the London School of Hygiene and Tropical Medicine (A340) and Oxford Tropical Research (OTR) (39-12, 42-14 and 37-15) Ethics Committees); Banfora, Burkina Faso (by Ministere de la Recherche Scientifique et de l’Innovation in Burkina Faso (2014-12-151) and the OTR Ethics Committees (41-12)); Soweto, South Africa (by the University of Witwatersrand Human Research (M130714) and the OTR Ethics Committees (1042-13 and 42-14)); and West Kiang, The Gambia (by the Gambian Government / Medical Research Council Ethics Committee (874/830)). The Jackson Heart Study (JHS) was approved by the IRB of the University of Mississippi Medical Center, Jackson State University, and Tougaloo College. All participants gave written informed consent prior to enrollment in the studies.

Study populations
Our discovery studies included five community-based cohorts of children from sub-Saharan Africa in Kenya, Uganda, Burkina Faso, South Africa, and The Gambia. Our replication population included African American adults from the Jackson Heart Study (JHS) in Mississippi, USA. We further analyzed GWAS data on severe malaria and bacteremia to assess whether the identified iron-related SNPs were associated with susceptibility to these infections. Below is a description of each of the study sites.

Discovery cohorts
Kilifi, Kenya: This included an ongoing rolling cohort evaluating malaria immunity in children in Kilifi, along the coast of Kenya.48 Within this cohort, children were followed-up for malaria episodes up to 8 years of age with weekly follow-up and annual single cross-sectional surveys during which anthropometry measurements, auxiliary temperature, and blood samples were taken. Iron and inflammatory markers were measured from blood samples collected at annual cross-sectional surveys (between 1998 and 2016) based on the availability of plasma samples archived at -800C.

Entebbe, Uganda: The Entebbe Mother and Baby Study (EMaBS) is a prospective birth cohort that was originally designed as a randomized controlled trial to test whether anthelminthic treatment during pregnancy and early childhood was associated with differential response to vaccination or incidence of infections including malaria (http://emabs.lshtm.ac.uk/).49 Pregnant women from Entebbe in their second or third trimester of pregnancy were initially enrolled at Entebbe Hospital antenatal clinic. Livebirths from these mothers were enrolled into the EMaBS study. Blood samples were collected in vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA) at birth and at subsequent birthdays up to five years of age. Iron and inflammatory biomarkers were measured from a single annual visit based on the availability of stored samples.

Banfora, Burkina Faso: The VAC050 ME-TRAP Malaria Vaccine Trial recruited infants between the ages of 6 and 18 months living in the Banfora region of Burkina Faso to participate in a Phase 1/2b clinical trial of the safety, immunogenicity and efficacy of a viral-vectored prime-boost liver-stage malaria vaccine.50 Samples from a total of 350 infants were then selected based on suitability of samples for DNA extraction. Plasma samples were available from the infants at multiple time-points following receipt of the experimental vaccine. Samples from individuals taken at time points as close to the 12-month age as possible were prioritized for this study.

South Africa: The Soweto Vaccine Response Study consisted of infants born in Chris Hani Baragwanath Hospital living in the Soweto region of Johannesburg, South Africa who were recruited from vaccine trials51 that are coordinated by the Respiratory and Meningeal Pathogens Unit (http://www.rmpru.com/). Mothers of the infants were approached if their infants had received all Expanded Programme on Immunization vaccines up to six months of age. The infants were sampled prospectively at six months of age and at 12 months after receipt of measles vaccine at nine months. Single whole blood samples were collected in EDTA vacutainer tubes for measurement of iron and inflammatory markers and DNA extraction.

The Gambia: West Kiang study: All children aged two to six years (except those with chronic illness) were recruited from 10 rural villages in the West Kiang region of The Gambia during the start of a malaria season to initially investigate seasonal genetic effects of iron status, hemoglobin and haptoglobin concentrations between July and August 2001.52 Ethnic groups were Mandinka (nine villages) and Fulani (one village). All children had a clinical examination, anthropometric measurements, and a three-day course of mebendazole for potential hookworm infection. A blood sample was collected for measurement of iron and inflammatory markers and DNA extraction.

Replication cohort
Jackson Heart Study: This is a population-based longitudinal study of African American adults aged ≥21 years living in the Jackson, Mississippi metropolitan area in the USA.53 This study was designed to evaluate risks of cardiovascular disease .53,54 Iron and inflammatory biomarkers were measured from blood samples collected at a single baseline clinic visit. Whole blood was used to extract DNA using Puregene reagents (Gentra System, Minneapolis, USA).

Infection GWAS
MalariaGEN consortium: This was a GWAS study of severe malaria in ~17,000 severe malaria cases and population controls from 11 countries in Africa (Kenya, Malawi, Tanzania, The Gambia, Mali, Burkina Faso, Ghana, Nigeria, Cameroon), Asia (Vietnam), and Oceania (Papua New Guinea).24 Sample collection, processing, genotyping, quality control, imputation, GWAS, and population characteristics are described in detail elsewhere.24,55 Severe malaria was defined according to WHO criteria56 and included diagnoses of severe malaria anemia, cerebral malaria, and other malaria-related symptoms. Association testing for each variant and severe malaria was performed using SNPTEST software in each study population adjusting for principal components under additive, dominant, recessive, heterozygote and general models of association. In the current study, we extracted additive model estimates for African populations. For the East-Africa specific GTF3C5 lead SNP, rs2905094, we retrieved population-specific results from all of the nine African populations in the MalariaGEN database and applied fixed-effects meta-analyses by East (Kenya, Malawi, and Tanzania) and West (The Gambia, Mali, Burkina Faso, Ghana, Nigeria, and Cameroon) African sites. For the association between the hepcidin lead SNP, rs73596248 and severe malaria, we meta-analyzed estimates from all the nine African populations in the MalariaGEN database. Since the sTfR GWAS signal is specific to Kenyan populations, we used data from the MalariaGEN Kenyan GWAS to determine the association between the top sTfR GWAS SNPs and severe malaria. The lead SNP, rs552439837, was not present in the severe malaria case–control dataset; therefore, we used estimates for rs141274959, a genome-wide significant sTfR GWAS variant in strong LD with rs552439837 (r² = 0.9).

The Kenyan Bacteremia GWAS: This GWAS included 1,970 Kenyan children (<13 years old) admitted to Kilifi County Hospital between 1998 and 2010 with culture-confirmed community-acquired bacteremia, and 4,013 healthy community controls.25 Controls were infants aged 3-12 months recruited between 2006 and 2008 from the same geographic area through the Kilifi Genetic Cohort Study. All participants were residents of Kilifi County on the coast of Kenya. Genotyping was performed in two phases: the discovery phase employed the Affymetrix SNP 6.0 array (787,861 SNPs) in 1,536 cases and 2,677 controls, while the replication phase used the Illumina ImmunoChip (143,100 SNPs) in an additional 434 cases and 1,336 controls, yielding a total of 1,970 bacteremia cases and 4,013 controls. Whole-genome imputation was carried out using SHAPEIT and IMPUTE2 with the 1000 Genomes Phase 3 reference panel. Demographic data for cases and controls and distribution of the most common bacterial species in the discovery and replication sets are described elsewhere.25 In the current study, associations between conditionally independent iron-related SNPs and bacteremia were assessed using additive logistic regression models, adjusted for age, sex, and principal components. Analyses were performed for overall bacteremia and then stratified by the most common bacterial species.

Assays for iron and inflammatory markers
The assayed biomarkers of iron (ferritin, soluble transferrin receptors (sTfR), hepcidin, serum iron, transferrin, and unsaturated iron binding capacity (UIBC) and inflammation (C-reactive protein (CRP) and α1-antichymotrypsin (ACT) are shown in Supplementary Table 11. Since iron biomarkers can be affected by inflammation or infection,1 it is important to account for inflammation when defining iron deficiency. We defined inflammation as CRP >5 mg/L or ACT >0.6 g/L (in Gambian children), consistent with established guidelines for identifying inflammation in children.57 The Gambian hepcidin values were harmonized with the rest of the cohorts’ hepcidin values (measured using high sensitive DRG kit) by converting to the old DRG hepcidin assay values and then to the new high sensitive DRG hepcidin assay values.58 Transferrin saturation (TSAT) was calculated as (serum iron (µmol/L)/transferrin (g/L) x 25.1) x 100 or as (serum iron/ UIBC + serum iron) x 100.59 Since EDTA chelates iron, serum iron measurements (and TSAT) were missing in Uganda and South Africa where blood samples were collected in EDTA vacutainer tubes. Transferrin (g/L) in JHS was calculated as 0.7 x total iron binding capacity (TIBC) [µg/dL] x 0.01. Transferrin in The Gambia was similarly calculated but after calculating TIBC as UIBC + serum iron. Red cell indices data included hemoglobin levels, Mean Corpuscular Volume (MCV), and Mean Corpuscular Hemoglobin Concentration (MCHC), measured using automated blood analyzers (Beckman Coulter).

Genotyping and imputation
Genotyping for DNA extracted from Uganda, Burkina Faso and South Africa was performed using the Illumina HumanOmni 2.5M-8 (‘octo’) BeadChip array version 1.1 (Illumina Inc., San Diego, USA), at the Genotyping Core facilities at the Wellcome Trust Sanger Institute. Genotyping for Kenyan and Gambian samples was performed using H3Africa Custom Genotyping Array v1.0 at the Wellcome Centre for Human Genetics, University of Oxford. JHS samples were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Genotypes were called from intensities using Illuminus and GenCall clustering algorithms in GenomeStudio (Illumina Inc., San Diego, USA) incorporating data from pre-determined genotypes.

We performed quality control (QC) for samples and single nucleotide polymorphisms (SNPs) using SNPs mapped to Human Genome Build 37. Using the appropriate strand files (http://www.well.ox.ac.uk/~wrayner/strand/), we removed low quality variants (those that mapped to multiple regions within the human genome or did not map to any region) and duplicate SNPs. Only autosomal SNPs were included in downstream QC analyses. We used a standardized H3A GWAS pipeline (https://github.com/h3abionet/h3agwas) to perform sample and SNP QC separately for each cohort (using identical steps).60,61 Samples having discordant sex information, call rates <98%, relatedness cutoffs of between 11 and 70% (accounts for cryptic relatedness), and heterozygosity <0.15 or >0.343 were removed. SNPs were retained if they passed thresholds of >99% call rate, minor allele frequency (MAF) >0.01, and Hardy-Weinberg equilibrium (HWE) adjusted p-value >0.008.

We imputed our quality-controlled genotypes using the African Genome Resources haplotype reference panel that contains 4,956 samples from 1000 Genomes Phase 3 and ~2000 Ugandan samples and ~100 Ethiopians/Egyptians/Namibians/South Africans (n= 93,421,145 biallelic SNPs). This reference panel is hosted by the Wellcome Sanger Institute (https://imputation.sanger.ac.uk/).62 We filtered out the resulting imputation dataset for SNPs with information score ≥ 0.3 and MAF ≥ 0.01 for association analyses.

Statistical analyses
We used inverse-normal transformation to normalize the distribution of iron biomarkers in R version 4.2.3. We performed association testing for each study site using genome-wide complex trait analysis (GCTA) software version 1.24.463 fitting a linear mixed model including a genetic relatedness matrix (GRM) as a random effect. We included age and sex as covariates. We also assessed the impact of additional adjustment for inflammatory markers. Inverse normal transformation measures were used throughout our GWAS analyses, and all results are reported as such. We meta-analyzed our discovery study-specific summary statistics using METASOFT version 2.0.1 that reports both fixed and random effects.64 Results were visualized using Manhattan and QQ plots generated using R packages GenABEL and qqman. Inflation factors (lambda) were calculated to check for population stratification. We used LocusZoom.js v0.12.0 (https://my.locuszoom.org/) to create GWAS regions and added LDs calculated using Plink version 1.9 from our discovery cohorts data. The R package coloc was used to estimate the probability of colocalization using the coloc’s default prior probability of colocalization of 1×10-5 and considered a posterior colocalization probability > 80% as a threshold for evidence of colocalization.

Genome-wide significant SNPs (P<5x10-8) from discovery GWAS meta-analyses were selected for testing in the JHS replication study. We considered a GWAS result to replicate if the effect in the replication was in the same direction as in the discovery sample, and if the association in the replication sample was statistically significant (P< 0.05).

To evaluate differences in the genetics of iron between European and African ancestry populations, we analyzed transferability of iron GWAS findings between continental African and European populations. We identified index SNPs from the largest genome-wide meta-analysis of European ancestry iron biomarkers GWAS14,15 for replication in our African iron GWAS. All lead/independent SNPs reported in the European ancestry iron GWAS were identified in African children with available iron GWAS data. We then compared effect sizes and P values between European and African populations. Plots comparing the effect estimates were generated using the R package ggplot2. A SNP was considered to replicate if the effect in Africans was in the same direction as in the European iron GWAS, and if the association in Africans was statistically significant (P< 0.05). For each iron biomarker, we further calculated a PRS,65 weighted using effect sizes reported in European GWAS and reflecting an increase in levels of the biomarker. A linear regression model adjusted for age, sex and study site was used to determine any association between the PRS and iron biomarkers measured in African children. In Africans, we used transferrin as a proxy measure for total iron binding capacity (TIBC), which was reported in European iron GWAS. TIBC indicates the amount of iron that can be bound to transferrin.

Since iron status might influence risk of malaria and bacterial infections,39,66 we investigated the association between our identified African-specific SNPs and these infections using the largest case-control GWAS of severe malaria24 and bacteremia25 as described above. Since the GTF3C5 lead SNP, rs2905094, showed an East-African specific effect on transferrin levels, we meta-analyzed severe malaria GWAS results from East African countries only (Kenya, Malawi and Tanzania). Since the SDR16C5 lead SNP appeared across all five iron GWAS African sites, we meta-analyzed severe malaria results from all nine MalariaGen African sites (The Gambia, Mali, Burkina Faso, Ghana, Nigeria, Cameroon, Malawi, Tanzania, and Kenya) using the ‘meta’ R package assuming fixed effects. For the DUP4 locus lead SNP, which is Kenyan specific and very rare in other populations across our cohorts or in reference panels such as 1000G, we extracted its effect size in the Kenya severe malaria GWAS only. For SNP-bacteremia associations, we identified our African-specific SNPs in the largest GWAS of bacteremia in African populations,25 merged the genotypes with the hospital clinical data, and conducted logistic regression analyses adjusting for age, sex, and principal components.

Functional annotation and statistical fine-mapping
To identify independent loci, we performed statistical fine-mapping by conducting conditional forward stepwise regression. This entailed conditioning on the lead SNP from our GWAS by treating it as an adjusting covariate and testing the remaining SNPs. Once a new independent secondary signal was identified, we performed sequential testing by adjusting for the lead SNP until no conditional tests were statistically significant.

To identify variants driving the East African GTF3C5 locus, we performed statistical fine-mapping using the susieR package67 in R version 4.2.3 based on meta-analyzed transferrin GWAS summary statistics from East African cohorts (Kenya and Uganda). A region-specific LD matrix, derived from the merged East African genotype data, was used to accurately model local LD structure. We specified up to 10 single-effect components (L = 10) to allow for multiple independent causal signals within the locus. Posterior inclusion probabilities (PIPs) were estimated for each variant, and 95% credible sets were defined to identify variants most likely to drive the association signal.

We performed functional annotation of lead GWAS variants using the Ensembl Variant Effect Predictor (VEP, GRCh37, https://www.ensembl.org/Tools/VEP), reporting the most severe consequence per variant based on canonical transcripts. To identify candidate target genes, we used the FUMA GWAS platform (https://fuma.ctglab.nl/), applying three complementary mapping strategies: (1) positional mapping within ±10 kb of gene boundaries, (2) cis-eQTL mapping using data from GTEx v8 across multiple tissues, and (3) chromatin interaction mapping based on Hi-C datasets from relevant cell types. In parallel, we queried the African Functional Genomics Resource (https://github.com/smontgomlab/AFGR) to identify eQTLs in African ancestry populations. Genes implicated by any of these approaches were considered candidate target genes.

Haplotype analyses
Haplotype patterns were derived from the imputed and phased genotypes using a custom R script that computed Manhattan distances followed by hierarchical clustering using the hclust function in R software (version 4.0.2). The resulting clusters were visualized with heat maps, and haplotype patterns were identified by visually assessing regions of homogeneity. We focused on a 72 kb region defined by 209 SNPs surrounding the rs2905094 SNP. Haplotype pattern analysis was performed separately for the ancestral and derived alleles of rs2905094, both across all countries combined and for each country individually. Since rs2905094 is common in European ancestry populations, we also analyzed the GTF3C5 haplotype configuration using 1000 Genomes Project EUR data.

To determine whether the association signal at GTF3C5 was driven by a specific haplotype carrying the derived rs2905094-T allele, we performed haplotype-level association analyses for transferrin levels among East African participants. Haplotype dosages (0/1/2) were estimated from phased genotype data and incorporated into linear regression models of inverse-normalized transferrin levels, adjusting for age, sex, study site, and the first ten principal components of ancestry.

Selection scan using integrated haplotype scores
Since the LD pattern around the CHCHD7/SDR16C5 locus on chromosome 8 was suggestive of an extended haplotype, we investigated extended haplotype homozygosity (EHH) patterns for each of the five African sites. Variants were restricted to chromosome 8 positions 57,050,000–57,300,000 bp (GRCh37/hg19 coordinates) to encompass the LD block surrounding the lead hepcidin GWAS variant (rs73596248). SNP positions within the target region were used to interpolate recombination distances from the 1000 Genomes Project Phase 3 genetic map. Integrated haplotype scores (iHS) were computed using selscan v2.1.0.68 SNPs with MAF ≥0.01 were included. The iHS statistic was calculated for variants in the target region, measuring the decay of EHH for ancestral and derived alleles. Using selscan's normalization procedure, raw iHS were standardized by allele frequency bins (n=10) across the genome to account for variation in allele frequencies. Haplotype signals were visualized using ggplot2 package in R v4.3.2, with loess smoothing applied to highlight local trends in normalized iHS values across the genomic region.

GWAS sample size and power
Power for the GWAS studies was calculated using the formulae given in Visscher et al 2017,69 implemented in: https://github.com/kaustubhad/gwas-power. Given an allele frequency ≥ 5% and effect size ≥ 0.33 standard deviation (SD), the overall discovery sample size of 3928 provided more than 80% power to observe true genotypic and phenotypic associations at a genome-wide significance level (type I error=5x10-8). For allele frequency ≥10%, we had over 80% power to see effect sizes ≥ 0.24 SD. For the association between lead SNPs and severe malaria or bacteraemia, we estimated that the severe malaria dataset (7,957 cases, 7,746 controls) had ~80% power to detect an odds ratio ≥1.15 for a variant with MAF = 5% at α = 0.05, while the bacteremia dataset (1,970 cases, 4013 controls) had ~80% power to detect an odds ratio ≥1.40.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have investigated the genetic control of iron-related biomarkers in Africa. They performed genome wide association studies of transferrin, hepcidin, sTfR, ferritin, serum iron, and TSAT in discovery cohorts including people from Kenya, Uganda, Burkina Faso, South Africa and The Gambia. In addition, they used data from the Jackson Heart study as a replication cohort, and used MalariaGEN GWAS and Kenyan Bacteremia data for assessing the association of candidate SNPs with malaria and bacteremia, respectively. The hypothesis underlying this study was that iron homeostasis has an effect on bacterial and plasmodial infection.\nThe paper is clear and well written, and the methods are appropriate for the purpose of the study. The results are convincing and very interesting. In particular, the authors report several SNPs associated with iron-related biomarkers, such as transferrin levels, hepcidin levels, or soluble transferrin receptor levels. These associations were detected either in the grouped five African populations, or in specific populations. Among them, some SNPs were replicated in an independent cohort and previous GWAS in European populations. In addition, previously unreported loci (GTF3C5 and CHCHD7) were associated with transferrin levels or hepcidin levels. Moreover, GTF3C5 locus was associated with transferrin levels, severe malaria and bacteremia, while CHCHD7 was associated with hepcidin levels and bacteremia.\nI have, nevertheless, some comments:\n-the authors should state whether the identified SNPs were imputed SNPs (or genotyped) in Table 1. If it is the case, they could state the quality of imputation. In addition, they should state this limitation in the discussion.\n- the authors should state relevant functional annotation for each SNP within Table 1. They should state in methods whether they used positional, eQTL, or 3D chromatin interaction mapping for mapping for identifying genes using FUMA.\n-the authors should provide a supplementary Table that provide the quality of imputation and the functional annotation for each SNP associated with transferrin levels, hepcidin levels, or soluble transferrin levels (see Figures 2, 3, 4, and 5).", "reviewer_2": "Muriuki et al. conducted a series of GWAS for iron-related biomarkers using five African samples and identified some novel associations. The authors also provide insights into the reasons behind the newly detected associations. GWAS for underrepresented populations is crucial for further biological research and the realization of equitable precision medicine. I have some comments on this paper from a scientific perspective.\nGTF3C5 locus for transferrin levels was detected in East Africa (Kenya and Uganda) through a meta-analysis, but not in other regions. This variant is also common in European populations, yet it hasn't been reported in studies of European populations. To explain this discrepancy, the authors analyzed haplotype configurations. They found that Haplotype 2 is common in Kenya, Uganda, and South Africa, but absent in The Gambia and Burkina Faso. However, based on S Fig 4, I believe Haplotype 2 is present in Burkina Faso. The haplotype configuration is unclear, so please provide the data as a supplementary material. Since the European population is also discussed, it would be beneficial to analyze haplotype configurations in European populations as well. I believe the 1000 Genomes data is sufficient for this purpose. To support the authors' hypothesis, I believe a haplotype-level association test is necessary to demonstrate the (negative) risk effect observed only by Haplotype 2, not Haplotype 1, even when the rs290504-T allele is present.\nAnother comment for GTF3C5 is that I suggest the authors conduct statistical fine mapping in East African populations first. SNPs with high PIP, excluding rs2905094, could potentially explain the discrepancy in the association between regions. This possibility has not been explored.\nThe authors identified the novel CHCHD7-SDR16C5 locus for hepcidin and noted that \"This region exhibits evidence of positive selection.\" Examining Figure 4, the core region of the haplotype involving rs73596248 spans from 57.13 to 57.30 Mb on chromosome 8. However, the reported selected region from PopHumanScan ranges from 56,967,474 to 57,015,445 (hg19), which does not align with the locuszoom plot presented in Figure 4 at a glance. In my opinion, the authors should provide additional explanations to establish a direct connection between the associated variant and the selected region.\nThe authors focused solely on the association of SMARCA5, which was only detected in a single Kenyan GWAS. As shown in S Fig1, there would also be other solitary significant associations, such as transferrin in Kenya and South Africa, ferritin in Uganda, and sTfR in The Gambia. Why did the authors ignore these associations and only focus on SMARCA5?\nMinor points:\n- The authors used “inflammation” as one of the traits; rather, would it be better to use the term “chronic inflammation”?\n- It's written in STab1 that participants in the Jackson Heart Study weren't exposed to malaria, but the table shows \"n/a\" for this study. Should it be 0, or if the ascertainment wasn't enough, it shouldn't be described as \"not exposed to malaria\"?\n- This reviewer believes the number of participants who failed the sex check, which is 58 (4.4%), in STab2 for Kenya, is too high. Is there any effort being made to prevent sample swapping?\n- Samples from Kenya and The Gambia should be genotyped using the same H3Africa array. However, only The Gambia's results showed a significantly higher number of duplicated variants (182,365) compared to Kenya's (28) in STab 2. This discrepancy raises questions about the reasons behind it.", "reviewer_3": "Thank you for asking me to review this manuscript. I agree with the authors’ view of the importance of topic and welcome their efforts to contribute to our understanding in an understudied population.\nKey results and significance\nThe key message that I got from the paper is that the genetic determinants of iron status in African populations are different from European populations and that this likely reflects the greater genetic diversity of the population and selective pressure exerted by malaria and other infections. This I believe was the main aim of the paper. I was less clear about the clinical and public health significance of the results. The stated new findings were the identification of a new loci for transferrin in GTF3C5 responsible for conferring protection against severe malaria and bacteriamia; a new loci for hepcidin in CHCHD7/SDR16C5, and variant rs73596248 associated with an increase hepcidin levels that conferring protected against bacteremia. However in the results section it is clearer that there is only an association with the risk of klebsiella and staph aureus bacteriaemia, but not with bacteraemia overall or with severe malaria. They also found that the rare Dantu blood group was associated with soluble transferrin receptor levels, (and confirmed previous reports that this blood group is associated with protection against severe malaria and bacteraemia). Interestingly the authors quote their own study (9) which shows link between iron status and malaria but which also did not show any relationship with hepcidin.\nAims and study design\nTt seems that there were two main aims: Firstly “To enhance discovery of genetic determinants of iron status in an understudied population (line 106) and secondly “To understand the relationship between iron and infection, we examined the effect of these variants on risk of severe malaria and bacterial infections” (line 109).\nIt felt to me that there was a much stronger rationale for the chosen study design for the first aim, and this was also reflected in the detail in which the findings were presented, and the robustness of the findings:\nFor the second aim, the presentation of results was not very clear, the interpretation of results less convincing and some of the details re. methods and statical analysis were missing.\nData and methodology\nAs mentioned above, I think the methods for Discovery and Replication GWAS seem appropriate although I have some concerns about the how the heterogeneity of contributing study designs and I and the resulting differences and gaps in the data collected. For example that transferrin was not measured in two sites and why α1-Antichymotrypsin being used as a marker of inflammation in one of the studies, rather than a more standard marker such as CRP. I also think it would have been useful to show red cell indices, other than haemaglobin, as more clinically relevant reflection of iron status than just the biochemical markers presented.\nFor the study looking for an association between the SNPs and severe malaria and bacteraemia I there was less description of the methods and the data making it difficult to comment on. Providing more information including a descriptive table of the population characteristics would be helpful. I was specifically interested to understand when these samples had been taken given the changing malaria epidemiology and had to go to other publications to find this. I was surprised to learn that that the original Malariagen study was conducted in Kilifi (conducted 1995-2008). Indeed a recently published study by the group designed specifically to explore the relationship between the Dantu and bacteriaemia, analysed separately data from 1998-2003, a period of high transmission and data from 2003-2010, a period of low transmission. They reported that the protective effect of Dantu against all-cause bacteremia was only significant during the high malaria transmission period suggesting that the protective effect was most likely mediated indirectly by its protective effect against malaria. It is not clear why a similar approach was not taken in this current study and somewhat contradicts which the authors state “….the Dantu NE blood group phenotype, is under strong selective pressure for protection against malaria along the Kenyan coast.” (Line 255).\nAnalytical approach\nIn the section of statistical analysis section (line 764-814), there is no description of how the data were analysed for the association between SNPs and bacteriaemia.\nIn the section on sample size and power (line 840-846), there is no description of the sample size and power calculations for the studies looking for association of SNPs with severe malaria and bacteriemia.\nClarity and context\nI this paper would benefit from being written more clearly, coherently and concisely. This would make it both stronger and more accessible. There are important details missing and very limited mention of any study limitations which I think are essential to address. I think it would also be helpful to clarify what’s new and significant in terms of implications for clinical practice or public health. Perhaps one way to make this clearer would be to give an example of how results of GWAS studies of iron status in other (eg European populations) have impacted on clinical care or outcomes?"}, "author_response": "Response: We thank the reviewer for this positive feedback, and for recognizing the clarity, methodological rigor, and significance of our study.\nI have, nevertheless, some comments:\n-the authors should state whether the identified SNPs were imputed SNPs (or genotyped) in Table 1. If it is the case, they could state the quality of imputation. In addition, they should state this limitation in the discussion.\nResponse: We appreciate the reviewer’s point that highlighting associations with poorly imputed SNPs could lead to erroneous conclusions. To address this concern, we have added Supplementary Table 4, which includes the source of each of the lead SNPs (whether genotyped or imputed) and the INFO score, where relevant. We have updated Table 1 to indicate which lead SNPs were directly genotyped and which were imputed.\nAll of the lead SNPs that we are presenting are either genotyped or of high quality (INFO >0.95), and we have added a statement to this effect in the Results section, as follows:\nPage 3: “All lead SNPs were either genotyped or imputed with high quality (INFO>0.95; Supplementary Table 4).”\nAdditionally, we have revised the Discussion to acknowledge this as a limitation as follows:\nPage 10: “Additionally, some lead SNPs were imputed, although imputation quality was high (INFO >0.95), and variants with small effect sizes may not have been detectable with the current sample size. Larger genomic studies within Africa are needed to detect smaller-effect variants and refine genetic architecture across populations.”\n- the authors should state relevant functional annotation for each SNP within Table 1. They should state in methods whether they used positional, eQTL, or 3D chromatin interaction mapping for mapping for identifying genes using FUMA.\nResponse: We have updated Table 1 to include the functional annotation of each lead SNP. We have also revised the Methods section to clarify how we conducted functional annotation as follows:\nPages 23-24: “We performed functional annotation of lead GWAS variants using the Ensembl Variant Effect Predictor (VEP, GRCh37, https://www.ensembl.org/Tools/VEP), reporting the most severe consequence per variant based on canonical transcripts. To identify candidate target genes, we used the FUMA GWAS platform (https://fuma.ctglab.nl/), applying three complementary mapping strategies: (1) positional mapping within ±10 kb of gene boundaries, (2) cis-eQTL mapping using data from GTEx v8 across multiple tissues, and (3) chromatin interaction mapping based on Hi-C datasets from relevant cell types. In parallel, we queried the African Functional Genomics Resource (https://github.com/smontgomlab/AFGR) to identify eQTLs in African ancestry populations. Genes implicated by any of these approaches were considered candidate target genes.”\n-the authors should provide a supplementary Table that provide the quality of imputation and the functional annotation for each SNP associated with transferrin levels, hepcidin levels, or soluble transferrin levels (see Figures 2, 3, 4, and 5).\nResponse: We have added functional annotations, as determined by the Ensembl Variant Effect Predictor (VEP), to Supplementary Tables 6, 7, 10, and 11 that report SNPs associated with transferrin at the TF locus, transferrin at the GTF3C5 locus, hepcidin, and soluble transferrin receptor (sTfR) levels. These annotations include the predicted consequence and genomic context of each variant. Additionally, we have expanded Supplementary Table 4 to include SNP annotations and imputation INFO scores for all genome-wide significant SNPs presented in Figures 2–5.\nReviewer #2 (Remarks to the Author):\nMuriuki et al. conducted a series of GWAS for iron-related biomarkers using five African samples and identified some novel associations. The authors also provide insights into the reasons behind the newly detected associations. GWAS for underrepresented populations is crucial for further biological research and the realization of equitable precision medicine. I have some comments on this paper from a scientific perspective.\nResponse: We thank the reviewer for noting the importance of GWAS in underrepresented populations.\nGTF3C5 locus for transferrin levels was detected in East Africa (Kenya and Uganda) through a meta-analysis, but not in other regions. This variant is also common in European populations, yet it hasn't been reported in studies of European populations. To explain this discrepancy, the authors analyzed haplotype configurations. They found that Haplotype 2 is common in Kenya, Uganda, and South Africa, but absent in The Gambia and Burkina Faso. However, based on S Fig 4, I believe Haplotype 2 is present in Burkina Faso. The haplotype configuration is unclear, so please provide the data as a supplementary material. Since the European population is also discussed, it would be beneficial to analyze haplotype configurations in European populations as well. I believe the 1000 Genomes data is sufficient for this purpose. To support the authors' hypothesis, I believe a haplotype-level association test is necessary to demonstrate the (negative) risk effect observed only by Haplotype 2, not Haplotype 1, even when the rs2905094-T allele is present.\nResponse: We thank the reviewer for these insightful comments, which have improved the clarity and depth of our analyses. We re-examined the GTF3C5 haplotype structure and confirmed that Haplotype 2 is indeed present, although at a low frequency (9%) in Burkina Faso. We have made this correction in the Results section as follows:\nPage 4: “Haplotype 2 accounted for 40%, 25% and 15% of chromosomes from Kenya, Uganda, and South Africa, respectively, but was absent in The Gambia and at low frequency in Burkina Faso (9%).”\nWhile we generated full haplotype configuration data for all study populations, we are unable to include these in the Supplementary Data. Our more recent studies are conducted with open access to sequence data in mind, but these data come from older studies with stricter ethical and data governance restrictions that prohibit sharing individual-level genomic information. However, we have shared the detailed haplotype configurations with the journal in this resubmission to allow sharing in confidence with the reviewer to enable verification of our results, on the understanding that the dataset is not for publication.\nGiven that rs2905094 is common in European ancestry populations, we also analyzed the GTF3C5 haplotype configuration using 1000G EUR data as suggested. However, due to differences in SNP coverage, the European haplotype block had only 110 SNPs (versus 209 in African datasets), precluding direct alignment with African Haplotype 1 and 2. Instead, we identified two major EUR haplotypes (Haplotype 3 and Haplotype 4). Based on shared SNP patterns within overlapping regions, Haplotype 3 shows partial similarity to African Haplotype 2, while Haplotype 4 resembles Haplotype 1. It is therefore possible that although rs2905094-T is common in Europeans, it resides on a distinct haplotype, which may explain the lack of association in European iron GWAS.\nWe have included the EUR haplotype results in Supplementary Figure 6 and in the Results section as follows:\nPage 5: “Although rs2905094 is more common in Europeans, analysis of 1000 Genomes EUR data revealed two predominant haplotypes (Haplotype 3 and Haplotype 4; Supplementary Fig. 6) that could differ from African haplotypes, suggesting that the rs2905094-T allele may segregate on a different haplotype background in Europeans, potentially explaining the absence of an association in European iron GWAS.”\nWe have also updated the Methods haplotype analyses section as follows:\nPage 24: “Since rs2905094 is common in European ancestry populations, we also analyzed the GTF3C5 haplotype configuration using 1000 Genomes Project EUR data.”\nTo test whether the association signal was driven by a specific haplotype carrying the derived rs2905094-T allele, we performed haplotype-level association tests for transferrin levels in East African participants, adjusting for age, sex, country, and ten genetic principal components. Both Haplotype 1 and Haplotype 2 were associated with lower transferrin levels. However, conditioning on rs2905094 genotype abolished both associations (P > 0.4), and among carriers of the rs2905094-T allele, neither haplotype showed additional effects suggesting that the East-African signal may be driven by rs2905094 or an unidentified variant in strong linkage disequilibrium with it, rather than by an independent haplotype variant. We have added these analyses to the Methods section as follows:\nPage 24: “To determine whether the association signal at GTF3C5 was driven by a specific haplotype carrying the derived rs2905094-T allele, we performed haplotype-level association analyses for transferrin levels among East African participants. Haplotype dosages (0/1/2) were estimated from phased genotype data and incorporated into linear regression models of inverse-normalized transferrin levels, adjusting for age, sex, study site, and the first ten principal components of ancestry.”\nWe have also included the findings in Supplementary Table 8 and in the Results and Discussion sections as follows:\nPages 4-5: “Through haplotype-level analyses in East Africans, both Haplotypes 1 and 2 were associated with lower transferrin levels, but these effects disappeared after conditioning on rs2905094, indicating that the signal is driven by rs2905094 or an unidentified variant in strong LD with rs2905094 (Supplementary Table 8).”\nPages 7-8, Discussion: “However, haplotype-level association analysis did not provide evidence for a haplotype-specific effect, suggesting the association is more likely to be driven by rs2905094 itself or a variant in strong LD with rs2905094.”\nAnother comment for GTF3C5 is that I suggest the authors conduct statistical fine mapping in East African populations first. SNPs with high PIP, excluding rs2905094, could potentially explain the discrepancy in the association between regions. This possibility has not been explored.\nResponse: Thank you for this insightful suggestion. We have now conducted statistical fine-mapping using the SuSiE-R package based on meta-analyzed summary statistics from Kenya and Uganda. These analyses identified a single credible set centered on rs2905094, which had the highest posterior inclusion probability (PIP = 0.57), followed by rs2905095 (PIP = 0.28) and rs2905089 (PIP = 0.08). All remaining variants had PIP < 0.03, suggesting a single likely causal signal at GTF3C5. We have updated the Methods section as follows:\nPage 23: “To identify variants driving the East African GTF3C5 locus, we performed statistical fine-mapping using the susieR package68 in R version 4.2.3 based on meta-analyzed transferrin GWAS summary statistics from East African cohorts (Kenya and Uganda). A region-specific LD matrix, derived from the merged East African genotype data, was used to accurately model local LD structure. We specified up to 10 single-effect components (L = 10) to allow for multiple independent causal signals within the locus. Posterior inclusion probabilities (PIPs) were estimated for each variant, and 95% credible sets were defined to identify variants most likely to drive the association signal.\nWe have also added these fine-mapping results to Supplementary Fig. 5 and updated the Results and Discussion sections as follows:\nPage 5, Results: “Through fine-mapping, we identified a single credible set centered on rs2905094 which had the highest posterior inclusion probability (PIP = 0.57), with rs2905095 and rs2905089 contributing minor probabilities (Supplementary Fig. 5), consistent with a single causal signal at GTF3C5.”\nPage 8, Discussion: “Consistent with this, fine mapping analyses also found most evidence for an effect of rs2905094 and little evidence for multiple independent associations in this region. Although these analyses do not fully explain the population-specific nature of the signal, they emphasize the importance of examining regional genomic diversity, particularly in African populations, and there remains the possibility that divergent LD patterns and/or gene–environment interactions may shape the underlying genetic effects at this locus.19,23”\nThe authors identified the novel CHCHD7-SDR16C5 locus for hepcidin and noted that \"This region exhibits evidence of positive selection.\" Examining Figure 4, the core region of the haplotype involving rs73596248 spans from 57.13 to 57.30 Mb on chromosome 8. However, the reported selected region from PopHumanScan ranges from 56,967,474 to 57,015,445 (hg19), which does not align with the locuszoom plot presented in Figure 4 at a glance. In my opinion, the authors should provide additional explanations to establish a direct connection between the associated variant and the selected region.\nResponse: We agree that the initially cited PopHumanScan region (56.97–57.02 Mb) does not perfectly overlap with the hepcidin-associated LD block shown in Figure 4. Upon inspection, we found that PopHumanScan contains no entries covering the narrower window (57.13–57.30 Mb), which explains the discrepancy. To directly assess evidence of selection at this locus, we re-computed the integrated haplotype score (iHS) using selscan (v2.1.0). While examining the region suggested by the reviewer (56.97–57.02 Mb), we observed that the signal of association and extended haplotype homozygosity actually begins around 57.05 Mb. We therefore expanded the window to chr8:57.05–57.30 Mb to capture the full extent of local linkage disequilibrium, as illustrated in Figure 4.\nWe computed iHS across all five African cohorts, and the results (now included in Supplementary Data 1) consistently show elevated absolute iHS values with a shift toward negative scores (Supplementary Fig. 8), indicative of recent positive selection acting on the derived allele at this locus. We have now updated the Methods as follows:\nPage 24: “Selection scan using integrated haplotype scores\nSince the LD pattern around the CHCHD7/SDR16C5 locus on chromosome 8 was suggestive of an extended haplotype, we investigated extended haplotype homozygosity (EHH) patterns for each of the five African sites. Variants were restricted to chromosome 8 positions 57,050,000–57,300,000 bp (GRCh37/hg19 coordinates) to encompass the LD block surrounding the lead hepcidin GWAS variant (rs73596248). SNP positions within the target region were used to interpolate recombination distances from the 1000 Genomes Project Phase 3 genetic map. Integrated haplotype scores (iHS) were computed using selscan v2.1.0.69 SNPs with MAF ≥0.01 were included. The iHS statistic was calculated for variants in the target region, measuring the decay of EHH for ancestral and derived alleles. Using selscan's normalization procedure, raw iHS were standardized by allele frequency bins (n=10) across the genome to account for variation in allele frequencies. Haplotype signals were visualized using ggplot2 package in R v4.3.2, with loess smoothing applied to highlight local trends in normalized iHS values across the genomic region.\nWe have also updated the Results and Discussion sections as follows:\nPage 5, Results: “The regional plot suggests that this association is within an extended haplotype (Fig. 4a) and we observed elevated absolute integrated haplotype score (iHS) values across all African cohorts (Fig. 4b and Supplementary Data 1), indicative of extended haplotype homozygosity. Genome-wide loess-smoothed profiles revealed a consistent trend toward negative iHS values suggesting recent selection acting on the derived allele (Supplementary Fig. 8).”\nPage 8, Discussion: “we observed elevated absolute iHS values, with a shift toward negative values, suggesting recent positive selection acting on the derived allele at the locus probably to protect against pathogens.”\nThe authors focused solely on the association of SMARCA5, which was only detected in a single Kenyan GWAS. As shown in S Fig1, there would also be other solitary significant associations, such as transferrin in Kenya and South Africa, ferritin in Uganda, and sTfR in The Gambia. Why did the authors ignore these associations and only focus on SMARCA5?\nResponse: We appreciate the reviewer’s careful attention to the country-specific GWAS results. As noted, several solitary genome-wide significant associations appear in Supplementary Figure 1, including for transferrin, ferritin, and sTfR in individual country-level analyses. We had examined these signals closely and found that they were driven by rare variants (MAF <2%), which limits statistical power for replication and increases the likelihood of false positives.\nIn contrast, we chose to highlight the Kenyan-specific SMARCA5 / Dantu locus because the associated variants were relatively common (~9% MAF) in this population and imputed with high quality. Importantly, the association lies within the well-known Dantu region, which is under strong positive selection along the Kenyan coast due to its known protective effect against severe malaria, further supporting the biological plausibility of this finding.\nTo clarify this rationale, we have added a note to the Supplementary Figure 1 legend stating that “Several genome-wide significant associations observed in individual country-level analyses (for example, sTfR in The Gambia) were driven by rare variants (MAF < 2%). Due to their low frequency and limited power for replication, these signals were not explored further in the current study.”\nMinor points:\n- The authors used “inflammation” as one of the traits; rather, would it be better to use the term “chronic inflammation”?\nResponse: In our study, “inflammation” was defined based on elevated acute-phase reactants, specifically C-reactive protein (CRP >5 mg/L) or α1-antichymotrypsin (ACT >0.6 g/L), consistent with established guidelines for identifying inflammation in children. Since CRP and ACT were measured cross-sectionally at one timepoint (together with markers of iron status), we cannot be certain whether inflammation has persisted over time as ‘chronic inflammation’ or if it is acute inflammation. We therefore believe that “inflammation” is the more accurate term. We have clarified this definition in the Methods as follows:\nPage 21: “Since iron biomarkers can be affected by inflammation or infection,1 and it is important to account for inflammation when defining iron deficiency, we defined inflammation as CRP >5 mg/L or ACT >0.6 g/L (in Gambian children), consistent with established guidelines for identifying inflammation in children.59\n- It's written in STab1 that participants in the Jackson Heart Study weren't exposed to malaria, but the table shows \"n/a\" for this study. Should it be 0, or if the ascertainment wasn't enough, it shouldn't be described as \"not exposed to malaria\"?\nResponse: We agree that “n/a” may be ambiguous in this context. As the Jackson Heart Study was conducted in a non-endemic region with no known exposure to malaria, we have updated Supplementary Table 1 to “report 0/2868 under the n/total column and 0.0% under the prevalence column for malaria parasitemia, consistent with how we reported data for South Africa”\n- This reviewer believes the number of participants who failed the sex check, which is 58 (4.4%), in STab2 for Kenya, is too high. Is there any effort being made to prevent sample swapping?\nResponse: We acknowledge that the sex mismatch rate in the Kenyan cohort (58 of 1,312 samples; 4.4%) is higher than typically expected. This cohort was part of a previously conducted longitudinal study of malaria immunity in children in Kilifi, Kenya. Blood samples were collected during annual cross-sectional surveys and archived at -80°C. For this study, samples were selected retrospectively from archived plasma based on availability, which may have introduced a higher risk of mislabeling or mismatch across visits. While this might have contributed to the elevated sex check failure rate, we applied rigorous genotype quality control procedures and excluded all affected samples from downstream analyses.\n- Samples from Kenya and The Gambia should be genotyped using the same H3Africa array. However, only The Gambia's results showed a significantly higher number of duplicated variants (182,365) compared to Kenya's (28) in STab 2. This discrepancy raises questions about the reasons behind it.\nResponse: We confirm that there was an error in reporting the number of individuals and variants at each QC step for the Gambian population. Initially, we reported that 2,297,939 variants were typed and mapped to Build 37 in the Gambian dataset. Upon investigation, we realized that these figures were based on an earlier version of the Gambian data, in which the 528- and 46-sample batches were merged prior to quality control. This pre-QC merge resulted in an inflated variant count due to multiple probes mapping to the same genomic positions on the H3Africa genotyping array. Some entries contained placeholder alleles (e.g., allele coded as “0”), introduced during genotype calling. For example, in the 528-sample file, a variant might appear as 1:821501:0:T, while in the 46-sample file, the same position appears as 1:821501:C:T. These mismatches led to a large number of apparent duplicates during the QC process.\nImportantly, this pre-QC merged file was not used for imputation or downstream analyses. Instead, we had merged the 528 and 46 files after QC, ensuring that only clean, non-duplicated variants were included in the final dataset.\nWe have now updated Supplementary Table 2 to reflect the correct number of individuals and variants at each QC step for the Gambian population. We confirm that this correction does not affect any downstream analyses or study conclusions. We have also verified that the QC steps reported for the other populations are accurate.\nReviewer #3 (Remarks to the Author):\nThank you for asking me to review this manuscript. I agree with the authors’ view of the importance of topic and welcome their efforts to contribute to our understanding in an understudied population.\nKey results and significance\nThe key message that I got from the paper is that the genetic determinants of iron status in African populations are different from European populations and that this likely reflects the greater genetic diversity of the population and selective pressure exerted by malaria and other infections. This I believe was the main aim of the paper. I was less clear about the clinical and public health significance of the results.\nResponse: We thank the reviewer for confirming the importance of the topic and the importance of studying under-represented populations. We agree that the central contribution of our study is the identification of African-specific genetic determinants of iron status and their contrast with European populations, likely reflecting both the greater genetic diversity in Africa and the selective pressures of infectious diseases such as malaria and bacteremia. Our study additionally highlights that genetic risk scores and instrumental variables for Mendelian randomization (MR) studies identified from European populations are unlikely to be useful in African populations, thus underlining the need for genomic studies in continental Africa to address important clinical questions that could be answered through MR studies. Our findings have potential public health relevance especially for iron supplementation programs since genetic differences in iron regulation may modify the balance between correcting deficiency and increasing infection risk. To better highlight these clinical and public health implications while acknowledging that further work is needed, we have revised the Discussion as follows:\nPage 10: “These findings have important implications for understanding genetic and environmental interactions shaping iron regulation and infection risk in African populations. While large European iron-GWAS have been foundational for several Mendelian randomization studies that clarified causal relationships relevant to clinical guidelines,45,46 our results highlight the need for Africa-centric genomic data since genetic risk scores and instrumental variables identified in European populations were poorly predictive in African populations. African-specific data is needed to guide diagnostic screening and inform targeted interventions and equitable global health strategies. Our findings have potential public health relevance for iron supplementation programs since genetic differences in iron regulation may modify the balance between correcting deficiency and increasing infection risk, underscoring the importance of population-specific approaches. For instance, routine or universal iron supplementation or fortification, especially in the context of elevated hepcidin, may lead to iron accumulation in the gut or dysregulated iron distribution, potentially increasing susceptibility to enteric and systemic infections.8,47 Future studies should explore the mechanistic pathways linking the identified variants to iron-pathogen interactions to inform safe and targeted interventions in African settings.”\nThe stated new findings were the identification of a new loci for transferrin in GTF3C5 responsible for conferring protection against severe malaria and bacteriamia; a new loci for hepcidin in CHCHD7/SDR16C5, and variant rs73596248 associated with an increase hepcidin levels that conferring protected against bacteremia. However in the results section it is clearer that there is only an association with the risk of klebsiella and staph aureus bacteriaemia, but not with bacteraemia overall or with severe malaria.\nResponse: We agree that our initial wording may have overstated the breadth of protection associated with the hepcidin locus. As the reviewer correctly points out, the variant rs73596248 at the CHCHD7/SDR16C5 locus was associated with increased hepcidin levels and reduced risk of Klebsiella pneumoniae and Staphylococcus aureus bacteremia, but not with overall bacteremia or with severe malaria. We have adjusted the wording in the Abstract and ensured consistency in Results, Discussion and Conclusion to avoid implying broader protection than was supported by the data.\nThey also found that the rare Dantu blood group was associated with soluble transferrin receptor levels, (and confirmed previous reports that this blood group is associated with protection against severe malaria and bacteraemia). Interestingly the authors quote their own study (9) which shows link between iron status and malaria but which also did not show any relationship with hepcidin.\nResponse: We agree that our previous study (https://doi.org/10.1093/cid/ciy791) demonstrated a relationship between iron status and malaria risk but did not identify an association with hepcidin. In the current study, we do not report a direct association between Dantu and hepcidin levels. Rather, we observed that the Dantu blood group locus was associated with soluble transferrin receptor (sTfR) levels, which we highlighted in the context of Dantu’s well-established protective effect against severe malaria and bacteremia. If the reviewer’s comment refers to sTfR, we agree that in our previous study we did not find any relationship between sTfR levels and malaria and have now made this clear in the Discussion as follows:\nPage 9: “Although sTfR levels may not be directly linked to malaria infection in observational analyses,9 elevated sTfR reflects expanded erythropoietic activity and can indicate ID, which is itself associated with protection against malaria infection.9”\nIf the reviewer’s comment instead refers to the CHCHD7/SDR16C5 locus, we note that this variant was associated with increased hepcidin levels and reduced risk of Klebsiella pneumoniae and Staphylococcus aureus bacteremia. However, it was not associated with severe malaria, consistent with our earlier mentioned study (https://doi.org/10.1093/cid/ciy791) that likewise found no evidence for a link between hepcidin and malaria risk. We have clarified this point in the Discussion as follows:\nPage 8: “However, we found no association with severe malaria, consistent with our previous prospective study that found no relationship between hepcidin concentrations and malaria risk.9”\nAims and study design\nTt seems that there were two main aims: Firstly “To enhance discovery of genetic determinants of iron status in an understudied population (line 106) and secondly “To understand the relationship between iron and infection, we examined the effect of these variants on risk of severe malaria and bacterial infections” (line 109).\nIt felt to me that there was a much stronger rationale for the chosen study design for the first aim, and this was also reflected in the detail in which the findings were presented, and the robustness of the findings:\nFor the second aim, the presentation of results was not very clear, the interpretation of results less convincing and some of the details re. methods and statical analysis were missing.\nResponse: We thank the reviewer for acknowledging the robustness of our analyses and findings from the iron GWAS, which formed the first aim of our study. To strengthen the presentation of the second aim, examining the effect of identified iron-related variants on severe malaria and bacteremia, we have substantially revised the manuscript. Specifically, we have expanded the Methods section to include detailed descriptions of the infection GWAS datasets, analytical approaches, and newly added power calculations for these follow-up analyses as follows:\nPages 20 and 21:\nMalariaGEN consortium: This was a GWAS study of severe malaria in ~17,000 severe malaria cases and population controls from 11 countries in Africa (Kenya, Malawi, Tanzania, The Gambia, Mali, Burkina Faso, Ghana, Nigeria, Cameroon), Asia (Vietnam), and Oceania (Papua New Guinea) as previously described.24 Sample collection, processing, genotyping, quality control, imputation, GWAS, and population characteristics are described in details elsewhere.24,57 Severe malaria was defined according to WHO criteria58 and included diagnoses of severe malaria anemia, cerebral malaria, and other malaria-related symptoms. Association testing for each variant and severe malaria was performed using SNPTEST software in each study population adjusting for principal components under additive, dominant, recessive, heterozygote and general models of association. In the current study, we extracted additive model estimates for African populations. For the East-Africa specific GTF3C5 lead SNP, rs2905094, we retrieved population-specific results from all of the nine African populations in the MalariaGEN database and applied fixed-effects meta-analyses by East (Kenya, Malawi, and Tanzania) and West (The Gambia, Mali, Burkina Faso, Ghana, Nigeria, and Cameroon) African sites. For the association between the hepcidin lead SNP, rs73596248 and severe malaria, we meta-analyzed estimates from all of the nine African populations in the MalariaGEN database. Since the sTfR GWAS signal is specific to Kenyan populations, we used data from the MalariaGEN Kenyan GWAS to determine the association between the top sTfR GWAS SNPs and severe malaria. The lead SNP, rs552439837, was not present in the severe malaria case–control dataset; therefore, we used estimates for rs141274959, a genome-wide significant sTfR GWAS variant in strong LD with rs552439837 (r² = 0.9).\nThe Kenyan Bacteremia GWAS: This GWAS included 1,970 Kenyan children (<13 years old) admitted to Kilifi County Hospital between 1998 and 2010 with culture-confirmed community-acquired bacteremia, and 4,013 healthy community controls, as previously described.25 Controls were infants aged 3-12 months recruited between 2006 and 2008 from the same geographic area through the Kilifi Genetic Cohort Study. All participants were residents of Kilifi County on the coast of Kenya. Genotyping was performed in two phases: the discovery phase employed the Affymetrix SNP 6.0 array (787,861 SNPs) in 1,536 cases and 2,677 controls, while the replication phase used the Illumina ImmunoChip (143,100 SNPs) in an additional 434 cases and 1,336 controls, yielding a total of 1,970 bacteremia cases and 4,013 controls. Whole-genome imputation was carried out using SHAPEIT and IMPUTE2 with the 1000 Genomes Phase 3 reference panel. Demographic data for cases and controls and distribution of the most common bacterial species in the discovery and replication sets are described elsewhere.25 In the current study, associations between conditionally independent iron-related SNPs and bacteremia were assessed using additive logistic regression models, adjusted for age, sex, and principal components. Analyses were performed for both overall bacteremia and stratified by the most common bacterial species.\nPage 23: “For SNP-bacteremia associations, we identified our African-specific SNPs in the largest GWAS of bacteremia in African populations,25 merged the genotypes with the hospital clinical data, and conducted logistic regression analyses adjusting for age, sex, and principal components.”\nPage 24: “For the association between lead SNPs and severe malaria or bacteraemia, we estimated that the severe malaria dataset (7,957 cases, 7,746 controls) had ~80% power to detect an odds ratio ≥1.15 for a variant with MAF = 5% at α = 0.05, while the bacteremia dataset (1,970 cases, 4013 controls) had ~80% power to detect an odds ratio ≥1.40.”\nWe have also added Supplementary Table 9, which summarizes the characteristics of the infection GWAS populations by identified African-specific iron-related lead variants, to improve clarity and interpretation. Furthermore, to provide clearer presentation and contextual interpretation of the SNP-infection associations, we revised the Results section as follows:\nPage 5: “Since GTF3C5 knockdown has been shown to increase endocytosis and internalization of transferrin,28 and cellular iron uptake influences risk of infection,29 we investigated the association between rs2905094 and susceptibility to infection using large case-control GWAS of severe malaria24 and bacteremia25 (Supplementary Table 9). Since the effect of rs2905094 on transferrin levels was restricted to East Africa, we compared fixed effects meta-analyses of severe malaria GWAS data from East Africa (i.e. Kenya, Malawi, and Tanzania) and West Africa (The Gambia, Mali, Burkina Faso, Ghana, Nigeria, and Cameroon; Supplementary Fig. 7). We found that rs2905094 was associated with 12% (OR=0.88, 95% CI 0.78, 0.98) protection against severe malaria in East Africa where it altered transferrin levels but was not associated with protection in West Africa where it did not alter transferrin levels (OR=0.95; 95% CI 0.87, 1.03; Fig. 3e and Supplementary Fig. 7). rs2905094 was also associated with 15% (OR=0.85, 95% CI 0.75, 0.97; P=0.02) protection against overall bacteremia in Kenyan children (Fig. 3e).”\nPage 6: “Since hepcidin is implicated in the pathophysiology of malaria and bacterial infections in mouse models,12,13,32 we determined the association between rs73596248 and these infections in the case-control GWAS of severe malaria and bacteremia described above.24,25 We found that rs73596248 was associated with protection against bacteremia due to Klebsiella pneumoniae (OR=0.22; 95% CI 0.05, 0.92; P=0.04) and Staphylococcus aureus (OR=0.56; 95% CI 0.33, 0.92; P=0.02). Protection against Escherichia coli and overall bacteremia did not reach statistical significance, while severe malaria was not associated with the rs73596248 variant (Fig. 4c).”\nPage 6: “The Dantu blood group locus, marked by variants associated with soluble transferrin receptor (sTfR) levels, showed strong and concordant protective effects against both bacteremia and severe malaria. The lead sTfR GWAS SNP, rs552439837, was 20% protective against bacteremia (OR=0.80; 95% CI 0.68, 0.95; P=0.01; Fig. 5c), as previously shown using rs186873296.34 Since rs552439837 was missing in the case-control study of severe malaria, we identified a tag SNP, rs141274959, from among the genome-wide significant sTfR GWAS SNPs (Supplementary Table 11). rs141274959 is in strong LD with rs552439837 (r2=0.9) and was highly protective against severe malaria (OR=0.56; 95% CI 0.46, 0.69; P=3.3x10-9).”\nData and methodology\nAs mentioned above, I think the methods for Discovery and Replication GWAS seem appropriate although I have some concerns about the how the heterogeneity of contributing study designs and I and the resulting differences and gaps in the data collected. For example that transferrin was not measured in two sites and why α1-Antichymotrypsin being used as a marker of inflammation in one of the studies, rather than a more standard marker such as CRP.\nResponse: We acknowledge that heterogeneity in study design and biomarker availability is a limitation of multi-site analyses. Transferrin was measured in all sites, however transferrin saturation (TSAT) could not be calculated in Uganda and South Africa because serum iron was unavailable, as blood samples had been collected in EDTA vacutainers, which chelated iron. In The Gambia, α1-Antichymotrypsin (ACT) was used instead of CRP, which was not available in the samples. ACT is a validated acute-phase reactant that correlates strongly with CRP, rising quickly like CRP but remaining elevated for longer, making it a reliable alternative marker of inflammation. To minimize the impact of site-specific heterogeneity, we conducted all analyses for each site, then combined by meta-analysis. We have now clarified in the Discussion that differences in biomarker availability across sites are a limitation of the study, as follows:\nPage 9: “A potential limitation is heterogeneity in the availability of iron biomarker and red cell indices data across sites. For example, TSAT could not be calculated in Uganda and South Africa due to missing serum iron measurements, and ACT was used instead of CRP in The Gambia. To minimize bias, all analyses were conducted within sites and combined by meta-analysis.”\nI also think it would have been useful to show red cell indices, other than haemaglobin, as more clinically relevant reflection of iron status than just the biochemical markers presented.\nResponse: We agree that red cell indices such as mean corpuscular volume (MCV) and mean corpuscular hemoglobin concentration (MCHC) provide clinically relevant measures of iron status. However, the main iron biomarkers used in our analyses (transferrin, ferritin, hepcidin, sTfR, serum iron, and TSAT) remain well-established indicators of iron homeostasis that have been used in genome-wide association analyses in European populations. We have now incorporated available MCV and MCHC data into our analyses (MCV: 2,237 children across Kenya, Uganda, and The Gambia; MCHC: 1,374 in Kenyan and Ugandan children) and updated Figure 3d, Supplementary Table 1, Supplementary Figure 8, and the Methods and Results sections accordingly.\nPage 5: “Associations between rs73596248 and other individual iron biomarkers and red cell indices did not reach statistical significance (Supplementary Fig. 9a).”\nPage 21: “Red cell indices data included hemoglobin levels, Mean Corpuscular Volume (MCV), and Mean Corpuscular Hemoglobin Concentration (MCHC), measured using automated blood analyzers (Beckman Coulter).”\nFor the study looking for an association between the SNPs and severe malaria and bacteraemia I there was less description of the methods and the data making it difficult to comment on. Providing more information including a descriptive table of the population characteristics would be helpful.\nResponse: We agree that the initial version of the manuscript included limited methodological detail regarding the analyses of SNP associations with severe malaria and bacteremia. We have now expanded the Methods section to describe these analyses in greater detail, including information on the severe malaria and bacteremia GWAS datasets, case–control sample sizes, analytical approach, and power calculations. To improve transparency and allow clearer interpretation, we have also added Supplementary Table 9, which summarizes the sample characteristics of the infection GWAS datasets by identified African-specific iron-related lead variants. These additions provide a more complete description of the data and analytical framework for the SNP–infection analyses.\nI was specifically interested to understand when these samples had been taken given the changing malaria epidemiology and had to go to other publications to find this. I was surprised to learn that that the original Malariagen study was conducted in Kilifi (conducted 1995-2008). Indeed a recently published study by the group designed specifically to explore the relationship between the Dantu and bacteriaemia, analysed separately data from 1998-2003, a period of high transmission and data from 2003-2010, a period of low transmission. They reported that the protective effect of Dantu against all-cause bacteremia was only significant during the high malaria transmission period suggesting that the protective effect was most likely mediated indirectly by its protective effect against malaria. It is not clear why a similar approach was not taken in this current study and somewhat contradicts which the authors state “….the Dantu NE blood group phenotype, is under strong selective pressure for protection against malaria along the Kenyan coast.” (Line 255).\nResponse: We agree that malaria epidemiology in Kilifi has changed substantially over time and appreciate the importance of considering transmission intensity when interpreting genetic associations. The iron and inflammatory biomarkers in our study were measured from archived plasma samples collected during annual cross-sectional surveys between 1998 and 2016, spanning both high (pre-2003) and subsequent lower malaria transmission periods. As the reviewer notes, our colleagues have already demonstrated, using overlapping sample sets, that the protective effect of the Dantu NE blood group against all-cause bacteremia is most likely mediated indirectly through protection against malaria during periods of high transmission. The lead SNP reported in that study is in strong linkage disequilibrium (r² > 0.9) with our lead SNP at this locus, and we therefore did not repeat the same stratified analyses here. Regarding our statement that the Dantu NE blood group is under strong selective pressure for protection against malaria along the Kenyan coast, we intended this as a reference to prior population-genetic and epidemiologic studies, rather than a conclusion from the present dataset. To avoid any ambiguity, we have clarified these points in the Results and Discussion sections as follows:\nPage 6, Results: “A complex structural variant at this locus, termed DUP4, encoding the Dantu NE blood group phenotype, has previously been shown to be under strong selective pressure due to its protective effect against malaria along the Kenyan coast.27,33 The lead sTfR SNP in our GWAS, rs552439837, was in strong LD with DUP4 (e.g. r2>0.9 with the previously identified tag SNP, rs186873296),33 which was also associated with increased sTfR levels (P=1.3x10-6).”\nPage 8, Discussion: “Recent analyses from the same population showed that another SNP tagging the Dantu polymorphism, rs186873296, and in strong LD with our lead SNP (r2>0.9), is associated with protection against bacteremia via its effect on malaria risk.34”\nWe have also explicitly stated in the Methods the timeframe of biomarker collection.\nPage 19: “Iron and inflammatory markers were measured from blood samples collected at annual cross-sectional surveys (between 1998 and 2016) based on the availability of plasma samples archived at -800C.”\nAnalytical approach\nIn the section of statistical analysis section (line 764-814), there is no description of how the data were analysed for the association between SNPs and bacteriaemia.\nResponse: We agree that our description of the SNP–bacteremia analysis was too brief in the original version. We have now clarified these analyses in the Methods section as follows:\nPage 23: “For SNP-bacteremia associations, we identified our African-specific SNPs in the largest GWAS of bacteremia in African populations,25 merged the genotypes with the hospital clinical data, and conducted logistic regression analyses adjusting for age, sex, and principal components.”\nIn the section on sample size and power (line 840-846), there is no description of the sample size and power calculations for the studies looking for association of SNPs with severe malaria and bacteriemia.\nResponse: We have now added power estimates for the association of SNPs with severe malaria and bacteraemia in the Methods section. The associations between SNPs and severe malaria and bacteriemia were targeted follow-up of lead variants rather than new genome-wide scans, and therefore formal a priori power calculations were not undertaken as for the discovery GWAS.\nPage 24: “For the association between lead SNPs and severe malaria or bacteraemia, we estimated that the severe malaria dataset (7,957 cases, 7,746 controls) had ~80% power to detect an odds ratio ≥1.15 for a variant with MAF = 5% at α = 0.05, while the bacteremia dataset (1,970 cases, 4013 controls) had ~80% power to detect an odds ratio ≥1.40.”\nClarity and context\nI this paper would benefit from being written more clearly, coherently and concisely. This would make it both stronger and more accessible. There are important details missing and very limited mention of any study limitations which I think are essential to address. I think it would also be helpful to clarify what’s new and significant in terms of implications for clinical practice or public health. Perhaps one way to make this clearer would be to give an example of how results of GWAS studies of iron status in other (eg European populations) have impacted on clinical care or outcomes?\nResponse: We have carefully edited the text throughout the manuscript for improved clarity, conciseness, and logical flow. We have expanded the Methods and Discussion to include additional details that were previously missing. For example, we have provided more details on the iron-infection studies and analyses and included additional comprehensive analyses of selective pressure, statistical fine-mapping, and haplotype level-associations. We now also provide a more comprehensive description of study limitations including heterogeneity in biomarker availability across sites, and limited power to detect small-effect variants.\nTo strengthen the clinical and public-health context, we have clarified what is new and significant about our findings in the Discussion, particularly the identification of African-specific loci influencing iron status and infection susceptibility, and how these results contribute to understanding iron regulation in diverse populations. We have also added a relevant example illustrating how large European iron-GWAS have been foundational for several Mendelian randomization studies that clarified causal relationships relevant to clinical guidelines.\nPage 10: “we identify African-specific genetic loci that influence iron biomarkers and risk of severe malaria and bacteremia. These findings have important implications for understanding genetic and environmental interactions shaping iron regulation and infection risk in African populations. While large European iron-GWAS have been foundational for several Mendelian randomization studies that clarified causal relationships relevant to clinical guidelines,45,46 our results highlight the need for Africa-centric genomic data since genetic risk scores and instrumental variables identified in European populations were poorly predictive in African populations. Africa-specific data is needed to guide diagnostic screening and inform targeted interventions and equitable global health strategies. Our findings have potential public health relevance for iron supplementation programs since genetic differences in iron regulation may modify the balance between correcting deficiency and increasing infection risk, underscoring the importance of population-specific approaches. For instance, routine or universal iron supplementation or fortification, especially in the context of elevated hepcidin, may lead to iron accumulation in the gut or dysregulated iron distribution, potentially increasing susceptibility to enteric and systemic infections.8,47 Future studies should explore the mechanistic pathways linking the identified variants to iron-pathogen interactions to inform safe and targeted interventions in African settings.”\nWe believe these revisions have substantially improved the clarity, completeness, and accessibility of the manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I appreciate the authors’ efforts to address my comments. However, I still have some concerns.\nI appreciate the authors included the statement that the frequency of rs2905094-T was 9 % in Burkina Faso. Nonetheless, they refer to this as a “low frequency.” Generally, “low frequency” denotes frequencies of 5 % or less, often even below 1 %. A 9 % frequency would be considered common rather than low.\nThe authors claim that they cannot publish the haplotype configuration for ethical reasons. I am not requesting individual-level data; it would be sufficient to provide a representative configuration and its count. The data in SFig 4 are too vague. For example, the manuscript states that Hap1 is present at less than 20 % in South Africa and Hap2 at about 15 %. Yet, looking at the third row from the top in SFig 4, Hap1 appears to be more than twice as frequent as Hap2, which is inconsistent with the manuscript. Displaying only the representative SNP alleles that define each haplotype and presenting them in a table with numeric values would resolve this. What matters most is identifying which haplotype carries the causal variant. Differences in haplotype configuration between EUR and other populations would also be clearer if shown using the representative alleles and their counts.\nFrom the haplotype association results, the hypothesis that the association observed in these populations is driven by Hap2 is not supported.\nSummarizing the findings thus far: rs2905094 is significantly associated with transferrin levels only in East African populations comprising Kenya and Uganda. In the fine-mapping of this group, rs2905094 has the highest PIP of about 0.57. The rs2905094‑T allele is common in East Africa, Other Africa, and Europe. The data do not support the notion that a causal variant lies on a specific haplotype present only in East Africans and absent in other groups.\nThe explanation for the lack of association with rs2905094 outside East Africa remains insufficient. We cannot rule out issues related to quality control or population structure, and the absence of association in other groups suggests that the signal was not replicated. The tone regarding the association of this SNP rs2905094 should be moderated.", "reviewer_3": "Thank you for asking me to review the revised manuscript.\nI am happy with how the authors addressed our concerns and believe that it has resulted in a much stronger and clearer manuscript."}, "author_response": "Response: We thank the reviewer for their careful re-examination of the haplotype frequencies and for highlighting the inconsistency between the manuscript text and SFig 4. Upon re-checking the South African data, we confirm that the reviewer is correct: Haplotype 1 is present at 31.3% and Haplotype 2 at 14.7% in South Africa. We have corrected the text to reflect these values and now report exact haplotype counts and frequencies in a new Supplementary Table 6.\nSupplementary Table 6. Distribution of haplotypes carrying the rs2905094-T allele at the GTF3C5 locus across African populations\nPopulation Haplotype 1, n (%) Haplotype 2, n (%) Other n (%)\nKenya 109 (32.2) 113 (33.3) 117 (34.5)\nUganda 115 (23.0) 100 (20.0) 285 (57.0)\nSouth Africa 66 (31.3) 31 (14.7) 114 (54.0)\nBurkina Faso 61 (52.1) 10 (8.6) 46 (39.3)\nThe Gambia 151 (62.1) 0 (0) 92 (37.9)\nWe have also rewritten the results section as follows:\nPage 4: “Haplotype 1 predominated in West Africa (The Gambia 62%, Burkina Faso 52%) but was less frequent in East and Southern Africa (Kenya 32%, Uganda 23%, South Africa 31%), whereas Haplotype 2 was absent in The Gambia, present at 9% in Burkina Faso and 15% in South Africa, and observed at moderate frequencies in East Africa (Kenya 33%, Uganda 20%; Supplementary Table 6). However, in haplotype-level analyses in East Africans, both Haplotypes 1 and 2 were associated with lower transferrin levels, and these effects disappeared after conditioning on rs2905094, indicating that the signal is driven by rs2905094 or an unidentified variant in strong LD with rs2905094 rather than by a haplotype-specific effect (Supplementary Table 7).”\nFrom the haplotype association results, the hypothesis that the association observed in these populations is driven by Hap2 is not supported.\nSummarizing the findings thus far: rs2905094 is significantly associated with transferrin levels only in East African populations comprising Kenya and Uganda. In the fine-mapping of this group, rs2905094 has the highest PIP of about 0.57. The rs2905094-T allele is common in East Africa, Other Africa, and Europe. The data do not support the notion that a causal variant lies on a specific haplotype present only in East Africans and absent in other groups.\nThe explanation for the lack of association with rs2905094 outside East Africa remains insufficient. We cannot rule out issues related to quality control or population structure, and the absence of association in other groups suggests that the signal was not replicated. The tone regarding the association of this SNP rs2905094 should be moderated.\nResponse: Reexamining our data, we agree with the reviewer that the haplotype results do not support the hypothesis that the observed association is driven by a unique haplotype present only in East African populations. We have revised the manuscript to remove references suggesting a Haplotype 2-specific causal mechanism.\nWe agree that the explanation for the lack of association outside East Africa remains unresolved. We have moderated the tone in the manuscript to acknowledge that alternative explanations including differences in environmental context or gene-environment interactions, linkage disequilibrium, and population or complex genomic structure, may contribute to the observed specificity in East African populations, and that a population-specific causal variant cannot be inferred from our data. We think it is unlikely that issues with quality control would explain a specific association in two separate East African populations, but not in West and South Africa especially as the genotypes have been used to identify associations in other published studies (example, https://www.nature.com/articles/s41591-024-02944-5). Other studies have similarly demonstrated substantial genetic heterogeneity among populations from East, West, and South Africa (example, https://www.nature.com/articles/s41467-025-58789-0).\nWe have revised the Discussion as follows:\nPage 7-8: “Although the effect allele at the GTF3C5 locus is common across African ancestry populations, and even more frequent in Europeans (33%), its association with transferrin levels was observed only in the East African cohorts. To investigate this geographic specificity, we examined local haplotype structure and found that rs2905094 occurs on multiple haplotypic backgrounds that differ in frequency across East and West Africa. Haplotype-level association analyses did not support a haplotype-specific effect suggesting that the observed association is not driven by a haplotype uniquely present in East Africa. Fine-mapping analyses identified rs2905094 as the variant with the highest posterior inclusion probability, although the possibility of a correlated variant in linkage disequilibrium cannot be excluded. Nonetheless, the population-specific nature of the association remains incompletely explained and may reflect differences in linkage disequilibrium, population structure, environmental context or gene-environment interactions, and unmodeled confounding, underscoring the importance of examining regional genomic diversity in African populations.19,23”\nWe believe these revisions improve the accuracy and balance of the interpretation and directly address the reviewer’s concerns."}]

Decision letter