# An Expression Quantitative Trait Locus of Fc Gamma Receptor Genes is Associated with Anti-Malarial IgG Responses and Infection Levels in Burkinabe Families Running Title: Association of rs1771575 with Malaria

Christelle Dieppois, Mathieu Adjemout, Jules Cretin, Frederic Gallardo, Magali Torres, Christophe Picard, Serge Aimé Sawadogo, Pascal Rihet, Pascale Paul

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Christelle Dieppois, Mathieu Adjemout, Jules Cretin, Frederic Gallardo, Magali Torres, et al. An Expression Quantitative Trait Locus of Fc Gamma Receptor Genes is Associated with Anti-Malarial IgG Responses and Infection Levels in Burkinabe Families Running Title: Association of rs1771575 with Malaria. Journal of Infectious Diseases, In press, 2231 (4), pp.1008-1019. 10.1093/infdis/jiae528. hal-04750444

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# An Expression Quantitative Trait Locus of Fc Gamma Receptor Genes is Associated with Anti-Malarial IgG Responses and Infection Levels in Burkinabe Families

**Running Title:** *Association of rs1771575 with Malaria*

Christelle Dieppois¹, Mathieu Adjemout¹, Jules Cretin¹, Frederic Gallardo¹, Magali Torres¹, Christophe Picard²,³, Serge Aimé Sawadogo ⁴,⁵, Pascal Rihet¹* and Pascale Paul¹*

¹Aix Marseille Univ, INSERM U1090, TAGC, Marseille, France

²Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France.

³Immunogenetics Laboratory, Etablissement Français du Sang PACA-Corse, Marseille, France (christophe.picard@efs.sante.fr)

⁴Université Joseph KI-ZERBO, Unité de Formation en Sciences de la Santé (UFR/SDS), 03 BP 7021 Ouagadougou 03. (serge.sawadogo@ujkz.bf)

⁵Centre Prüm-O-Nelson Mandela, 84 rue Sao Tomé et Principe, 09 BP 706 Ouagadougou 09. (sergesawadogo@priimocenter.com)

* Corresponding author: Pascale PAUL (P.P.): pascale.paul@inserm.fr or pascale.paul@univ-amu.fr and Pascal RIHET: pascal.rihet@univ-amu.fr

**Manuscript word count:** 3496

**40-word summary of the article's main point:** This study identifies the non-coding variant rs1771575 in the *FCGR* gene cluster as a key marker for IgG levels and malaria infection in children. It highlights the role of regulatory and coding variants in shaping immune responses to Plasmodium falciparum.
**Abstract**

**Background**

The interaction between antibodies and Fc gamma receptors (FcγRs) plays a critical role in regulating immune responses to *Plasmodium falciparum*. Polymorphisms in genes encoding FcγRs influence the host's capacity to control parasite infection. This study investigates whether non-coding variants influencing FcγR expression are associated with anti-malarial immunization and infection traits.

**Methods**

We utilized eQTL databases and functional annotations to identify non-coding variants, specifically rs1771575, rs2099684, and rs6700241, within the *FCGR* gene cluster. In addition, we examined the coding variants rs1801274 (p.His167Arg) and rs1050501 (p.Ile231Thr), which affect the affinity of FcγRIIa and FcγRIIb for IgG. These variants were genotyped in 163 individuals from Burkinabe families. Family-based linear mixed regression and Quantitative Transmission Disequilibrium Tests (QTDT) analyses were performed to assess associations with IgG levels and malaria infection, accounting for relevant covariates.

**Results**

Linear mixed models identified rs1771575 as associated with total IgG levels, while both rs1771575 and rs1801274 were linked to IgG2, and rs1050501 to IgG1 levels. A haplotype combining rs2099684 and rs6700241 was positively associated with IgG1. The rs1771575-CC and rs1050501-TT genotypes correlated with higher infection levels in children. QTDT models confirmed the association of rs1771575 with IgG2 and infection in children.

**Conclusions**

Our findings suggest that the intergenic variant rs1771575 serves as an independent marker for IgG levels and blood infection in children. This highlights the interplay between regulatory variants and coding mutations in *FCGR*, which may influence immune function and antibody
production. These results underscore the potential for personalized strategies to monitor humoral responses in malaria-endemic regions.

**Keywords:** Fc Gamma Receptors- *Plasmodium falciparum* immunization- *FCGR* Expression
Quantitative Trait Locus- *FCGR* gene polymorphism- Malaria- blood infectious levels
**Background**

The malaria parasite *Plasmodium falciparum* (*Pf*) is a leading cause of child mortality worldwide[1], exerting significant selective pressure on the human genome in endemic regions.
Repeated exposure to *Pf* triggers a humoral response, parasitemia and clinical malaria.
Naturally acquired or vaccinal immunity to *Pf*, primarily mediated by immunoglobulin G (IgG) antibodies, relies on memory B cells for protective responses that inhibit parasite replication[2,3]. Immune activation is triggered by the recognition of the Fc domains of IgG subclasses 1-4 by Fc-gamma receptors (FcγRs). Each IgG subclass has unique characteristics that determine their interaction with FcγRs, resulting in varying capacities to stimulate immune functions[4-7] and neutralize toxins[8]. Cytophilic IgG1 and IgG3 antibody subclasses have high affinity to most Fc- receptors and play critical roles in pathogen opsonization, complement activation, and antibody-dependent cellular cytotoxicity (ADCC)[9-11]. In contrast, IgG2 and IgG4 antibodies exhibit lower affinity for both complement and FcγRs, functioning primarily as neutralizing antibodies with limited protective efficacy against malaria[5,12].

Single nucleotide polymorphisms (SNPs) can affect the expression and function of Fc
receptors, thereby influencing the antibody-dependent activation of host monocytes and natural
killer (NK) cells. These genetic variations modulate the immune response, contributing to the
effective destruction of parasites. Studies have identified associations between coding
mutations that result in amino acid substitutions in activating Fcγ receptors, such as
p.His167Arg (rs1801274) in FcγRIIa and p.Phe176Val (rs396991) in FcγRIIIa, and their
effects on IgG levels, affinity for IgG antibody subclasses, and the host's ability to control
parasite density and malaria severity[13–19].
The c.692T>C (p.Ile231Thr-rs1050501) variant in *FCGR2B* leads to the substitution of isoleucine with threonine in the transmembrane domain of the FcγRIIb inhibitory receptor. FcγRIIb, expressed on B cells, dendritic cells, monocytes, macrophages, and mast cells, modulates inhibitory signaling pathways, balancing activation signals from receptors such as FcγRIIa and FcγRIIIa, or regulating antibody production by B cells[20–22]. The c.692T>C variant exhibits variable allelic frequencies across populations, suggesting malaria-driven selective pressure in endemic regions[23]. An association has been observed between decreased susceptibility to severe malaria and homozygosity for the C allele[24]. The protective effect is believed to be due to both ITIM-dependent and independent suppression of B cell activation, enhanced antibody-mediated responses, and increased parasite elimination through improved phagocytosis of *P. falciparum*-infected erythrocytes[24,25].

A genome-wide association study (GWAS) identified variants in *FCGR2B* associated with circulating immunoglobulin levels[26]. More recently, the non-coding variant c.-73+446C>T(rs1771575), an expression quantitative trait locus (eQTL) for several *FCGR* genes in immune cells was linked to *Pf* infection [27].

In this study, we investigated whether intergenic eQTL variants within the chromosome 1q23 *FCGR* gene cluster, along with known coding *FCGR2A/FCGR2B* variants, influence anti-*Pf* antigen IgG levels in families from Burkina Faso. Additionally, we aimed to explore the relationship between *Pf* infection, IgG levels, and *FCGR* genetic variants.

# METHODS

## Study Population, Blood Samples, and Parasitological Phenotypes

The study was conducted in Logofourousso, a village of 3,500 inhabitants near Bobo-Dioulasso, Burkina Faso, where population and malaria transmission data have been previously published[28]. The study received approval from the Ethics Committee for Health
Research (ref: 2023000192/MSHP/MESRI/CERS) and the Committee for Informatics and
Liberties of Burkina Faso (ref: 2024-341/CIL/SG/DAJC). Participant families were randomly
selected from the endemic village, and individuals were included only after voluntary
informed consent was obtained from participants or their guardians. Over a period of two
years, each family was visited 28 times. If participants developed a fever, additional visits
were conducted, and they were treated for malaria if diagnosed positive. Parasitemia data
collected during mild malaria episodes were excluded from the analysis.

A total of 163 participants from 38 pedigrees were included, comprising 68 parents and 95 siblings. The annual incidence of infective mosquito bites per person exceeded 230. During the 28 visits, peripheral blood samples were collected from all volunteers present during each visit, and thin blood films were stained with Giemsa to assess parasite density (PD), as described previously[28]. PD was quantified microscopically by counting infected red blood cells per µL of blood. The mean adjusted parasite density (MaPD) for each participant was calculated through a logarithmic transformation of PD using log(1 + PD) (LPD). To account for visit-specific effects, the mean LPD for each visit was subtracted from the individual LPD, allowing the calculation of MaPD for each participant as a quantitative phenotype without a specific threshold.

IgG and IgG Subclass Level Phenotype Determination

Blood samples were collected from 68 parents and 95 siblings to allow antibody measurements
at the end of the dry season (P1) and the end of the rainy season (P2)[19]. Pf antigens were
extracted from a synchronized culture of the W2 strain and coated on ELISA plates. Levels of
Immunoglobulin G isotypes (IgG1, IgG2, IgG3, and IgG4) were measured using specific
monoclonal antibodies, while total IgG was measured using a goat F(ab')2 anti-human IgG.
Enzymatic activities were measured with p-nitrophenyl phosphate, and optical densities were
read at 405 nm. Standard curves were drawn using a pool of 200 sera, and antibody levels were expressed as arbitrary units (AU). A logarithmic transformation based on log (1 + AU) (LAU) was applied to account for zero values. Standardization of LAU at P1 and P2 and calculation of the mean adjusted LAU (MaLAU) for each participant were performed to correct for visit effects[19].

## Bioinformatic identification of non-coding regulatory variants

Non-coding SNPs with potential regulatory function were identified through a linkage disequilibrium (LD) analysis using genotyping data from the 1000 Genomes Project. LD data of five African populations (ESN, GWD, LWK, MSL, YRI) were downloaded from the Ensembl browser (https://www.ensembl.org/index.html). The analysis was limited to SNPs in strong LD (D' > 0.9) in at least two populations. eQTL data from the GTEx Project (https://gtexportal.org/home/) and ImmuNexUT (https://www.immunexut.org/) were used to specify eQTL associations in whole blood and immune cell types. RegVar (https://regvar.omic.tech/) and VarNote-REG (http://www.mulinlab.org/varnote/index.html) were used to prioritize SNPs based on their regulatory potential and tissue/cell-type-specific impact on gene expression. A threshold of 0.6 was set for the RegVar score and the cepip score from VarNote-REG. Positions and annotations of ENCODE candidate cis-Regulatory Elements (cCREs) were obtained through the UCSC Table Browser to locate variants within regulatory regions. Chromatin status in primary cells were visualized with Roadmap Epigenomics tracks (https://egg2.wustl.edu/roadmap/web_portal/). ReMap ChIP-seq tracks were used to visualize overlapping transcription factor binding sites (TFBS) in primary cells and immune cell lines in the UCSC Genome Browser (https://genome.ucsc.edu/) on Human GRCh37/hg19. The RSAT software (https://rsat.france-bioinformatique.fr/metazoa/) was used
to predict the impact of candidate variants on TFBS, using motif collections from ENCODE and JASPAR. Only TFBS supported by ChIP-seq data were considered relevant.

## Genotyping of *FCGR* variants

Blood samples were collected using venipuncture, and DNA was extracted from mononuclear cells separated by a Ficoll-Hypaque density gradient, the samples underwent whole-genome amplification using the primer extension pre-amplification method prior genotyping. For genotyping variations located on chromosome 1q23.3 (NC_000001.10), Taqman assays C_26275257_10, C_16118535_20, C_57479994_10, and C_9077561_20 were used as recommended by the manufacturer (Thermo Fisher Scientific, Massachusetts 02451, USA) for the specific variations g.161617497C>T (c.-73+446C>T, rs1771575), g.161500130A>G (rs2099684), g.161500975A>G (rs6700241), and g.161479745A>G (p.His167Arg rs1801274), respectively. PCR amplification and genotyping of g.161643798T>C (p.Ile231Thr- rs1050501) were performed as described, using a homemade TaqMan discrimination assay[27].

## Statistical Analyses

Multiple linear mixed regression models were used to investigate the effects of genotypes on IgG antibody levels and parasitemia-associated phenotypes. Three genetic inheritance models—additive, dominant, and recessive—were constructed, except in cases where the low frequency of the minor G alleles at rs6700241 and rs2099684 lacked sufficient power to build additive and recessive models. Each model incorporated familial effects and potential confounding variables such as age and sex. Residuals from these models were calculated to visualize the distribution of Total IgG and IgG4 antibody levels, as well as to explore the
relationship between genotypes and the MaPD phenotype. Non-parametric Mann-Whitney U tests were performed to compare median IgG levels and MaPD across different genotypes.

The Quantitative Transmission Disequilibrium Test (QTDT) program was used for a comprehensive assessment of allelic transmission effects on quantitative phenotypes within families, taking into account familial relationships and potential confounding factors[29]. The Family Based Association Test (FBAT) program allowed us to assess haplotype transmission effects on phenotypes within families[30]. To tackle multiple test issue, the adaptive false discovery rate (FDR) method was applied[31]. All nominal p-values were included in this procedure, with a significance threshold of 0.046 corresponding to an FDR of 10%. Linkage disequilibrium statistics and plots were generated using Haploview v4.2 (https://www.broadinstitute.org/haploview/haploview) [32].

# RESULTS

## Prioritization and Annotation of Putative Regulatory SNPs in the non-coding *FCGR* regions

To elucidate the role of specific SNPs in regulating *FCGR* gene expression, we focused on identifying and annotating putative regulatory SNPs. In a previous study, we identified two non-coding SNPs, rs2099684 and rs1771575, associated with *FCGR* gene expression and influencing transcription factor binding sites in immune cells [27]. Our current study aimed to identify additional regulatory variants through linkage disequilibrium (LD) analysis in African populations. This revealed one SNP, rs380125, in high LD with rs1771575, and three SNPs— rs6700241, rs10919543, and rs10919544—in strong linkage with rs2099684 (Supplemental Table 1). Among these, only rs6700241 showed high regulatory potential based on RegVar and cepip scores; the others were excluded due to lower scores (Supplemental Table 2). We used ENCODE cCREs data to locate regulatory regions overlapping with rs6700241, finding it
within a distal enhancer downstream of rs2099684 (Figure 1A). Epigenomic mapping showed rs6700241 in an open chromatin region and H3K27ac peaks in monocytes, B cells, NK cells, and neutrophils. The ReMap ChIP-seq catalogue indicated multiple transcription factors binding at this variant location. RSAT analysis predicted that rs6700241-G significantly enhanced the binding of Transcription Factor 3 (TCF3/E2A), a key regulator in B cell development and differentiation (Supplemental Figure 1). This aligns with ImmuNexUT eQTL data, associating this allele with increased FCGR expression in B cells (Supplemental Table 3).

## Analysis of Genotypic Frequencies of Non-coding and Coding *FCGR* Variants in Burkina Faso Population

To establish the genetic diversity and distribution of *FCGR* variants in the local population, we analyzed genotypic frequencies. The study analyzed 95 siblings and 68 parents from the Bobo ethnic group, who underwent genotyping and IgG data collection. Age and sex distribution, as well as phenotypic and genotypic data, are presented in Table 1.

We assessed the heterozygous and homozygous genotype frequencies for five target SNPs among the 68 parents, finding no deviations from Hardy-Weinberg equilibrium (Supplemental Table 4). Haploview analysis confirmed strong LD between rs2099684 and rs6700241 (Figure 1B and Supplemental Table 5), consistent with observations in other African populations (Figure 1C). These genotype frequencies were compared to African and European populations from the 1000 Genomes Project.

Genotype distributions for rs1771575 and rs1801274 were similar across Burkina Faso, African, and European populations (Figure 2), while those of the rs2099684 and rs6700241
non-coding variants (Figure 2B-C), and *FCGR2B* coding variant rs1050501 (p.Ile231Thr) differed significantly between the three populations (Figure 2D).

## Analysis of FCGR Variants and IgG Immunization

To investigate the impact of genetic variants on immune response, we analyzed the association between specific eQTL and coding variants with IgG immunization against Plasmodium falciparum. We analyzed the association between specific eQTL variants and coding variants FcγRIIa p.His167Arg (rs1801274) and FcγRIIb p.Ile231Thr (rs1050501) with IgG immunization against *Pf*.

Total IgG levels were higher in females but not significantly correlated with age (Figure 3A). Using linear mixed models, we found a significant association between rs1771575 and total IgG levels (Table 2), with the CC genotype associated with higher IgG levels (Figure 3B). No significant associations were found for other SNPs.

IgG subclass analyses revealed that total IgG was primarily driven by IgG1 and IgG3 (Supplemental Table 6). Notably, higher IgG2 levels were associated with rs1771575-CC (Figure 3C) and rs1801274-AA genotypes (Figure 3D, Table 2), both in the full population and in children. QTDT analysis confirmed a significant association between rs1771575 and IgG2 levels (P = 0.041, Supplemental Table 7).

Lower IgG1 levels were linked to the rs1050501-CT/CC genotypes, but only in children (Table 2, Supplemental Figure 2), a result not corroborated by QTDT analysis (Supplemental Table 7). Furthermore, haplotype analysis of rs2099684-A allele combined with rs6700241-A identified a positive association with IgG1 levels (P = 0.046) (Supplemental Table 8). No other significant associations were found with the other SNPs or haplotypes analyzed (Supplemental Tables 7 and 8).
Association of the rs1771575 T Allele with Lower Levels of P. falciparum Infection

To explore the relationship between genetic variants and malaria infection levels, we modeled
the impact of genetic variants on P. falciparum blood infection levels (Mean of Adjusted
Parasite Density, MaPD) in infants, who showed a 31-fold higher variance in MaPD than
parents (Supplemental Figure 3). The analysis accounted for age, antibody levels, and family
structure.

Infants with the rs1771575-T allele (CT/TT genotypes) had lower infection levels than those with the CC genotype. After adjusting for age, MaPD was inversely correlated with total IgG (P = 0.023) and IgG4 (P = 0.004), but not with IgG1, IgG2, or IgG3 levels (Supplemental Table 9). Individuals with both rs1771575-CC and rs1050501-TT genotypes had higher MaPD compared to those with rs1771575-CT/TT and rs1050501-CT/CC, even after adjusting for age, total IgG, and/or IgG4 levels (Table 3, Figure 4).

QTDT confirmed a significant association between rs1771575-C and higher MaPD (P = 0.006), persisting after adjusting for IgG4 levels (P = 0.012). However, QTDT did not replicate the rs1050501 association observed in mixed models or any other variant besides rs1771575 (Supplemental Table 7). FBAT analysis further linked enhanced MaPD to the haplotype rs2099684-A, rs6700241-A, and rs1771575-C (Supplemental Table 8).**DISCUSSION** Recent research has highlighted the complex relationship between Fc gamma receptor (FcγR) genetic variations and humoral immune responses, particularly in malaria[14]. While much focus has been on coding variants affecting IgG subclass affinity, this study explores non-coding variants regulating FcγR gene expression.

Using bioinformatic tools, we identified eQTL variants with regulatory potential that influence
the expression of activating and inhibitory FcγRs. We then examined these variants'
associations with malaria phenotypes using both family-based linear mixed models and QTDT and FBAT tests, which assess allele transmission. As previously reported[4], total IgG levels were associated with lower parasitemia. However, QTDT analyses did not confirm significant associations of total IgG with the studied variants in the study cohort.

The rs1801274-AA genotype was associated with elevated anti-plasmodial IgG2 levels but not with infection levels. This genotype corresponds to p.167His in FcγRIIa, known for enhancing IgG2 binding, unlike the alternative p.167Arg isoform, which binds cytophilic IgG1 more efficiently[12]. The association between rs1801274 and IgG2 has been reported in malaria contexts[13,15,17,33,34], aligning with our observations. However, the role of FcγRIIa-IgG2 mediated immune activation remains debated. Some studies suggest that p.167Arg which binds IgG1 more effectively, may offer better protection against high parasite densities and clinical malaria compared to p.167His[8,35–37]. Conversely, other studies have linked this isoform with malaria protection[15,38,39], particularly in Fulani populations[40]. Our findings align with those in southern Benin[41] and Allada populations[16], showing that rs1801274 did not exhibit a protective effect. These inconsistencies in African subpopulations likely stem from differences in sample size, ethnic origin, genetic diversity, clinical definitions of malaria, gene-environment interactions, or the lack of family-based studies for this FcγRIIa variant concerning parasitemia and clinical malaria. This highlights the variability in how coding variants influence FcγR-dependent humoral immune responses across populations.

In contrast to the extensively studied FcγRIIa variant, our family-based study identified the non-coding rs1771575 variant as the only significant marker associated with both parasitemia and IgG2 levels. This association between rs1771575 and blood infection levels is consistent with our previous results showing its association with malaria infection prevalence[27].

In exposed children, rs1771575-CC individuals had higher infection and IgG2 levels compared to CT/TT individuals. Although this suggests that this variant influences FcγRIIb-dependent
inhibition of FcγRIIa-mediated immune activation during parasite challenge, this hypothesis is unsupported by the lack of association between IgG2 levels and infection rates in infants.

The mechanisms by which rs1771575 influences IgG2 and infection levels remain unclear.

However, as we previously described[27], allele-specific binding of CCCTC-binding factor (CTCF) may contribute to the regulatory potential of this eQTL variant to modulate the expression of inhibitory (FcγRIIb) and activating receptors (FcγRIIa, FcγRIIc, FcγRIIIa, FcγRIIIb) on immune cells involved in parasite clearance, such as neutrophils, dendritic cells, monocytes, and NK cells. This complexity makes it difficult to decipher how rs1771575 influences the balance between inhibitory and activating signals, particularly in conjunction with coding functional variants. Public eQTL data reveal that the rs1771575-CC genotype is linked to lower gene expression of activating FCGR2A, FCGR2C, FCGR3A and inhibitory FCGR2B in various immune cell types. This genotype is expected to reduce FcγRIIb expression on monocytes and neutrophils, potentially lessening the impairment of FcγRIIa-mediated parasite phagocytosis. Furthermore, rs1771575 is linked to FCGR2B expression in dendritic cells, which may affect antigen presentation, T lymphocyte activation, and B cell-dependent antibody production.

Finally, eQTL associations in the ImmuNexUT database suggest that the rs1771575-TT genotype may upregulate FCGR2C expression on immune cells. FCGR2C was long considered a pseudogene due to a splice variant polymorphism preventing expression in most individuals, while an open reading frame mutation allows its expression on immune cells. Although less studied, FcγRIIc expression on immune cells promotes humoral activation. Polymorphisms in FCGR2C have been associated with its expression in human B cells and with vaccinal protection against HIV and anthrax trials[42–44], and recently identified as predictors of sepsis outcomes in infectious diseases[45].
Our results suggest that rs1771575-TT may enhance IgG and FcγRIIc-dependent activation of NK or B lymphocytes in individuals with this open reading frame polymorphism.

Lower infection levels were observed in children carrying the rs1050501-C allele compared to TT homozygotes, and in those carrying both rs1050501-C and the rs1771575-T alleles, compared to individuals with rs1050501-TT and rs1771575-CC genotypes. Consistent with our observations, homozygosity for the FcγRIIb rs1050501-CC polymorphism (T-to-C in exon 5 leading to the p.Ile231Thr substitution) has been associated with protection against severe malaria in East Africa[25].

This protective genotype is more prevalent in malaria-endemic regions but also linked to an increased risk of autoimmune diseases like systemic lupus erythematosus (SLE) in Caucasian and Southeast Asian populations[24,25].

We observed a higher frequency of the rs1050501-C allele in Burkina Faso compared to European populations in the 1000 Genomes Project. This increased frequency has been attributed to malaria-driven selective pressure[23]. In regions with high malaria exposure, this polymorphism may influence host capacity to mount protective immune responses against parasites, which could explain the prevalence of the rs1050501-CC genotype in Logofourousso, Burkina Faso.

Interestingly, the rs1050501-CC genotype was previously associated with enhanced humoral responses, notably with herpes simplex virus type 1-specific IgG3 antibodies in Alzheimer's disease[46]. Although the rs1050501-TT genotype was associated with higher serum IgG1 levels and increased infection rates, this association did not persist in QTDT models, and IgG1 levels were not correlated with protection in our cohort.

Haplotype analysis revealed a positive association between IgG1 levels and the haplotype containing rs2099684-A and rs6700241-A alleles, suggesting this haplotype may influence IgG1 production. While the underlying mechanisms remain unclear, the reduced expression of
FcγRIIb associated with this haplotype may alleviate its inhibitory effect on B cells, promoting increased B cell activation and higher IgG1 levels. As previously observed [47], although no specific variants were associated with IgG4 levels, elevated IgG4 were linked to lower parasitemia. Multivariate analysis suggests that the association between rs1771575 and parasitemia appears to be partially independent of IgG4. The mechanisms by which this variant and IgG4 may cooperate to influence parasitemia remain unclear, but prior research indicates that IgG4 may indirectly contribute to increased FCGR2B expression and the differentiation of M2 macrophages, which produce immunosuppressive cytokines [48].

One limitation of this study is that the identification of regulatory variants using the GTEx dataset predominantly represents individuals of European ancestry, which restricts the ability to capture population-specific eQTLs relevant to African populations. However, it is important to note that the rs1771575 variant identified in our study does not show significant differences in allele frequencies between populations in endemic regions and European populations. Despite the relatively small sample size, analyses based on QTDT and mixed model methods demonstrated sufficient statistical power to detect the association of the rs1771575-T allele with both lower IgG2 levels and reduced parasitemia in children. Another limitation is the lack of information on confounding factors, such as co-infections and prior infection history, which could influence parasitemia levels.

In conclusion, this study provides compelling evidence that, alongside the known coding variants rs1050501 and rs1801274, non-coding intergenic variants may significantly influence the expression of both activating and inhibitory Fc receptors, as well as overall immune functions. Notably, our findings indicate that rs1771575 is an independent factor associated with IgG2 levels and blood infection rates. These results highlight the intricate interplay between coding and non-coding genetic determinants in shaping FcγR-mediated humoral immune responses against malaria in children. Moreover, they suggest that IgG2 may play a
nuanced role in immunity, potentially influencing antibody-mediated responses to combat the
parasite in individuals with specific FCGR2A or FCGR2B genetic backgrounds. Further
research is essential to validate the association of rs1771575 with malaria phenotypes and to
assess responses to natural or vaccine-induced immunization in independent populations
residing in malaria-endemic regions.

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**Data availability:** The data underlying this article are available in the article and in its online supplementary material.

**Acknowledgments:** We thank all the families who participated in the study.

**Conflict of Interests:** the authors have No conflict of interest to declare

**Funding Statement** This work was supported by Institut national de la santé et de la recherche médicale (INSERM) and Aix-Marseille University (AMU) and the French government under the France 2030 investment plan, as part of the initiative excellence d'Aix Marseille Université -A*MIDEX (AMX-19-IET-007). C. D. was supported by a PhD fellowship 'emploi jeune doctorant 2023-2026' from the Région Sud Alpes Cote Azur P. M. A. was supported by a PhD fellowship from the French Ministry of Research. J.C was supported by a master fellowship from Marmara Institute. This project was carried out in the framework of the Inserm GOLD Cross-Cutting program (to P. R.).

**Author Contributions:** Conceptualization: P.P, P.R.- Data Curation: P.P, C.D, S.S, P.R, C.P, M.A- Formal Analysis: M.A., P.P., C.D., P.R., M.T., F.G., C.P. Investigation: C.D, J.C, F.G, M.T. Funding Acquisition: P.P, P.R Methodology: P.R., P.P., S.S. Supervision: P.P., P.R., Writing-original draft: P.P, P.R- Writing-review and editing: C.D., M.A., P.P., J.C, P.R., M.T., F.G., C.P, S.S.

**Ethics declaration:** Informed consent was obtained from participants or their guardians. The study is approved by the Ethics Committee for Health Research (ref: 2023000192/MSHP/MESRI/CERS), and the use of genetic data by the Commission for Informatics and Liberties of Burkina Faso (ref: 2024-341/CIL/SG/DAJC).
## Figure legends

**Figure 1. Epigenetic and Linkage Disequilibrium Profiling of FCGR variants.** Candidate variants rs2099684, rs6700241 and rs1771575 are in DNase I hypersensitivity sites and overlap with histone marks (H3K4me1 or H3K27ac) found in B cells, NK cells, monocytes, and neutrophils (A). The variants are also in regulatory regions defined by ENCODE cCREs. The positions of *FCGR2A* and *FCGR2B* coding variants, rs1801274 and rs1050501, are also shown. rs2099684 and rs6700241, are in strong linkage disequilibrium in (B) the studied population and (C) African populations from the 1000 Genomes Project. Linkage Disequilibrium plots show correlation coefficient between a pair of variants, represented in each square by the R2 value\*100. The color key also provides a visual reference with a blue to orange gradient reflecting lower to higher values. Abbreviations: H3K4me1, Histone H3 lysine 4 mono-methylation- H3K27ac, Histone H3 lysine 27 acetylation- cCREs, candidate cis-regulatory elements.

**Figure 2. Distribution of FCGR genotypes analyzed in the parental cohort.**

The genotype frequencies of analyzed variants among individuals from Europe, Africa (1000 Genomes Project), and the study population of parents from the cohort enrolled in Burkina Faso (n = 68) were compared. This includes the distribution of genotypes for non-coding variants rs1771575 (A), rs2099684 (B), and rs6700241 (C), as well as coding variants rs1050501 (D) and rs1801274 (E). Genotype frequencies were analyzed using the Chi-square test. For rs2099684 and rs6700241, GG homozygous were combined with heterozygous, and for rs1050501, CC homozygous were combined with heterozygous. Only significant differences (p-values < 0.05) are shown. Abbreviations: EUR, Europeans; AFR, Africans; BF, Burkina Faso.
<caption>**Figure 3.** Comparative analysis of IgG levels across genotypes.</caption>

Box plots show total serum IgG levels in males and females (A) and across rs1771575 genotypes (B) or serum IgG2 levels according to rs1771575 (C) and rs1801274 genotypes (D). Boxes depict the interquartile range (IQR) with medians as lines within the boxes, whiskers extending to the 10th and 90th percentiles, and outliers as individual points. P-values (< 0.046), which remained significant after correcting for multiple tests, are shown in the plots and resulted from nonparametric Mann-Whitney U-tests. Abbreviations: IgG, immunoglobulin G-IgG2, immunoglobulin G subclass 2.

**Figure 4.** Genotypic comparison of *P. falciparum* blood infection levels in children. Box plots show parasitemia (MaPD) adjusted for age and total IgG according to rs1771575 (A) or rs1050501 genotypes (B). MaPD adjusted for age, total IgG and IgG4 was also compared across rs1771575 (C) and rs1050501 genotypes (D). Boxes represent the interquartile range (IQR) with medians indicated by lines, whiskers extending to the 10th and 90th percentiles, and outliers as individual points. Significant differences in MaPD between genotypes were tested with the nonparametric Mann-Whitney U-test, with p-values < 0.046 considered significant after correcting for multiple tests. Abbreviations: MaPD, Mean of adjusted parasite density- IgG, immunoglobulin G- IgG4, immunoglobulin G subclass 4.
<table><caption>Table 1: Characteristics of participants</caption><thead><tr><th></th><th colspan="3">Median (25; 75 percentile) or No. (%)</th></tr><tr><th></th><th>All (N = 163)</th><th>Parents (n = 68)</th><th>Children (n = 95)</th></tr></thead><tbody><tr><td>Gender</td><td></td><td></td><td></td></tr><tr><td>Female</td><td>93 (57)</td><td>44 (65)</td><td>49 (52)</td></tr><tr><td>Male</td><td>70 (43)</td><td>24 (35)</td><td>46 (48)</td></tr><tr><td>Age</td><td>14.0 (7.50; 39.0)</td><td>40.0 (31.2; 45.7)</td><td>8.00 (6.00; 12.0)</td></tr><tr><td>IgG levels</td><td></td><td></td><td></td></tr><tr><td>Total IgG</td><td>0.054 (-0.540; 0.523)</td><td>0.188 (-0.626; 0.778)</td><td>0.013 (-0.443; 0.361)</td></tr><tr><td>IgG1</td><td>0.074 (-0.457; 0.521)</td><td>0.130 (-0.390; 0.538)</td><td>0.070 (-0.461; 0.509)</td></tr><tr><td>IgG2</td><td>0.165 (-0.608; 0.605)</td><td>-0.058 (-0.426; 0.504)</td><td>0.333 (-0.906; 0.726)</td></tr><tr><td>IgG3</td><td>0.105 (-0.436; 0.694)</td><td>-0.030 (-0.455; 0.618)</td><td>0.199 (-0.363; 0.750)</td></tr><tr><td>IgG4</td><td>-0.116 (-0.560; 0.405)</td><td>-0.203 (-0.611; 0.276)</td><td>-0.092 (-0.522; 0.450)</td></tr><tr><td>Parasitemia</td><td></td><td></td><td></td></tr><tr><td>Measures per individual</td><td>21.0 (15.5; 24.0)</td><td>20.0 (16.0; 24.0)</td><td>21.0 (15.0; 24.0)</td></tr><tr><td>MaPD</td><td>-0.395 (-0.744; 0.231)</td><td>-0.778 (-0.828; -0.709)</td><td>0.115 (-0.318; -0.512)</td></tr><tr><td>Non-coding variants</td><td></td><td></td><td></td></tr><tr><td>rs1771575 (C>T)</td><td></td><td></td><td></td></tr><tr><td>CC</td><td>36 (22)</td><td>14 (21)</td><td>22 (23)</td></tr><tr><td>CT</td><td>73 (45)</td><td>28 (41)</td><td>45 (47)</td></tr><tr><td>TT</td><td>54 (33)</td><td>26 (38)</td><td>28 (29)</td></tr><tr><td>rs2099684 (A>G)</td><td></td><td></td><td></td></tr><tr><td>AA</td><td>124 (77)</td><td>54 (79)</td><td>70 (75)</td></tr><tr><td>AG</td><td>33 (20)</td><td>13 (19)</td><td>20 (21)</td></tr><tr><td>GG</td><td>5 (3)</td><td>1 (2)</td><td>4 (4)</td></tr><tr><td>rs6700241 (A>G)</td><td></td><td></td><td></td></tr><tr><td>AA</td><td>117 (74)</td><td>49 (74)</td><td>68 (73)</td></tr><tr><td>AG</td><td>37 (23)</td><td>15 (23)</td><td>22 (24)</td></tr><tr><td>GG</td><td>5 (3)</td><td>2 (3)</td><td>3 (3)</td></tr><tr><td>Coding variants</td><td></td><td></td><td></td></tr><tr><td>rs1050501 (T>C)</td><td></td><td></td><td></td></tr><tr><td>CC</td><td>12 (7)</td><td>5 (8)</td><td>7 (8)</td></tr><tr><td>CT</td><td>75 (48)</td><td>33 (50)</td><td>42 (46)</td></tr><tr><td>TT</td><td>70 (45)</td><td>28 (42)</td><td>42 (46)</td></tr><tr><td>rs1801274 (A>G)</td><td></td><td></td><td></td></tr><tr><td>AA</td><td>26 (16)</td><td>13 (19)</td><td>13 (14)</td></tr><tr><td>AG</td><td>77 (49)</td><td>32 (48)</td><td>45 (49)</td></tr><tr><td>GG</td><td>56 (35)</td><td>22 (33)</td><td>34 (37)</td></tr></tbody></table>
Qualitative variables are presented as numbers (No.) with corresponding percentages (%).

Quantitative variables are reported as median values with interquartile ranges (25th-75th percentile).

Abbreviations: IgG (immunoglobulin G), iRBC (infected red blood cells), MaPD (Mean of adjusted parasite density)
<table><caption>Table 2. Family-Based Association between IgG and IgG2 levels and FCGR variants</caption><thead><tr><th></th><th colspan="2">beta (95% CI); P Value</th></tr><tr><th></th><th>All (N = 163)</th><th>Children (n = 95)</th></tr></thead><tbody><tr><td>rs1771575<sup>a</sup></td><td></td><td></td></tr><tr><td>Total IgG</td><td>0.46 (0.10; 0.83); 0.013</td><td>0.14 (-0.21; 0.49); 0.444</td></tr><tr><td>Total IgG adjusted to Sex</td><td>0.39 (0.12; 0.70); 0.036</td><td>0.08 (-0.27; 0.42); 0.665</td></tr><tr><td>Sex</td><td>0.41 (0.12; 0.70); 0.013</td><td>0.37 (0.09; 0.65); 0.011</td></tr><tr><td>IgG2 subclass</td><td>0.60 (0.23; 0.97); 0.002</td><td>0.66 (0.13; 1.19); 0.017</td></tr><tr><td>rs1801274<sup>b</sup></td><td></td><td></td></tr><tr><td>IgG2 subclass</td><td>0.33 (0.11; 0.55); 0.004</td><td>0.40 (0.06; 0.74); 0.022</td></tr><tr><td>rs1050501<sup>c</sup></td><td></td><td></td></tr><tr><td>IgG1 subclass</td><td>-0.18 (-0.50; 0.13); 0.262</td><td>-0.44 (-0.84; -0.05); 0.031</td></tr></tbody></table>

Family-based Linear Mixed models were used to test the association of coding and non-coding variants with immunoglobulins levels, when considering family structure. P-values < 0.046 were considered significant after correcting for multiple tests. Beta coefficients are presented along with their 95% confidence intervals (CI), which are provided as (lower; upper bounds).

Abbreviations: IgG, Immunoglobulin G.

<footnote><sup>a</sup>Analysis according to the recessive model, comparing genotype CC versus CT/TT.</footnote>

<footnote><sup>b</sup>Analysis according to the additive model, comparing genotype GG versus AG versus AA.</footnote>

<footnote><sup>c</sup>Analysis according to the dominant model, comparing genotype TT versus CT/CC</footnote>
<table><caption>Table 3. Family-Based Association between the Mean of adjusted Parasite Density (MaPD) and FCGR variants</caption><thead><tr><th rowspan="2"></th><th colspan="2">beta (95% CI); P Value</th></tr><tr><th>MaPD adjusted for age and total IgG</th><th>MaPD adjusted for age, total IgG and IgG4</th></tr></thead><tbody><tr><td>rs1771575<sup>a</sup></td><td>0.23 (0.01; 0.45); 0.045</td><td>0.23 (0.02; 0.04); 0.036</td></tr><tr><td>Age</td><td>-0.06 (-0.07; -0.04); &lt;0.001</td><td>-0.05 (-0.07; -0.04); &lt;0.001</td></tr><tr><td>Total IgG</td><td>-0.17 (-0.30; -0.04); 0.012</td><td>-0.13 (-0.26; -0.003); 0.049</td></tr><tr><td>IgG4</td><td>...</td><td>-0.12 (-0.20; -0.03); 0.011</td></tr><tr><td>rs1050501<sup>b</sup></td><td>-0.26 (-0.45; -0.08); 0.007</td><td>-0.25 (-0.44; -0.07); 0.007</td></tr><tr><td>Age</td><td>-0.06 (-0.08; -0.04); &lt;0.001</td><td>-0.06 (-0.07; -0.04); &lt;0.001</td></tr><tr><td>Total IgG</td><td>-0.17 (-0.30; -0.05); 0.009</td><td>-0.13 (-0.26; 0.003); 0.058</td></tr><tr><td>IgG4</td><td>...</td><td>-0.11 (-0.21; -0.02); 0.024</td></tr><tr><td>rs1771575-rs1050501<sup>c</sup></td><td>-0.23 (-0.41; -0.05); 0.017</td><td>-0.23 (-0.41; -0.05); 0.013</td></tr><tr><td>Age</td><td>-0.06 (-0.07; -0.04); &lt;0.001</td><td>-0.05 (-0.07; -0.04); &lt;0.001</td></tr><tr><td>Total IgG</td><td>-0.16 (-0.28; -0.03); 0.017</td><td>-0.12 (-0.24; 0.01); 0.069</td></tr><tr><td>IgG4</td><td>...</td><td>-0.12 (-0.20; -0.03); 0.010</td></tr></tbody></table>

Family-based Linear Mixed Models were used to test the association of coding and non-coding variants with MaPD in children, when considering family structure, age, total IgG or IgG4. P-values < 0.046 were considered significant after correcting for multiple tests. Beta coefficients are presented along with their 95% confidence intervals (CI), which are provided as (lower; upper bounds). Abbreviations: IgG, immunoglobulin G; MaPD, Mean of adjusted Parasite Density. <sup>a</sup>Analysis according to the recessive model, comparing genotype CC versus CT/TT. <sup>b</sup>Analysis according to the dominant model; comparing genotype TT versus CT/CC.
cAnalysis comparing combined genotypes rs1771575-CC and rs1050501-TT with combined genotypes rs1771575-CT/TT and rs1050501-CT/CC.
<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>
<caption>Figure 2: Distribution of *FCGR* genotypes analyzed in the parental cohort.</caption>

Non-Coding Variants

<figure><img src="image_3.png" /></figure>

Coding Variants

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>Figure 3.</figcaption></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>Figure 4.</figcaption></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>
## Supplemental Data

<table><caption>Supplemental Table 1: Pairwise Linkage Disequilibrium statistics of candidate variants in African populations from the 1000 Genomes Project</caption><thead><tr><th rowspan="2">Population</th><th colspan="3">Variants in LD with rs2099684</th><th>Variant in LD with rs1771575</th></tr><tr><th>rs6700241</th><th>rs10919543</th><th>rs10919544</th><th>rs380125</th></tr></thead><tbody><tr><td><strong>ESN</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>1.000</td><td>0.932</td><td>0.932</td><td>0.958</td></tr><tr><td>D'</td><td>1.000</td><td>1.000</td><td>1.000</td><td>0.979</td></tr><tr><td><strong>GWD</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>0.900</td><td>0.871</td><td>0.871</td><td>0.948</td></tr><tr><td>D'</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td></tr><tr><td><strong>LWK</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>0.961</td><td>0.961</td><td>0.960</td><td>0.941</td></tr><tr><td>D'</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td></tr><tr><td><strong>MSL</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>0.931</td><td>0.857</td><td>0.857</td><td>0.977</td></tr><tr><td>D'</td><td>1.000</td><td>0.926</td><td>0.926</td><td>1.000</td></tr><tr><td><strong>YRI</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>R<sup>2</sup></td><td>1.000</td><td>0.958</td><td>0.958</td><td>0.982</td></tr><tr><td>D'</td><td>1.000</td><td>1.000</td><td>1.000</td><td>1.000</td></tr></tbody></table>

Variants in high LD with the two candidate regulatory variants were retrieved from the Ensembl genome browser 110. Abbreviations: LD, linkage disequilibrium; ESN, Esan in Nigeria; GWD; Gambian in Western Division; LWK, Luhya in Webuye Kenya; MSL, Mende in Sierra Leone; YRI, Yoruba in Ibadan, Nigeria.
<table><caption>Supplemental Table 2: Prioritization scores of candidate regulatory variants.</caption><thead><tr><th rowspan="2">Variant</th><th colspan="6">RegVar score in Whole Blood</th><th rowspan="2">cepip score in whole blood</th></tr><tr><th>FCGR2A</th><th>FCGR2B</th><th>FCGR2C</th><th>FCGR3A</th><th>FCGR3B</th><th>B cell</th><th>Monocyte</th></tr></thead><tbody><tr><td>rs6700241</td><td><strong>0.797</strong></td><td><strong>0.711</strong></td><td><strong>0.758</strong></td><td><strong>0.694</strong></td><td>0.551</td><td><strong>0.659</strong></td><td><strong>0.763</strong></td></tr><tr><td>rs10919543</td><td>0.124</td><td>0.154</td><td>0.197</td><td>0.107</td><td>0.085</td><td>0.187</td><td>0.187</td></tr><tr><td>rs10919544</td><td>0.162</td><td>0.114</td><td>0.209</td><td>0.109</td><td>0.116</td><td>0.240</td><td>0.240</td></tr><tr><td>rs380125</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>0.055</td><td>0.055</td></tr><tr><td>rs1771575</td><td>0.584</td><td><strong>0.872</strong></td><td><strong>0.888</strong></td><td><strong>0.651</strong></td><td><strong>0.851</strong></td><td>0.499</td><td><strong>0.677</strong></td></tr><tr><td>rs2099684</td><td><strong>0.777</strong></td><td>0.584</td><td><strong>0.641</strong></td><td><strong>0.614</strong></td><td>0.484</td><td><strong>0.845</strong></td><td><strong>0.711</strong></td></tr></tbody></table>

RegVar and cepip scores were computed to evaluate the regulatory potential of the variants within whole blood and various immune cell types. Only rs6700241 and the previously identified non-coding variants, rs1771575 and rs2099684, demonstrated high regulatory potential, with most of their scores exceeding the 0.6 threshold (in bold characters) established to identify the best candidates.
<table><caption>Supplemental Table 3: Significant eQTL associations of Candidate Regulatory SNPs from the ImmuNexUT Database.</caption><thead><tr><td>Variant</td><td>Gene</td><td>P-value</td><td>Slope</td><td>Immune cell type</td></tr></thead><tbody><tr><td>rs1771575</td><td>FCGR2B</td><td>3.098E-38</td><td>0.537</td><td>Neutrophils</td></tr><tr><td>rs1771575</td><td>FCGR3A</td><td>6.025E-26</td><td>0.558</td><td>Neutrophils</td></tr><tr><td>rs1771575</td><td>FCGR2B</td><td>9.959E-21</td><td>0.404</td><td>Classical monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>4.906E-16</td><td>0.411</td><td>Naive B cells</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>1.373E-10</td><td>0.333</td><td>Classical monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>2.277E-10</td><td>0.361</td><td>Switched memory B cells</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>1.222E-09</td><td>0.317</td><td>CD16+ monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>1.681E-09</td><td>0.430</td><td>Intermediate monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>3.900E-09</td><td>0.322</td><td>Unswitched memory B cells</td></tr><tr><td>rs1771575</td><td>FCGR2B</td><td>8.087E-09</td><td>0.272</td><td>CD16+ monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>1.963E-07</td><td>0.338</td><td>Neutrophils</td></tr><tr><td>rs1771575</td><td>FCGR2B</td><td>7.320E-07</td><td>0.305</td><td>Intermediate monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2B</td><td>4.687E-06</td><td>0.289</td><td>Non-Classical monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>5.114E-06</td><td>0.340</td><td>Non-Classical monocytes</td></tr><tr><td>rs1771575</td><td>FCGR3B</td><td>1.166E-05</td><td>0.297</td><td>Natural Killer cells</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>2.148E-04</td><td>0.228</td><td>Plasmablasts</td></tr><tr><td>rs1771575</td><td>FCGR3A</td><td>3.215E-04</td><td>0.172</td><td>Intermediate monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2A</td><td>4.862E-04</td><td>0.198</td><td>Naive B cells</td></tr><tr><td>rs1771575</td><td>FCGR3B</td><td>2.035E-03</td><td>0.170</td><td>CD16+ monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2A</td><td>3.914E-03</td><td>0.092</td><td>Classical monocytes</td></tr><tr><td>rs1771575</td><td>FCGR2C</td><td>3.971E-03</td><td>0.130</td><td>Double Negative B cells</td></tr></tbody></table>
<table>
  <tr>
    <td>rs1771575</td>
    <td>FCGR3B</td>
    <td>6.502E-03</td>
    <td>0.190</td>
    <td>Non-Classical monocytes</td>
  </tr>
  <tr>
    <td>rs1771575</td>
    <td>FCGR3B</td>
    <td>9.253E-03</td>
    <td>0.193</td>
    <td>Intermediate monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR3B</td>
    <td>8.543E-78</td>
    <td>1.091</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR3B</td>
    <td>9.882E-20</td>
    <td>0.498</td>
    <td>CD16+ monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR3B</td>
    <td>1.612E-18</td>
    <td>0.677</td>
    <td>Intermediate monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2C</td>
    <td>2.632E-17</td>
    <td>0.526</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>6.682E-15</td>
    <td>0.230</td>
    <td>Neutrophils</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR3B</td>
    <td>2.225E-11</td>
    <td>0.506</td>
    <td>Non-Classical monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2C</td>
    <td>6.558E-07</td>
    <td>0.269</td>
    <td>Naive B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>8.577E-07</td>
    <td>0.289</td>
    <td>Naive B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>8.909E-07</td>
    <td>0.321</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2B</td>
    <td>1.227E-05</td>
    <td>0.205</td>
    <td>Neutrophils</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>1.326E-05</td>
    <td>0.185</td>
    <td>Intermediate monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>4.580E-05</td>
    <td>0.135</td>
    <td>Classical monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>5.339E-05</td>
    <td>0.257</td>
    <td>Unswitched memory B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>3.483E-04</td>
    <td>0.169</td>
    <td>Double Negative B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>4.831E-04</td>
    <td>0.218</td>
    <td>Switched memory B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2C</td>
    <td>1.064E-03</td>
    <td>0.189</td>
    <td>Unswitched memory B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR3A</td>
    <td>1.272E-03</td>
    <td>0.147</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2B</td>
    <td>1.609E-03</td>
    <td>0.157</td>
    <td>CD16+ monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2A</td>
    <td>2.119E-03</td>
    <td>0.096</td>
    <td>CD16+ monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2B</td>
    <td>7.512E-03</td>
    <td>0.192</td>
    <td>Non-Classical monocytes</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2B</td>
    <td>7.691E-03</td>
    <td>0.114</td>
    <td>Naive B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2C</td>
    <td>9.116E-03</td>
    <td>0.158</td>
    <td>Switched memory B cells</td>
  </tr>
  <tr>
    <td>rs2099684</td>
    <td>FCGR2B</td>
    <td>9.311E-03</td>
    <td>0.124</td>
    <td>Classical monocytes</td>
  </tr>
</table>
<table>
  <tr>
    <td>rs6700241</td>
    <td>FCGR3B</td>
    <td>8.543E-78</td>
    <td>1.091</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR3B</td>
    <td>9.882E-20</td>
    <td>0.498</td>
    <td>CD16+ monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR3B</td>
    <td>1.612E-18</td>
    <td>0.677</td>
    <td>Intermediate monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2C</td>
    <td>2.632E-17</td>
    <td>0.526</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>6.682E-15</td>
    <td>0.230</td>
    <td>Neutrophils</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR3B</td>
    <td>2.225E-11</td>
    <td>0.506</td>
    <td>Non-Classical monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2C</td>
    <td>6.558E-07</td>
    <td>0.269</td>
    <td>Naive B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>8.577E-07</td>
    <td>0.289</td>
    <td>Naive B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>8.909E-07</td>
    <td>0.321</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2B</td>
    <td>1.227E-05</td>
    <td>0.205</td>
    <td>Neutrophils</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>1.326E-05</td>
    <td>0.185</td>
    <td>Intermediate monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>4.580E-05</td>
    <td>0.135</td>
    <td>Classical monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>5.339E-05</td>
    <td>0.257</td>
    <td>Unswitched memory B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>3.483E-04</td>
    <td>0.169</td>
    <td>Double Negative B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>4.831E-04</td>
    <td>0.218</td>
    <td>Switched memory B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2C</td>
    <td>1.064E-03</td>
    <td>0.189</td>
    <td>Unswitched memory B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR3A</td>
    <td>1.272E-03</td>
    <td>0.147</td>
    <td>Natural Killer cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2B</td>
    <td>1.609E-03</td>
    <td>0.157</td>
    <td>CD16+ monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2A</td>
    <td>2.119E-03</td>
    <td>0.096</td>
    <td>CD16+ monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2B</td>
    <td>7.512E-03</td>
    <td>0.192</td>
    <td>Non-Classical monocytes</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2B</td>
    <td>7.691E-03</td>
    <td>0.114</td>
    <td>Naive B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2C</td>
    <td>9.116E-03</td>
    <td>0.158</td>
    <td>Switched memory B cells</td>
  </tr>
  <tr>
    <td>rs6700241</td>
    <td>FCGR2B</td>
    <td>9.311E-03</td>
    <td>0.124</td>
    <td>Classical monocytes</td>
  </tr>
</table>
The non-coding variants rs6700241 (A>G), rs1771575 (C>T) and rs2099684 (A>G) are eQTLs
for several *FCGR* genes in many immune cell types. ImmuNexUT eQTL data were
downloaded from the National Bioscience Database Center Human Database (accession
number E-GEAD-420). All associations with a nominal p-value < 0.001 are represented. The
slope represents the effect sizes of the alternative allele (ref>alt). The data are sorted from the
highest to the lowest p-value for each variant.
<table><caption>**Supplemental Table 4: Genotyping statistics generated with Haploview.**</caption><thead><tr><th>Variant</th><th>Position<sup>a</sup></th><th>ObsHET</th><th>PredHET</th><th>HWpval</th><th>%Geno</th><th>MAF</th><th>major:minor<sup>b</sup></th></tr></thead><tbody><tr><td>rs1802174 chr1:161509955</td><td>0.46</td><td>0.49</td><td>0.78</td><td>98.60</td><td>0.44</td><td>G:A</td></tr><tr><td>rs2099684 chr1:161530340</td><td>0.2</td><td>0.20</td><td>1.0</td><td>100.00</td><td>0.11</td><td>A:G</td></tr><tr><td>rs6700241 chr1:161531185</td><td>0.22</td><td>0.24</td><td>0.74</td><td>97.10</td><td>0.14</td><td>A:G</td></tr><tr><td>rs1771575 chr1:161647707</td><td>0.41</td><td>0.48</td><td>0.30</td><td>97.10</td><td>0.41</td><td>T:C</td></tr><tr><td>rs1050501 chr1:161674008</td><td>0.5</td><td>0.44</td><td>0.44</td><td>94.30</td><td>0.33</td><td>T:C</td></tr></tbody></table>

Abbreviations: ObsHET, observed heterozygosity; PredHET, predicted heterozygosity; HWpval, Hardy-Weinberg equilibrium p-value; %Geno, percentage of non-missing genotypes; MAF, minor allele frequency.

<footnote><sup>a</sup>Position of the variants on the reference genome hg38.</footnote>

<footnote><sup>b</sup>Major and Minor alleles for each variant.</footnote>
<table><caption>Supplemental Table 5: Pairwise Linkage Disequilibrium statistics from Haploview.</caption><thead><tr><th rowspan="2">Variant 1</th><th rowspan="2">Variant 2</th><th rowspan="2">Distance<sup>a</sup></th><th colspan="3">1000 Genomes Project</th><th colspan="2">Studied Population</th></tr><tr><th>D'</th><th>LOD</th><th>R<sup>2</sup></th><th>D'</th><th>LOD</th></tr></thead><tbody><tr><td>rs1802174</td><td>rs2099684</td><td>20385</td><td>0.12</td><td>0.35</td><td>&lt;0.01</td><td>0.26</td><td>0.23</td></tr><tr><td>rs1802174</td><td>rs6700241</td><td>21230</td><td>0.07</td><td>0.10</td><td>&lt;0.01</td><td>0.133</td><td>0.07</td></tr><tr><td>rs1802174</td><td>rs1771575</td><td>137752</td><td>0.07</td><td>0.58</td><td>&lt;0.01</td><td>0.009</td><td>0.00</td></tr><tr><td>rs1802174</td><td>rs1050501</td><td>164053</td><td>0.06</td><td>0.13</td><td>&lt;0.01</td><td>0.022</td><td>0.00</td></tr><tr><td>rs2099684</td><td>rs6700241</td><td>845</td><td>0.96</td><td>160.54</td><td>0.92</td><td>0.925</td><td>11.05</td></tr><tr><td>rs2099684</td><td>rs1771575</td><td>117367</td><td>0.02</td><td>0.01</td><td>0.00</td><td>0.034</td><td>0.00</td></tr><tr><td>rs2099684</td><td>rs1050501</td><td>143668</td><td>0.38</td><td>0.96</td><td>&lt;0.01</td><td>0.023</td><td>0.00</td></tr><tr><td>rs6700241</td><td>rs1771575</td><td>116522</td><td>&lt;0.01</td><td>0.00</td><td>0.0</td><td>0.065</td><td>0.02</td></tr><tr><td>rs6700241</td><td>rs1050501</td><td>142823</td><td>0.42</td><td>1.22</td><td>0.01</td><td>0.034</td><td>0.01</td></tr><tr><td>rs1771575</td><td>rs1050501</td><td>26301</td><td>0.08</td><td>0.24</td><td>&lt;0.01</td><td>0.038</td><td>0.01</td></tr></tbody></table>

Linkage Disequilibrium (LD) between non-coding and coding variants was analyzed using Haploview. High LD and strong correlation between two variants are indicated by D' and R² values close to 1, respectively. Abbreviations: LOD, log of the likelihood odds ratio.ªDistance between the two variants in the base pair.
<table><caption>**Supplemental Table 6: Correlation between Total IgG and IgG subclasses in the whole population and in children.**</caption><thead><tr><th></th><th>P value</th><th>Standard Error</th><th>beta (95% CI)</th></tr></thead><tbody><tr><td colspan="4">Whole Population</td></tr><tr><td>IgG1</td><td>&lt;0.001</td><td>0.060</td><td>0.344 (0.226; 0.462)</td></tr><tr><td>IgG2</td><td>0.041</td><td>0.058</td><td>0.119 (0.006; 0.232)</td></tr><tr><td>IgG3</td><td>&lt;0.001</td><td>0.059</td><td>0.436 (0.321; 0.552)</td></tr><tr><td>IgG4</td><td>0.182</td><td>0.057</td><td>0.076 (-0.035; 0.187)</td></tr><tr><td colspan="4">Children</td></tr><tr><td>IgG1</td><td>0.003</td><td>0.059</td><td>0.181 (0.066; 0.296)</td></tr><tr><td>IgG2</td><td>0.125</td><td>0.050</td><td>0.077 (-0.020; 0.174)</td></tr><tr><td>IgG3</td><td>&lt;0.001</td><td>0.056</td><td>0.349 (0.239; 0.459)</td></tr><tr><td>IgG4</td><td>0.073</td><td>0.054</td><td>0.099 (-0.008; 0.205)</td></tr></tbody></table>

Correlation analysis was conducted using linear mixed models to account for family structure. P-values less than 0.046 were considered significant following correction for multiple comparisons. Beta coefficients are presented along with their 95% confidence intervals (CI), which are provided as (lower; upper bounds).
<table><caption>**Supplemental Table 7. Association of Variants with MaPD, Total IgG, and IgG Subclasses.**</caption><thead><tr><th rowspan="2">Variant</th><th rowspan="2">Models</th><th colspan="4">P Value</th></tr><tr><th>Total IgG adj.<sup>a</sup></th><th>IgG1</th><th>IgG2</th><th>IgG3</th></tr></thead><tbody><tr><td rowspan="3">rs2099684<br>Dominant Model<br>AA vs. AG-GG</td><td>Linear Mixed Models</td><td></td><td></td><td></td><td></td></tr><tr><td>Whole population</td><td>0.462</td><td>0.260</td><td>0.087</td><td>0.506</td></tr><tr><td>Children</td><td>0.117</td><td>0.065</td><td>0.510</td><td>0.993</td></tr><tr><td></td><td>QTDT</td><td>0.607</td><td>0.095</td><td>0.966</td><td>0.954</td></tr><tr><td rowspan="3">rs6700241<br>Dominant Model<br>AA vs. AG-GG</td><td>Linear Mixed Models</td><td></td><td></td><td></td><td></td></tr><tr><td>Whole population</td><td>0.395</td><td>0.464</td><td>0.091</td><td>0.908</td></tr><tr><td>Children</td><td>0.186</td><td>0.088</td><td>0.450</td><td>0.39</td></tr><tr><td></td><td>QTDT</td><td>0.937</td><td>0.147</td><td>0.806</td><td>0.518</td></tr><tr><td rowspan="3">rs1771575<br>Recessive Model<br>CC vs CT-TT</td><td>Linear Mixed Models</td><td></td><td></td><td></td><td></td></tr><tr><td>Whole population</td><td><strong>0.036</strong></td><td>0.169</td><td><strong>0.002</strong></td><td>0.083</td></tr><tr><td>Children</td><td>0.665</td><td>0.409</td><td><strong>0.017</strong></td><td>0.500</td></tr><tr><td></td><td>QTDT</td><td>0.742</td><td>0.806</td><td><strong>0.041</strong></td><td>0.864</td></tr><tr><td rowspan="3">rs1050501<br>Dominant Model<br>TT vs CT-CC</td><td>Linear Mixed Models</td><td></td><td></td><td></td><td></td></tr><tr><td>Whole population</td><td>0.685</td><td>0.262</td><td>0.145</td><td>0.54</td></tr><tr><td>Children</td><td>0.882</td><td><strong>0.031</strong></td><td>0.247</td><td>0.456</td></tr><tr><td></td><td>QTDT</td><td>0.343</td><td>0.386</td><td>0.239</td><td>0.123</td></tr><tr><td rowspan="3">rs1801274<br>Additive Model<br>GG vs AG vs AA</td><td>Linear Mixed Models</td><td></td><td></td><td></td><td></td></tr><tr><td>Whole population</td><td>0.313</td><td>0.065</td><td><strong>0.004</strong></td><td>0.187</td></tr><tr><td>Children</td><td>0.913</td><td>0.103</td><td><strong>0.022</strong></td><td>0.797</td></tr><tr><td></td><td>QTDT</td><td>0.630</td><td>0.256</td><td>0.176</td><td>0.633</td></tr></tbody></table>

Family-based linear mixed models and the Quantitative Transmission Disequilibrium Test (QTDT) were used to test the association of the variants with MaPD, total IgG, and IgG subclasses. P-values < 0.046 were considered significant after correcting for multiple tests and are indicated in bold. Abbreviations: IgG, immunoglobulin G; MaPD, Mean of adjusted Parasite Density.

<footnote>aTotal IgG adjusted for sex</footnote>

<footnote>bMaPD adjusted for age and total IgG</footnote>
cMaPD adjusted for age, total IgG and IgG
<table><caption>Supplemental Table 8. Family-Based Association Test (FBAT) for Associations of Haplotype with MaPD, Total IgG, and IgG Subclasses</caption><thead><tr><th rowspan="2">Haplotype<br>(allele frequency %)</th><th colspan="6">Z score (P Value)</th></tr><tr><th>Total IgG</th><th>Total IgG<sup>a</sup></th><th>IgG1</th><th>IgG2</th><th>IgG3</th><th>IgG4</th></tr></thead><tbody><tr><td>rs2099684-rs6700241</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>A A (89%)</td><td>0.59 (0.554)</td><td>0.78 (0.436)</td><td>2.00 (<strong>0.046</strong>)</td><td>-0.49 (0.628)</td><td>0.17 (0.863)</td><td>-1.06 (0.287)</td></tr><tr><td>G G (8%)</td><td>-1.15 (0.249)</td><td>-1.26 (0.209)</td><td>-2.05 (<strong>0.040</strong>)</td><td>-0.09 (0.931)</td><td>-0.23 (0.817)</td><td>1.32 (0.186)</td></tr><tr><td>rs2099684-rs6700241-rs1771575</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>A A T (49%)</td><td>-0.14 (0.886)</td><td>0.14 (0.893)</td><td>0.57 (0.571)</td><td>-1.06 (0.288)</td><td>0.13 (0.895)</td><td>0.24 (0.810)</td></tr><tr><td>A A C (41%)</td><td>0.64 (0.521)</td><td>0.44 (0.662)</td><td>1.16 (0.244)</td><td>0.706 (0.480)</td><td>-0.15 (0.879)</td><td>-1.32 (0.186)</td></tr></tbody></table>

Family-Based Association Test (FBAT) was used to test the association of haplotypes with MaPD, total IgG, and IgG subclasses. P-values < 0.046 were considered significant after correcting for multiple tests and are indicated in bold. Z scores indicate the strength and direction of the association. Abbreviations: IgG, immunoglobulin G; MaPD, Mean of adjusted Parasite Density.ªTotal IgG adjusted for sex. ᵇMaPD adjusted for age and total IgG. ᶜMaPD adjusted for age, total IgG, and IgG4.
<table><caption>**Supplemental Table 9: Correlation between MaPD and IgG levels in children.**</caption><thead><tr><th></th><th>P value</th><th>Standard Error</th><th>beta (95% CI)</th></tr></thead><tbody><tr><td>Total IgG</td><td>0.023</td><td>0.066</td><td>-0.153 (-0.283; -0.023)</td></tr><tr><td>Age</td><td>&lt;0.001</td><td>0.008</td><td>-0.061 (-0.077; -0.046)</td></tr><tr><td>IgG4</td><td>0.004</td><td>0.045</td><td>-0.132 (-0.220; -0.045)</td></tr><tr><td>Age</td><td>&lt;0.001</td><td>0.008</td><td>-0.057 (-0.073; -0.041)</td></tr></tbody></table>

P-values <0.046 were considered significant after correcting for multiple tests. Beta coefficients are presented along with their 95% confidence intervals (CI), which are provided as (lower; upper bounds). Abbreviations: MaPD, Mean of adjusted Parasite Density.
P-values<0.046 were considered significant after correcting for multiple tests. Beta coefficients
are presented along with their 95% confidence intervals (CI), which are provided as (lower;
upper bounds). Abbreviations: MaPD, Mean of adjusted Parasite Density.
<figure><img src="image_13.png" /><figcaption>Supplemental Figure 1: Alteration of TCF3 binding site at position rs6700241.</figcaption></figure>

(A) ReMap ChIP-seq tracks showing the overlap between rs6700241 and TCF3 binding sites in several immune cell lines. (B) Position weight matrix of TCF3 reveals that the transcription factor is less likely to bind to its site in presence of rs6700241 alternative allele, as indicated by the p-value with the reference (ref) or alternative (alt) allele and their p-value ratio (worse p-value/best p-value).
<figure><img src="image_14.png" /><figcaption>Supplemental Figure 2: Comparative analysis of IgG1 levels according to rs1050501 genotypes.</figcaption></figure>

Comparison of IgG1 levels shows significant differences between rs1050501 genotypes. Box plots are represented with the interquartile range (IQR) of IgG1 levels and medians as lines. Whiskers extend to the 10th and 90th percentiles and outliers are shown as individual points. Significant p-values (<0.05) were obtained using the nonparametric Mann-Whitney U-test. Abbreviations: IgG1, immunoglobulin G subclass 1.
<figure><img src="image_15.png" /><figcaption>Supplemental Figure 3: Comparison of MaPD between Parents and Children.</figcaption></figure>

Comparative analysis of MaPD reveals significantly higher parasitemia in children compared to parents. A box plot showing MaPD for all participants is included. Box plots depict the interquartile range (IQR), with medians as lines, whiskers extending to the 10th and 90th percentiles and outliers as individual points. The p-value was obtained using the nonparametric Mann-Whitney U-test. Abbreviations: MaPD, Mean of adjusted parasite density.