Differences in the frequency of genetic variants associated with iron imbalance among global populations

Abstract

Iron deficiency anaemia is a major health problem affecting approximately 1.2 billion people worldwide. Young children, women of reproductive age and pregnant women living in sub-Saharan Africa are the most vulnerable. It is estimated that iron deficiency accounts for half of anaemia cases. Apart from nutritional deficiency, infection, inflammation and genetic factors are the major drivers of anaemia. However, the role of genetic risk factors has not been thoroughly investigated. This is particularly relevant in African populations, as they carry high genetic diversity and have a high prevalence of anaemia. Multiple genetic variations in iron regulatory genes have been linked to impaired iron status. Here we conducted a literature review to identify genetic variants associated with iron imbalance among global populations. We compare their allele frequencies and risk scores and we investigated population-specific selection among populations of varying geographic origin using data from the Keneba Biobank representing individuals in rural Gambia and the 1000 Genomes Project. We identified a significant lack of data on the genetic determinants of iron status in sub-Saharan Africa. Most of the studies on genetic determinants of iron status have been conducted in Europeans. Also, we identified population differences in allele frequencies in candidate putative genetic risk factors. Given the disproportionately high genetic diversity in African populations coupled with their high prevalence of iron deficiency, there is need to investigate the genetic influences of low iron status in Sub-Saharan Africa. The resulting insights may inform the future implementation of iron intervention strategies.

Fig 1. Geographical locations of the sixty-four studies that reported genetic variants associated with iron imbalance.

Nine studies involved multi-ethnic populations. AUS, Australia; EUR, Europe; SAS, South Asia.

Fig 2. Minor allele frequencies (MAF) of 13 SNPs across African populations.

Comparing MAF between the two Gambian datasets, Yoruba (YRI) from Nigeria and overall African populations included in the 1000 Genomes Project. The minor alleles were defined by the 1000 Genomes Project.

Fig 3. Correlation of minor allele frequencies between different geographic regions.

Correlation coefficients were obtained by pairwise comparisons of each of the 50 SNPs identified across two populations. They are coloured according to the value using a gradient from white (representing 0 for no correlation) to dark blue (1 for perfect correlation). The minor allele variant was defined by the 1000 Genomes Project. AFR, African; EUR, European; AMR, American; EAS, East Asian; SAS, South Asian.

Fig 4. The differences in minor allele frequencies of SNPs in the six genes investigated, across different geographic regions.

The comparisons were made between Africans and other global populations (A) Africa vs. Europe; (B) Africa vs. South Asia; (C) Africa vs. East Asia; (D) Africa vs. America. The thick grey lines indicated borders between SNPs in different genes: HAMP, SLC40A1, TMPRSS6, TF, HFE and TFR2. The minor alleles were defined according to the 1000 Genomes Project database [34]. AFR, African; EUR, European; AMR, American; EAS, East Asian; SAS, South Asian.

Fig 5. Linkage disequilibrium (LD) plots in SNPs in HAMP, SLC40A1, TMPRSS6, TF, HFE and TFR2 genes.

LD plot showing r² values in SNPS associated with iron imbalances in: (A) African populations, (B) European populations and (C) Gambian population in the Keneba Biobank.

Fig 6. Distribution of the number of low iron risk alleles across global populations.

(A) Distribution of the number of low iron risk alleles in eleven SNPs associated with low iron status across five populations. (B) Distribution of the number of low iron risk alleles in six SNPs with genotype data in the MRC Keneba Biobank population. Designation of the allele (risk or not) was determined by their previously published information as presented in S1 and S2 Tables. AFR, African; EUR, European; AMR, American; EAS, East Asian; SAS, South Asian.

Fig 7. Distribution of the number of risk alleles for haemochromatosis among global populations.

Designation of the risk allele was determined by previously published information as presented in S1 and S2 Tables. AFR, African; EUR, European; AMR, American; EAS, East Asian; SAS, South Asian.

Fig 8. Distribution of the number of high iron risk alleles across global populations.

(A) Distribution of the number of high iron risk alleles across five populations. (B) Distribution of the number of high iron risk alleles in three SNPs in the MRC Keneba Biobank (Gambian) and other populations. Designation of the allele (risk or not) was determined by their previously published information as presented in S1 and S2 Tables. AFR, African; EUR, European; AMR, American; EAS, East Asian; SAS, South Asian.

Fig 9. Pairwise F_ST values for iron related SNPs between African and non-African populations.

This figure illustrates the comparison of F_ST scores between African and other global populations. AFR, African; AMR, American; EUR, European; EAS, East Asian; SAS, South Asian, F_ST, fixation index.