Identifying genetic variants and pathways associated with extreme levels of fetal hemoglobin in Sickle Cell Disease in Tanzania.

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Siana Nkya, Liberata Mwita, Josephine Mgaya, Happiness Kumburu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.22041/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Jun, 2020 Read the published version in BMC Medical Genetics → Version 3 posted 4 You are reading this latest preprint version Show more versions Abstract Background: Sickle cell disease (SCD) is a blood disorder caused by a point mutation on the beta globin gene resulting in the synthesis of abnormal hemoglobin. Fetal hemoglobin (HbF) reduces disease severity, but the levels vary from one individual to another. Most research has focused on common variants which differ across populations and hence do not fully account for HbF variation. Methods: We investigated rare and common genetic variants that influence HbF levels in 14 SCD patients to elucidate variants and pathways in SCD patients with extreme HbF levels (≥7.7% for high HbF) and (≤2.5% for low HbF) in Tanzania. We performed targeted next generation sequencing (Illumina_Miseq) covering exonic and other significant fetal hemoglobin-associated loci, including BCL11A , MYB , HOXA9 , HBB , HBG1 , HBG2 , CHD4 , KLF1 , MBD3 , ZBTB7A and PGLYRP1 . Results: Results revealed a range of genetic variants, including bi-allelic and multi-allelic SNPs, frameshift insertions and deletions, some of which have functional importance. Notably, there were significantly more deletions in individuals with high HbF levels (11% vs 0.9%). We identified deletions with high HbF levels and frameshift insertions in individuals with low HbF. CHD4 and MBD3 genes, interacting in the same sub-network, were identified to have a significant number of pathogenic or non-synonymous mutations in individuals with low HbF levels, suggesting an important role of epigenetic pathways in the regulation of HbF synthesis. Conclusions: This study provides new insights in selecting essential variants and identifying potential biological pathways associated with extreme HbF levels in SCD using multiple genomic variants associated with HbF in SCD. Medical Genetics sickle cell disease genetic disorder fetal hemoglobin hemoglobinopathy tanzania Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Sickle cell disease (SCD) and thalassemia are the most common hemoglobinopathies worldwide, with 270 million carriers and 300,000 to 500,000 annual births [1]. Up to 70% of global SCD annual births occur in sub-Saharan Africa. Reports show that 50% to 80% of affected children in these countries die annually [2]. Tanzania ranks fifth worldwide regarding the number of children born with SCD, estimated at 8,000-11,000 births annually. 15-20% of the population are SCD carriers (HbAS) and therefore potential parents of future babies with SCD [3, 4]. Without intervention, it is estimated that up to 50% of children with SCD will die before the age of 5 years [1]. Thus, SCD intervention at early stages of life may prevent premature deaths and reduce under-five mortality. SCD is a monogenic condition resulting from a single mutation in the β-globin gene ( HBB ), on chromosome 11, leading to the production of an abnormal β-hemoglobin chain (HbS). SCD is a complex hemoglobin disorder with multiple phenotypic expressions that manifest as both chronic and acute complications, affecting multiple organs. Clinical manifestations vary immensely, with some individuals being entirely asymptomatic while others suffer from severe forms of the disease. The marked phenotypic heterogeneity of SCD is due to both genetic and environmental determinants [5]. A major disease modifier is the presence of fetal hemoglobin (HbF): high HbF levels are associated with reduced morbidity and mortality [6, 7]. Hemoglobin is a tetrametric molecule composed by 2-alpha-globin and 2 gamma globin molecules in HbF and 2 alpha-globin and two 2 beta-globin molecules in HbA [8]. HbF is normally expressed during the development of the fetus and starts to decline just before birth, when it is replaced by adult hemoglobin (HbA) in normal individuals and hemoglobin S (HbS) in individuals with SCD [9]. Red blood cells of normal adults (HbAA) contain mainly hemoglobin A (HbA), with 2.5-3.5% Hemoglobin A 2 (HbA 2 ), and < 1% HbF [10]. However, 10% to 15% of adults possess higher HbF levels (up to 5.0%). Although this has no significant consequences in healthy individuals, HbF background variability in SCD can reach levels with clinical benefit to patients [11]. Consequently, efforts to understand and control the production of HbF in SCD patients may result in interventions of significant clinical benefit to individuals with SCD. The levels of both HbF and F cells (erythrocytes with measurable amounts of HbF) are highly heritable traits [12] with up to 89% of variation being influenced by genetic factors. The remaining proportion is accounted for by age, sex and environmental factors. It is now clear that HbF is a quantitative trait which is shaped by genetic factors both linked and unlinked to the β-globin gene. Three main loci, namely BCL11A on chromosome 2, HMIP on chromosome 6, and HBG on chromosome 11, have been identified across populations as associated with HbF levels [13, 14, 15]. The variants in these loci have been reported to contribute 20-50% of HbF variation in non-African populations, however the impact of these variants is different from one population to another. An example is a strong variant at HMIP , which is rare in the Tanzanian population and hence has a smaller impact on HbF levels there [16, 17]. HbF levels in SCD, as a quantitative trait, is expected to be influenced by other polymorphisms, including insertions/deletions, rare mutations or copy number variations [15]. New genetic and proteomic techniques have led to the identification of several HbF expression regulators. Kruppel like factor ( KLF 1) has been reported as one of the key regulators of HbF expression with dual functions: direct activation of HbF expression through activation of β-globin [18] and an indirect silencing of γ-globin gene through BCL11A1 [19]. Other players within the HbF regulation network that have been reported include GATA1, FOG1 and SOX6 , which are erythroid transcription factors and are believed to interact with BCL11A in HbF regulation [20]. In addition, nuclear receptors TR2/TR4 which are associated with corepressors of DNA methyltransferase 1 ( DNMT1 ) and lysine-specific demethylase 1 ( LSD1 ) have also been implicated. DNMT1 and LSD1 are a part of the DRED complex, a known repressor of embryonic and fetal globin genes in adults [21]. Recently, studies of epigenetic pathways of HbF regulation have elucidated the involvement of the nucleosome remodeling and deacetylase (NuRD) complex [22, 23]. Despite the high prevalence of SCD in Africa, African patient populations remain understudied. Unique insight can be obtained from these patients, considering the substantial African genetic diversity and exceptional mapping resolution. The high burden of SCD in sub Saharan Africa makes it important that genetic studies, ultimately aimed at improved therapeutic intervention, are carried out in African countries. To address this, we conducted a Genome Wide Association Study (GWAS) [16, 17, 24] and candidate genotyping for HbF in Tanzanian individuals with SCD, which led to validation of known HbF variants and identification of novel ones. This report documents a follow-up study aimed at performing in-depth targeted sequencing around previously identified loci to descriptively compare, in detail, discovered polymorphisms between individuals with extreme HbF levels. For the first time, we have conducted targeted next-generation sequencing to investigate known and novel genetic variants and pathways associated with extreme HbF levels in individuals with Sickle cell disease (SCD) in Tanzania. From these selected individuals, we have identified different types of polymorphisms, including SNPs, INDELS, suggesting potential modifier effects. Interestingly, key discovered variants, together with previously identified variants, are enriched in biological pathways that underlie the HbF regulation. Methods Study design and population : We performed a cross-sectional study involving the Dar-es-Salaam (Tanzania) Muhimbili National Hospital SCD cohort, which consisted of 1,725 SCD patients, recruited between 2004 and 2009, for prospective surveillance, with three monthly interval visits for routine check-up [3]. These patients were subjected to folic acid (5mg/day) and penicillin. Different hematological factors, including complete blood counts and foetal haemoglobin (HbF) quantifications, were measured during hospital visits. Written informed consent was obtained for each adult patient (>18 years) and ethical approval given by the Muhimbili University Research and Publications Committee (MU/RP/AEC/VOLX1/33 and 2017-03-06/AEC/Vol X11/65). Informed and written consent was obtained from parents or guardians for all minor patients (≤18 years) with an oral assent for children older than 7 years. The study involved 14 individuals confirmed to have SCD (HbSS or S-β°thalassaemia), over five years old, with extreme HbF levels. Excluded were individuals confirmed to be AS or AA following Hb electrophoresis and HPLC, those with HbF measured at an age of less than 5 years, with inconclusive SCD laboratory diagnosis where a repeat test for confirmation could not be performed, and individuals who were on hydroxyurea therapy. Phenotyping Individuals were selected using previously collected HbF data. In this population, the median HbF was 4.6 [Interquartile range (IQR): 2.5-7.7)] [17] and therefore 0-2.5% was considered a low HbF level while 7.7% and above was considered a high HbF level. Sequencing DNA was extracted from archived buffy coat samples using the Nucleon BACC II system (GE Healthcare, Little Chalfont, UK). The sequencing panel was adopted from a research panel at King’s College London and customized using Illumina DesignStudio (https://designstudio.illumina.com/). Targeted sequencing covered exons and non-coding regions around validated and candidate fetal hemoglobin-influencing loci, including B-cell lymphoma/leukemia 11A ( BCL11A ), proto-oncogene, transcription factor ( MYB ), homeobox A9 ( HOXA9 ), hemoglobin subunit beta ( HBB), hemoglobin subunit gamma 1 ( HBG1 ), hemoglobin subunit gamma 2 ( HBG2 ), chromodomain helicase DNA binding protein 4 ( CHD4 ), Kruppel like factor 1 ( KLF1 ), methyl-CpG binding domain protein 3 ( MBD3 ), zinc finger and BTB domain containing 7A ( ZBTB7A ), peptidoglycan recognition protein 1 ( PGLYRP1 ) on chromosomes 2, 6, 7, 11, 12 and 19, respectively ( Table 2 ). Selection of target regions was based on previous associated known and novel loci in the studied population and those reported recently in other populations. Sequencing was performed on the Illumina MiSeq platform at the Kilimanjaro Clinical Research Institute ( KCRI ), Tanzania, following TruSeq Custom Amplicon Low Input Kit protocol. Reads Mapping, Alignment, Variant Calling and Variant Calling Quality Control Figure 1 illustrates and summarizes the pipeline used from alignment to prioritization of mutation. We reconstructed the reads by realigning them to the complete reference genome build hg38 using BWA [25]. The Picard tool kit [26] was used to sort and mark reads duplication, after alignment. We used an ensemble approach implemented in VariantMetaCaller [27] that may find a call consensus in detecting SNPs and short indels [28]. The best practice specific to each caller were adopted [29]. We combined information generated from two independent variant caller pipelines: (1) An incremental joint variant discovery implemented in GATK 3.0 HaplotypeCaller [26], which calls samples independently to produce gVCF files and leverages the information from the independent gVCF file to produce a final call-set at the genotyping step; (2) bcftools via mpileup [30, 31] variant callers ( Figure 1 ). The final call-set from each subject group was produced from VariantMetaCaller [27]. Annotation, In Silico Prediction of Mutation and Prioritization High confidence variants were called using VariantMetaCaller [27] from the dataset including 14 Tanzanian SCD patients (nine with high HbF and five with low HbF levels). We used ANNOVAR [32] to perform gene-based annotation to detect whether SNPs cause protein coding changes and to produce a list of the amino acids that are affected. ANNOVAR contains up to 21 different functional scores including SIFT [33, 34], LRT [35], MutationTaster, MutationAssessor [36], FATHMM [37], fathmm-MKL [38], RadialSVM, LR, PROVEAN, MetaSVM, MetaLR, DANN, M-CAP, Eigen, GenoCanyon [39], CADD [40], GERP++ [41], Polyphen2 HVAR, Polyphen2 HDIV[42] and PhyloP, SiPhy [43]. From the resulting functional annotated dataset, we first filtered variants for rarity, exonic variants, non-synonymous, stop codons, predicted functional significance and deleteriousness [33, 34]. First, the resulting functional annotated data set was independently filtered for predicted functional status (of which each predicted functional status is "deleterious" (D), "probably damaging" (D), "disease_causing_automatic" (A) or "disease_causing" (D) [44, 45, 46] from these 21 in silico prediction mutation tools. Recent evaluation of in silico prediction tools for mutation effects suggested these tools are quite similar [47]. However, the evaluation of these tools was conducted mostly in non-African populations. Here we opted for an extreme casting vote approach to retain only a variant if it had at least 17 predicted functional status “D” or “A” out of 21, as one can expect a true in silico mutant variant to similarly be reported from most of these tools. Second, the retained variants were further filtered for rarity, exonic variants, nonsynonymous mutations, yielding a final candidate list of predicted mutant and genetic modifier variants. Network and Enrichment Analysis To find out how predicted in silico mutant and modifier genes interact with others at the systems level, we analyzed how the set of all interactive genes from knowledge-based Protein-Protein Interaction (PPI) interacted with our identified in silico mutant genes and the rest of targeted genes, respectively. This has enabled the identification of potential biological pathways in which these genes participate. To achieve this, we first mapped the identified mutant SNPs to their closest genes. We mapped genes to a comprehensive human Protein-Protein Interaction (PPI) network [48, 49] to identify sub-networks containing mutant and genetics modifier variant genes and their interactions. Using the Enrichr software [50], we examined how closely these genes within the extracted sub-networks are associated with human phenotypes and elucidate biological processes and pathways in which these genes participate, molecular functions and association with potential human phenotypes. The most significant pathway enriched for genes in the networks were selected from KEGG [51], Panther [52], Biocarta [53] and Reactome [54]. Gene ontologies, including molecular functions and biological processes, from the Gene Ontology database [55]. Results Sample Characterization This study involved 14 SCD individuals with extreme (9 with high and 5 with low) HbF levels. Table 1 , describes the age and HbF ranges of the included individuals. Summary of variants found in individuals with high and low HbF levels A total of 873 and 1196 highly confident variants were determined in SCD patients with high and low HbF levels, respectively, on chromosomes 2, 6, 7, 11, 12 and 19. Surprisingly, this shows a difference in the overall variation between the two groups of individuals with SCD. The identified variants are comprised of 77% and 82 % biallelic SNPs, 0.15% and 0.11% multi-allelic SNPs, 11% and 0.9% deletions and 0.9% and 0.7% insertions in patients with high and low HbF levels (adjusted χ2 p-values = 1.16e-03 and 2.96e-06, as compared to uniform distribution), respectively. From these discovered variants, we detect 1 and 0 frameshift-deletions, 2 and 4 frameshift-insertions, 1 and 1 non-frameshift-insertions, 34 and 41 nonsynonymous, 3 and 3 stop-gain, 49 and 60 synonymous variants in SCD individuals with high/low HbF level, respectively. Based on our targeted chromosomal sequencing, we found significant difference in coverage of variants in the molecular structure ( Figure 2 ) between SCD patients with high and low HbF level (adjusted Fisher exact p-value = 6.1e-04), at 3’untranslated region (3’UTR) (2.98% versus 4.24%), 5’ untranslated region (5’UTR) (4.24% versus 0.69%), upstream (0.23%, 2.98%). Critically, we observed that patients with high HbF have 0% variants in splicing regions, while patients with low HbF level have 1.49% ( Figure 2 ). Potential pathogenic variants Because African-specific reported pathogenic variants are underrepresented in current databases of pathogenic variants [56], here we aimed at descriptively characterizing possible pathogenic variants from the set of polymorphisms in the retained candidate in silico mutant genes and our initial target genes discovery variants between the two patient groups. Following our pipeline and mutation prioritization, we identified six SNPs in genes ( ZBTB7A, CHD 4, HBB, PGLYRP1, MBD3 and MYB ) with functional impact ( Table 2 and Supplementary File: Table S2 ) in both data generated from the SCD patients with high and low HbF levels. Two genes, CHD4 and the MBD3, were found with a difference in the number of pathogenic variants ( Table 3 and Supplementary File: Table S2 ): individuals with SCD with low HbF levels were found to have more pathogenic, benign or uncertain significant pathogenic variants. Individuals with SCD with lower HbF levels had a significantly higher number of variants with insertions at both CHD4 and MBD3 than patients with high HbF levels ( Table 2 and Supplementary File: Table S1 ). While both groups have small numbers of deletion variants, individuals with low HbF level had fewer deletions than those with high HbF level. Based on Exome Aggregation Consortium (ExAC) database of pathogenic mutation [25], we found no significant difference in the number of pathogenic variants in both SCD patients with high or low HbF levels in genes ( BCL11A ), proto-oncogene, transcription factor ( MYB ), Homeobox A9 ( HOXA9 ), hemoglobin subunit gamma 2 ( HBG2 ), Kruppel like factor 1 ( KLF1 ), zinc finger and BTB domain containing 7A ( ZBTB7A ) in chromosomes 2, 6, 7, 11, 12 and 19, respectively. Overall, our targeted next generation sequencing of HbF associated genetic loci identified a disproportional number of loci with a few variants, particularly deletions, present in patients with high levels of HbF. Biological pathways and processes associated with genes with high mutational burdens Independent roles of the identified candidate in silico mutant genes (Supplementary File: Table S1, Figure 2 ) or our initial targeted nine genes are known in Sickle Cell disease. However, how these genes interact with others at the systems level is currently unknown in various populations of African SCD patients. As described in the method section, using the set of all interactive genes including our identified mutant genes and the rest of targeted genes may contribute in identifying potential Sickle Cell-specific pathways in which modifier and mutant genes participate together in conferring variation in Sickle Cell Disease severity. The identified Protein-Protein Interaction (PPI) sub-network formed from 2 genes ( Figure 3A ) showed an enrichment of rare variants with deleterious effects was enriched for the PRC2 complex which influence long-term gene silencing through modification of histone tails (P = 0.000004; Figure 3B ), and is highly associated with or involved in the TP-dependent chromatin remodeling (P = 1.6e-12, Figure 3B ) biological process, nominally associated with pallor (P = 0.0014, Figure 3B ). CHD4 and MBD3 were found to be the most important genes (hubs) of sub-network ( Figure 4A ), which are nominally associated with the B cell survival pathway (P = 0.018, Figure 4B ), known to be implicated in the ATP-dependent chromatin re-modeling biological process (P = 6e-15, Figure 4B ) and associated with polycythemia disorder (P = 0.0001, Figure 4B ). Discussion This is the first study in Africa to conduct targeted next generation sequencing to investigate genetic modifiers and pathways associated with extreme fetal hemoglobin (HbF) in individuals with SCD. Most of the loci (SNPs) that have been found to associate with HbF by GWAS only show possible associations with variants covered by the array chip used. The approach taken in this study was to perform in-depth sequencing around previously identified loci to descriptively compare, in detail, discovered polymorphisms between individuals with extreme HbF levels. We have identified single nucleotide polymorphisms (SNPs), insertions (IN) and deletions (DEL) across 8 targeted regions in chromosomes 2, 6, 7, 11, 12 and 19. We found differing types of polymorphisms, including SNPs and INDELS between individuals with low HbF versus those with high HbF, suggesting potential modifier effect. Interestingly, key discovered variants, together with previously identified variants, are enriched in biological pathways that underlie the HbF regulation. It is worth also noting that possible structural variants in these patient groups may make the sequencing off between the two groups. Furthermore, current challenges, including (1) limitation of variant calling tools in African data [57], (2) sequencing errors and structural variants in African data [58] and (3) under-representation of African samples in the current reference genome [58, 59], may contribute to the observed difference in variants discovered in both high and low HbF level in individuals with SCD. We found more deletions in individuals with high HbF than those with low HbF levels indicating their role in HbF synthesis pathways. A number of significant deletions have been reported before, particularly in the globin cluster [60, 61, 62]. In this study, we have identified additional potential deletions across the targeted regions ( Table 2 ). We observed more insertions in individuals with high HbF than in those with low HbF. However, frameshift deletions were more prevalent in individuals with high HbF, while frameshift insertions were more prevalent in individuals with low HbF. Frameshift deletions and insertion may lead to abnormal proteins due to shorter or longer sequences, respectively. We also looked at variants located at untranslated regions (UTR) both at 3’ and 5’ ends which are involved differently in regulation of gene expression. Interestingly, in individuals with high HbF levels, variants in the 5’UTR were more prevalent as opposed to more variants in the 3’UTR in individuals with low HbF levels. Molecular mechanisms of the 5’UTR include regulating translation of main coding sequences while the 3’UTR contain binding sites for microRNA (miRNA) which takes part in the timing and rate of translation of the corresponding mRNA. Hence the difference in variants in these two regions between individuals with high HbF versus those with low HbF is notable and may contribute differently in the regulation of HbF synthesis. We looked specifically at non-synonymous mutations and found that out of the eight targets, six were found to have mutations with functional impact. Of interest, the genes CHD4 and MBD3 , functionally interacting in the same sub-network (see Figure 3 ), had more pathogenic mutations in individuals with low HbF levels than those with high HbF. CHD4 is a chromatin organization modifier which confers the chromatin remodeling function of the NuRD complex. CHD4 has been reported to repress γ-globin gene expression in mice [63, 64]. Similarly, MBD3 operates as a NuRD complex and is associated with the transcription factors GATA-1 and FOG-1, which directly regulate genes within the β-globin locus. The human protein-protein interaction (PPI) ( Figure 3A ) for CHD4 and MBD3 proteins indicates that they are essential to system survival and hence their biological functions tend to be evolutionary conserved [63]. Thus, in presence of non-synonymous mutations, it is expected that individual components (proteins and interactions) in the system must adapt to a changing environment while maintaining the system’s primary function. In this study, we observed that, to maintain its robustness while sustaining its function under fluctuating environmental conditions, the system possibly triggers different mechanisms. This ensures that the network retains the modularity degree in order to provide a selective advantage for the host system by conserving and/or gaining useful functional interactions within the network to ensure an increase of HbF levels. As an illustration, CHD4 , as well as MBD3 , indirectly interact with KLF1 and MYB , which are potent activators of BCL11A . CHD4 is believed to exert its gamma globin silencing effect by positively regulating the BCL11A and KLF1 genes. In addition, BCL11A and MYB are known to be involved in γ-globin gene regulation, leading to either elevation or reduction of HbF levels [64]. The difference in frequency of non-synonymous mutations in the individuals with high HbF levels versus those with low levels reflect different interactions within this network and the resulting levels of HbF. Though these post-analysis results are consistent with the literature and are biologically relevant, it is worth noting that, due to relatively high noise related to high-throughput data or experiments from which interactions are inferred, the protein-protein interaction network used may contain incorrectly classified interactions, i.e., failing to detect interactions (false negatives) or wrongly identifying some other interactions (false positives). This suggests these results still need to be validated experimentally. In this study, we minimized the likelihood of incorrectly classified interaction computationally by: (1) using a data integration model, combining information from multiple interacting data sources into one unified network, and (2) applying a strict interaction reliability or confidence score cutoff. These techniques are expected to significantly reduce the false negative and positive rate of the network produced, leading to a PPI network of high confidence interactions with an increased coverage [65]. Given our study design, we did not perform genetics differentiation tests or statistical tests of differences in minor allele frequencies or genotype counts. Instead, we have aimed at descriptively characterizing the proportion of variants between the low/high HbF from high confident variants calling, compare the count of pathogenic variants between the groups and identify potential Sickle Cell-specific pathways in which modifier and mutant genes participate in conferring variation in Sickle Cell severity. Importantly, our current study suggests (1) a difference in the overall genetic variation between Sickle Cell patients with high and low HbF level and, (2) biological pathways, including the PRC2 complex which sets long-term gene silencing through modification of histone tails (P = 0.000004; Figure 3B ), hemoglobin’s Chaperone (P = 0.001, Figure 4B ) and B-cell Survival (P = 0.018, Figure 4B ). These identified pathways may harbour potential interactive Sickle Cell-specific genes including modifier, mutant and other genes ( Figures 3-4 ) in conferring variation in severity among individuals with SCD. This work has focused on the importance of studying both genetic and epigenetic pathways in HbF regulation. Our findings suggest an in-depth whole genome sequencing study to fully characterize modifier genes implicated in the variation of SCD severity. This approach may contribute to future development of interventions for SCD, including drugs and gene therapy. Finally to note is, the modest sample size limited the expected statistical power, which could yield false positive associations and missed others. However, different results obtained provide a strong hypothesis for future studies. With a larger sample size, it would be possible to perform genetics differentiation tests or statistical tests of differences in minor allele frequencies or genotype counts and possibly identify additional essential variants and biological pathways associated with extreme HbF levels in SCD using the model set by this study. Conclusions This study has shown that the analysis of genetic modifiers associated with HbF in SCD patients can elucidate genetic factors underlying extreme (low or high) HbF levels in these patients. The study has identified frameshift deletion in SCD patients with high HbF levels and frameshift insertions in both CHD4 and MBD3 for those with low HbF, and some of these insertions are associated with the SCD pathogenesis. Declarations Ethics approval and consent to participate This study was performed in accordance with the Declaration of Helsinki and with the approval of the Muhimbili University Research and Publications Committee (MU/RP/AEC/VOLX1/33 and 2017-03-06/AEC/Vol X11/65). Informed and written consent was obtained from all patients that were all adult participants (>18 years). Informed and written consent was requested from parents or guardians for all minor participants (≤18 years) with an oral assent for children older than 7 years. Consent for publication Not applicable Availability of data and materials Additional supporting information can be found in the supplementary file at the end of the article. Competing interests The authors declare that they have no conflict of interests. Funding This work was supported by Wellcome Trust (Grant no: 095009, 093727, 080025 & 084538) and Fogarty Global Health Fellowship sponsored by the National Institutes of Health (NIH). Authors' Contributions SN, HK, SM and JBM designed the study, JAM collected data, MZ, LM, RS, GKM and EC processed and analyzed the data. All authors contributed to the drafts of the manuscript, GKM and SN finalized the manuscript. Acknowledgments The authors thank the patients and staff of Muhimbili National Hospital, Muhimbili University of Health and Allied Sciences, Tanzania and the Sickle Cell Program. The authors extend special gratitude to Dr Barnaby Clark (PhD) who was Principal Clinical Scientist at King’s College Hospital. Dr Clark shared the next generation sequencing panel that was customized and adopted for this study. Author details 1 Department of Biological Sciences, Dar es Salaam University College of Education, Dar es Salaam, Tanzania. 2 Sickle Cell Program, Department of Hematology and Blood Transfusion, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania. 3 Department of Biotechnology Laboratory, Kilimanjaro Clinical Research Institute, Kilimanjaro, Tanzania. 4 Department of Molecular Hematology, King’s College of London, London, UK. 5 Department of Pathology, Division of Human Genetics, University of Cape Town, IDM, Cape Town, South Africa. 6 Department of Biomedical Sciences, Computational Biology Division, University of Cape Town, South Africa. 7 African Institute for Mathematical Sciences, Muizenberg 7945, Cape Town, South Africa. 8 Department of Pharmaceutical Microbiology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania. 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Validation and assessment of variant calling pipelines for next-generation sequencing. Human genomics 2014;8(1):14. Teo YY, Small KS, Kwiatkowski DP. Methodological challenges of genome-wide association analysis in Africa. Nature Reviews Genetics 2010;11(2):149. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733-745. doi:10.1093/nar/gkv1189. Chalaow N, Thein SL, Viprakasit V. The 12.6 kb-deletion in the β-globin gene cluster is the known Thai/Vietnamese (δβ)0-thalassemia commonly found in Southeast Asia. Haematologica 2013;98:e117–e118. doi:10.3324/haematol.2013.090613. Hamid M, Nejad LD, Shariati G, Galehdari H, Saberi A, Mohammadi-Anaei M, et al. The First Report of a 290-bp Deletion in β-Globin Gene in the South of Iran. Iranian Biomedical Journal 2017;21(2):126–128. doi:10.18869/acadpub.ibj.21.2.126. Thein SL, Craig JE. Genetics of Hb F/F cell variance in adults and heterocellular hereditary persistence of fetal hemoglobin. Hemoglobin 1998;22:401–414. Akinola RO, Mazandu GK, Mulder NJ. A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein–Protein Interaction Networks: A Case Study of Mycobacterium leprae. Front Genet 2016;7:39. doi:10.3389/fgene.2016.00039. Jiang J, Best S, Menzel S, Silver N, Lai MI, Surdulescu GL, et al. cMYB is involved in the regulation of fetal hemoglobin production in adults. Blood 2006;108:1077–1083. doi:10.1182/blood-2006-01-008912. Mazandu GK, Mulder NJ. Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification. Advances in Bioinformatics 2011, 2011(Article ID 801478), 14 pages. Doi:10.1155/2011/801478. Tables Table 1. Characteristics of Tanzanian individuals sickle cell disease (SCD) with extreme fetal hemoglobin levels. High HbF ≥ 7.7% Low HbF ≤ 2.5% N 9 5 Age range (Years) 5 - 19 8 - 21 HbF (%) 15-32 0.3-2.2 Table 2. Characterization of polymorphisms within mutant and modifiers genes in SCA patients from Tanzania. Details of gene variants can be found in Supplementary File: Table S1 . (High; low HbF level) GENE #Polymorphisms #MNP #SNPs #Deletion #Insertion #Pathogenic #Benign #USig * PGLYRP1 4; 4 0; 0 4; 4 0; 0 0; 0 0; 0 1; 0 3;4 ZBTB7A 13; 11 0; 1 10; 9 0; 0 1; 1 0; 0 2; 2 11;9 CHD4 25; 32 3; 3 19; 27 1; 0 1; 3 2; 5 3; 4 20;23 MBD3 14; 19 0; 2 12; 14 1; 1 1; 4 1; 2 0; 1 12;17 KLF1 11; 4 1; 0 10; 4 0; 0 0; 0 0; 0 1; 1 10;3 MYB 24; 27 1; 1 20; 23 0; 2 3; 1 0; 0 3; 1 21;26 BCL11A 27; 27 1; 2 25; 21 1; 2 0; 2 0; 0 4; 1 23; 26 HBG2 5; 17 1; 1 3; 12 0; 2 1; 2 0; 0 0; 0 5; 17 HOXA9 2; 2 0; 0 1; 1 0; 0 1; 1 0; 0 0; 0 2; 2 HBB 9; 10 0; 0 9; 10 0; 0 0; 0 0; 0 1; 1 8; 9 Abbreviations: USig * is the number variant with uncertain significance of pathogenicity. Supplementary Files TableS2.xlsx TableS1.docx Cite Share Download PDF Status: Published Journal Publication published 05 Jun, 2020 Read the published version in BMC Medical Genetics → Version 3 posted Editorial decision: Minor revision 12 May, 2020 Editor assigned by journal 11 May, 2020 Editor invited by journal 10 May, 2020 Submission checks completed at journal 24 Jan, 2020 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-12622","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":566394,"identity":"f39b8732-2cf8-4f7e-b3d6-4a33f1fa7e83","order_by":1,"name":"Siana Nkya","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Siana","middleName":"","lastName":"Nkya","suffix":""},{"id":566395,"identity":"b2260e6c-54df-4823-8a9d-76affd476b23","order_by":2,"name":"Liberata Mwita","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liberata","middleName":"","lastName":"Mwita","suffix":""},{"id":566396,"identity":"259b4e01-bb9b-4fed-b2f2-2b3a9a9f0693","order_by":3,"name":"Josephine Mgaya","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Josephine","middleName":"","lastName":"Mgaya","suffix":""},{"id":566397,"identity":"5b66a91e-8e61-403d-9b88-fbbdf10c1b12","order_by":4,"name":"Happiness Kumburu","email":"","orcid":"","institution":"Kilimajaro Clinical Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Happiness","middleName":"","lastName":"Kumburu","suffix":""},{"id":566398,"identity":"3cdf040d-8666-4ffb-96bf-971f568cac23","order_by":5,"name":"Marco van Zwetselaar","email":"","orcid":"","institution":"Kilimanjaro Clinical Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Marco","middleName":"van","lastName":"Zwetselaar","suffix":""},{"id":566399,"identity":"59be84ec-1b78-48a1-b94b-09f5f9ea924e","order_by":6,"name":"Stephan Menzel","email":"","orcid":"","institution":"King's College London","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Stephan","middleName":"","lastName":"Menzel","suffix":""},{"id":566400,"identity":"90328400-68dd-4902-8827-fc423127755a","order_by":7,"name":"Gaston Kuzamunu Mazandu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYPACZgZ+EJVQQIoWyQaQFgNStBgcANHEaJEPO/z4w88d1ombz69O/PDAgEGeX+wAfi2Gt9MMDHvPpCduu/F2swTQYYYzZycQ0DI7wSCBt+0wUMvZDSAtCQa3CWpJ/3DwL1DL5hlnN/8gSou8dI5hM8iWDfy924izxUA6p5hZti3deMYN3m0WCQYShP0iPzt988e3bday/f1nN9/8UWEjzy9NyJYDMJYEWKUEfuVgWxpgLP4DuFWNglEwCkbByAYApVBIV2fI8dEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6441-8006","institution":"University of Cape Town","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Gaston","middleName":"Kuzamunu","lastName":"Mazandu","suffix":""},{"id":566401,"identity":"5db96510-9372-441a-8e52-3ca62539196c","order_by":8,"name":"Raphael Z. 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Describes the bioinformatics pipelines from alignment of DNA reads, variants calling to in silico mutation prioritization.","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-12622/v3/1.png"},{"id":1141922,"identity":"7278c452-82ec-4c9d-9532-edacf71a101b","added_by":"auto","created_at":"2020-05-20 18:14:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135607,"visible":true,"origin":"","legend":"Characterization of SCD gene function and exome map from the targeted next generation sequencing: This included the exon and full regions for validated and novel fetal hemoglobin-associated loci, including B-cell lymphoma/leukemia 11A (BCL11A), proto-oncogene, transcription factor (MYB), Homeobox A9 (HOXA9), Hemoglobin subunit beta (HBB), hemoglobin subunit gamma 1 (HBG1), hemoglobin subunit gamma 2 (HBG2), chromodomain helicase DNA binding protein 4 (CHD4), Kruppel like factor 1 (KLF1), methyl-CpG binding domain protein 3 (MBD3), zinc finger and BTB domain containing 7A (ZBTB7A), Peptidoglycan recognition protein 1 (PGLYRP1) on chromosomes 2, 6, 7, 11, 12 and 19, respectively . (A) gene functions from patients with high HbF levels and (B) gene functions from patients with low HbF levels.","description":"","filename":"Figure2Bis.png","url":"https://assets-eu.researchsquare.com/files/rs-12622/v3/Figure2Bis.png"},{"id":1141923,"identity":"699b5f99-7eac-4ac2-96da-b155c956219d","added_by":"auto","created_at":"2020-05-20 18:14:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":163319,"visible":true,"origin":"","legend":"Biological sub-network of the candidate mutant gene and identified modifier genes in 14 SCD patients from Tanzania. A) sub-networks of the mutant gene and identified candidate genetic modifiers include CHD4 and MBD3. B) description of the top most significant pathways, GO biological process, and Human Phenotypes associated with the identified variants.","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-12622/v3/3.png"},{"id":1141924,"identity":"93e5c123-3ced-4283-93c1-f815ba6e4bdf","added_by":"auto","created_at":"2020-05-20 18:14:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124394,"visible":true,"origin":"","legend":"Biological sub-network of the candidate mutant gene and identified modifier genes in 14 SCD patients from Tanzania. A) sub-networks of our target sequencing variants include ZBTB7A, BCL11A, MYB, HBB, HOXA9, HBG2, CHD4, KLF1, MBD3. B) description of the top most significant pathways, GO biological process, and Human Phenotypes associated with the identified variants.","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-12622/v3/4.png"},{"id":13504772,"identity":"bb9eb698-9c94-4e4b-a74f-f1c262bb6ed1","added_by":"auto","created_at":"2021-09-16 23:24:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1110360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-12622/v3/95212ee4-1951-4323-9b1c-a579e4f4d758.pdf"},{"id":1141921,"identity":"7cf66cc8-43d4-4153-a6b2-1a5fc79bdbe2","added_by":"auto","created_at":"2020-05-20 18:14:35","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10515,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-12622/v3/TableS2.xlsx"},{"id":1141920,"identity":"89353ca1-8e35-4b2f-98df-26292606d8e7","added_by":"auto","created_at":"2020-05-20 18:14:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12074,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-12622/v3/TableS1.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIdentifying genetic variants and pathways associated with extreme levels of fetal hemoglobin in Sickle Cell Disease in Tanzania.\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSickle cell disease (SCD) and thalassemia are the most common hemoglobinopathies worldwide, with 270 million carriers and 300,000 to 500,000 annual births\u0026nbsp;[1]. Up to 70% of global SCD annual births occur in sub-Saharan Africa. Reports show that 50% to 80% of affected children in these countries die annually\u0026nbsp;[2]. Tanzania ranks fifth worldwide regarding the number of children born with SCD, estimated at 8,000-11,000 births annually. 15-20% of the population are SCD carriers (HbAS) and therefore potential parents of future babies with SCD\u0026nbsp;[3, 4]. Without intervention, it is estimated that up to 50% of children with SCD will die before the age of 5 years\u0026nbsp;[1]. Thus, SCD intervention at early stages of life may prevent premature deaths and reduce under-five mortality.\u003c/p\u003e\n\u003cp\u003eSCD is a monogenic condition resulting from a single mutation in the \u0026beta;-globin gene (\u003cem\u003eHBB\u003c/em\u003e), on chromosome 11, leading to the production of an abnormal \u0026beta;-hemoglobin chain (HbS). SCD is a complex hemoglobin disorder with multiple phenotypic expressions that manifest as both chronic and acute complications, affecting multiple organs. Clinical manifestations vary immensely, with some individuals being entirely asymptomatic while others suffer from severe forms of the disease. The marked phenotypic heterogeneity of SCD is due to both genetic and environmental determinants\u0026nbsp;[5]. A major disease modifier is the presence of fetal hemoglobin (HbF): high HbF levels are associated with reduced morbidity and mortality\u0026nbsp;[6, 7].\u003c/p\u003e\n\u003cp\u003eHemoglobin is a tetrametric molecule composed by 2-alpha-globin and 2 gamma globin molecules in HbF and 2 alpha-globin and two 2 beta-globin molecules in HbA\u0026nbsp;[8]. HbF is normally expressed during the development of the fetus and starts to decline just before birth, when it is replaced by adult hemoglobin (HbA) in normal individuals and hemoglobin S (HbS) in individuals with SCD [9]. Red blood cells of normal adults (HbAA) contain mainly hemoglobin A (HbA), with 2.5-3.5% Hemoglobin A\u003csub\u003e2\u003c/sub\u003e (HbA\u003csub\u003e2\u003c/sub\u003e), and \u0026lt; 1% HbF\u0026nbsp;[10]. However, 10% to 15% of adults possess higher HbF levels (up to 5.0%). Although this has no significant consequences in healthy individuals, HbF background variability in SCD can reach levels with clinical benefit to patients\u0026nbsp;[11]. Consequently, efforts to understand and control the production of HbF in SCD patients may result in interventions of significant clinical benefit to individuals with SCD.\u003c/p\u003e\n\u003cp\u003eThe levels of both HbF and F cells (erythrocytes with measurable amounts of HbF) are highly heritable traits\u0026nbsp;[12] with up to 89% of variation being influenced by genetic factors. The remaining proportion is accounted for by age, sex and environmental factors. It is now clear that HbF is a quantitative trait which is shaped by genetic factors both linked and unlinked to the \u0026beta;-globin gene. Three main loci, namely \u003cem\u003eBCL11A\u003c/em\u003e on chromosome 2, \u003cem\u003eHMIP\u003c/em\u003e on chromosome 6, and \u003cem\u003eHBG\u003c/em\u003e on chromosome 11, have been identified across populations as associated with HbF levels\u0026nbsp;[13, 14, 15]. The variants in these loci have been reported to contribute 20-50% of HbF variation in non-African populations, however the impact of these variants is different from one population to another. An example is a strong variant at \u003cem\u003eHMIP\u003c/em\u003e, which is rare in the Tanzanian population and hence has a smaller impact on HbF levels there\u0026nbsp;[16, 17]. HbF levels in SCD, as a quantitative trait, is expected to be influenced by other polymorphisms, including insertions/deletions, rare mutations or copy number variations\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003eNew genetic and proteomic techniques have led to the identification of several HbF expression regulators. \u003cem\u003eKruppel like factor\u003c/em\u003e (\u003cem\u003eKLF\u003c/em\u003e1) has been reported as one of the key regulators of HbF expression with dual functions: direct activation of HbF expression through activation of \u0026beta;-globin\u0026nbsp;[18] and an indirect silencing of \u0026gamma;-globin gene through \u003cem\u003eBCL11A1 \u003c/em\u003e[19]. Other players within the HbF regulation network that have been reported include \u003cem\u003eGATA1, FOG1\u003c/em\u003e and \u003cem\u003eSOX6\u003c/em\u003e, which are erythroid transcription factors and are believed to interact with \u003cem\u003eBCL11A\u003c/em\u003e in HbF regulation [20]. In addition, nuclear receptors \u003cem\u003eTR2/TR4\u003c/em\u003e which are associated with \u003cem\u003ecorepressors of DNA methyltransferase 1\u003c/em\u003e (\u003cem\u003eDNMT1\u003c/em\u003e) and \u003cem\u003elysine-specific demethylase 1\u003c/em\u003e (\u003cem\u003eLSD1\u003c/em\u003e) have also been implicated. \u003cem\u003eDNMT1\u003c/em\u003e and \u003cem\u003eLSD1\u003c/em\u003e are a part of the DRED complex, a known repressor of embryonic and fetal globin genes in adults [21]. Recently, studies of epigenetic pathways of HbF regulation have elucidated the involvement of the \u003cem\u003enucleosome remodeling and deacetylase\u003c/em\u003e (NuRD) complex\u0026nbsp;[22, 23].\u003c/p\u003e\n\u003cp\u003eDespite the high prevalence of SCD in Africa, African patient populations remain understudied. Unique insight can be obtained from these patients, considering the substantial African genetic diversity and exceptional mapping resolution. The high burden of SCD in sub Saharan Africa makes it important that genetic studies, ultimately aimed at improved therapeutic intervention, are carried out in African countries. To address this, we conducted a Genome Wide Association Study (GWAS)\u0026nbsp;[16, 17, 24] and candidate genotyping for HbF in Tanzanian individuals with SCD, which led to validation of known HbF variants and identification of novel ones. This report documents a follow-up study aimed at performing in-depth targeted sequencing around previously identified loci to descriptively compare, in detail, discovered polymorphisms between individuals with extreme HbF levels. For the first time, we have conducted targeted next-generation sequencing to investigate known and novel genetic variants and pathways associated with extreme HbF levels in individuals with Sickle cell disease (SCD) in Tanzania. From these selected individuals, we have identified different types of polymorphisms, including SNPs, INDELS, suggesting potential modifier effects. Interestingly, key discovered variants, together with previously identified variants, are enriched in biological pathways that underlie the HbF regulation.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eStudy design and population\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e: \u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eWe performed a cross-sectional study involving the Dar-es-Salaam (Tanzania) Muhimbili National Hospital SCD cohort, which consisted of 1,725 SCD patients, recruited between 2004 and 2009, for prospective surveillance, with three monthly interval visits for routine check-up\u0026nbsp;[3]. These patients were subjected to folic acid (5mg/day) and penicillin. Different hematological factors, including complete blood counts and foetal haemoglobin (HbF) quantifications, were measured during hospital visits. Written informed consent was obtained for each adult patient (\u0026gt;18 years) and ethical approval given by the Muhimbili University Research and Publications Committee (MU/RP/AEC/VOLX1/33 and 2017-03-06/AEC/Vol X11/65). Informed and written consent was obtained from parents or guardians for all minor patients (\u0026le;18 years) with an oral assent for children older than 7 years. The study involved 14 individuals confirmed to have SCD (HbSS or S-\u0026beta;\u0026deg;thalassaemia), over five years old, with extreme HbF levels. Excluded were individuals confirmed to be AS or AA following Hb electrophoresis and HPLC, those with HbF measured at an age of less than 5 years, with inconclusive SCD laboratory diagnosis where a repeat test for confirmation could not be performed, and individuals who were on hydroxyurea therapy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePhenotyping\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals were selected using previously collected HbF data. In this population, the median HbF was 4.6 [Interquartile range (IQR): 2.5-7.7)]\u0026nbsp;[17] and therefore 0-2.5% was considered a low HbF level while 7.7% and above was considered a high HbF level.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eSequencing\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from archived buffy coat samples using the Nucleon BACC II system (GE Healthcare, Little Chalfont, UK). The sequencing panel was adopted from a research panel at King\u0026rsquo;s College London and customized using Illumina DesignStudio (https://designstudio.illumina.com/). Targeted sequencing covered exons and non-coding regions around validated and candidate fetal hemoglobin-influencing loci, including \u003cem\u003eB-cell lymphoma/leukemia 11A\u003c/em\u003e (\u003cem\u003eBCL11A\u003c/em\u003e), \u003cem\u003eproto-oncogene, transcription factor\u003c/em\u003e (\u003cem\u003eMYB\u003c/em\u003e), \u003cem\u003ehomeobox A9\u003c/em\u003e (\u003cem\u003eHOXA9\u003c/em\u003e), \u003cem\u003ehemoglobin subunit beta\u003c/em\u003e (\u003cem\u003eHBB), hemoglobin subunit gamma 1\u003c/em\u003e (\u003cem\u003eHBG1\u003c/em\u003e), \u003cem\u003ehemoglobin subunit gamma 2\u003c/em\u003e (\u003cem\u003eHBG2\u003c/em\u003e), \u003cem\u003echromodomain helicase DNA binding protein 4\u003c/em\u003e (\u003cem\u003eCHD4\u003c/em\u003e), \u003cem\u003eKruppel like factor 1\u003c/em\u003e (\u003cem\u003eKLF1\u003c/em\u003e), \u003cem\u003emethyl-CpG binding domain protein 3\u003c/em\u003e (\u003cem\u003eMBD3\u003c/em\u003e), \u003cem\u003ezinc finger and BTB domain containing 7A\u003c/em\u003e (\u003cem\u003eZBTB7A\u003c/em\u003e), \u003cem\u003epeptidoglycan recognition protein 1\u003c/em\u003e (\u003cem\u003ePGLYRP1\u003c/em\u003e) on chromosomes 2, 6, 7, 11, 12 and 19, respectively (\u003cstrong\u003eTable 2\u003c/strong\u003e). Selection of target regions was based on previous associated known and novel loci in the studied population and those reported recently in other populations. Sequencing was performed on the Illumina MiSeq platform at the Kilimanjaro Clinical Research Institute (\u003cem\u003eKCRI\u003c/em\u003e), Tanzania, following TruSeq Custom Amplicon Low Input Kit protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eReads Mapping, Alignment, Variant Calling and Variant Calling Quality Control\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e illustrates and summarizes the pipeline used from alignment to prioritization of mutation. We reconstructed the reads by realigning them to the complete reference genome build hg38 using BWA\u0026nbsp;[25]. The Picard tool kit\u0026nbsp;[26] was used to sort and mark reads duplication, after alignment. We used an ensemble approach implemented in VariantMetaCaller\u0026nbsp;[27] that may find a call consensus in detecting SNPs and short indels\u0026nbsp;[28]. The best practice specific to each caller were adopted [29]. We combined information generated from two independent variant caller pipelines: (1) An incremental joint variant discovery implemented in GATK 3.0 HaplotypeCaller\u0026nbsp;[26], which calls samples independently to produce gVCF files and leverages the information from the independent gVCF file to produce a final call-set at the genotyping step; (2) bcftools via mpileup\u0026nbsp;[30, 31] variant callers (\u003cstrong\u003eFigure 1\u003c/strong\u003e). The final call-set from each subject group was produced from VariantMetaCaller\u0026nbsp;[27].\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eAnnotation, In Silico Prediction of Mutation and Prioritization\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eHigh confidence variants were called using VariantMetaCaller\u0026nbsp;[27] from the dataset including 14 Tanzanian SCD patients (nine with high HbF and five with low HbF levels). We used ANNOVAR\u0026nbsp;[32] to perform gene-based annotation to detect whether SNPs cause protein coding changes and to produce a list of the amino acids that are affected. ANNOVAR contains up to 21 different functional scores including SIFT\u0026nbsp;[33, 34], LRT\u0026nbsp;[35], MutationTaster, MutationAssessor\u0026nbsp;[36], FATHMM\u0026nbsp;[37], fathmm-MKL\u0026nbsp;[38], RadialSVM, LR, PROVEAN, MetaSVM, MetaLR, DANN, M-CAP, Eigen, GenoCanyon\u0026nbsp;[39], CADD\u0026nbsp;[40], GERP++\u0026nbsp;[41], Polyphen2 HVAR, Polyphen2 HDIV[42] and PhyloP, SiPhy\u0026nbsp;[43].\u003c/p\u003e\n\u003cp\u003eFrom the resulting functional annotated dataset, we first filtered variants for rarity, exonic variants, non-synonymous, stop codons, predicted functional significance and deleteriousness\u0026nbsp;[33, 34]. First, the resulting functional annotated data set was independently filtered for predicted functional status (of which each predicted functional status is \"deleterious\" (D), \"probably damaging\" (D), \"disease_causing_automatic\" (A) or \"disease_causing\" (D)\u0026nbsp;[44, 45, 46] from these 21 in silico prediction mutation tools. Recent evaluation of in silico prediction tools for mutation effects suggested these tools are quite similar\u0026nbsp;[47]. However, the evaluation of these tools was conducted mostly in non-African populations. Here we opted for an extreme casting vote approach to retain only a variant if it had at least 17 predicted functional status \u0026ldquo;D\u0026rdquo; or \u0026ldquo;A\u0026rdquo; out of 21, as one can expect a true in silico mutant variant to similarly be reported from most of these tools. Second, the retained variants were further filtered for rarity, exonic variants, nonsynonymous mutations, yielding a final candidate list of predicted mutant and genetic modifier variants.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eNetwork and Enrichment Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eTo find out how predicted in silico mutant and modifier genes interact with others at the systems level, we analyzed how the set of all interactive genes from knowledge-based Protein-Protein Interaction (PPI) interacted with our identified in silico mutant genes and the rest of targeted genes, respectively. This has enabled the identification of potential biological pathways in which these genes participate. To achieve this, we first mapped the identified mutant SNPs to their closest genes. We mapped genes to a comprehensive human Protein-Protein Interaction (PPI) network\u0026nbsp;[48, 49] to identify sub-networks containing mutant and genetics modifier variant genes and their interactions. Using the Enrichr software\u0026nbsp;[50], we examined how closely these genes within the extracted sub-networks are associated with human phenotypes and elucidate biological processes and pathways in which these genes participate, molecular functions and association with potential human phenotypes. The most significant pathway enriched for genes in the networks were selected from KEGG\u0026nbsp;[51], Panther\u0026nbsp;[52], Biocarta\u0026nbsp;[53] and Reactome\u0026nbsp;[54]. Gene ontologies, including molecular functions and biological processes, from the Gene Ontology database\u0026nbsp;[55].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample Characterization\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved 14 SCD individuals with extreme (9 with high and 5 with low) HbF levels. \u003cstrong\u003eTable 1\u003c/strong\u003e, describes the age and HbF ranges of the included individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSummary of variants found in individuals with high and low HbF levels\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 873 and 1196 highly confident variants were determined in SCD patients with high and low HbF levels, respectively, on chromosomes 2, 6, 7, 11, 12 and 19. Surprisingly, this shows a difference in the overall variation between the two groups of individuals with SCD.\u003c/p\u003e\n\u003cp\u003eThe identified variants are comprised of 77% and 82 % biallelic SNPs, 0.15% and 0.11% multi-allelic SNPs, 11% and 0.9% deletions and 0.9% and 0.7% insertions in patients with high and low HbF levels (adjusted \u0026chi;2 p-values = 1.16e-03 and 2.96e-06, as compared to uniform distribution), respectively. From these discovered variants, we detect 1 and 0 frameshift-deletions, 2 and 4 frameshift-insertions, 1 and 1 non-frameshift-insertions, 34 and 41 nonsynonymous, 3 and 3 stop-gain, 49 and 60 synonymous variants in SCD individuals with high/low HbF level, respectively. Based on our targeted chromosomal sequencing, we found significant difference in coverage of variants in the molecular structure (\u003cstrong\u003eFigure 2\u003c/strong\u003e) between SCD patients with high and low HbF level (adjusted Fisher exact p-value = 6.1e-04), at 3\u0026rsquo;untranslated region (3\u0026rsquo;UTR) (2.98% versus 4.24%), 5\u0026rsquo; untranslated region (5\u0026rsquo;UTR) (4.24% versus 0.69%), upstream (0.23%, 2.98%). Critically, we observed that patients with high HbF have 0% variants in splicing regions, while patients with low HbF level have 1.49% (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePotential pathogenic variants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBecause African-specific reported pathogenic variants are underrepresented in current databases of pathogenic variants [56], here we aimed at descriptively characterizing possible pathogenic variants from the set of polymorphisms in the retained candidate in silico mutant genes and our initial target genes discovery variants between the two patient groups. Following our pipeline and mutation prioritization, we identified six SNPs in genes (\u003cem\u003eZBTB7A, CHD\u003c/em\u003e4, \u003cem\u003eHBB, PGLYRP1, MBD3 \u003c/em\u003eand\u003cem\u003e MYB\u003c/em\u003e) with functional impact (\u003cstrong\u003eTable 2 \u003c/strong\u003eand Supplementary File:\u003cstrong\u003e Table S2\u003c/strong\u003e) in both data generated from the SCD patients with high and low HbF levels. Two genes, \u003cem\u003eCHD4\u003c/em\u003e and the \u003cem\u003eMBD3,\u003c/em\u003e were found with a difference in the number of pathogenic variants (\u003cstrong\u003eTable 3 \u003c/strong\u003eand Supplementary File:\u003cstrong\u003e Table S2\u003c/strong\u003e): individuals with SCD with low HbF levels were found to have more pathogenic, benign or uncertain significant pathogenic variants.\u003c/p\u003e\n\u003cp\u003eIndividuals with SCD with lower HbF levels had a significantly higher number of variants with insertions at both \u003cem\u003eCHD4\u003c/em\u003e and \u003cem\u003eMBD3\u003c/em\u003e than patients with high HbF levels (\u003cstrong\u003eTable 2\u003c/strong\u003e and Supplementary File: \u003cstrong\u003eTable S1\u003c/strong\u003e). While both groups have small numbers of deletion variants, individuals with low HbF level had fewer deletions than those with high HbF level.\u003c/p\u003e\n\u003cp\u003eBased on Exome Aggregation Consortium (ExAC) database of pathogenic mutation [25], we found no significant difference in the number of pathogenic variants in both SCD patients with high or low HbF levels in genes (\u003cem\u003eBCL11A\u003c/em\u003e), proto-oncogene, \u003cem\u003etranscription factor\u003c/em\u003e (\u003cem\u003eMYB\u003c/em\u003e), \u003cem\u003eHomeobox A9\u003c/em\u003e (\u003cem\u003eHOXA9\u003c/em\u003e), \u003cem\u003ehemoglobin subunit gamma 2\u003c/em\u003e (\u003cem\u003eHBG2\u003c/em\u003e), \u003cem\u003eKruppel like factor 1\u003c/em\u003e (\u003cem\u003eKLF1\u003c/em\u003e), \u003cem\u003ezinc finger and BTB domain containing 7A\u003c/em\u003e (\u003cem\u003eZBTB7A\u003c/em\u003e) in chromosomes 2, 6, 7, 11, 12 and 19, respectively. Overall, our targeted next generation sequencing of HbF associated genetic loci identified a disproportional number of loci with a few variants, particularly deletions, present in patients with high levels of HbF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiological pathways and processes associated with genes with high mutational burdens\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent roles of the identified candidate in silico mutant genes (Supplementary File: \u003cstrong\u003eTable S1, Figure 2\u003c/strong\u003e) or our initial targeted nine genes are known in Sickle Cell disease. However, how these genes interact with others at the systems level is currently unknown in various populations of African SCD patients. As described in the method section, using the set of all interactive genes including our identified mutant genes and the rest of targeted genes may contribute in identifying potential Sickle Cell-specific pathways in which modifier and mutant genes participate together in conferring variation in Sickle Cell Disease severity. The identified Protein-Protein Interaction (PPI) sub-network formed from 2 genes (\u003cstrong\u003eFigure 3A\u003c/strong\u003e) showed an enrichment of rare variants with deleterious effects was enriched for the\u003cem\u003e PRC2 complex \u003c/em\u003ewhich influence long-term gene silencing through modification of histone tails (P = 0.000004; \u003cstrong\u003eFigure 3B\u003c/strong\u003e), and is highly associated with or involved in the \u003cem\u003eTP-dependent chromatin remodeling\u003c/em\u003e (P = 1.6e-12, \u003cstrong\u003eFigure 3B\u003c/strong\u003e) biological process, nominally associated with \u003cem\u003epallor\u003c/em\u003e (P = 0.0014, \u003cstrong\u003eFigure 3B\u003c/strong\u003e). \u003cem\u003eCHD4\u003c/em\u003e and \u003cem\u003eMBD3\u003c/em\u003e were found to be the most important genes (hubs) of sub-network (\u003cstrong\u003eFigure 4A\u003c/strong\u003e), which are nominally associated with the B cell survival pathway (P = 0.018, \u003cstrong\u003eFigure 4B\u003c/strong\u003e), known to be implicated in the \u003cem\u003eATP-dependent chromatin re-modeling\u003c/em\u003e biological process (P = 6e-15, \u003cstrong\u003eFigure 4B\u003c/strong\u003e) and associated with \u003cem\u003epolycythemia disorder\u003c/em\u003e (P = 0.0001, \u003cstrong\u003eFigure 4B\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study in Africa to conduct targeted next generation sequencing to investigate genetic modifiers and pathways associated with extreme fetal hemoglobin (HbF) in individuals with SCD. Most of the loci (SNPs) that have been found to associate with HbF by GWAS only show possible associations with variants covered by the array chip used. The approach taken in this study was to perform in-depth sequencing around previously identified loci to descriptively compare, in detail, discovered polymorphisms between individuals with extreme HbF levels. We have identified single nucleotide polymorphisms (SNPs), insertions (IN) and deletions (DEL) across 8 targeted regions in chromosomes 2, 6, 7, 11, 12 and 19. We found differing types of polymorphisms, including SNPs and INDELS between individuals with low HbF versus those with high HbF, suggesting potential modifier effect. Interestingly, key discovered variants, together with previously identified variants, are enriched in biological pathways that underlie the HbF regulation.\u003c/p\u003e\n\u003cp\u003eIt is worth also noting that possible structural variants in these patient groups may make the sequencing off between the two groups. Furthermore, current challenges, including (1) limitation of variant calling tools in African data\u0026nbsp;[57], (2) sequencing errors and structural variants in African data\u0026nbsp;[58] and (3) under-representation of African samples in the current reference genome\u0026nbsp;[58, 59], may contribute to the observed difference in variants discovered in both high and low HbF level in individuals with SCD. We found more deletions in individuals with high HbF than those with low HbF levels indicating their role in HbF synthesis pathways. A number of significant deletions have been reported before, particularly in the globin cluster\u0026nbsp;[60, 61, 62]. In this study, we have identified additional potential deletions across the targeted regions (\u003cstrong\u003eTable 2\u003c/strong\u003e). We observed more insertions in individuals with high HbF than in those with low HbF. However, frameshift deletions were more prevalent in individuals with high HbF, while frameshift insertions were more prevalent in individuals with low HbF. Frameshift deletions and insertion may lead to abnormal proteins due to shorter or longer sequences, respectively.\u003c/p\u003e\n\u003cp\u003eWe also looked at variants located at untranslated regions (UTR) both at 3\u0026rsquo; and 5\u0026rsquo; ends which are involved differently in regulation of gene expression. Interestingly, in individuals with high HbF levels, variants in the 5\u0026rsquo;UTR were more prevalent as opposed to more variants in the 3\u0026rsquo;UTR in individuals with low HbF levels. Molecular mechanisms of the 5\u0026rsquo;UTR include regulating translation of main coding sequences while the 3\u0026rsquo;UTR contain binding sites for microRNA (miRNA) which takes part in the timing and rate of translation of the corresponding mRNA. Hence the difference in variants in these two regions between individuals with high HbF versus those with low HbF is notable and may contribute differently in the regulation of HbF synthesis.\u003c/p\u003e\n\u003cp\u003eWe looked specifically at non-synonymous mutations and found that out of the eight targets, six were found to have mutations with functional impact. Of interest, the genes \u003cem\u003eCHD4\u003c/em\u003e and \u003cem\u003eMBD3\u003c/em\u003e, functionally interacting in the same sub-network (see \u003cstrong\u003eFigure 3\u003c/strong\u003e), had more pathogenic mutations in individuals with low HbF levels than those with high HbF. \u003cem\u003eCHD4\u003c/em\u003e is a chromatin organization modifier which confers the chromatin remodeling function of the NuRD complex. \u003cem\u003eCHD4\u003c/em\u003e has been reported to repress \u003cem\u003e\u0026gamma;-globin\u003c/em\u003e gene expression in mice\u0026nbsp;[63, 64]. Similarly, \u003cem\u003eMBD3\u003c/em\u003e operates as a NuRD complex and is associated with the transcription factors GATA-1 and FOG-1, which directly regulate genes within the \u0026beta;-globin locus.\u003c/p\u003e\n\u003cp\u003eThe human protein-protein interaction (PPI) (\u003cstrong\u003eFigure 3A\u003c/strong\u003e) for CHD4 and MBD3 proteins indicates that they are essential to system survival and hence their biological functions tend to be evolutionary conserved\u0026nbsp;[63]. Thus, in presence of non-synonymous mutations, it is expected that individual components (proteins and interactions) in the system must adapt to a changing environment while maintaining the system\u0026rsquo;s primary function. In this study, we observed that, to maintain its robustness while sustaining its function under fluctuating environmental conditions, the system possibly triggers different mechanisms. This ensures that the network retains the modularity degree in order to provide a selective advantage for the host system by conserving and/or gaining useful functional interactions within the network to ensure an increase of HbF levels. As an illustration, \u003cem\u003eCHD4\u003c/em\u003e, as well as \u003cem\u003eMBD3\u003c/em\u003e, indirectly interact with \u003cem\u003eKLF1\u003c/em\u003e and \u003cem\u003eMYB\u003c/em\u003e, which are potent activators of \u003cem\u003eBCL11A\u003c/em\u003e. \u003cem\u003eCHD4\u003c/em\u003e is believed to exert its gamma globin silencing effect by positively regulating the \u003cem\u003eBCL11A\u003c/em\u003e and \u003cem\u003eKLF1\u003c/em\u003e genes. In addition, \u003cem\u003eBCL11A\u003c/em\u003e and \u003cem\u003eMYB\u003c/em\u003e are known to be involved in \u0026gamma;-globin gene regulation, leading to either elevation or reduction of HbF levels\u0026nbsp;[64]. The difference in frequency of non-synonymous mutations in the individuals with high HbF levels versus those with low levels reflect different interactions within this network and the resulting levels of HbF.\u003c/p\u003e\n\u003cp\u003eThough these post-analysis results are consistent with the literature and are biologically relevant, it is worth noting that, due to relatively high noise related to high-throughput data or experiments from which interactions are inferred, the protein-protein interaction network used may contain incorrectly classified interactions, i.e., failing to detect interactions (false negatives) or wrongly identifying some other interactions (false positives). This suggests these results still need to be validated experimentally. In this study, we minimized the likelihood of incorrectly classified interaction computationally by: (1) using a data integration model, combining information from multiple interacting data sources into one unified network, and (2) applying a strict interaction reliability or confidence score cutoff. These techniques are expected to significantly reduce the false negative and positive rate of the network produced, leading to a PPI network of high confidence interactions with an increased coverage [65].\u003c/p\u003e\n\u003cp\u003eGiven our study design, we did not perform genetics differentiation tests or statistical tests of differences in minor allele frequencies or genotype counts. Instead, we have aimed at descriptively characterizing the proportion of variants between the low/high HbF from high confident variants calling, compare the count of pathogenic variants between the groups and identify potential Sickle Cell-specific pathways in which modifier and mutant genes participate in conferring variation in Sickle Cell severity. Importantly, our current study suggests (1) a difference in the overall genetic variation between Sickle Cell patients with high and low HbF level and, (2) biological pathways, including the\u003cem\u003e PRC2 complex \u003c/em\u003ewhich sets long-term gene silencing through modification of histone tails (P = 0.000004; \u003cstrong\u003eFigure 3B\u003c/strong\u003e), hemoglobin\u0026rsquo;s Chaperone (P = 0.001, \u003cstrong\u003eFigure 4B\u003c/strong\u003e) and B-cell Survival (P = 0.018, \u003cstrong\u003eFigure 4B\u003c/strong\u003e). These identified pathways may harbour potential interactive Sickle Cell-specific genes including modifier, mutant and other genes (\u003cstrong\u003eFigures 3-4\u003c/strong\u003e) in conferring variation in severity among individuals with SCD. This work has focused on the importance of studying both genetic and epigenetic pathways in HbF regulation. Our findings suggest an in-depth whole genome sequencing study to fully characterize modifier genes implicated in the variation of SCD severity. This approach may contribute to future development of interventions for SCD, including drugs and gene therapy. Finally to note is, the modest sample size limited the expected statistical power, which could yield false positive associations and missed others. However, different results obtained provide a strong hypothesis for future studies. With a larger sample size, it would be possible to perform genetics differentiation tests or statistical tests of differences in minor allele frequencies or genotype counts and possibly identify additional essential variants and biological pathways associated with extreme HbF levels in SCD using the model set by this study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study has shown that the analysis of genetic modifiers associated with HbF in SCD patients can elucidate genetic factors underlying extreme (low or high) HbF levels in these patients. The study has identified frameshift deletion in SCD patients with high HbF levels and frameshift insertions in both CHD4 and MBD3 for those with low HbF, and some of these insertions are associated with the SCD pathogenesis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch5\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eThis study was performed in accordance with the Declaration of Helsinki and with the approval of the Muhimbili University Research and Publications Committee (MU/RP/AEC/VOLX1/33 and 2017-03-06/AEC/Vol X11/65). Informed and written consent was obtained from all patients that were all adult participants (\u0026gt;18 years). Informed and written consent was requested from parents or guardians for all minor participants (\u0026le;18 years) with an oral assent for children older than 7 years.\u003c/p\u003e\n\u003ch5\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditional supporting information can be found in the supplementary file at the end of the article.\u003c/p\u003e\n\u003ch5\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003ch5\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eThis work was supported by Wellcome Trust (Grant no: 095009, 093727, 080025 \u0026amp; 084538) and Fogarty Global Health Fellowship sponsored by the National Institutes of Health (NIH).\u003c/p\u003e\n\u003ch5\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eSN, HK, SM and JBM designed the study, JAM collected data, MZ, LM, RS, GKM and EC processed and analyzed the data. All authors contributed to the drafts of the manuscript, GKM and SN finalized the manuscript.\u003c/p\u003e\n\u003ch5\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/h5\u003e\n\u003cp\u003eThe authors thank the patients and staff of Muhimbili National Hospital, Muhimbili University of Health and Allied Sciences, Tanzania and the Sickle Cell Program.\u003c/p\u003e\n\u003cp\u003eThe authors extend special gratitude to Dr Barnaby Clark (PhD) who was Principal Clinical Scientist at King\u0026rsquo;s College Hospital. Dr Clark shared the next generation sequencing panel that was customized and adopted for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Biological Sciences, Dar es Salaam University College of Education, Dar es Salaam, Tanzania. \u003csup\u003e2\u003c/sup\u003eSickle Cell Program, Department of Hematology and Blood Transfusion, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania. \u003csup\u003e3\u003c/sup\u003eDepartment of Biotechnology Laboratory, Kilimanjaro Clinical Research Institute, Kilimanjaro, Tanzania. \u003csup\u003e4 \u003c/sup\u003eDepartment of Molecular Hematology, King\u0026rsquo;s College of London, London, UK. \u003csup\u003e5\u003c/sup\u003eDepartment of Pathology, Division of Human Genetics, University of Cape Town, IDM, Cape Town, South Africa. \u003csup\u003e6\u003c/sup\u003eDepartment of Biomedical Sciences, Computational Biology Division, University of Cape Town, South Africa. \u003csup\u003e7\u003c/sup\u003eAfrican Institute for Mathematical Sciences, Muizenberg 7945, Cape Town, South Africa. \u003csup\u003e8\u003c/sup\u003eDepartment of Pharmaceutical Microbiology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWeatherall D, Akinyanju O, Fucharoen S, Olivieri N, Musgrove P. 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New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47:D590-D595. doi: 10.1093/nar/gky962.\u003c/li\u003e\n\u003cli\u003eMi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 2019;47(D1):D419\u0026ndash;D426. doi: 10.1093/nar/gky1038\u003c/li\u003e\n\u003cli\u003eNishimura D. BioCarta. Biotech Softw. Internet Rep. 2001; 2:117\u0026ndash;120. doi: 10.1089/152791601750294344.\u003c/li\u003e\n\u003cli\u003eFabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018; 46(D1):D649-D655. doi: 10.1093/nar/gkx1132.\u003c/li\u003e\n\u003cli\u003eThe Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res. 2019; 47(D1):D330-D338. doi: 10.1093/nar/gky1055.\u003c/li\u003e\n\u003cli\u003eKarczewski KJ, Weisburd B, Thomas B, Solomonson M, Ruderfer DM, Kavanagh D, et al. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res. 2017;45:D840\u0026ndash;D845. doi:10.1093/nar/gkw971.\u003c/li\u003e\n\u003cli\u003ePirooznia M, Kramer M, Parla J, Goes FS, Potash JB, McCombie WR, Zandi PP. Validation and assessment of variant calling pipelines for next-generation sequencing. Human genomics 2014;8(1):14.\u003c/li\u003e\n\u003cli\u003eTeo YY, Small KS, Kwiatkowski DP. Methodological challenges of genome-wide association analysis in Africa. Nature Reviews Genetics 2010;11(2):149.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733-745. doi:10.1093/nar/gkv1189.\u003c/li\u003e\n\u003cli\u003eChalaow N, Thein SL, Viprakasit V. The 12.6 kb-deletion in the \u0026beta;-globin gene cluster is the known Thai/Vietnamese (\u0026delta;\u0026beta;)0-thalassemia commonly found in Southeast Asia. Haematologica 2013;98:e117\u0026ndash;e118. doi:10.3324/haematol.2013.090613.\u003c/li\u003e\n\u003cli\u003eHamid M, Nejad LD, Shariati G, Galehdari H, Saberi A, Mohammadi-Anaei M, et al. The First Report of a 290-bp Deletion in \u0026beta;-Globin Gene in the South of Iran. Iranian Biomedical Journal 2017;21(2):126\u0026ndash;128. doi:10.18869/acadpub.ibj.21.2.126.\u003c/li\u003e\n\u003cli\u003eThein SL, Craig JE. Genetics of Hb F/F cell variance in adults and heterocellular hereditary persistence of fetal hemoglobin. Hemoglobin 1998;22:401\u0026ndash;414.\u003c/li\u003e\n\u003cli\u003eAkinola RO, Mazandu GK, Mulder NJ. A Quantitative Approach to Analyzing Genome Reductive Evolution Using Protein\u0026ndash;Protein Interaction Networks: A Case Study of Mycobacterium leprae. Front Genet 2016;7:39. doi:10.3389/fgene.2016.00039.\u003c/li\u003e\n\u003cli\u003eJiang J, Best S, Menzel S, Silver N, Lai MI, Surdulescu GL, et al. cMYB is involved in the regulation of fetal hemoglobin production in adults. Blood 2006;108:1077\u0026ndash;1083. doi:10.1182/blood-2006-01-008912.\u003c/li\u003e\n\u003cli\u003eMazandu GK, Mulder NJ. Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification. Advances in Bioinformatics 2011, 2011(Article ID 801478), 14 pages. Doi:10.1155/2011/801478.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 14px; color: rgb(0, 0, 0);\"\u003eTable 1.\u003c/span\u003e\u003c/strong\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 14px;\"\u003e\u0026nbsp;Characteristics of Tanzanian individuals sickle cell disease (SCD) with extreme fetal hemoglobin levels.\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width:256.6pt;margin-left:61.7pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:62.8pt;border:solid #000001 1.0pt;border-right: none;background:#DDDDDD;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:94.3pt;border:solid #000001 1.0pt;border-right: none;background:#DDDDDD;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003eHigh HbF\u0026nbsp;\u003cspan style=\"font-size: 16px; line-height: 200%;\"\u003e\u0026ge;\u003c/span\u003e\u0026nbsp;7.7%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:99.5pt;border:solid #000001 1.0pt;background:#DDDDDD;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003eLow HbF\u0026nbsp;\u003cspan style=\"font-size: 16px; line-height: 200%;\"\u003e\u0026le;\u003c/span\u003e\u0026nbsp;2.5%\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:62.8pt;border-top:none;border-left:solid #000001 1.0pt;border-bottom:solid #000001 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003eN\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:94.3pt;border-top:none;border-left:solid #000001 1.0pt;border-bottom:solid #000001 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:99.5pt;border:solid #000001 1.0pt;border-top:none;background:white;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e5\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:62.8pt;border-top:none;border-left:solid #000001 1.0pt;border-bottom:solid #000001 1.0pt;border-right:none;background:#EEEEEE;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003eAge range (Years)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:94.3pt;border-top:none;border-left:solid #000001 1.0pt;border-bottom:solid #000001 1.0pt;border-right:none;background:#EEEEEE;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e5 - 19\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:99.5pt;border:solid #000001 1.0pt;border-top:none;background:#EEEEEE;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e8 - 21\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:62.8pt;border-top:none;border-left:solid #000001 1.0pt;border-bottom:solid #000001 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003eHbF (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:94.3pt;border-top:none;border-left:solid #000001 1.0pt;border-bottom:solid #000001 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e15-32\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:99.5pt;border:solid #000001 1.0pt;border-top:none;background:white;padding:0in 5.4pt 0in 2.15pt;height:24.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e0.3-2.2\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:normal;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 14px;\"\u003eTable 2.\u003c/span\u003e\u003c/strong\u003e\u003cspan style=\"font-size: 14px;\"\u003e\u0026nbsp;Characterization of polymorphisms within mutant and modifiers genes in SCA patients from Tanzania. Details of gene variants can be found in Supplementary File: \u003cstrong\u003eTable S1\u003c/strong\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width:436.1pt;margin-left:12.75pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" style=\"width: 436.05pt;border-top: 1.5pt solid rgb(0, 0, 1);border-left: none;border-bottom: 1.5pt solid rgb(0, 0, 1);border-right: none;background: white;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:center;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e(High; low HbF level)\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:45.35pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003eGENE\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:75.5pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#Polymorphisms\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.85pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#MNP\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.35pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#SNPs\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.2pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#Deletion\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:.75in;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#Insertion\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:58.25pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#Pathogenic\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.05pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#Benign\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.5pt;border:none;border-bottom:solid #000001 1.5pt;background:#DDDDDD;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e#USig\u003csup\u003e*\u003c/sup\u003e\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:45.35pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003ePGLYRP1\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:75.5pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e4; 4\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.85pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e0; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.35pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e4; 4\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.2pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e0; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:.75in;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e0; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:58.25pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e0; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.05pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e1; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.5pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e3;4\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:45.35pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003eZBTB7A\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:75.5pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e13; 11\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.85pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e0; 1\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.35pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e10; 9\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.2pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e0; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:.75in;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e1; 1\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:58.25pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e0; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.05pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e2; 2\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.5pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;height:16.1pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e11;9\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:45.35pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cem\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003eCHD4\u003c/span\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:75.5pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e25; 32\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.85pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e3; 3\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.35pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e19; 27\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:49.2pt;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e1; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:.75in;border:none;border-bottom:solid #000001 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; 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line-height: 200%;\"\u003e0; 0\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:45.05pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e1; 1\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.5pt;border:none;border-bottom:solid #000001 1.0pt;background:#EEEEEE;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;'\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%;\"\u003e8; 9\u003c/span\u003e\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv style='margin-top:0in;margin-right:-.3in;margin-bottom:10.0pt;margin-left:0in;line-height:115%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;border:none;border-bottom:solid #000001 1.0pt;padding:0in 0in 0in 0in;'\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:10.0pt;margin-left:0in;line-height:200%;font-size:13px;font-family:\"Calibri\",sans-serif;color:#00000A;text-align:justify;border:none;padding:0in;'\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12px; line-height: 200%; color: rgb(0, 0, 0);\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Abbreviations: USig\u003csup\u003e*\u0026nbsp;\u003c/sup\u003eis the number variant with uncertain significance of pathogenicity.\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgtc","sideBox":"Learn more about [BMC Medical Genetics](http://bmcmedgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgtc/default.aspx","title":"BMC Medical Genetics","twitterHandle":"BMC_series","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sickle cell disease; genetic disorder; fetal hemoglobin; hemoglobinopathy; tanzania","lastPublishedDoi":"10.21203/rs.2.22041/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.22041/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Sickle cell disease (SCD) is a blood disorder caused by a point mutation on the beta globin gene resulting in the synthesis of abnormal hemoglobin. Fetal hemoglobin (HbF) reduces disease severity, but the levels vary from one individual to another. Most research has focused on common variants which differ across populations and hence do not fully account for HbF variation. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We investigated rare and common genetic variants that influence HbF levels in 14 SCD patients to elucidate variants and pathways in SCD patients with extreme HbF levels (≥7.7% for high HbF) and (≤2.5% for low HbF) in Tanzania. We performed targeted next generation sequencing (Illumina_Miseq) covering exonic and other significant fetal hemoglobin-associated loci, including \u003cstrong\u003e\u003cem\u003eBCL11A\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eMYB\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eHOXA9\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eHBB\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eHBG1\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eHBG2\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eCHD4\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eKLF1\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eMBD3\u003c/em\u003e\u003c/strong\u003e, \u003cstrong\u003e\u003cem\u003eZBTB7A\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003ePGLYRP1\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Results revealed a range of genetic variants, including bi-allelic and multi-allelic SNPs, frameshift insertions and deletions, some of which have functional importance. Notably, there were significantly more deletions in individuals with high HbF levels (11% vs 0.9%). We identified deletions with high HbF levels and frameshift insertions in individuals with low HbF. \u003cstrong\u003e\u003cem\u003eCHD4\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eMBD3\u003c/em\u003e\u003c/strong\u003e genes, interacting in the same sub-network, were identified to have a significant number of pathogenic or non-synonymous mutations in individuals with low HbF levels, suggesting an important role of epigenetic pathways in the regulation of HbF synthesis. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e This study provides new insights in selecting essential variants and identifying potential biological pathways associated with extreme HbF levels in SCD using multiple genomic variants associated with HbF in SCD.\u003c/p\u003e","manuscriptTitle":"Identifying genetic variants and pathways associated with extreme levels of fetal hemoglobin in Sickle Cell Disease in Tanzania.","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-05-20 18:14:34","doi":"10.21203/rs.2.22041/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-05-12T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-05-11T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-05-10T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-01-24T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgtc","sideBox":"Learn more about [BMC Medical Genetics](http://bmcmedgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgtc/default.aspx","title":"BMC Medical Genetics","twitterHandle":"BMC_series","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2020-04-14 13:57:45","doi":"10.21203/rs.2.22041/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-04-17T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-04-07T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-04-06T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-04-06T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgtc","sideBox":"Learn more about [BMC Medical Genetics](http://bmcmedgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgtc/default.aspx","title":"BMC Medical Genetics","twitterHandle":"BMC_series","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-01-28 20:40:34","doi":"10.21203/rs.2.22041/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-03-08T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-03-07T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept after discretionary revisions\nForm responses:\n---\n\nComments to Author:\n---\nBrief summary.\nThis paper discussed determinants of low and high fetal hemoglobin (HbF) levels in Tanzanian patients with sickle cell disease. Tanzania ranks fifth worldwide regarding the number of children born with SCD, estimated at 8,000-11,000 births annually, with 15-20% of the population are being carriers (HbAS). Without intervention, it is estimated that up to 50% of children with SCD will die before the age of 5 years. Despite being a monogenic condition, there is marked variability in individuals with this disorder, due in part to genetic and environmental determinants, with one these the level of HbF. The study aims to study genetic contributions to low and high HbF.\nFourteen patients were enrolled in the study, 9 with high HbF and 5 with low HbF, some were \u003c18 yrs. Targeted sequencing of covered exons and non-coding regions around validated and candidate fetal hemoglobin-influencing loci, including BCL11A, MYB, HOXA9, HBB, HBG1, KLF1, MBD3, ZBTB7A, and PGLYRP1, previously established at King's College London.\nA total of 873 and 1196 highly confident variants were determined in SCD patients with high and low HbF levels. There were significantly more deletions in individuals with high HbF levels (11% vs\n0.9%). Frameshift deletions in individuals with high HbF levels and\nframeshift insertions in individuals with low HbF were identified. CHD4 and MBD3 genes, interacting in the same sub-network, were identified to have a significant number of pathogenic or\nnon-synonymous mutations in individuals with low HbF levels, suggesting an important role of epigenetic pathways in the regulation of HbF synthesis.\nConclusion: This study provides new insights in selecting essential variants and in identifying potential biological pathways associated with extreme HbF levels in SCD.\n\nReviewer's comments:\nAll in all, the authors achieved what they set out to do, studying a population of patients with SCD, an important monogenic disease, understudied in Africa and in Tanzania. Mortality in SCD, as the authors mention, is high in Tanzania, possibly leading therefore to selection of survivors, if adult are studied. In the case, the number of subjects is low, and the study can qualify mostly as exploratory. For a larger study, a birth cohort, identified through newborn screening would be preferred, as biases removed. Nevertheless, this is a well considered study, well executed, identifying determinants of high and low HbF that can be further explored in a larger population, and compared between various SCD populations, even within Africa.\nThere are a few grammatical errors and typos, that should be addressed. Also, possible bias due to low number of enrolled patients should be discussed briefly.\n\nEg.\np3, 43: We identified frameshift deletion in individuals, must read deletions.\np4, 50: SCD affects multiple organs and hence is a multisystem disease. This is a bit clumsy...\np7, 39: explain the cohort study in a few sentences\np11, 36: This to identify potential biological pathways in which these genes participate. This sentence has no verb.\np14, 40: Although African-specific reported pathogenic variants are underrepresented in current databases of pathogenic variants [56]. Probably should read: Because etc.\np16, 43: particularly in African population of Sickle cell patients. Prob should read: various populations of African SCD patients.\n\n\n\n\n* Are the methods appropriate and well described?: **Yes**\n* Does the work include the necessary controls?: **Yes**\n* Are the conclusions drawn adequately supported by the data shown?: **Yes**\n* Are you able to assess any statistics in the manuscript or would you recommend an additional statistical review?: **I am able to assess the statistics**\n* Quality of written English: **Needs some language corrections before being published**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n"},{"type":"editorInvitedReview","content":"","date":"2020-02-08T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after discretionary revisions\nForm responses:\n---\n\nComments to Author:\n---\nI assume this is a revised version since it is coded R2\n\nI have no specific. However, I think the authors should show caution in the use of predicted interaction networks. These generate predictions which will need experimental testing in the future. The authors should make a clear comment on this.* Are the methods appropriate and well described?: **Yes**\n* Does the work include the necessary controls?: **Yes**\n* Are the conclusions drawn adequately supported by the data shown?: **No**\n* Are you able to assess any statistics in the manuscript or would you recommend an additional statistical review?: **I recommend additional statistical review**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **'I declare that I have no competing interests**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n"},{"type":"reviewersInvited","content":"","date":"2020-02-07T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-02-07T12:00:00+00:00","index":1,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-02-07T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorAssigned","content":"","date":"2019-12-16T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2019-12-15T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2019-12-15T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgtc","sideBox":"Learn more about [BMC Medical Genetics](http://bmcmedgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgtc/default.aspx","title":"BMC Medical Genetics","twitterHandle":"BMC_series","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b71c3c33-12cf-45f5-b85b-054781d0e3ec","owner":[],"postedDate":"May 20th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":83181,"name":"Medical Genetics"}],"tags":[],"updatedAt":"2021-07-22T20:05:19+00:00","versionOfRecord":{"articleIdentity":"rs-12622","link":"https://doi.org/10.1186/s12881-020-01059-1","journal":{"identity":"bmc-medical-genetics","isVorOnly":false,"title":"BMC Medical Genetics"},"publishedOn":"2020-06-05 20:05:19","publishedOnDateReadable":"June 5th, 2020"},"versionCreatedAt":"2020-05-20 18:14:34","video":"","vorDoi":"10.1186/s12881-020-01059-1","vorDoiUrl":"https://doi.org/10.1186/s12881-020-01059-1","workflowStages":[]},"version":"v3","identity":"rs-12622","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-12622","version":["v3"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}