Genetic Modifiers of Sickle Cell Anemia Phenotype in a Cohort of Angolan Children

Overview

Journal Genes (Basel)

Publisher MDPI

Date 2024 Apr 27

PMID 38674403

Authors

Catarina Ginete

Mariana Delgadinho

Brigida Santos

Armandina Miranda

Carina Silva

Paulo Guerreiro

Emile R Chimusa

Miguel Brito

Affiliations

Soon will be listed here.

Abstract

The aim of this study was to identify genetic markers in the Cluster; intergenic region; and , , , and genes associated with the heterogeneous phenotypes of Sickle Cell Anemia (SCA) using next-generation sequencing, as well as to assess their influence and prevalence in an Angolan population. Hematological, biochemical, and clinical data were considered to determine patients' severity phenotypes. Samples from 192 patients were sequenced, and 5,019,378 variants of high quality were registered. A catalog of candidate modifier genes that clustered in pathophysiological pathways important for SCA was generated, and candidate genes associated with increasing vaso-occlusive crises (VOC) and with lower fetal hemoglobin (HbF) were identified. These data support the polygenic view of the genetic architecture of SCA phenotypic variability. Two single nucleotide polymorphisms in the intronic region of 2q16.1, harboring the gene, are genome-wide and significantly associated with decreasing HbF. A set of variants was identified to nominally be associated with increasing VOC and are potential genetic modifiers harboring phenotypic variation among patients. To the best of our knowledge, this is the first investigation of clinical variation in SCA in Angola using a well-customized and targeted sequencing approach.

References

Kim S, Jhong J, Lee J, Koo J . Meta-analytic support vector machine for integrating multiple omics data. BioData Min. 2017; 10:2. PMC: 5270233. DOI: 10.1186/s13040-017-0126-8. View

Gurdasani D, Carstensen T, Tekola-Ayele F, Pagani L, Tachmazidou I, Hatzikotoulas K . The African Genome Variation Project shapes medical genetics in Africa. Nature. 2014; 517(7534):327-32. PMC: 4297536. DOI: 10.1038/nature13997. View

Zuberi K, Franz M, Rodriguez H, Montojo J, Lopes C, Bader G . GeneMANIA prediction server 2013 update. Nucleic Acids Res. 2013; 41(Web Server issue):W115-22. PMC: 3692113. DOI: 10.1093/nar/gkt533. View

Chang C, Chow C, Tellier L, Vattikuti S, Purcell S, Lee J . Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015; 4:7. PMC: 4342193. DOI: 10.1186/s13742-015-0047-8. View

Liu X, Jian X, Boerwinkle E . dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum Mutat. 2011; 32(8):894-9. PMC: 3145015. DOI: 10.1002/humu.21517. View

Wonkam A, Chimusa E, Mnika K, Pule G, Ngo Bitoungui V, Mulder N . Genetic modifiers of long-term survival in sickle cell anemia. Clin Transl Med. 2020; 10(4):e152. PMC: 7423184. DOI: 10.1002/ctm2.152. View

Shihab H, Gough J, Cooper D, Day I, Gaunt T . Predicting the functional consequences of cancer-associated amino acid substitutions. Bioinformatics. 2013; 29(12):1504-10. PMC: 3673218. DOI: 10.1093/bioinformatics/btt182. View

Dong C, Wei P, Jian X, Gibbs R, Boerwinkle E, Wang K . Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015; 24(8):2125-37. PMC: 4375422. DOI: 10.1093/hmg/ddu733. View

Lu Q, Hu Y, Sun J, Cheng Y, Cheung K, Zhao H . A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci Rep. 2015; 5:10576. PMC: 4444969. DOI: 10.1038/srep10576. View

10.

Seidman D, Shenoy S, Kim M, Babu R, Woods I, Dyer T . Rapid, Phase-free Detection of Long Identity-by-Descent Segments Enables Effective Relationship Classification. Am J Hum Genet. 2020; 106(4):453-466. PMC: 7118564. DOI: 10.1016/j.ajhg.2020.02.012. View

11.

Jagadeesh K, Wenger A, Berger M, Guturu H, Stenson P, Cooper D . M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat Genet. 2016; 48(12):1581-1586. DOI: 10.1038/ng.3703. View

12.

Chang A, Ginter Summarell C, Birdie P, Sheehan V . Genetic modifiers of severity in sickle cell disease. Clin Hemorheol Microcirc. 2018; 68(2-3):147-164. DOI: 10.3233/CH-189004. View

13.

Speed D, Kaphle A, Balding D . SNP-based heritability and selection analyses: Improved models and new results. Bioessays. 2022; 44(5):e2100170. DOI: 10.1002/bies.202100170. View

14.

Kato G, Piel F, Reid C, Gaston M, Ohene-Frempong K, Krishnamurti L . Sickle cell disease. Nat Rev Dis Primers. 2018; 4:18010. DOI: 10.1038/nrdp.2018.10. View

15.

Ionita-Laza I, McCallum K, Xu B, Buxbaum J . A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet. 2016; 48(2):214-20. PMC: 4731313. DOI: 10.1038/ng.3477. View

16.

Michalopoulos S . The Origins of Ethnolinguistic Diversity. Am Econ Rev. 2014; 102(4):1508-1539. PMC: 4172340. DOI: 10.1257/aer.102.4.1508. View

17.

Davydov E, Goode D, Sirota M, Cooper G, Sidow A, Batzoglou S . Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput Biol. 2010; 6(12):e1001025. PMC: 2996323. DOI: 10.1371/journal.pcbi.1001025. View

18.

Liu L, Pertsemlidis A, Ding L, Story M, Steinberg M, Sebastiani P . Original Research: A case-control genome-wide association study identifies genetic modifiers of fetal hemoglobin in sickle cell disease. Exp Biol Med (Maywood). 2016; 241(7):706-18. PMC: 4950389. DOI: 10.1177/1535370216642047. View

19.

Chun S, Fay J . Identification of deleterious mutations within three human genomes. Genome Res. 2009; 19(9):1553-61. PMC: 2752137. DOI: 10.1101/gr.092619.109. View

20.

Han B, Eskin E . Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am J Hum Genet. 2011; 88(5):586-98. PMC: 3146723. DOI: 10.1016/j.ajhg.2011.04.014. View