Genomic polymorphisms in sickle cell disease: implications for clinical diversity and treatment

Figures & data

Figure 1. Timeline of major scientific achievements relevant to the study of sickle cell disease.

HbF: Fetal hemoglobin; SCD: Sickle cell disease; SNP: Single nucelotide polymorphism.

Table 1. Comparison among different approaches to genomic study in human diseases.

Type of study	Main characteristics	Advantages	Limitations	Examples in sickle cell disease
Genetic-epidemiological studies (Linkage analysis: family-based - ‘pedigree’, sibling and twin-pair studies)	Defined by a heritable phenotype, uses a low number of genetic markers to define linkage disequilibrium	• Lower number of samples needed (tens)	• Low statistical power if involvement of multiple genes, better suited to monogenic diseases • Familial clustering due to environmental factors • Dependent on high penetrance of genetic mutation/polymorphism	Sickle cell disease and α-thalassemia
Candidate-gene based studies	Screening cases and controls for new mutations and variable SNPs in genes possibly influencing phenotype	• Possible more direct clinical application • Low-complexity technology needed (regular PCR, restriction enzymes or direct sequencing of low number of samples)	• Depends on previous knowledge of the pathogenesis of the disease • Multiple testing needed if several genes/proteins known to be involved in the disease pathogenesis • Multiple tests increase false-positive results • False-negative results in small samples and low relative risk • Large sample size needed depending on minor allele frequency and statistical power (hundreds to thousands)	UGT1A polymorphism and cholelithiasis in sickle cell disease
Genome-wide association studies (SNPs and copy-number variation arrays and Bayesian networks)	Matching and comparing DNA sequences of populational samples of cases and controls to millions of genetic variants previously described in a high-resolution genetic map	• Detection of variants in genes with lower genetic relative risk for disease • Detection of genes not pathogenetically related to a certain disease • Identification of noncoding regions associated with disease • Detection of dependency across different genes and chromosomes	• Need for specific data management of large quantities of information to avoid false-positive results • Larger sample sizes (than candidate gene-based studies) needed to detect genome-wide low relative risks, to account for high number of variants and to validate data (thousands to tens of thousands) • False-negative results if SNPs analyzed separately instead of simultaneously • Replication needed in populations with different ancestries (e.g., European vs African)	Fetal hemoglobin levels SNPs associated with sickle cell disease severity
Deep DNA sequencing (ChIP assays followed by massive sequencing)	Genome-scale view of protein–DNA interactions by hybridizing fragmented immunoprecipitated chromatin to a microarray (ChIP-chip) followed by direct sequencing (ChIP-seq)	• Characterization of whole genomes with resolution to detect single nucleotide differences with low amounts of DNA (10–50 ng in ChIP-seq)	• High cost • Lack of easy access to user-friendly computational platforms	Identification of transcription factors able to activate γ-globin gene expression in sickle cell disease

ChIP: Chromatin immunoprecipitation; SNP: Single nucleotide polymorphism.

Table 2. Main associations between genes and complications in sickle cell disease.

Main importance	Gene	Pain	ACS	Infection	Stroke	Leg ulcer	Priapism	AVN	PH	Bil	RF	Ref.
Cell adhesion	SELP				×							[116–118]
	ITGAV											[148]
	NRCAM		×									[77]
	PIK3CG		×									[77]
	VCAM1				×							[111,112]
Angiogenesis	TEK					×	×					[138,148]
Cell growth	ANXA2							×				[89]
	IGF1R			×								[100]
Inflammation	IL4R				×							[112]
	TNFA				×							[114,115]
	CCL5			×								[101]
Cholesterol metabolism	LDLR				×							[112]
Coagulation	F13A1						×					[148]
	HPA3	×										[86]
	ITGA2		×		×		×	×				[85]
	MTHFR							×^†				[80–84]
Immunity	HLA (several)			×	×							[94–97,110,113]
	MBL2	×		×								[75,76,98]
	MPO			×								[99]
Nitric oxide metabolism	GCH1	×										[74]
	NOS3		×									[79]
	KL		×			×	×	×	×			[77,89,137,143,148]
TGF-β/SMAD pathway	ACVRL1								×			[143]
	BMP6			×	×	×		×	×			[89,91,92,100,118,137,143]
	BMPR1A			×								[100]
	BMPR1B					×						[137]
	BMPR2								×			[143]
	MAP2K1					×						[137]
	MAP3K7					×						[137]
	SMAD1		×									[77]
	SMAD3		×	×								[1,77]
	SMAD6			×								[100]
	SMAD7		×			×						[77,137]
	SMAD9					×						[137]
	SMURF1					×						[137]
	TGFBR1										×	[135]
	TGFBR2				×	×		×				[92,116,137,148]
	TGFBR3		×	×	×	×	×	×				[92,100,116,118,137]
Bilirubin metabolism	UGT1A									×		[122–127]
Unknown	STARD13		×									[77]

^†Controversial reports.

ACS: Acute chest syndrome; AVN: Avascular necrosis; Bil: Biliary disease and bilirubin elevation; PH: Pulmonary hypertension; RF: Renal failure.

Table 3. Polymorphisms in genes involved in the TGF-β/SMAD/BMP pathway and in the klotho gene, and genotypes associated with subphenotypes in sickle cell disease.

Subphenotype	Gene	Polymorphism	Genotypes (higher→lower risk)	Patients (n)	Ref.
Lower glomerular filtration rate	TGFBR1	rs2240036	CC→CT→TT	1140	[105]
		rs4145993	TT→CT→CC
		rs17022863	GG→AG→AA
		rs1434549	TT→CT→CC
Leg ulcers	TGFBR2	rs1019856	G_→AA	759	[107]
	TGFBR3	rs2038931	TT→C_
	BMPR1B	rs1560909 rs7661539	AA→G_ T_→CC
	BMP6	rs270393	GG→C_
	KL	rs685417 rs516306	AG→GG CT→TT
	SMAD9	rs9576135	A_→GG
	SMAD7	rs736839^†	CC→T_
Pulmonary hypertension	BMP6	rs267192	CT→CC	111	[113]
	ACVRL1	rs3847859 rs706814	AA→AG→GG TT→AT→AA
	TGFBR3	rs10874940	TT→CT→CC
	BMPR2	rs35711585 rs17199249	NR
Infection	BMPR1A	rs6586039 hCV1663921	GG→A_ CC→T_	145	[73]
	BMP6	rs270387 rs267188 rs408505 rs449853	GG→AA AA→T_ TT→C_ TT→C_
	SMAD3	rs10518707	A_→GG
	SMAD6	rs5014202	C_→ TT
	TGFBR3	rs2765888 rs6662385^†	TT→CT TT→C_
Stroke	TGFBR2	rs1019856 rs876687 rs3773658	^‡	1398	[88]
	TGFBR3	rs1555889 rs284875 rs2038931 rs901917 rs2148322 rs2765888 rs2007686 rs284874	^‡
	BMP6	rs199205 rs267196 rs267201 rs408505 rs449853 rs1225934 rs270387 rs270393 rs267192	^‡
Priapism	TGFBR3	rs7526590	TT→AT→AA	199	[118]
	KL	rs2249358 rs211239 rs211234/rs211239	C_→TT G_→AA Haplotype G-G	768	[119]^§
Acute chest syndrome	TGFBR3	rs284157	NR	1442	[51]^¶
	SMAD3	NR	NR
	SMAD7	rs736839^†	NR
Acute chest syndrome in patients aged over 5 years	SMAD1	rs2068991	NR	1442	[51]^¶
	KL	rs2149860 rs656525	NR
Avascular necrosis	TGFBR2	rs934328	CC→T_	514	[63]
	TGFBR3	rs284157	A_→GG	792	[63]
	BMP6	rs3812163 rs270393 rs267196 rs449853 rs1225934	TT→A_ GG→C_ TT→AT TT→C_ CC→AC	244 539 673 764 766	[65,63]
	KL	rs480780 rs211235 rs2149860 rs685417 rs516306 rs565587 rs211239 rs211234 rs499091 rs576404	AC→CC C_→AA G_→AA AG→GG T_→CC AG→AA A_→G G _→AA A_→GG C_→AA	124 399 803 651 803 557 782 767 730 804	[63]

Single nucelotide polymorphisms occurring more than once in the table are bold.

^†Single nucleotide polymorphism is adjacent to the gene.

^‡Genotype effect requires simultaneous consideration of other single nucleotide polymorphisms (see [88]).

^§Association not replicated in [118].

^¶Data reported in abstract form only.

NR: Not reported in the original publication.

Elliott L, Ashley-Koch AE, De Castro L et al. Genetic polymorphisms associated with priapism in sickle cell disease. Br. J. Haematol.137(3), 262–267 (2007).

 PubMed Web of Science ®Google Scholar

Martinez-Castaldi C, Nolan VG, Baldwin CT et al. Association of genetic polymorphisms in the TGF-β pathway with the acute chest syndrome of sickle cell anemia. Blood118, 666a (2007).

 Google Scholar

Baldwin C, Nolan VG, Wyszynski DF et al. Association of klotho, bone morphogenic protein 6 and annexin A2 polymorphisms with sickle cell osteonecrosis. Blood106(1), 372–375 (2005).

 PubMed Web of Science ®Google Scholar

Adewoye AH, Nolan VG, Ma Q et al. Association of polymorphisms of IGF1R and genes in the transforming growth factor-β/bone morphogenetic protein pathway with bacteremia in sickle cell anemia. Clin. Infect. Dis.43, 593–598 (2006).

 PubMed Web of Science ®Google Scholar

Hoppe C, Klitz W, Cheng S et al. Gene interactions and stroke risk in children with sickle cell anemia. Blood103(6), 2391–2396 (2004).

 PubMed Web of Science ®Google Scholar

Dossou-Yovo OP, Zaccaria I, Benkerrou M et al. Effects of RANTES and MBL2 gene polymorphisms in sickle cell disease clinical outcomes: association of the g.In1.1T>C RANTES variant with protection against infections. Am. J. Hematol.84(6), 378–380 (2009).

 PubMed Web of Science ®Google Scholar

Al-Subaie AM, Fawaz NA, Mahdi N et al. Human platelet alloantigens (HPA) 1, HPA2, HPA3, HPA4, and HPA5 polymorphisms in sickle cell anemia patients with vaso-occlusive crisis. Eur. J. Haematol.83(6), 579–585 (2009).

 PubMedGoogle Scholar

Castro V, Alberto FL, Costa RN et al. Polymorphism of the human platelet antigen-5 system is a risk factor for occlusive vascular complications in patients with sickle cell anemia. Vox Sanguinis87, 118–123 (2004).

 PubMed Web of Science ®Google Scholar

Costa RNP, Conran N, Albuquerque DM, Soares PH, Saad STO, Costa FF. Association of the G-463A myeloperoxidase polymorphism with infection in sickle cell anemia. Haematologica90, 977–979 (2005).

 PubMedGoogle Scholar

Taylor JG, Belfer I, Desai K et al. A GCH1 haplotype associated with susceptibility to vasoocclusive pain and impaired vascular function in sickle cell anemia. Blood114, 575 (2009).

 Google Scholar

Sharan K, Surrey S, Ballas S et al. Association of T-786C eNOS gene polymorphism with increased susceptibility to acute chest syndrome in females with sickle cell disease. Br. J. Haematol.124(2), 240–243 (2004).

 PubMed Web of Science ®Google Scholar

Ashley-Koch AE, Elliott L, Kail ME et al. Identification of genetic polymorphisms associated with risk for pulmonary hypertension in sickle cell disease. Blood111(12), 5721–5726 (2008).

 PubMed Web of Science ®Google Scholar

Nolan VG, Adewoye A, Baldwin C et al. Sickle cell leg ulcers: associations with haemolysis and SNPs in Klotho, TEK and genes of the TGF-β/BMP pathway. Br. J. Haematol.133, 570–578 (2006).

 PubMed Web of Science ®Google Scholar

Nolan VG, Ma Q, Cohen HT et al. Estimated glomerular filtration rate in sickle cell anemia is associated with polymorphisms of bone morphogenetic protein receptor 1B. Am. J. Hematol.82, 179–184 (2007).

 PubMed Web of Science ®Google Scholar

Neonato MG, Guilloud-Bataille M, Beauvais P et al. Acute clinical events in 299 homozygous sickle cell patients living in France. French Study Group on Sickle Cell Disease. Eur. J. Haematol.65, 155–164 (2000).

 PubMed Web of Science ®Google Scholar

Ohene-Frempong K, Weiner SJ, Sleeper LA et al. Cerebrovascular accidents in sickle cell disease: rates and risk factors. Blood91, 288–294 (1998).

 PubMed Web of Science ®Google Scholar

Hoppe C, Klitz W, Noble J et al. Distinct HLA associations by stroke subtype in children with sickle cell anemia. Blood101(7), 2865–2869 (2003).

 PubMed Web of Science ®Google Scholar

Darbari DS, van Schaik RHN, Capparelli EV, Rana S, McCarter R, van den Anker J. UGT2B7 promoter variant -840G>A contributes to the variability in hepatic clearance of morphine in patients with sickle cell disease. Am. J. Hematol.83, 200–202 (2008).

 PubMed Web of Science ®Google Scholar

Milner PF, Kraus AP, Sebes JI et al. Sickle cell disease as a cause of osteonecrosis of the femoral head. N. Engl. J. Med.325(21), 1476–1481 (1991).

 PubMed Web of Science ®Google Scholar

Sebastiani P, Milton JN, Timofeev N et al. Genome-wide association study of stroke in sickle cell anemia. Blood114, 1528 (2009).

 PubMedGoogle Scholar

Barrett-Connor E. Cholelithiasis in sickle cell anemia. Am. J. Med.45, 889–898 (1968).

 PubMed Web of Science ®Google Scholar

Chang YPC, Maier-Redelsperger M, Smith KD et al. The relative importance of the X-linked FCP locus and β-globin haplotypes in determining hemoglobin F levels: a study of SS patients homozygous for βS haplotypes. Br. J. Haematol.96, 806–814 (1997).

 PubMed Web of Science ®Google Scholar

Dworkis D, Sebastiani P, Melista E et al. Fetal hemoglobin in sickle cell anemia: a novel method for high-resolution discovery of associated genomic copy number variations. Blood112, 2491 (2008).

 Google Scholar