Reference
Complete the bibliographic reference for the article according to AJE format.

Hoppe C., Klitz W, Cheg S. et al. Gene interactions and stroke risk in children with sickle cell anemia. Blood. 2004 Mar 15;103(6):2391-6.

Category of HuGE information
Specify the types of information (from the list below) available in the article:

1. Prevalence of gene variant
2. Gene-disease association
3. Gene-environment interaction
4. Gene-gene interaction
5. Genetic test evaluation/monitoring

1. Prevalence of gene variants
2. Gene-disease association

Study hypotheses or purpose
The authors study hypotheses or main purpose for conducting the study

Gene(s)
Identification of the following:

• Gene name
• Chromosome location
• Gene product/function
• Alleles
• OMIM #

The following genes were found to be significantly associated with risk of stroke:

Gene name: IL4R *
Chromosome location: 16p12.1-p11.2
Gene product/function: Interleukin-4 plays a major role in immunoglobulin E (IgE) production. Its signal is conferred to effector cells through binding to the alpha chain of the IL4 receptor (IL4RA).
Alleles: 503
OMIM #: 147781

Gene name: TNF
Chromosome location: 6p21.3
Gene product/function: Tumor necrosis factor is a multifunctional proinflammatory cytokine, with effects on lipid metabolism, coagulation, insulin resistance, and endothelial function.
Alleles: 308 - TNFA promoter polymorphisms at position -308
OMIM #: 191160

Gene name: ADRB2
Gene product/function: 5q32-q34
Gene product/function: beta-adrenergic receptor; serotonin 5-HT-1A receptor Alleles: 27
OMIM #: 109690

Gene name: VCAM1
Chromosome location: 1p32-p31
Gene product/function: 1 Vascular cell adhesion molecule-1, a cell surface glycoprotein expressed by cytokine-activated endothelium, mediates the adhesion of monocytes and lymphocytes.
Alleles: (-1594)
OMIM #: 192225

Gene name: LDLR NcoI
Chromosome location: 19p13.2
Gene product/function: The low density lipoprotein receptor is a cell surface receptor that plays an important role in cholesterol homeostasis.
Alleles:
OMIM #: 606945

* combination also significant associated with stroke

Environmental factor(s)
Identification of the major environmental factors studied (infectious, chemical, physical, nutritional, and behavioral)

Stroke (Defined as MRI-documented cerebral infarction)

1. Case-control
2. Cohort 
3. Cross-sectional
4. Descriptive or case series
5. Clinical trial
6. Population screening

1. Disease case definition
2. Exclusion criteria
3. Gender
4. Race/ethnicity
5. Age
6. Time period
7. Geographic location
8. Number of participants (% of total eligible)

Disease case definition: Children with SCA (homozygous Hb S) and MRI-documented cerebral infarction (asymptomatic or symptomatic). This was further stratified into large-vessel stroke and small-vessel stroke.
Exclusion criteria: Children with MRI evidence of atrophy or isolated cerebral hemorrhage without evidence of preexisting infarction
Gender: not specified. Total group altogether was equally distributed amongst males and females (115 males, 115 females)
Race/ethnicity: Not specified
Age: Distribution not specified. Mean age of entire group= 8.4+ 1.7 years (median, 7.9 years; range 5.0-14.6 years) at time of baseline MRI
Time period: 13+ years; 1978-1988 phase 1; 1989-presnt phase 2; MRIs measured at 6 years of age and each subsequent 2 years.
Geographic location: United States , national multi-center study
Number of participants: LV =36, SV=35. Total cases 71.

1. Control selection criteria
2. Matching variables
3. Exclusion criteria
4. Gender
5. Race/ethnicity
6. Age
7. Time period
8. Geographic location
9. Number of participants (% of total eligible)

Control selection criteria: Children with sickle cell anemia from the CSSCD newborn cohort with a normal MRI at age of ten or older.
Matching variables: not specified
Exclusion criteria: not specified
Gender: not specified. Total group altogether was equally distributed amongst males and females (115 males, 115 females)
Race/ethnicity: not specified
Age: Distribution not specified. Mean age of entire group 8.4+ 1.7 years (median, 7.9 years; range 5.0-14.6 years) at time of baseline MRI
Time period: 13+ years; 1978-1988 phase 1; 1989-present phase 2; MRIs measured at 6 years of age and each subsequent 2 years.
Geographic location: United States , national multi-center study
Number of participants: 159

For study designs 2, 3, and 6, define the following if available:

• Cohort selection criteria
• Exclusion criteria
• Gender
• Race/ethnicity
• Age
• Time period
• Geographic location
• Number of participants

Cohort selection criteria: The CSSCD was a multi-institutional investigation of the natural history of sickle cell disease from birth to adulthood. It involved data collection at 23 institutions in a uniform standardized fashion on 3,800 patients. According to the other references cited, a special effort was made to include patients of all ages, mildly affected patients (to ensure that the study did not collect only a severe hospital-based population), and patients from rural areas.
Exclusion criteria: n/a
Gender: n/a
Race/ethnicity: n/a
Age: n/a
Time period: n/a
Geographic location: n/a
Number of participants: n/a

• Environmental factor
• Exposure assessment
• Exposure definition
• Number of participants with exposure data (% of total eligible)

• Gene
• DNA source
• Methodology
• Number of participants genotyped (% of total eligible) 

Gene: 104 polymorphisms on 65 genes were assessed.
DNA source: Not specified in this paper; methods cited from another paper cited suggest “ apheresis lymphocytes” were used.
Methodology: multilocus PCR-based assay
Number of participants genotyped: N (% of total eligible) Assumed to be all 230, but not specified in document

The markers are listed below. (See Table 1 from Hoppe et al.'s paper)

This information is summarized in Table 4 from Hoppe el al's paper, which follows.

1. Prevalence of gene variant
2. Gene-disease association
3. Gene-environment interaction
4. Gene-gene interaction
5. Genetic test evaluation/monitoring

Of the 104 variant sites examined, only 57 were sufficiently informative for statistical testing. The prevalence of the gene variants that were found significant via univariate analysis ranged from 0.13 (TNF(-308)A) to 0.39 (IL4R503P)

These variants were entered into a multivariate logistic model where either large vessel stroke or small vessel stroke was the outcome.

In the model for large vessel stroke, four variables were significantly associated with that outcome. These included IL4R 503P (OR: 2.50, pvalue 0.0006), HLA-A (OR=7.71, p=0.013), ADRB2 27E (OR=0.53,p=0.033), and TNF(-308)A (OR=0.52, p=0.048).

For the model for small vessel stroke the following genetic loci were significantly associated: VCAM1 (-1594)C (OR=1.98, p=0.002), HLA-DPB1 (OR=3.50, pvalue=0.002), HLA homozygosity (OR=1.58, p=0.023), and LDLR NcoI- (OR=0.53, p=0.002).

The researchers also looked for interactions between markers thought to be in similar physiologic pathways.

In their assessment for gene-gene interaction, they determined that in the population, that IL4R 503P and TNF G (-308) were associated in the LV stroke group.

This information is summarized in Table 4, which follows.

Risk of Combined IL4R/TNF Genotype with LV Stroke

The authors conclude that their results provide evidence for the involvement of multiple candidate genes that predispose children with SCA to stroke. In addition, since different alleles were observed to be associated with the two subclassifications of stroke, the authors suggest their findings demonstrate the possible existence of different pathogenetic mechanisms In this study, both single gene effects and gene-gene in tera ctions appear to influence the risk of specific vascular subtypes (LV and SV) of stroke.

More specifically, large vessel stroke appeared to be associated with four genetic variants. Two variants appeared to have an increased association IL4R 503P (OR: 2.50, pvalue 0.0006), HLA-A (OR=7.71, p=0.013) while two variants appeared to be protective: ADRB2 27E (OR=0.53,p=0.033), and TNF(-308)A(OR=0.52, p=0.048).

For the model for small vessel stroke the following genetic loci were significantly associated: VCAM1 (-1594)C (OR=1.98, p=0.002), HLA-DPB1 (OR=3.50, pvalue=0.002), HLA homozygosity (OR=1.58, p=0.023), and LDLRNcoI- (OR=0.53, p=0.002).

The researchers also indicated that the IL4R 503P and TNF G (-308) combination was significantly associated with LV stroke (OR=5.54, p=0.000).

This study attempted to investigate a large number of genes in a fairly small population and the role of chance explaining these findings was not discussed. The authors in their methods, did indicate that of the 104 polymorphisms from 65 candidate vascular genes they selected for further study only the alleles with frequency greater than 10% (n=70) and also excluded alleles from pairs of genes in linkage disequilibrium. The remaining 57 alleles were examined via univariate analysis and the indication appears to be that only alleles for which p<0.10 were entered into the logistic model, presumably the eight alleles listed in table 2. However, it does not preclude the possibility that the findings of this study are due to chance.

In addition, information on the cohort – including ethnicity, age and gender by case/control status was not provided. It is unclear then whether any of these factors may contribute to or confound the overall findings. Information on clinical progression and severity of sickle cell disease also was not provided and it was not clear whether any of this information may also potentially explain some of the observed associations. Information on environmental factors was not provided or included in the analysis. Gene-environment interactions could not be evaluated or assessed.

In addition, it was unclear whether the cases and controls were alike in terms of their underlying hemoglobin mutations (e.g., variant B-globin genes) that all can cause sickle cell disease.

The classification of the outcome, stroke, was not based on a clinical finding, but rather evaluation of MRIs conducted by two neurologists who were blinded to patient status and genetic outcomes. A third neurologist was consulted when the two neurologists disagreed on their findings and the three parties came to a consensus on the outcome. This may indicate a potential for misclassification of the outcome; whether this misclassification could be differential or non-differential can not be evaluated.

While the authors state that in the normal populace there is thought to be a genetic contribution to the occurrence of stroke, sickle cell phenotype itself generates cells that are capable of damaging the blood vessels and thereby inducing stroke. The high prevalence of the outcome in this study makes it difficult to calculate an attributable fraction and assess the relative contribution made by these alleles to the occurrence of stroke in children with sickle cell disease.

Additional studies will be needed to verify the observed associations and to evaluate further the role of potential factors which could confound or modify the associations.