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Genomic architecture of sickle cell disease in West African children.

Quinlan J, Idaghdour Y, Goulet JP, Gbeha E, de Malliard T, Bruat V, Grenier JC, Gomez S, Sanni A, Rahimy MC, Awadalla P - Front Genet (2014)

Bottom Line: Here we used the joint analysis of gene expression and whole genome genotyping data to identify the genetic regulatory effects contributing to gene expression variation among groups of patients exhibiting clinical variability, as well as unaffected siblings, in Benin, West Africa.Genome-wide association mapping of gene expression revealed 390 peak genome-wide significant expression SNPs (eSNPs) and 6 significant eSNP-by-clinical status interaction effects.The strong modulation of the transcriptome implicates pathways affecting core circulating cell functions and shows how genotypic regulatory variation likely contributes to the clinical variation observed in SCD.

View Article: PubMed Central - PubMed

Affiliation: Department of Social and Preventive Medicine, Faculty of Medicine, School of Public Health, University of Montreal Montreal, QC, Canada ; Department of Pediatrics, Faculty of Medicine, Sainte-Justine Research Center, University of Montreal Montreal, QC, Canada.

ABSTRACT
Sickle cell disease (SCD) is a congenital blood disease, affecting predominantly children from sub-Saharan Africa, but also populations world-wide. Although the causal mutation of SCD is known, the sources of clinical variability of SCD remain poorly understood, with only a few highly heritable traits associated with SCD having been identified. Phenotypic heterogeneity in the clinical expression of SCD is problematic for follow-up (FU), management, and treatment of patients. Here we used the joint analysis of gene expression and whole genome genotyping data to identify the genetic regulatory effects contributing to gene expression variation among groups of patients exhibiting clinical variability, as well as unaffected siblings, in Benin, West Africa. We characterized and replicated patterns of whole blood gene expression variation within and between SCD patients at entry to clinic, as well as in follow-up programs. We present a global map of genes involved in the disease through analysis of whole blood sampled from the cohort. Genome-wide association mapping of gene expression revealed 390 peak genome-wide significant expression SNPs (eSNPs) and 6 significant eSNP-by-clinical status interaction effects. The strong modulation of the transcriptome implicates pathways affecting core circulating cell functions and shows how genotypic regulatory variation likely contributes to the clinical variation observed in SCD.

No MeSH data available.


Related in: MedlinePlus

Examples of significant SNP-by-clinical status interaction effects. Five SNP-by-clinical status interaction effects are shown. All are local eSNP interactions. Expression levels are shown on the y-axis, and SNP genotype on the x-axis. The eSNP interaction involving gene zinc finger and SCAN domain containing 12 pseudogene 1 (ZSCAN12L1) is shown in (A); chromosome 9 open reading frame 173 (C9ORF173) is shown in (B); capping protein (actin filament) muscle Z-line, alpha 1 (CAPZA1) is shown in (C); supervillin (SVIL) is shown in (D); and myocyte enhancer factor 2A (MEF2A) is shown in (E). Linear regression for each group is plotted and colored: yellow for follow-up, FU; purple for entry, E; and red for controls, Ctls. See also Figure S7.
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Figure 4: Examples of significant SNP-by-clinical status interaction effects. Five SNP-by-clinical status interaction effects are shown. All are local eSNP interactions. Expression levels are shown on the y-axis, and SNP genotype on the x-axis. The eSNP interaction involving gene zinc finger and SCAN domain containing 12 pseudogene 1 (ZSCAN12L1) is shown in (A); chromosome 9 open reading frame 173 (C9ORF173) is shown in (B); capping protein (actin filament) muscle Z-line, alpha 1 (CAPZA1) is shown in (C); supervillin (SVIL) is shown in (D); and myocyte enhancer factor 2A (MEF2A) is shown in (E). Linear regression for each group is plotted and colored: yellow for follow-up, FU; purple for entry, E; and red for controls, Ctls. See also Figure S7.

Mentions: Differential expression analysis revealed 7002 probes significantly differentially expressed (1% FDR) for the clinical status effect. In order to identify which of these genes are under strong genetic regulatory effects that are dependent on clinical status we tested for the SNP-by-ClinStatus interaction effect by including it as term in Model 1 (See Materials and Methods for details). Bonferroni correction for multiple testing in this analysis was applied. The markers included in this analyses were limited to SNPs with MAF >5% in each clinical group (See Materials and Methods for details). This analysis revealed 11 significant interaction effects, six of which remained genome-wide significant after accounting for relatedness in the entire sample using a Q-K mixed model (Yu et al., 2006) (see Materials and Methods for details): ZSCAN12L1 (p-value = 4.26 × 10−10), C9ORF173 (p-value = 8.94 × 10−9), CAPZA1 (p-value = 1.33 × 10−8), SVIL (p-value = 2.41 × 10−8), MEF2A (p-value = 1.69 × 10−8), and C1ORF88 (p-value = 5.42 × 10−9). These interactions are visualized in Figures 4, S7. Figures 4A–C shows three local eSNP interaction effects where higher expression levels of the corresponding gene in the FU group relative to both the E group and the Ctls is driven by the minor allele of the eSNP in question. Figures 4D,E shows two associations where the higher expression levels in the Ctls relative to SCD patients is observed only in the presence of the minor allele for the corresponding eSNP.


Genomic architecture of sickle cell disease in West African children.

Quinlan J, Idaghdour Y, Goulet JP, Gbeha E, de Malliard T, Bruat V, Grenier JC, Gomez S, Sanni A, Rahimy MC, Awadalla P - Front Genet (2014)

Examples of significant SNP-by-clinical status interaction effects. Five SNP-by-clinical status interaction effects are shown. All are local eSNP interactions. Expression levels are shown on the y-axis, and SNP genotype on the x-axis. The eSNP interaction involving gene zinc finger and SCAN domain containing 12 pseudogene 1 (ZSCAN12L1) is shown in (A); chromosome 9 open reading frame 173 (C9ORF173) is shown in (B); capping protein (actin filament) muscle Z-line, alpha 1 (CAPZA1) is shown in (C); supervillin (SVIL) is shown in (D); and myocyte enhancer factor 2A (MEF2A) is shown in (E). Linear regression for each group is plotted and colored: yellow for follow-up, FU; purple for entry, E; and red for controls, Ctls. See also Figure S7.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924578&req=5

Figure 4: Examples of significant SNP-by-clinical status interaction effects. Five SNP-by-clinical status interaction effects are shown. All are local eSNP interactions. Expression levels are shown on the y-axis, and SNP genotype on the x-axis. The eSNP interaction involving gene zinc finger and SCAN domain containing 12 pseudogene 1 (ZSCAN12L1) is shown in (A); chromosome 9 open reading frame 173 (C9ORF173) is shown in (B); capping protein (actin filament) muscle Z-line, alpha 1 (CAPZA1) is shown in (C); supervillin (SVIL) is shown in (D); and myocyte enhancer factor 2A (MEF2A) is shown in (E). Linear regression for each group is plotted and colored: yellow for follow-up, FU; purple for entry, E; and red for controls, Ctls. See also Figure S7.
Mentions: Differential expression analysis revealed 7002 probes significantly differentially expressed (1% FDR) for the clinical status effect. In order to identify which of these genes are under strong genetic regulatory effects that are dependent on clinical status we tested for the SNP-by-ClinStatus interaction effect by including it as term in Model 1 (See Materials and Methods for details). Bonferroni correction for multiple testing in this analysis was applied. The markers included in this analyses were limited to SNPs with MAF >5% in each clinical group (See Materials and Methods for details). This analysis revealed 11 significant interaction effects, six of which remained genome-wide significant after accounting for relatedness in the entire sample using a Q-K mixed model (Yu et al., 2006) (see Materials and Methods for details): ZSCAN12L1 (p-value = 4.26 × 10−10), C9ORF173 (p-value = 8.94 × 10−9), CAPZA1 (p-value = 1.33 × 10−8), SVIL (p-value = 2.41 × 10−8), MEF2A (p-value = 1.69 × 10−8), and C1ORF88 (p-value = 5.42 × 10−9). These interactions are visualized in Figures 4, S7. Figures 4A–C shows three local eSNP interaction effects where higher expression levels of the corresponding gene in the FU group relative to both the E group and the Ctls is driven by the minor allele of the eSNP in question. Figures 4D,E shows two associations where the higher expression levels in the Ctls relative to SCD patients is observed only in the presence of the minor allele for the corresponding eSNP.

Bottom Line: Here we used the joint analysis of gene expression and whole genome genotyping data to identify the genetic regulatory effects contributing to gene expression variation among groups of patients exhibiting clinical variability, as well as unaffected siblings, in Benin, West Africa.Genome-wide association mapping of gene expression revealed 390 peak genome-wide significant expression SNPs (eSNPs) and 6 significant eSNP-by-clinical status interaction effects.The strong modulation of the transcriptome implicates pathways affecting core circulating cell functions and shows how genotypic regulatory variation likely contributes to the clinical variation observed in SCD.

View Article: PubMed Central - PubMed

Affiliation: Department of Social and Preventive Medicine, Faculty of Medicine, School of Public Health, University of Montreal Montreal, QC, Canada ; Department of Pediatrics, Faculty of Medicine, Sainte-Justine Research Center, University of Montreal Montreal, QC, Canada.

ABSTRACT
Sickle cell disease (SCD) is a congenital blood disease, affecting predominantly children from sub-Saharan Africa, but also populations world-wide. Although the causal mutation of SCD is known, the sources of clinical variability of SCD remain poorly understood, with only a few highly heritable traits associated with SCD having been identified. Phenotypic heterogeneity in the clinical expression of SCD is problematic for follow-up (FU), management, and treatment of patients. Here we used the joint analysis of gene expression and whole genome genotyping data to identify the genetic regulatory effects contributing to gene expression variation among groups of patients exhibiting clinical variability, as well as unaffected siblings, in Benin, West Africa. We characterized and replicated patterns of whole blood gene expression variation within and between SCD patients at entry to clinic, as well as in follow-up programs. We present a global map of genes involved in the disease through analysis of whole blood sampled from the cohort. Genome-wide association mapping of gene expression revealed 390 peak genome-wide significant expression SNPs (eSNPs) and 6 significant eSNP-by-clinical status interaction effects. The strong modulation of the transcriptome implicates pathways affecting core circulating cell functions and shows how genotypic regulatory variation likely contributes to the clinical variation observed in SCD.

No MeSH data available.


Related in: MedlinePlus