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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

Differential gene expression between SCD disease status. (A) Number of differentially expressed probes for the following effects: SCD clinical status (E, Entry; FU, Follow-up; Ctls, Controls; A, Acute), Hb genotypes (HbSS, HbSC, Ctls), and between sexes (M, males; F, females). The 3way-ClinStatus effect is between E vs. FU vs. Ctls. These results were obtained from an analysis of covariance (ANCOVA, FDR 1%) of the discovery, replication and combined datasets I and II and accounts for sex and total blood cell counts (RBC and WBC). (B) Venn diagram of the 7002 differentially expressed probes for the 3-way clinical status effect in the combined data set II. In red, 735 probes are shown to be differentially expressed uniquely between E vs. FU SCD patients. (C) Two-way hierarchical clustering of the mean expression levels for the 7002 differentially expressed probes in the combined data set II for each group of patients (E, FU, Ctls) is shown. Mean expression from this class of genes cluster controls from SCD entry and follow-up patients. (D) Gene Set Enrichment Analysis (GSEA) was performed for each contrast of the clinical status effect using the combined dataset II. This analysis identified biological pathways and sets of individual genes that are significantly enriched in each contrast. Selection of the most distinctive significantly enriched pathways between entry and follow-up groups is shown. Cells are colored by their respective Normalized Enrichment Scores for a given contrast. See also Figure S6.
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Figure 2: Differential gene expression between SCD disease status. (A) Number of differentially expressed probes for the following effects: SCD clinical status (E, Entry; FU, Follow-up; Ctls, Controls; A, Acute), Hb genotypes (HbSS, HbSC, Ctls), and between sexes (M, males; F, females). The 3way-ClinStatus effect is between E vs. FU vs. Ctls. These results were obtained from an analysis of covariance (ANCOVA, FDR 1%) of the discovery, replication and combined datasets I and II and accounts for sex and total blood cell counts (RBC and WBC). (B) Venn diagram of the 7002 differentially expressed probes for the 3-way clinical status effect in the combined data set II. In red, 735 probes are shown to be differentially expressed uniquely between E vs. FU SCD patients. (C) Two-way hierarchical clustering of the mean expression levels for the 7002 differentially expressed probes in the combined data set II for each group of patients (E, FU, Ctls) is shown. Mean expression from this class of genes cluster controls from SCD entry and follow-up patients. (D) Gene Set Enrichment Analysis (GSEA) was performed for each contrast of the clinical status effect using the combined dataset II. This analysis identified biological pathways and sets of individual genes that are significantly enriched in each contrast. Selection of the most distinctive significantly enriched pathways between entry and follow-up groups is shown. Cells are colored by their respective Normalized Enrichment Scores for a given contrast. See also Figure S6.

Mentions: All statistical analyses of the gene expression data were performed using JMP Genomics v5.0 (SAS), and SAS 9.3 (SAS). Principal Component analysis (PCA) and Variance Component analysis (VCA) of the gene expression data were performed such that the first three expression PCs (ePCs) were modeled either simultaneously or individually as a function of various effects in the data: Hemoglobin genotype, clinical status (E vs. FU vs. Ctls), sex, and pair-wise combination of fixed effects. SAS GLM was used to evaluate the magnitude and significance of differentially expressed probes. Probe-level differential expression analysis was performed using analysis of covariance. Variance was partitioned among the Hemoglobin genotype (Hb), clinical status effect, sex, and total blood cell counts (RBCs and WBCs) as covariates. The effects of date of sampling, phase (discovery vs. replication), age (in years), and gPCs were tested and found to be marginal. Pairwise contrasts (Hb genotype × Sex, Hb genotype × ClinStatus, and ClinStatus × Sex) also were evaluated and found to be insignificant. Results from the following full ANCOVA model are detailed in Figure 2: Expression = μ + Hb genotype + ClinStatus + Sex + WBC + RBC + ε.


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)

Differential gene expression between SCD disease status. (A) Number of differentially expressed probes for the following effects: SCD clinical status (E, Entry; FU, Follow-up; Ctls, Controls; A, Acute), Hb genotypes (HbSS, HbSC, Ctls), and between sexes (M, males; F, females). The 3way-ClinStatus effect is between E vs. FU vs. Ctls. These results were obtained from an analysis of covariance (ANCOVA, FDR 1%) of the discovery, replication and combined datasets I and II and accounts for sex and total blood cell counts (RBC and WBC). (B) Venn diagram of the 7002 differentially expressed probes for the 3-way clinical status effect in the combined data set II. In red, 735 probes are shown to be differentially expressed uniquely between E vs. FU SCD patients. (C) Two-way hierarchical clustering of the mean expression levels for the 7002 differentially expressed probes in the combined data set II for each group of patients (E, FU, Ctls) is shown. Mean expression from this class of genes cluster controls from SCD entry and follow-up patients. (D) Gene Set Enrichment Analysis (GSEA) was performed for each contrast of the clinical status effect using the combined dataset II. This analysis identified biological pathways and sets of individual genes that are significantly enriched in each contrast. Selection of the most distinctive significantly enriched pathways between entry and follow-up groups is shown. Cells are colored by their respective Normalized Enrichment Scores for a given contrast. See also Figure S6.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Differential gene expression between SCD disease status. (A) Number of differentially expressed probes for the following effects: SCD clinical status (E, Entry; FU, Follow-up; Ctls, Controls; A, Acute), Hb genotypes (HbSS, HbSC, Ctls), and between sexes (M, males; F, females). The 3way-ClinStatus effect is between E vs. FU vs. Ctls. These results were obtained from an analysis of covariance (ANCOVA, FDR 1%) of the discovery, replication and combined datasets I and II and accounts for sex and total blood cell counts (RBC and WBC). (B) Venn diagram of the 7002 differentially expressed probes for the 3-way clinical status effect in the combined data set II. In red, 735 probes are shown to be differentially expressed uniquely between E vs. FU SCD patients. (C) Two-way hierarchical clustering of the mean expression levels for the 7002 differentially expressed probes in the combined data set II for each group of patients (E, FU, Ctls) is shown. Mean expression from this class of genes cluster controls from SCD entry and follow-up patients. (D) Gene Set Enrichment Analysis (GSEA) was performed for each contrast of the clinical status effect using the combined dataset II. This analysis identified biological pathways and sets of individual genes that are significantly enriched in each contrast. Selection of the most distinctive significantly enriched pathways between entry and follow-up groups is shown. Cells are colored by their respective Normalized Enrichment Scores for a given contrast. See also Figure S6.
Mentions: All statistical analyses of the gene expression data were performed using JMP Genomics v5.0 (SAS), and SAS 9.3 (SAS). Principal Component analysis (PCA) and Variance Component analysis (VCA) of the gene expression data were performed such that the first three expression PCs (ePCs) were modeled either simultaneously or individually as a function of various effects in the data: Hemoglobin genotype, clinical status (E vs. FU vs. Ctls), sex, and pair-wise combination of fixed effects. SAS GLM was used to evaluate the magnitude and significance of differentially expressed probes. Probe-level differential expression analysis was performed using analysis of covariance. Variance was partitioned among the Hemoglobin genotype (Hb), clinical status effect, sex, and total blood cell counts (RBCs and WBCs) as covariates. The effects of date of sampling, phase (discovery vs. replication), age (in years), and gPCs were tested and found to be marginal. Pairwise contrasts (Hb genotype × Sex, Hb genotype × ClinStatus, and ClinStatus × Sex) also were evaluated and found to be insignificant. Results from the following full ANCOVA model are detailed in Figure 2: Expression = μ + Hb genotype + ClinStatus + Sex + WBC + RBC + ε.

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