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Integrative genomic analysis of the human immune response to influenza vaccination.

Franco LM, Bucasas KL, Wells JM, Niño D, Wang X, Zapata GE, Arden N, Renwick A, Yu P, Quarles JM, Bray MS, Couch RB, Belmont JW, Shaw CA - Elife (2013)

Bottom Line: Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy.We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine.More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Human Genetics , Baylor College of Medicine , Houston , United States ; Department of Medicine , Baylor College of Medicine , Houston , United States.

ABSTRACT
Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits. DOI:http://dx.doi.org/10.7554/eLife.00299.001.

No MeSH data available.


The study design permits causal and reactive model analyses.(A) Three models were evaluated, each showing a candidate hypothesis for the three-way association between genotype (G), expression (E) and trait (T). In the independent model, expression and trait each associate with genotype but are not themselves directly related. In the causal model, expression mediates the association between genotype and trait. In the reactive model, genotype and expression relate through the trait, so that gene expression changes are a downstream response to the trait. (B) p-values for independent-versus-reactive and independent-versus-causal hypothesis tests. Each point shows the result for one SNP-transcript pair. Points to the right of the solid vertical line are significant (p<0.05) for the reactive hypothesis and points above the solid horizontal line are significant for the causal hypothesis. The dashed line shows a p=0.1 threshold. (C) Power for rejection of the independent hypothesis. Non-independent data were simulated with effect sizes and variances similar to those in the enrichment set (the set of SNP-transcript pairs that were found to be significant in our study). The curve shows the proportion of cases in which the simulated data rejected the independent () hypothesis. The dotted line indicates the combined sample size in our study.DOI:http://dx.doi.org/10.7554/eLife.00299.009
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fig7: The study design permits causal and reactive model analyses.(A) Three models were evaluated, each showing a candidate hypothesis for the three-way association between genotype (G), expression (E) and trait (T). In the independent model, expression and trait each associate with genotype but are not themselves directly related. In the causal model, expression mediates the association between genotype and trait. In the reactive model, genotype and expression relate through the trait, so that gene expression changes are a downstream response to the trait. (B) p-values for independent-versus-reactive and independent-versus-causal hypothesis tests. Each point shows the result for one SNP-transcript pair. Points to the right of the solid vertical line are significant (p<0.05) for the reactive hypothesis and points above the solid horizontal line are significant for the causal hypothesis. The dashed line shows a p=0.1 threshold. (C) Power for rejection of the independent hypothesis. Non-independent data were simulated with effect sizes and variances similar to those in the enrichment set (the set of SNP-transcript pairs that were found to be significant in our study). The curve shows the proportion of cases in which the simulated data rejected the independent () hypothesis. The dotted line indicates the combined sample size in our study.DOI:http://dx.doi.org/10.7554/eLife.00299.009

Mentions: We explored three types of associations in our work: genotype to gene expression (eQTL), gene expression to antibody titer, and genotype to antibody titer (QTL). We now considered alternative models for the relationships between these distinct types of association (Figure 7A), and we evaluated our data to determine which of these alternatives appears most consistent with our observations. The alternative models considered were: (i) genotype association with gene expression is independent of genotype association or trends of association with antibody response (independent model); (ii) genotype association or trends of association with antibody response are mediated by gene expression patterns that are strongly correlated with genotype (causal model); and (iii) genotype associations to antibody response are not mediated by expression, but instead gene expression patterns are a response to the antibody trait or its early correlates (reactive model). To perform a comparative analysis of these alternatives we extended the framework for causal modeling (Pearl, 2010) in eQTL data recently developed by others (Millstein et al., 2009) and applied the method to our time-course gene expression study. We used the 137 eQTL SNP-transcript pairs from the 20 loci with the strongest evidence of genetic variation influencing the immune response to the vaccine, as described above. We found that the patterns in the data trend toward the causal model compared to the reactive model (Figure 7B), but a power analysis based on the distribution of the empirical effect sizes of our observed associations also indicates that our sample size is too modest to support definitive conclusions (Figure 7C).10.7554/eLife.00299.009Figure 7.The study design permits causal and reactive model analyses.


Integrative genomic analysis of the human immune response to influenza vaccination.

Franco LM, Bucasas KL, Wells JM, Niño D, Wang X, Zapata GE, Arden N, Renwick A, Yu P, Quarles JM, Bray MS, Couch RB, Belmont JW, Shaw CA - Elife (2013)

The study design permits causal and reactive model analyses.(A) Three models were evaluated, each showing a candidate hypothesis for the three-way association between genotype (G), expression (E) and trait (T). In the independent model, expression and trait each associate with genotype but are not themselves directly related. In the causal model, expression mediates the association between genotype and trait. In the reactive model, genotype and expression relate through the trait, so that gene expression changes are a downstream response to the trait. (B) p-values for independent-versus-reactive and independent-versus-causal hypothesis tests. Each point shows the result for one SNP-transcript pair. Points to the right of the solid vertical line are significant (p<0.05) for the reactive hypothesis and points above the solid horizontal line are significant for the causal hypothesis. The dashed line shows a p=0.1 threshold. (C) Power for rejection of the independent hypothesis. Non-independent data were simulated with effect sizes and variances similar to those in the enrichment set (the set of SNP-transcript pairs that were found to be significant in our study). The curve shows the proportion of cases in which the simulated data rejected the independent () hypothesis. The dotted line indicates the combined sample size in our study.DOI:http://dx.doi.org/10.7554/eLife.00299.009
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fig7: The study design permits causal and reactive model analyses.(A) Three models were evaluated, each showing a candidate hypothesis for the three-way association between genotype (G), expression (E) and trait (T). In the independent model, expression and trait each associate with genotype but are not themselves directly related. In the causal model, expression mediates the association between genotype and trait. In the reactive model, genotype and expression relate through the trait, so that gene expression changes are a downstream response to the trait. (B) p-values for independent-versus-reactive and independent-versus-causal hypothesis tests. Each point shows the result for one SNP-transcript pair. Points to the right of the solid vertical line are significant (p<0.05) for the reactive hypothesis and points above the solid horizontal line are significant for the causal hypothesis. The dashed line shows a p=0.1 threshold. (C) Power for rejection of the independent hypothesis. Non-independent data were simulated with effect sizes and variances similar to those in the enrichment set (the set of SNP-transcript pairs that were found to be significant in our study). The curve shows the proportion of cases in which the simulated data rejected the independent () hypothesis. The dotted line indicates the combined sample size in our study.DOI:http://dx.doi.org/10.7554/eLife.00299.009
Mentions: We explored three types of associations in our work: genotype to gene expression (eQTL), gene expression to antibody titer, and genotype to antibody titer (QTL). We now considered alternative models for the relationships between these distinct types of association (Figure 7A), and we evaluated our data to determine which of these alternatives appears most consistent with our observations. The alternative models considered were: (i) genotype association with gene expression is independent of genotype association or trends of association with antibody response (independent model); (ii) genotype association or trends of association with antibody response are mediated by gene expression patterns that are strongly correlated with genotype (causal model); and (iii) genotype associations to antibody response are not mediated by expression, but instead gene expression patterns are a response to the antibody trait or its early correlates (reactive model). To perform a comparative analysis of these alternatives we extended the framework for causal modeling (Pearl, 2010) in eQTL data recently developed by others (Millstein et al., 2009) and applied the method to our time-course gene expression study. We used the 137 eQTL SNP-transcript pairs from the 20 loci with the strongest evidence of genetic variation influencing the immune response to the vaccine, as described above. We found that the patterns in the data trend toward the causal model compared to the reactive model (Figure 7B), but a power analysis based on the distribution of the empirical effect sizes of our observed associations also indicates that our sample size is too modest to support definitive conclusions (Figure 7C).10.7554/eLife.00299.009Figure 7.The study design permits causal and reactive model analyses.

Bottom Line: Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy.We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine.More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Human Genetics , Baylor College of Medicine , Houston , United States ; Department of Medicine , Baylor College of Medicine , Houston , United States.

ABSTRACT
Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits. DOI:http://dx.doi.org/10.7554/eLife.00299.001.

No MeSH data available.