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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.


Study samples cluster with the HapMap CEU population.Pairwise identity-by-descent metrics were estimated based on genotype data from our two study samples and six HapMap populations. Multidimensional scaling analysis was performed on the resulting pairwise distances. Components 1–3 of this analysis are plotted for the male (top) and female (bottom) cohorts, and the comparison populations. As expected, the study samples cluster with the HapMap CEU population. 12 outliers were identified in each cohort and were removed prior to analysis.DOI:http://dx.doi.org/10.7554/eLife.00299.011
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fig9: Study samples cluster with the HapMap CEU population.Pairwise identity-by-descent metrics were estimated based on genotype data from our two study samples and six HapMap populations. Multidimensional scaling analysis was performed on the resulting pairwise distances. Components 1–3 of this analysis are plotted for the male (top) and female (bottom) cohorts, and the comparison populations. As expected, the study samples cluster with the HapMap CEU population. 12 outliers were identified in each cohort and were removed prior to analysis.DOI:http://dx.doi.org/10.7554/eLife.00299.011

Mentions: Array quality was initially assessed using GenomeStudio software (Illumina, Inc.). Default algorithms were used to normalize, generate SNP clusters, and make genotype calls. SNPs with minor allele frequency (MAF) <0.05 and Hardy-Weinberg Equilibrium (HWE) χ2 < 1 × 10−7 were removed prior to analysis. For local eQTL mapping, we restricted the analyses to SNPs within 1 Mb (500 kb upstream or downstream) of each unambiguously mapped expression reporter sequence. We used the genotype information to reliably establish that the individuals in each cohort were indeed unrelated and ethnically matched. For this, we estimated pairwise identity-by-descent (IBD) metrics on the basis of identity-by-state (IBS) information from their genome-wide SNP genotyping results, followed by multidimensional scaling (MDS) analysis on the resulting matrix of pairwise distances, using PLINK (Purcell et al., 2007). The first three components of the MDS analysis on our study samples and the founders from six HapMap populations were plotted with R package Scatterplot3D (Ligges and Machler, 2003) and are shown in Figure 9. As expected, our study samples from both cohorts cluster with the HapMap CEU population. In the male cohort, we identified four pairs of individuals with cryptic or undisclosed familial relationships (pi-hat ≥ 0.125). 12 outliers were identified by component 2; two of the four pairs of cryptic or undisclosed relatives were among the outliers. One individual from each related pair and all remaining outliers were removed prior to analysis. In the female cohort, we identified one pair of individuals with cryptic or undisclosed familial relationship (pi-hat ≥ 0.125). 11 outliers were identified by component 2 and one by component 1. One individual from the related pair and all outliers were removed prior to analysis.10.7554/eLife.00299.011Figure 9.Study samples cluster with the HapMap CEU population.


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)

Study samples cluster with the HapMap CEU population.Pairwise identity-by-descent metrics were estimated based on genotype data from our two study samples and six HapMap populations. Multidimensional scaling analysis was performed on the resulting pairwise distances. Components 1–3 of this analysis are plotted for the male (top) and female (bottom) cohorts, and the comparison populations. As expected, the study samples cluster with the HapMap CEU population. 12 outliers were identified in each cohort and were removed prior to analysis.DOI:http://dx.doi.org/10.7554/eLife.00299.011
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3713456&req=5

fig9: Study samples cluster with the HapMap CEU population.Pairwise identity-by-descent metrics were estimated based on genotype data from our two study samples and six HapMap populations. Multidimensional scaling analysis was performed on the resulting pairwise distances. Components 1–3 of this analysis are plotted for the male (top) and female (bottom) cohorts, and the comparison populations. As expected, the study samples cluster with the HapMap CEU population. 12 outliers were identified in each cohort and were removed prior to analysis.DOI:http://dx.doi.org/10.7554/eLife.00299.011
Mentions: Array quality was initially assessed using GenomeStudio software (Illumina, Inc.). Default algorithms were used to normalize, generate SNP clusters, and make genotype calls. SNPs with minor allele frequency (MAF) <0.05 and Hardy-Weinberg Equilibrium (HWE) χ2 < 1 × 10−7 were removed prior to analysis. For local eQTL mapping, we restricted the analyses to SNPs within 1 Mb (500 kb upstream or downstream) of each unambiguously mapped expression reporter sequence. We used the genotype information to reliably establish that the individuals in each cohort were indeed unrelated and ethnically matched. For this, we estimated pairwise identity-by-descent (IBD) metrics on the basis of identity-by-state (IBS) information from their genome-wide SNP genotyping results, followed by multidimensional scaling (MDS) analysis on the resulting matrix of pairwise distances, using PLINK (Purcell et al., 2007). The first three components of the MDS analysis on our study samples and the founders from six HapMap populations were plotted with R package Scatterplot3D (Ligges and Machler, 2003) and are shown in Figure 9. As expected, our study samples from both cohorts cluster with the HapMap CEU population. In the male cohort, we identified four pairs of individuals with cryptic or undisclosed familial relationships (pi-hat ≥ 0.125). 12 outliers were identified by component 2; two of the four pairs of cryptic or undisclosed relatives were among the outliers. One individual from each related pair and all remaining outliers were removed prior to analysis. In the female cohort, we identified one pair of individuals with cryptic or undisclosed familial relationship (pi-hat ≥ 0.125). 11 outliers were identified by component 2 and one by component 1. One individual from the related pair and all outliers were removed prior to analysis.10.7554/eLife.00299.011Figure 9.Study samples cluster with the HapMap CEU population.

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.