| Encoding: | UTF-8 |
| Type: | Package |
| Title: | Hierarchical Joint Analysis of Marginal Summary Statistics |
| Version: | 1.0.0 |
| Author: | Lai Jiang <jian848@usc.edu> |
| Maintainer: | Lai Jiang <jian848@usc.edu> |
| Description: | Provides functions to implement a hierarchical approach which is designed to perform joint analysis of summary statistics using the framework of Mendelian Randomization or transcriptome analysis. Reference: Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). "A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis." <bioRxiv><doi:10.1101/2020.02.03.924241>. |
| License: | MIT + file LICENSE |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| URL: | https://github.com/lailylajiang/hJAM |
| BugReports: | https://github.com/lailylajiang/hJAM/issues |
| Imports: | ggplot2, ggpubr, dplyr, reshape2 |
| NeedsCompilation: | no |
| Packaged: | 2020-02-12 01:12:52 UTC; jian848 |
| Repository: | CRAN |
| Date/Publication: | 2020-02-20 14:50:05 UTC |
Example reference data of hJAM
Description
The real data example from hJAM paper
Usage
Gl
Format
The Gl object is a data matrix with 2467 individual of 210 SNPs from 1000 Genome project.
References
Consortium GP. A global reference for human genetic variation. Nature 2015; 526: 68.
Heatmap for all the SNPs used in the analysis
Description
To generate the heatmap of all the SNPs that the user use in the analysis
Usage
SNPs_heatmap(Gl)
Arguments
Gl |
The reference panel (Gl) of the SNPs that the user use in the analysis, such as 1000 Genome |
Author(s)
Lai Jiang
Examples
data(Gl)
t = SNPs_heatmap(Gl = Gl)
t
Example SNPs' information of hJAM
Description
Example SNPs' information of hJAM
Usage
SNPs_info
Format
The SNPs_info is the information of the 210 SNPs that we used in this data example. It includes three columns: the rsID, major allele, and minor allele frequency of each SNP. The minor allele frequencies were calculated in the 503 European-ancestry subjects in 1000 Genome project.
References
Consortium GP. A global reference for human genetic variation. Nature 2015; 526: 68.
Scatter plot for all the SNPs used in the analysis
Description
To generate the scatter plot of all the SNPs that the user use in the analysis
Usage
SNPs_scatter_plot(A, betas.Gy, num_X)
Arguments
A |
The effects of SNPs on the exposures (Gx). |
betas.Gy |
The betas in the paper: the marginal effects of SNPs on the phenotype (Gy) |
num_X |
The number of intermediates in the research question. |
Value
A set of scatter plots with x-axis being the conditional \alpha estimates for each
intermediate and y-axis being the \beta estimates.
Author(s)
Lai Jiang
Examples
data(conditional_A)
data(betas.Gy)
t = SNPs_scatter_plot(A = conditional_A, betas.Gy = betas.Gy, num_X = 2)
t
Example beta list of hJAM
Description
Example beta list of hJAM
Usage
betas.Gy
Format
The betas.Gy is the beta vector in the hJAM model: the association estimates between 210 SNPs and myocardial infarction. The summary data was collected from UK Biobank (n=459,324).
References
Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015; 12: e1001779.
Example conditional A matrix of hJAM
Description
Example conditional A matrix of hJAM
Usage
conditional_A
Format
The conditional_A is the conditional estimates alpha matrix in the hJAM model: the association estimates between 210 SNPs and body mass index (BMI) and type 2 diabetes (T2D). The summary data was collected from GIANT consortium (n=339,224) and DIAGRAM+GERA+UKB (n=659316) for BMI and T2D, respectively. We converted it from marginal_A, using get_cond_A function in hJAM package.
References
1. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015; 518: 197-206. 2. Xue A, Wu Y, Zhu Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 2018; 9: 2941.
Compute conditional Z matrix
Description
The get_cond_A function is to get the conditional A matrix by using marginal A matrix
Usage
get_cond_A(marginal_A, Gl, N.Gx, ridgeTerm = FALSE)
Arguments
marginal_A |
the marginal effects of SNPs on the exposures (Gx). |
Gl |
the reference panel (Gl), such as 1000 Genome |
N.Gx |
the sample size of each Gx. It can be a scalar or a vector. If there are multiple X's from different Gx, it should be a vector including the sample size of each Gx. If all alphas are from the same Gx, it could be a scalar. |
ridgeTerm |
ridgeTerm = TRUE when the matrix L is singular. Matrix L is obtained from the cholesky decomposition of G0'G0. Default as FALSE. |
Value
A matrix with conditional estimates which are converted from marginal estimates using the JAM model.
Author(s)
Lai Jiang
Examples
data(Gl)
data(betas.Gy)
data(marginal_A)
get_cond_A(marginal_A = marginal_A, Gl = Gl, N.Gx = c(339224, 659316), ridgeTerm = TRUE)
Compute conditional alphas
Description
The get_cond_alpha function is to compute the conditional alpha vector for each X If only one X in the model, please use get_cond_alpha instead of get_cond_A A sub-step in the get_cond_A function
Usage
get_cond_alpha(alphas, Gl, N.Gx, ridgeTerm = FALSE)
Arguments
alphas |
the marginal effects of SNPs on one exposure (Gx). |
Gl |
the reference panel (Gl), such as 1000 Genome |
N.Gx |
the sample size of the Gx. It can be a scalar. |
ridgeTerm |
ridgeTerm = TRUE when the matrix L is singular. Matrix L is obtained from the cholesky decomposition of G0'G0. Default as FALSE |
Value
A vector with conditional estimates which are converted from marginal estimates using the JAM model.
Author(s)
Lai Jiang
References
Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis. bioRxiv https://doi.org/10.1101/2020.02.03.924241.
Examples
data(Gl)
data(betas.Gy)
data(marginal_A)
get_cond_alpha(alphas = marginal_A[, 1], Gl = Gl, N.Gx = 339224, ridgeTerm = TRUE)
Fit hJAM with Egger regression
Description
The hJAM_egger function is to get the results from the hJAM model with Egger regression. It is for detecting potential pleiotropy
Usage
hJAM_egger(betas.Gy, N.Gy, Gl, A, ridgeTerm = FALSE)
Arguments
betas.Gy |
The betas in the paper: the marginal effects of SNPs on the phenotype (Gy) |
N.Gy |
The sample size of Gy |
Gl |
The reference panel (Gl), such as 1000 Genome |
A |
The A matrix in the paper: the marginal/conditional effects of SNPs on the exposures (Gx) |
ridgeTerm |
ridgeTerm = TRUE when the matrix L is singular. Matrix L is obtained from the cholesky decomposition of G0'G0. Default as FALSE. |
Value
An object of the hJAM with egger regression results.
- Exposure
The intermediates, such as the modifiable risk factors in Mendelian Randomization and gene expression in transcriptome analysis.
- numSNP
The number of SNPs that the user use in the instrument set.
- Estimate
The conditional estimates of the associations between intermediates and the outcome.
- StdErr
The standard error of the conditional estimates of the associations between intermediates and the outcome.
- Lower.CI
The lower bound of the 95% confidence interval of the estimates.
- Upper.CI
The upper bound of the 95% confidence interval of the estimates.
- Pvalue
The p value of the estimates with a type-I error equals 0.05.
- Est.Int
The intercept of the regression of intermediates on the outcome.
- StdErr.Int
The standard error of the intercept of the regression of intermediates on the outcome.
- Lower.CI.Int
The lower bound of the 95% confidence interval of the intercept.
- Upper.CI.Int
The upper bound of the 95% confidence interval of the intercept.
- Pvalue.Int
The p value of the intercept with a type-I error equals 0.05.
An object of hJAM with egger regression results.
Author(s)
Lai Jiang
References
Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis. bioRxiv https://doi.org/10.1101/2020.02.03.924241.
Examples
data(Gl)
data(betas.Gy)
data(conditional_A)
hJAM_egger(betas.Gy = betas.Gy, Gl = Gl, N.Gy = 459324, A = conditional_A, ridgeTerm = TRUE)
Fit hJAM with linear regression
Description
The hJAM function is to get the results from the hJAM model using input data
Usage
hJAM_lnreg(betas.Gy, N.Gy, Gl, A, ridgeTerm = FALSE)
Arguments
betas.Gy |
The betas in the paper: the marginal effects of SNPs on the phenotype (Gy) |
N.Gy |
The sample size of Gy |
Gl |
The reference panel (Gl), such as 1000 Genome |
A |
The A matrix in the paper: the marginal/conditional effects of SNPs on the exposures (Gx) |
ridgeTerm |
ridgeTerm = TRUE when the matrix L is singular. Matrix L is obtained from the cholesky decomposition of G0'G0. Default as FALSE. |
Value
An object of the hJAM with linear regression results.
- Exposure
The intermediates, such as the modifiable risk factors in Mendelian Randomization and gene expression in transcriptome analysis.
- numSNP
The number of SNPs that the user use in the instrument set.
- Estimate
The conditional estimates of the associations between intermediates and the outcome.
- StdErr
The standard error of the conditional estimates of the associations between intermediates and the outcome.
- Lower.CI
The lower bound of the 95% confidence interval of the estimates.
- Upper.CI
The upper bound of the 95% confidence interval of the estimates.
- Pvalue
The p value of the estimates with a type-I error equals 0.05.
Author(s)
Lai Jiang
References
Lai Jiang, Shujing Xu, Nicholas Mancuso, Paul J. Newcombe, David V. Conti (2020). A Hierarchical Approach Using Marginal Summary Statistics for Multiple Intermediates in a Mendelian Randomization or Transcriptome Analysis. bioRxiv https://doi.org/10.1101/2020.02.03.924241.
Examples
data(Gl)
data(betas.Gy)
data(conditional_A)
hJAM_lnreg(betas.Gy = betas.Gy, Gl = Gl, N.Gy = 459324, A = conditional_A, ridgeTerm = TRUE)
Example marginal A matrix of hJAM
Description
Example marginal A matrix of hJAM
Usage
marginal_A
Format
The marginal_A is the marginal estimates alpha matrix in the hJAM model: the association estimates between 210 SNPs and body mass index (BMI) and type 2 diabetes (T2D). The summary data was collected from GIANT consortium (n=339,224) and DIAGRAM+GERA+UKB (n=659316) for BMI and T2D, respectively.
References
1. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015; 518: 197-206. 2. Xue A, Wu Y, Zhu Z, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 2018; 9: 2941.
Keep the output as three digits
Description
Keep the output as three digits
Usage
output.format(x, ...)
Arguments
x |
input |
... |
other options you want to put in |
Author(s)
Lai Jiang
Print out for hJAM_egger
Description
Print out for hJAM_egger
Usage
## S3 method for class 'hJAM_egger'
print(x, ...)
Arguments
x |
input |
... |
other options you want to put in |
Author(s)
Lai Jiang
Print out for hJAM_lnreg
Description
Print out for hJAM_lnreg
Usage
## S3 method for class 'hJAM_lnreg'
print(x, ...)
Arguments
x |
input |
... |
other options you want to put in |
Author(s)
Lai Jiang