MI: Example data of hJAM
In USCbiostats/hJAM: Hierarchical Joint Analysis of Marginal Summary Statistics
MI R Documentation
Example data of hJAM
Description
Real data for BMI/T2D on the risk of Myocardial infarction
Format
The MI object is a set of data sets which was used to estimate the causal effect of body mass index and type 2 diabetes on the risk of myocardial infarction.
- MI.marginal.Amatrix
The marginal \hat{A} matrix. Column one and two are the marginal estimates of the SNPs on body mass index from GIANT consortium (n = 339,224) (Locke et al., 2015) and type 2 diabetes from DIAGRAM+GERA+UKB (n = 659,316) (Xue et al., 2018), respectively
- MI.Amatrix
The conditional \hat{A} matrix composed by JAM and the marginal \hat{A} matrix. Column one and two are the conditional effect estimates of the SNPs on body mass index and type 2 diabetes, respectively.
- MI.Geno
The reference genotype data from the European-ancestry population in 1000 Genome Project (Consortium, 2015).
- MI.betas.gwas
The b vector. The association estimates between selected SNPs and the risk of myocardial infarction from UK Biobank (Sudlow et al., 2015).
- MI.SNPs_info
The SNP information. Five columns included: the RSID, reference allele, reference allele frequency, if BMI significant and if T2D significant. The last two columns are indicator variables for the SNPs which are genome-wide significant associated with BMI/T2D.
References
Consortium GP. A global reference for human genetic variation. Nature 2015; 526: 68.
Locke, Adam E., et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518.7538 (2015): 197-206.
Xue, Angli, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nature communications 9.1 (2018): 1-14.
Sudlow, Cathie, 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 12.3 (2015): e1001779.
USCbiostats/hJAM documentation built on Jan. 26, 2024, 5:27 p.m.
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| MI | R Documentation |
Example data of hJAM
Description
Real data for BMI/T2D on the risk of Myocardial infarction
Format
The MI object is a set of data sets which was used to estimate the causal effect of body mass index and type 2 diabetes on the risk of myocardial infarction.
- MI.marginal.Amatrix
The marginal
\hat{A}matrix. Column one and two are the marginal estimates of the SNPs on body mass index from GIANT consortium (n = 339,224) (Locke et al., 2015) and type 2 diabetes from DIAGRAM+GERA+UKB (n = 659,316) (Xue et al., 2018), respectively- MI.Amatrix
The conditional
\hat{A}matrix composed by JAM and the marginal\hat{A}matrix. Column one and two are the conditional effect estimates of the SNPs on body mass index and type 2 diabetes, respectively.- MI.Geno
The reference genotype data from the European-ancestry population in 1000 Genome Project (Consortium, 2015).
- MI.betas.gwas
The b vector. The association estimates between selected SNPs and the risk of myocardial infarction from UK Biobank (Sudlow et al., 2015).
- MI.SNPs_info
The SNP information. Five columns included: the RSID, reference allele, reference allele frequency, if BMI significant and if T2D significant. The last two columns are indicator variables for the SNPs which are genome-wide significant associated with BMI/T2D.
References
Consortium GP. A global reference for human genetic variation. Nature 2015; 526: 68.
Locke, Adam E., et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518.7538 (2015): 197-206.
Xue, Angli, et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nature communications 9.1 (2018): 1-14.
Sudlow, Cathie, 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 12.3 (2015): e1001779.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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