The data-set contains a matrix of 574 individuals and 1,001 variables. These variables are real-world genotypes centered and scaled, and therefore retains the correlation structure of variables in the original genotype data. 3 out of the variables have non-zero effects. The response data is generated under a multivariate linear regression model. See Wang et al (2020) for more details.
N3finemapping is a list with the following elements:
N by P variable matrix of centered and scaled genotype data.
Chromomsome of the original data, in hg38 coordinate.
Chromomosomal positoin of the original data, in hg38 coordinate. The information can be used to compare impact of using other genotype references of the same variables in susie_rss application.
The simulated effect sizes.
The simulated residual covariance matrix.
The simulated response variables.
Allele frequency of the original genotype data.
Prior covariance matrix for effect size of the three non-zero effect variables.
G. Wang, A. Sarkar, P. Carbonetto and M. Stephens (2020). A simple new approach to variable selection in regression, with application to genetic fine-mapping. Journal of the Royal Statistical Society, Series B doi: 10.1101/501114.
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