Description Format References Examples

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:

- X
N by P variable matrix of centered and scaled genotype data.

- chrom
Chromomsome of the original data, in hg38 coordinate.

- pos
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.

- true_coef
The simulated effect sizes.

- residual_variance
The simulated residual covariance matrix.

- Y
The simulated response variables.

- allele_freq
Allele frequency of the original genotype data.

- V
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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