Last updated: 2018-06-27

Code version: f6754bb

The summary statistics are from Mixed model association for biobank-scale data sets.

The $\beta$ are coefficients from mix effects model. Since fitting the mix model with a large sample size is intractable, the authors estimate them using $\chi^2_{BOLT\_LMM\_inf}$ statistics. There is no information about how to compute se. The p value is based on $\chi^2_{BOLT\_LMM}$ statistics. Detail about the BOLT LMM is in BOLT_LMM

There are 23 phenotypes. The tri-allelic SNPs are excluded from the data. We took the union of the SNPs from different phenotype, so there are missing values in $\hat{B}$ matrix. The total data set contains 11988455 SNPs.

The phenotypes are

The procedure to select strong SNPs subset:

1. Select SNPs with p value less than $5 \times 10^{-8}$.
2. For each chromosome, pick SNPs that at least 15000 bp away.

The procedure to select random SNPs subset:

1. Random select 800000 SNPs
2. For each chromosome, pick SNPs that at least 15000 bp away.

The strong subset contains 60070 SNPs. The random subset contains 142075 SNPs.

• Flash on Strong SNPs
• Mash on UKBio

sessionInfo()

R version 3.4.4 (2018-03-15)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] compiler_3.4.4  backports_1.1.2 magrittr_1.5    rprojroot_1.3-2
 [5] tools_3.4.4     htmltools_0.3.6 yaml_2.1.19     Rcpp_0.12.17   
 [9] stringi_1.2.2   rmarkdown_1.9   knitr_1.20      git2r_0.21.0   
[13] stringr_1.3.0   digest_0.6.15   evaluate_0.10.1

This R Markdown site was created with workflowr