Last updated: 2020-02-07

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LD score

library(tidyverse)
Registered S3 methods overwritten by 'ggplot2':
  method         from 
  [.quosures     rlang
  c.quosures     rlang
  print.quosures rlang
── Attaching packages ────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1     ✔ purrr   0.3.2
✔ tibble  2.1.2     ✔ dplyr   0.8.1
✔ tidyr   0.8.3     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0
── Conflicts ───────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
work.dir ="~/Downloads/ldscore/"

ld score regression

## read ld-score in chr 22 calculated by Yanyu Liang using the GTEx V8 SNP set

ldscore = read.table(paste0(work.dir,"chr22.l2.ldscore.gz"),header=T,as.is=T)
head(ldscore)
  CHR                         SNP       BP    L2
1  22 chr22_10562747_AGTTTT_A_b38 10562747 2.985
2  22      chr22_10633788_G_A_b38 10633788 1.706
3  22      chr22_10648794_G_A_b38 10648794 1.343
4  22      chr22_10648974_G_A_b38 10648974 0.914
5  22      chr22_10659479_A_C_b38 10659479 0.853
6  22      chr22_10660248_C_T_b38 10660248 8.909
## plot ldscore to get a sense of variability

hist(ldscore$L2,xlab="LD Score" , main ="Histogram of LD score")

plot(ldscore$BP, ldscore$L2,xlab="Chr 22 Position",ylab="LD Score", main = "LD Score across chr 22")

ind = 1:5000; ind = ind + 10000;plot(ldscore$BP[ind], ldscore$L2[ind],xlab="Chr 22 Position",ylab="LD Score")

## read GWAS result for height from the GIANT consortium (chr22 only)

giant = read.table(paste0(work.dir,"gwas_giant_chr22.txt"),header=T, as.is=T)

names(giant) = c("variant_id",      "panel_variant_id",        "chromosome",      "position",        "effect_allele",   "non_effect_allele",       "frequency",       "pvalue",  "zscore",  "effect_size",     "standard_error",  "sample_size",     "n_cases")
tempo = inner_join(giant,ldscore,by=c("panel_variant_id"="SNP") )

## plot Chi2 vs LD score

plot(tempo$zscore^2~ tempo$L2)

ggplot(tempo, aes(L2, zscore^2)) + geom_point(alpha=0.5,size=3) +geom_smooth(method="lm") 

fit = lm(tempo$zscore^2~ tempo$L2)
summary(fit)

Call:
lm(formula = tempo$zscore^2 ~ tempo$L2)

Residuals:
   Min     1Q Median     3Q    Max 
-4.666 -1.836 -1.271  0.379 70.077 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 1.671988   0.032579   51.32   <2e-16 ***
tempo$L2    0.005222   0.000256   20.40   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.885 on 32151 degrees of freedom
Multiple R-squared:  0.01278,   Adjusted R-squared:  0.01275 
F-statistic: 416.3 on 1 and 32151 DF,  p-value: < 2.2e-16

sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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     

other attached packages:
[1] forcats_0.4.0   stringr_1.4.0   dplyr_0.8.1     purrr_0.3.2    
[5] readr_1.3.1     tidyr_0.8.3     tibble_2.1.2    ggplot2_3.1.1  
[9] tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2       cellranger_1.1.0 plyr_1.8.4       pillar_1.4.1    
 [5] compiler_3.6.0   git2r_0.25.2     workflowr_1.3.0  tools_3.6.0     
 [9] digest_0.6.19    lubridate_1.7.4  jsonlite_1.6     evaluate_0.14   
[13] nlme_3.1-139     gtable_0.3.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.1      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_2.1.0      xfun_0.7         withr_2.1.2      xml2_1.2.0      
[25] httr_1.4.0       knitr_1.23       hms_0.4.2        generics_0.0.2  
[29] fs_1.3.1         rprojroot_1.3-2  grid_3.6.0       tidyselect_0.2.5
[33] glue_1.3.1       R6_2.4.0         readxl_1.3.1     rmarkdown_1.13  
[37] modelr_0.1.4     magrittr_1.5     backports_1.1.4  scales_1.0.0    
[41] htmltools_0.4.0  rvest_0.3.4      assertthat_0.2.1 colorspace_1.4-1
[45] labeling_0.3     stringi_1.4.3    lazyeval_0.2.2   munsell_0.5.0   
[49] broom_0.5.2      crayon_1.3.4