Example Analysis with CAUSE: LDL -> CAD

Last updated: 2020-11-13

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Introduction

This document will walk through a real genome-sized example of how to use CAUSE. Some of the steps will take 5-10 minutes. The LD pruning steps will benefit from access to a cluster or multiple cores. For steps that require long computation we also provide output files that can be downloaded to make it easier to run through the example.

We will be analyzing GWAS data for LDL cholesterol and for coronary artery disease to test for a causal relationship of LDL on CAD. The analysis will have the following steps:

Format the data for use with CAUSE
Calculate nuisance parameters
LD pruning
Fit CAUSE
Look at results

Step 3 will require LD information estimated from the 1000 Genomes CEU population using LDshrink here. LD data are about 11 Gb. The GWAS data we will use are about 320 Gb. However, in this tutorial you will be able to skip the large data steps and simply download the results.

Step 0: Install CAUSE

Follow installation instructions here

Step 1: Format Data for CAUSE

We will use read_tsv to read in summary statistics for a GWAS of LDL cholesterol and a GWAS of coronary artery disease from the internet. We will then combine these and format them for use with CAUSE. First read in the data. For LDL Cholesterol, we use summary statistics from Willer et al (2013) [PMID: 24097068]. For CAD we use summary statistics from van der Harst et al. (2017) [PMID: 29212778]. Downloading and formatting the data takes several minutes. If you want to skip this step, we provide a formatted data file that you can download below.

library(readr)
library(dplyr)


Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(cause)

X1 <- read_tsv("http://csg.sph.umich.edu/abecasis/public/lipids2013/jointGwasMc_LDL.txt.gz")
X2 <- read_tsv("ftp://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/vanderHarstP_29212778_GCST005194/CAD_META.gz")

CAUSE needs the following information from each data set: SNP or variant ID, effect size, and standard error, effect allele and other allele. For convenience, we provide a simple function that will merge data sets and produce a cause_data object that can be used with later functions. This step and the rest of the analysis are done in R.

The function gwas_merge will try to merge two data sets and and align effect sizes to correspond to the same allele. It will remove variants with ambiguous alleles (G/C or A/T) or with alleles that do not match between data sets (e.g A/G in one data set and A/C in the other). It will also remove variants that are duplicated in either data set. It will not remove variants that are simply strand flipped between the two data sets (e. g. A/C in one data set, T/G in the other).

LDL column headers:

SNP: rsid
Effect: beta
Standard Error: se
Effect Allele: A1
Other Allele: A2

CAD column headers:

SNP: oldID
Effect: Effect
Standard Error: StdErr
Effect Allele: Allele1
Other Allele: Allele2

X <- gwas_merge(X1, X2, snp_name_cols = c("rsid", "oldID"), 
                       beta_hat_cols = c("beta", "Effect"), 
                       se_cols = c("se", "StdErr"), 
                       A1_cols = c("A1", "Allele1"), 
                       A2_cols = c("A2", "Allele2"))

Alternatively, you can download already formatted data here and read it in using readRDS.

system("mkdir example_data/")
download.file("https://github.com/jean997/cause/blob/master/example_data/LDL_CAD_merged.RDS", destfile = "example_data/LDL_CAD_merged.RDS")

X <- readRDS("example_data/LDL_CAD_merged.RDS")

head(X)

        snp beta_hat_1   seb1 beta_hat_2   seb2 A1 A2
1 rs4747841     0.0037 0.0052     0.0106 0.0056  A  G
2 rs4749917    -0.0033 0.0052    -0.0108 0.0056  A  G
3  rs737656     0.0099 0.0054     0.0196 0.0058  A  G
4  rs737657     0.0084 0.0054     0.0195 0.0058  A  G
5 rs7086391    -0.0075 0.0067     0.0115 0.0072  A  G
6  rs878177    -0.0073 0.0055    -0.0225 0.0059  A  G

There are likely more efficient ways to do this merge. If you would like to process the data yourself, you can construct a cause_data object from a data frame using the constructor new_cause_data(X) where X is any data frame that includes the columns snp, beta_hat_1, seb1, beta_hat_2, and seb2.

Step 2: Calculate nuisance parameters

The next step is to estimate the parameters that define the prior distribution of $\beta_{M}$ and $\theta$ and to estimate $\rho$, the correlation between summary statistics that is due to sample overlap or population structure. We will do this with a random subset of 1,000,000 variants since our data set is large. est_cause_params estimates the nuisance parameters by finding the maximum a posteriori estimate of $\rho$ and the mixing parameters when $\gamma = \eta = 0$. This step takes a several minutes.

set.seed(100)
varlist <- with(X, sample(snp, size=1000000, replace=FALSE))
params <- est_cause_params(X, varlist)

Estimating CAUSE parameters with  1000000  variants.
1 0.2180944 
2 0.0006398994 
3 7.445625e-06 
4 4.878722e-08

The object params is of class cause_params and contains information about the fit as well as the maximum a posteriori estimates of the mixing parameters ($\pi$) and $\rho$. The object params$mix_grid is a data frame defining the distribution of summary statistics. The column S1 is the variance for trait 1 ($M$), the column S2 is the variance for trait 2 ($Y$) and the column pi is the mixture proportion assigned to that variance combination.

class(params)

[1] "cause_params"

names(params)

[1] "rho"       "pi"        "mix_grid"  "loglik"    "PIS"       "RHO"      
[7] "LLS"       "converged" "prior"

params$rho

[1] 0.06465784

head(params$mix_grid)

           S1          S2           pi
1 0.000000000 0.000000000 0.3105286643
2 0.000000000 0.003440730 0.2381082227
3 0.000000000 0.004865928 0.1490899299
4 0.003533615 0.004865928 0.1660234953
5 0.004997287 0.009731855 0.1177553318
6 0.019989147 0.009731855 0.0006521846

So, for example, in this case, we have estimated that 31% of variants have trait 1 variance and trait 2 equal to 0 meaning that they have no association with either trait.

Tip: Do not try to estimate the nuisance parameters with substantially fewer than 100,000 variants. This can lead to poor estimates of the mixing parameters whih leads to bad model comparisons.

Step 3: LD Pruning

We estimate CAUSE posterior distributions using an LD pruned set of variants, prioritizing variants with low trait $M$ (LDL) $p$-values.

There are two ways to do this step. The CAUSE R package contains a function that performs LD pruning given a file that contains pairwise estimates of $r^2$. This is the method we used in our paper, coupled with LD estimates 1000 Genomes European samples computed via LDshrin. This method however, is very slow.

An alternative is to use Plink to perform LD clumping. The ieugwasr package provides a convenient R interface to Plink. This method is much faster but requires a reference sample. You can download reference data from here.

LD pruning using plink built in function

The function ld_prune uses a greedy algorithm that selects the variant with the lowest LDL p-value and removes all variants in LD with the selected variant and then repeats until no variants are left. This step requires LD estimates. You can download estimates made in the 1000 Genomes CEU population here. We first demonstrate use of the function for one chromosome and then show an example of how to parallelize the analysis over all 22 autosomes.

download.file("https://zenodo.org/record/1464357/files/chr22_AF0.05_0.1.RDS?download=1", destfile = "example_data/chr22_AF0.05_0.1.RDS")
download.file("https://zenodo.org/record/1464357/files/chr22_AF0.05_snpdata.RDS?download=1", destfile="example_data/chr22_AF0.05_snpdata.RDS")

ld <- readRDS("example_data/chr22_AF0.05_0.1.RDS")
snp_info <- readRDS("example_data/chr22_AF0.05_snpdata.RDS")

head(ld)

      rowsnp      colsnp        r2
1 rs62224609 rs376238049 0.9012642
2 rs62224609  rs62224614 0.9907366
3 rs62224609   rs7286962 0.9907366
4 rs62224609  rs55926024 0.1103000
5 rs62224609 rs117246541 0.9012642
6 rs62224609  rs62224618 0.9907366

head(snp_info)

# A tibble: 6 x 9
     AF SNP         allele    chr      pos   snp_id region_id   map ld_snp_id
  <dbl> <chr>       <chr>   <int>    <int>    <int>     <int> <dbl>     <int>
1 0.884 rs62224609  T,C        22 16051249 79758556        22     0  79758556
2 0.904 rs4965031   G,A        22 16052080 79758578        22     0  79758578
3 0.646 rs375684679 A,AAAAC    22 16052167 79758584        22     0  79758584
4 0.894 rs376238049 C,T        22 16052962 79758602        22     0  79758602
5 0.934 rs200777521 C,A        22 16052986 79758604        22     0  79758604
6 0.934 rs80167676  A,T        22 16053444 79758627        22     0  79758627

The ld data frame should contain the column names rowsnp, colsnp, and r2. The snp_info data frame contains information about all of the chromosome 22 variants with allele frequency greater than 0.05. The only piece of information we need from this data frame is the list of variants snp_info$SNP which provides the total list of variants used in the LD calculations.

LD pruning for one chromosome

The ld_prune function is somewhat flexible in its arguments, see help(ld_prune).

variants <- X %>% mutate(pval1 = 2*pnorm(abs(beta_hat_1/seb1), lower.tail=FALSE))
pruned <- ld_prune(variants = variants, 
                            ld = ld, total_ld_variants = snp_info$SNP, 
                            pval_cols = c("pval1"), 
                            pval_thresh = c(1e-3))

You have suppplied information for  2023354  variants.
Of these,  22835  have LD information.

length(pruned)

[1] 15

If length(pval_cols) =1, ld_prune returns a vector of selected variants. If There are multipld $p$-value columns provided, ld_prune will return a list of vectors, one for each column. The length of pval_thresh should be equal to the length of pval_cols and provides a threshold for each column. Excluding variants with high $p$-values speeds up the pruning step. We can fit CAUSE without high $p$-value variants because these variants have almost no influence on the posterior distributions or the ELPD test statistic. The exact value of the threshold isn’t important as long as it is fairly lenient. Including additional variants may slow down computation but shouldn’t change the results

Parallelizing over chromosomes

We highly recommend parallelizing for whole genome LD pruning. One way to do this is with the parallel pacakge, though this option uses a lot of memory. A better option is to submit separate jobs to nodes of a compute cluster and then combine results.

If you are analyzing many phenotypes, the most efficient way to complete this step is to first obtain a list of variants present in all data sets and use only these variants in your analysis. You can then obtain an LD-pruned set of variants prioritized for low $p$-values for each traits ($N$ lists if there are $N$ phenotypes). In any analysis, you will use the list of variants prioritized for the trait $M$ phenotype.

Download the LD-pruned variant list for our example analysis: top LDL list

download.file("https://github.com/jean997/cause/blob/master/example_data/top_ldl_pruned_vars.RDS", destfile = "example_data/top_ldl_pruned_vars.RDS")

LD pruning using Plink

To perform LD pruning using the ieugwasr wrapper to Plink LD clumping function use

X_clump <- X %>% rename(rsid = snp,
                  pval = p_value) %>%
           ieugwasr::ld_clump(dat = .,
                     clump_r2 = r2_thresh,
                     clump_p = pval_thresh,
                     plink_bin = genetics.binaRies::get_plink_binary(),
                     bfile = ref_path)
keep_snps <- X_clump$rsid

where r2_thresh is the $r^2$ threshold you would like to use, pval_thresh is the p-value threshold you will use to compute the CAUSE posteriors, and ref_path is a path to the reference data. We recomend using a lower $r^2$ threshold for plink LD clumping than for LD clumping based on estimates from LD shrink, since these are not shrunken.

Step 4: Fit CAUSE

Now that we have formatted data, an LD pruned set of variants, and nuisance parameters estimated, we can fit CAUSE! The function cause estimates posterior distributions under the sharing and causal models and calculates the ELPD for both models as well as for the null model in which there is neither a causal or a shared factor effect. This might take 5-10 minutes.

top_vars <- readRDS("example_data/top_ldl_pruned_vars.RDS")
res <- cause(X=X, variants = top_vars, param_ests = params)

Estimating CAUSE posteriors using  1215  variants.

Step 5: Look at Results

The resulting cause object contains an object for the partial sharing model fit (sharing), and object for the causal model fit (causal) and a table of ELPD results.

class(res)

[1] "cause"

names(res)

[1] "sharing" "causal"  "elpd"    "loos"    "data"    "sigma_g" "qalpha" 
[8] "qbeta"

res$elpd

   model1  model2 delta_elpd se_delta_elpd         z
1    null sharing -66.887583      8.350653 -8.009862
2    null  causal -75.072952      9.497320 -7.904646
3 sharing  causal  -8.185368      1.246101 -6.568783

class(res$sharing)

[1] "cause_post"

class(res$causal)

[1] "cause_post"

The elpd table has the following columns:

model1, model2: The models being compared
delta_elpd: Estimated difference in elpd. If delta_elpd is negative, model 2 is a better fit
se_delta_elpd: Estimated standard error of delta_elpd
z: delta_elpd/se_delta_elpd. A z-score that can be compared to a normal distribution to test if the difference in model fit is significant.

In this case we see that the causal model is significantly better than the sharing model from the thrid line of the table. The $z$-score is -6.57 corresponding to a p-value of 2.5^{-11}.

For each model (partial sharing and full) we can plot the posterior distributions of the parameters. Dotted lines show the prior distributions.

plot(res$sharing)

Version	Author	Date
81a1a48	Jean Morrison	2020-06-02
70e2b97	Jean Morrison	2019-12-04
bfa7d28	Jean Morrison	2019-07-09
d8d1486	Jean Morrison	2019-06-25
33b3732	Jean Morrison	2019-06-25
286f4e9	Jean Morrison	2019-06-25
1753b22	Jean Morrison	2018-11-09
2eb09d8	Jean Morrison	2018-11-06
4a8f76c	Jean Morrison	2018-11-06
a34393d	Jean Morrison	2018-10-24
bbe4901	Jean Morrison	2018-10-17
73690eb	Jean Morrison	2018-10-17

plot(res$causal)

Version	Author	Date
81a1a48	Jean Morrison	2020-06-02
70e2b97	Jean Morrison	2019-12-04
bfa7d28	Jean Morrison	2019-07-09
d8d1486	Jean Morrison	2019-06-25
33b3732	Jean Morrison	2019-06-25
286f4e9	Jean Morrison	2019-06-25
1753b22	Jean Morrison	2018-11-09
2eb09d8	Jean Morrison	2018-11-06
4a8f76c	Jean Morrison	2018-11-06
a34393d	Jean Morrison	2018-10-24
bbe4901	Jean Morrison	2018-10-17
73690eb	Jean Morrison	2018-10-17

The summary method will summarize the posterior medians and credible intervals.

summary(res, ci_size=0.95)

p-value testing that causal model is a better fit:  2.5e-11 
Posterior medians and  95 % credible intervals:
     model     gamma              eta                   q                  
[1,] "Sharing" NA                 "0.38 (0.31, 0.45)"   "0.78 (0.65, 0.89)"
[2,] "Causal"  "0.34 (0.28, 0.4)" "-0.01 (-0.66, 0.53)" "0.03 (0, 0.25)"

The plot method applied to a cause object will arrange all of this information on one spread.

plot(res)

Version	Author	Date
81a1a48	Jean Morrison	2020-06-02
70e2b97	Jean Morrison	2019-12-04
1a90dd6	Jean Morrison	2019-07-15
bfa7d28	Jean Morrison	2019-07-09
d8d1486	Jean Morrison	2019-06-25
33b3732	Jean Morrison	2019-06-25
286f4e9	Jean Morrison	2019-06-25
1753b22	Jean Morrison	2018-11-09
2eb09d8	Jean Morrison	2018-11-06
4a8f76c	Jean Morrison	2018-11-06
a34393d	Jean Morrison	2018-10-24
bbe4901	Jean Morrison	2018-10-17
73690eb	Jean Morrison	2018-10-17

The plot method can also produce scatter plots of the data showing for each model, the probability that each variant is acting through the shared factor ($U$) and the contribution of each variant to the ELPD test statistic.

plot(res, type="data")

Version	Author	Date
81a1a48	Jean Morrison	2020-06-02
1cfcb0b	Jean Morrison	2020-05-29
70e2b97	Jean Morrison	2019-12-04
1a90dd6	Jean Morrison	2019-07-15
bfa7d28	Jean Morrison	2019-07-09
d8d1486	Jean Morrison	2019-06-25
33b3732	Jean Morrison	2019-06-25
286f4e9	Jean Morrison	2019-06-25
1cc4860	Jean Morrison	2018-11-09
2eb09d8	Jean Morrison	2018-11-06
4a8f76c	Jean Morrison	2018-11-06

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] cause_1.2.0     dplyr_1.0.2     readr_1.4.0     workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0   xfun_0.18          ashr_2.2-47        purrr_0.3.4       
 [5] lattice_0.20-41    colorspace_2.0-0   vctrs_0.3.4        generics_0.1.0    
 [9] htmltools_0.5.0    loo_2.3.0          yaml_2.2.1         utf8_1.1.4        
[13] rlang_0.4.8        mixsqp_0.3-43      later_1.1.0.1      pillar_1.4.6      
[17] glue_1.4.2         matrixStats_0.57.0 lifecycle_0.2.0    stringr_1.4.0     
[21] munsell_0.5.0      gtable_0.3.0       evaluate_0.14      labeling_0.4.2    
[25] knitr_1.30         httpuv_1.5.4       invgamma_1.1       irlba_2.3.3       
[29] parallel_4.0.3     fansi_0.4.1        Rcpp_1.0.5         promises_1.1.1    
[33] backports_1.2.0    scales_1.1.1       RcppParallel_5.0.2 truncnorm_1.0-8   
[37] farver_2.0.3       fs_1.5.0           gridExtra_2.3      ggplot2_3.3.2     
[41] hms_0.5.3          digest_0.6.27      stringi_1.5.3      rprojroot_1.3-2   
[45] grid_4.0.3         cli_2.1.0          tools_4.0.3        magrittr_1.5      
[49] tibble_3.0.4       crayon_1.3.4       whisker_0.4        tidyr_1.1.2       
[53] pkgconfig_2.0.3    ellipsis_0.3.1     Matrix_1.2-18      SQUAREM_2020.5    
[57] assertthat_0.2.1   rmarkdown_2.3      rstudioapi_0.12    R6_2.5.0          
[61] intervals_0.15.2   git2r_0.27.1       compiler_4.0.3

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] cause_1.2.0     dplyr_1.0.2     readr_1.4.0     workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0   xfun_0.18          ashr_2.2-47        purrr_0.3.4       
 [5] lattice_0.20-41    colorspace_2.0-0   vctrs_0.3.4        generics_0.1.0    
 [9] htmltools_0.5.0    loo_2.3.0          yaml_2.2.1         utf8_1.1.4        
[13] rlang_0.4.8        mixsqp_0.3-43      later_1.1.0.1      pillar_1.4.6      
[17] glue_1.4.2         matrixStats_0.57.0 lifecycle_0.2.0    stringr_1.4.0     
[21] munsell_0.5.0      gtable_0.3.0       evaluate_0.14      labeling_0.4.2    
[25] knitr_1.30         httpuv_1.5.4       invgamma_1.1       irlba_2.3.3       
[29] parallel_4.0.3     fansi_0.4.1        Rcpp_1.0.5         promises_1.1.1    
[33] backports_1.2.0    scales_1.1.1       RcppParallel_5.0.2 truncnorm_1.0-8   
[37] farver_2.0.3       fs_1.5.0           gridExtra_2.3      ggplot2_3.3.2     
[41] hms_0.5.3          digest_0.6.27      stringi_1.5.3      rprojroot_1.3-2   
[45] grid_4.0.3         cli_2.1.0          tools_4.0.3        magrittr_1.5      
[49] tibble_3.0.4       crayon_1.3.4       whisker_0.4        tidyr_1.1.2       
[53] pkgconfig_2.0.3    ellipsis_0.3.1     Matrix_1.2-18      SQUAREM_2020.5    
[57] assertthat_0.2.1   rmarkdown_2.3      rstudioapi_0.12    R6_2.5.0          
[61] intervals_0.15.2   git2r_0.27.1       compiler_4.0.3