Here we show how to compute polygenic risk scores using LDpred2.

New: if you install {bigsnpr} >= v1.10.4, LDpred2-grid and LDpred2-auto should be much faster for large data. You can install {bigsnpr} from CRAN using install.packages("bigsnpr"), or the latest version from GitHub using remotes::install_github("privefl/bigsnpr").

This tutorial uses fake data for educational purposes only. Another tutorial using another dataset can be found at https://privefl.github.io/bigsnpr-extdoc/polygenic-scores-pgs.html.

In practice, until we find a better set of variants, we recommend using the HapMap3 variants used in the PRS-CS and LDpred2 papers. If you do not have enough data to use as LD reference (e.g. at least 2000 individuals), we provide an LD reference to be used directly at https://doi.org/10.6084/m9.figshare.13034123, along with an example R script on how to use it. New: we now provide a new version of these LD references at https://doi.org/10.6084/m9.figshare.19213299 by forming independent LD blocks in the matrices, which can be useful for robustness and extra speed gains (see this new paper).

Information about these variants can be retrieved with

# $pos is in build GRCh37 / hg19, but we provide positions in 3 other builds info <- readRDS(runonce::download_file( "https://ndownloader.figshare.com/files/25503788", dir = "tmp-data", fname = "map_hm3_ldpred2.rds")) str(info) ## Classes 'tbl_df', 'tbl' and 'data.frame': 1054330 obs. of 10 variables: ##$ chr     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $pos : int 752721 754182 760912 768448 779322 838555 846808 853954 854250 864938 ... ##$ a0      : chr  "A" "A" "C" "G" ...
##  $a1 : chr "G" "G" "T" "A" ... ##$ rsid    : chr  "rs3131972" "rs3131969" "rs1048488" "rs12562034" ...
##  $af_UKBB : num 0.841 0.87 0.84 0.106 0.128 ... ##$ ld      : num  3.69 3.73 3.69 1.4 3.68 ...
##  $pos_hg17: int 792584 794045 800775 808311 819185 878418 886671 893817 894113 904801 ... ##$ pos_hg18: int  742584 744045 750775 758311 769185 828418 836671 843817 844113 854801 ...
##  $pos_hg38: int 817341 818802 825532 833068 843942 903175 911428 918574 918870 929558 ... Note that you should run LDpred2 genome-wide; just build the SFBM (the sparse LD matrix on disk) so that it contains all 1M HapMap3 variants genome-wide (see the for-loop below). ## Downloading genotype data and summary statistics You can download the tutorial data and unzip files in R. We store those files in a directory called "tmp-data" here. # install.packages("runonce") zip <- runonce::download_file( "https://github.com/privefl/bigsnpr/raw/master/data-raw/public-data3.zip", dir = "tmp-data") unzip(zip) First, you need to read genotype data from the PLINK files (or BGEN files) as well as the text file containing summary statistics. # Load packages bigsnpr and bigstatsr library(bigsnpr) ## Loading required package: bigstatsr # Read from bed/bim/fam, it generates .bk and .rds files. snp_readBed("tmp-data/public-data3.bed") ## [1] "C:\\Users\\au639593\\Desktop\\bigsnpr\\tmp-data\\public-data3.rds" # Attach the "bigSNP" object in R session obj.bigSNP <- snp_attach("tmp-data/public-data3.rds") # See how the file looks like str(obj.bigSNP, max.level = 2, strict.width = "cut") ## List of 3 ##$ genotypes:Reference class 'FBM.code256' [package "bigstatsr"] with 16 ..
##   ..and 26 methods, of which 12 are  possibly relevant:
##   ..  add_columns, as.FBM, bm, bm.desc, check_dimensions,
##   ..  check_write_permissions, copy#envRefClass, initialize,
##   ..  initialize#FBM, save, show#envRefClass, show#FBM
##  $fam :'data.frame': 503 obs. of 6 variables: ## ..$ family.ID  : int [1:503] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$sample.ID : chr [1:503] "HG00096" "HG00097" "HG00099" "HG00100" ... ## ..$ paternal.ID: int [1:503] 0 0 0 0 0 0 0 0 0 0 ...
##   ..$maternal.ID: int [1:503] 0 0 0 0 0 0 0 0 0 0 ... ## ..$ sex        : int [1:503] 1 2 2 2 1 2 1 1 2 1 ...
##   ..$affection : num [1:503] -0.547 0.188 -0.407 -0.28 0.398 ... ##$ map      :'data.frame':   45337 obs. of  6 variables:
##   ..$chromosome : int [1:45337] 1 1 1 1 1 1 1 1 1 1 ... ## ..$ marker.ID   : chr [1:45337] "rs3934834" "rs12726255" "rs11260549" "..
##   ..$genetic.dist: num [1:45337] 0.359 0.408 0.932 0.986 1.107 ... ## ..$ physical.pos: int [1:45337] 1005806 1049950 1121794 1162435 1314015..
##   ..$allele1 : chr [1:45337] "T" "G" "A" "A" ... ## ..$ allele2     : chr [1:45337] "C" "A" "G" "C" ...
##  - attr(*, "class")= chr "bigSNP"
# Get aliases for useful slots
G   <- obj.bigSNP$genotypes CHR <- obj.bigSNP$map$chromosome POS <- obj.bigSNP$map$physical.pos y <- obj.bigSNP$fam$affection (NCORES <- nb_cores()) ## [1] 4 # Read external summary statistics sumstats <- bigreadr::fread2("tmp-data/public-data3-sumstats.txt") str(sumstats) ## 'data.frame': 50000 obs. of 9 variables: ##$ rsid   : chr  "rs3934834" "rs12726255" "rs11260549" "rs3766186" ...
##  $chr : int 1 1 1 1 1 1 1 1 1 1 ... ##$ pos    : int  995669 1039813 1111657 1152298 1303878 1495118 1833906 2041373 2130121 2201709 ...
##  $a0 : chr "G" "T" "C" "C" ... ##$ a1     : chr  "A" "C" "T" "A" ...
##  $beta : num 0.0125 0.027 0.0171 -0.0195 -0.0057 ... ##$ beta_se: num  0.0157 0.0167 0.0179 0.0199 0.0213 ...
##  $N : int 15155 15155 15155 15155 15155 15155 15155 15155 15155 15155 ... ##$ p      : num  0.426 0.107 0.342 0.328 0.789 ...

We split the genotype data using part of the data to choose hyper-parameters and another part of the data to evaluate statistical properties of polygenic risk score such as AUC. Here we consider that there are 350 individuals to be used as validation set to tune hyper-parameters for LDpred2-grid. The other 153 individuals are used as test set to evaluate the final models.

set.seed(1)
ind.val <- sample(nrow(G), 350)
ind.test <- setdiff(rows_along(G), ind.val)

## Matching variants between genotype data and summary statistics

To match variants contained in genotype data and summary statistics, the variables "chr" (chromosome number), "pos" (physical genetic position in bp), "a0" (reference allele) and "a1" (alternative allele) should be available in the summary statistics and in the genotype data. These 4 variables are used to match variants between the two data frames. From the summary statistics, you need to get "beta", "beta_se" (standard errors), and "n_eff" (the effective sample sizes per variant for a GWAS using logistic regression, and simply the sample size for continuous traits).

# sumstats$n_eff <- 4 / (1 / sumstats$n_case + 1 / sumstats$n_control) # sumstats$n_case <- sumstats$n_control <- NULL sumstats$n_eff <- sumstats$N map <- setNames(obj.bigSNP$map[-3], c("chr", "rsid", "pos", "a1", "a0"))
df_beta <- snp_match(sumstats, map)
## 50,000 variants to be matched.
## 0 ambiguous SNPs have been removed.
## 4 variants have been matched; 2 were flipped and 2 were reversed.
## Error: Not enough variants have been matched.

Here, there is problem with the matching; this is due to having different genome builds. You can either convert between builds with snp_modifyBuild() (or directly use the converted positions in info), or match by rsIDs instead.

df_beta <- snp_match(sumstats, map, join_by_pos = FALSE)  # use rsid instead of pos
## 50,000 variants to be matched.
## 0 ambiguous SNPs have been removed.
## 45,337 variants have been matched; 22,758 were flipped and 15,092 were reversed.

If no or few variants are actually flipped, you might want to disable the strand flipping option (strand_flip = FALSE) and maybe remove the few that were flipped (errors?).

## Computing LDpred2 scores genome-wide

Some quality control on summary statistics is highly recommended (see paper and other tutorial). A new refined QC is described in this new paper. See e.g. the code that was used to prepare the sumstats there.

### Correlation

First, you need to compute correlations between variants. We recommend to use a window size of 3 cM (see the LDpred2 paper).

# POS2 <- snp_asGeneticPos(CHR, POS, dir = "tmp-data", ncores = NCORES)
# To avoid downloading "large" files, POS2 has been precomputed
POS2 <- obj.bigSNP$map$genetic.dist

We create the on-disk sparse genome-wide correlation matrix on-the-fly:

tmp <- tempfile(tmpdir = "tmp-data")

for (chr in 1:22) {

# print(chr)

## indices in 'df_beta'
ind.chr <- which(df_beta$chr == chr) ## indices in 'G' ind.chr2 <- df_beta$_NUM_ID_[ind.chr]

corr0 <- snp_cor(G, ind.col = ind.chr2, size = 3 / 1000,
infos.pos = POS2[ind.chr2], ncores = NCORES)

if (chr == 1) {
ld <- Matrix::colSums(corr0^2)
corr <- as_SFBM(corr0, tmp, compact = TRUE)
} else {
ld <- c(ld, Matrix::colSums(corr0^2))
corr$add_columns(corr0, nrow(corr)) } } To use the “compact” format for SFBMs, you need packageVersion("bigsparser") >= package_version("0.5"). Make sure to reinstall {bigsnpr} after updating {bigsparser} to this new version (to avoid crashes). file.size(corr$sbk) / 1024^3  # file size in GB
## [1] 0.03205058

Note that you will need at least the same memory as this file size (to keep it cached for faster processing) + some other memory for all the results returned. If you do not have enough memory, processing will be very slow (because you would read the data from disk all the time). If using the one million HapMap3 variants, having 60 GB of memory should be enough.

### LDpred2-inf: infinitesimal model

# Estimate of h2 from LD Score regression
(ldsc <- with(df_beta, snp_ldsc(ld, length(ld), chi2 = (beta / beta_se)^2,
sample_size = n_eff, blocks = NULL)))
##       int        h2
## 0.9496371 0.2981213
h2_est <- ldsc[["h2"]]
beta_inf <- snp_ldpred2_inf(corr, df_beta, h2 = h2_est)
pred_inf <- big_prodVec(G, beta_inf, ind.row = ind.test, ind.col = df_beta[["_NUM_ID_"]])
pcor(pred_inf, y[ind.test], NULL)
## [1] 0.3171149 0.1668316 0.4529985

LDpred2-inf would very likely perform worse than the other models presented hereinafter. We actually recommend not to use it anymore.

### LDpred2(-grid): grid of models

In practice, we recommend to test multiple values for h2 and p.

(h2_seq <- round(h2_est * c(0.3, 0.7, 1, 1.4), 4))
## [1] 0.0894 0.2087 0.2981 0.4174
(p_seq <- signif(seq_log(1e-5, 1, length.out = 21), 2))
##  [1] 1.0e-05 1.8e-05 3.2e-05 5.6e-05 1.0e-04 1.8e-04 3.2e-04 5.6e-04
##  [9] 1.0e-03 1.8e-03 3.2e-03 5.6e-03 1.0e-02 1.8e-02 3.2e-02 5.6e-02
## [17] 1.0e-01 1.8e-01 3.2e-01 5.6e-01 1.0e+00
(params <- expand.grid(p = p_seq, h2 = h2_seq, sparse = c(FALSE, TRUE)))
##          p     h2 sparse
## 1  1.0e-05 0.0894  FALSE
## 2  1.8e-05 0.0894  FALSE
## 3  3.2e-05 0.0894  FALSE
## 4  5.6e-05 0.0894  FALSE
## 5  1.0e-04 0.0894  FALSE
## 6  1.8e-04 0.0894  FALSE
## 7  3.2e-04 0.0894  FALSE
## 8  5.6e-04 0.0894  FALSE
## 9  1.0e-03 0.0894  FALSE
## 10 1.8e-03 0.0894  FALSE
##  [ reached 'max' / getOption("max.print") -- omitted 158 rows ]
# takes less than 2 min with 4 cores
beta_grid <- snp_ldpred2_grid(corr, df_beta, params, ncores = NCORES)
pred_grid <- big_prodMat(G, beta_grid, ind.col = df_beta[["_NUM_ID_"]])
params$score <- apply(pred_grid[ind.val, ], 2, function(x) { if (all(is.na(x))) return(NA) summary(lm(y[ind.val] ~ x))$coef["x", 3]
# summary(glm(y[ind.val] ~ x, family = "binomial"))$coef["x", 3] }) Note that missing values represent models that diverged substantially. library(ggplot2) ggplot(params, aes(x = p, y = score, color = as.factor(h2))) + theme_bigstatsr() + geom_point() + geom_line() + scale_x_log10(breaks = 10^(-5:0), minor_breaks = params$p) +
facet_wrap(~ sparse, labeller = label_both) +
labs(y = "GLM Z-Score", color = "h2") +
theme(legend.position = "top", panel.spacing = unit(1, "lines"))

library(dplyr)
params %>%
mutate(sparsity = colMeans(beta_grid == 0), id = row_number()) %>%
arrange(desc(score)) %>%
mutate_at(c("score", "sparsity"), round, digits = 3) %>%
slice(1:10)
##      p     h2 sparse  score sparsity  id
## 1 0.01 0.4174  FALSE 10.302    0.000  76
## 2 0.01 0.4174   TRUE 10.277    0.839 160
## 3 0.01 0.2087   TRUE 10.239    0.782 118
## 4 0.01 0.2087  FALSE 10.227    0.000  34
## 5 0.01 0.2981  FALSE 10.202    0.000  55
##  [ reached 'max' / getOption("max.print") -- omitted 5 rows ]

You can then choose the best model according to your preferred criterion (e.g. max AUC). Here, we use the Z-Score from the (linear or logistic) regression of the phenotype by the PRS since we have found it more robust than using the correlation or the AUC. It also enables adjusting for covariates in this step.

Also note that we separate both sparse and non-sparse models to show that their predictive performance are similar (in the paper). In practice, if you do not really care about sparsity, you could choose the best LDpred2-grid model among all sparse and non-sparse models. If you do, choose the best sparse one (if it is close enough to the best one).

best_beta_grid <- params %>%
mutate(id = row_number()) %>%
# filter(sparse) %>%
arrange(desc(score)) %>%
slice(1) %>%
print() %>%
pull(id) %>%
beta_grid[, .]
##      p     h2 sparse    score id
## 1 0.01 0.4174  FALSE 10.30208 76
pred <- big_prodVec(G, best_beta_grid, ind.row = ind.test,
ind.col = df_beta[["_NUM_ID_"]])
pcor(pred, y[ind.test], NULL)
## [1] 0.4980540 0.3684984 0.6086318

### LDpred2-auto: automatic model

We recommend to run many chains in parallel with different initial values for p. In this new paper, we have introduced two new parameters in LDpred2-auto for improving its robustness, allow_jump_sign and shrink_corr, and recommend to use them.

# takes less than 2 min with 4 cores
multi_auto <- snp_ldpred2_auto(corr, df_beta, h2_init = h2_est,
vec_p_init = seq_log(1e-4, 0.2, length.out = 30),
allow_jump_sign = FALSE, shrink_corr = 0.95,
ncores = NCORES)
str(multi_auto, max.level = 1)
## List of 30
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
##  $:List of 10 ##$ :List of 10
str(multi_auto[[1]], max.level = 1)
## List of 10
##  $beta_est : num [1:45337] 6.50e-05 3.25e-04 1.01e-04 -1.18e-04 2.38e-05 ... ##$ postp_est  : num [1:45337] 0.00615 0.01415 0.00691 0.00708 0.00486 ...
##  $corr_est : num [1:45337] 7.08e-05 3.44e-04 5.75e-05 1.93e-06 -8.05e-05 ... ##$ sample_beta: num[1:45337, 0 ]
##  $p_est : num 0.0122 ##$ h2_est     : num 0.217
##  $path_p_est : num [1:700] 0.00103 0.00181 0.00344 0.00453 0.00464 ... ##$ path_h2_est: num [1:700] 0.0808 0.1253 0.1527 0.1533 0.1501 ...
##  $h2_init : num 0.298 ##$ p_init     : num 1e-04

You can verify whether the chains “converged” by looking at the path of the chains:

library(ggplot2)
auto <- multi_auto[[1]]  # first chain
plot_grid(
qplot(y = auto$path_p_est) + theme_bigstatsr() + geom_hline(yintercept = auto$p_est, col = "blue") +
scale_y_log10() +
labs(y = "p"),
qplot(y = auto$path_h2_est) + theme_bigstatsr() + geom_hline(yintercept = auto$h2_est, col = "blue") +
labs(y = "h2"),
ncol = 1, align = "hv"
)

In the LDpred2 paper, we proposed an automatic way of filtering bad chains by comparing the scale of the resulting predictions (see this code). We have tested a somewhat equivalent and simpler alternative since, which we recommend here:

# range should be between 0 and 1
(range <- sapply(multi_auto, function(auto) diff(range(auto$corr_est)))) ## [1] 0.1202303 0.1191589 0.1198559 0.1193584 0.1186733 0.1201270 0.1200940 ## [8] 0.1183752 0.1207500 0.1195862 0.1201593 0.1192859 0.1197396 0.1190085 ## [15] 0.1187442 0.1201806 0.1187239 0.1192436 0.1185955 0.1171342 0.1172936 ## [22] 0.1189853 0.1199237 0.1189011 0.1190304 0.1204099 0.1205797 0.1209386 ## [29] 0.1185130 0.1178552 (keep <- (range > (0.9 * quantile(range, 0.9)))) ## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [15] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE ## [29] TRUE TRUE To get the final effects / predictions (after filtering): beta_auto <- rowMeans(sapply(multi_auto[keep], function(auto) auto$beta_est))
pred_auto <- big_prodVec(G, beta_auto, ind.row = ind.test, ind.col = df_beta[["_NUM_ID_"]])
pcor(pred_auto, y[ind.test], NULL)
## [1] 0.4961249 0.3662820 0.6070162

### lassosum2: grid of models

lassosum2 is a re-implementation of the lassosum model that now uses the exact same input parameters as LDpred2 (corr and df_beta). It should be fast to run. It can be run next to LDpred2 and the best model can be chosen using the validation set. Note that parameter ‘s’ from lassosum has been replaced by a new parameter ‘delta’ in lassosum2, in order to better reflect that the lassosum model also uses L2-regularization (therefore, elastic-net regularization).

beta_lassosum2 <- snp_lassosum2(corr, df_beta, ncores = NCORES)
params2 <- attr(beta_lassosum2, "grid_param")
pred_grid2 <- big_prodMat(G, beta_lassosum2, ind.col = df_beta[["_NUM_ID_"]])
params2$score <- apply(pred_grid2[ind.val, ], 2, function(x) { if (all(is.na(x))) return(NA) summary(lm(y[ind.val] ~ x))$coef["x", 3]
# summary(glm(y[ind.val] ~ x, family = "binomial"))$coef["x", 3] }) library(ggplot2) ggplot(params2, aes(x = lambda, y = score, color = as.factor(delta))) + theme_bigstatsr() + geom_point() + geom_line() + scale_x_log10(breaks = 10^(-5:0)) + labs(y = "GLM Z-Score", color = "delta") + theme(legend.position = "top") + guides(colour = guide_legend(nrow = 1)) ## Warning: Removed 10 rows containing missing values (geom_point). ## Warning: Removed 10 row(s) containing missing values (geom_path). library(dplyr) best_grid_lassosum2 <- params2 %>% mutate(id = row_number()) %>% arrange(desc(score)) %>% print() %>% slice(1) %>% pull(id) %>% beta_lassosum2[, .] ## lambda delta num_iter time sparsity score id ## 1 0.02086632 0.001 21 0.02 0.9864570 9.715332 8 ## 2 0.02086632 0.010 20 0.03 0.9864790 9.709724 38 ## 3 0.02086632 0.100 16 0.02 0.9862364 9.667190 68 ## 4 0.02432834 0.001 19 0.03 0.9942652 9.404453 7 ## [ reached 'max' / getOption("max.print") -- omitted 116 rows ] # Choose the best among all LDpred2-grid and lassosum2 models best_grid_overall <- if(max(params2$score, na.rm = TRUE) > max(params\$score, na.rm = TRUE),
best_grid_lassosum2, best_beta_grid)
# Some cleaning
rm(corr); gc(); file.remove(paste0(tmp, ".sbk"))
##            used  (Mb) gc trigger  (Mb) max used  (Mb)
## Ncells  3474373 185.6    6003534 320.7  6003534 320.7
## Vcells 33893628 258.6   58101092 443.3 58101090 443.3
## [1] TRUE