# Scalable Generalized Mixed Models in PheWAS using SAIGEgds

#### Xiuwen Zheng (Genomics Research Center, AbbVie, North Chicago)

#### Mar, 2020

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A phenome-wide association study (PheWAS) is known to be a powerful tool in discovery and replication of genetic association studies. The recent development in the UK Biobank resource with deep genomic and phenotyping data has provided unparalleled research opportunities. To reduce the computational complexity and cost of PheWAS, the SAIGE (scalable and accurate implementation of generalized mixed model [1]) method was proposed recently, controlling for case-control imbalance and sample structure in single variant association studies. However, it is still computationally challenging to analyze the associations of thousands of phenotypes with whole-genome variant data, especially for disease diagnoses using the ICD-10 codes of deep phenotyping.

Here we develop a new high-performance statistical package (SAIGEgds) for large-scale PheWAS using mixed models [2]. In this package, the SAIGE method is implemented with optimized C++ codes taking advantage of sparse structure of genotype dosages. SAIGEgds supports efficient genomic data structure (GDS) files [3] including both integer genotypes and numeric imputed dosages. Benchmarks using the UK Biobank White British genotype data (N=430K) with coronary heart disease and simulated cases, show that SAIGEgds is 5 to 6 times faster than the SAIGE R package in the steps of fitting null models and p-value calculations. When used in conjunction with high-performance computing (HPC) clusters and/or cloud resources, SAIGEgds provides an efficient analysis pipeline for biobank-scale PheWAS.

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# 1 Installation

- Bioconductor repository (available soon):

    if (!requireNamespace("BiocManager", quietly=TRUE))
        install.packages("BiocManager")
    BiocManager::install("SAIGEgds")

The `BiocManager::install()` approach may require that you build from source, i.e. `make` and compilers must be installed on your system – see the R FAQ for your operating system; you may also need to install dependencies manually.

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# 2 Examples

    library(SeqArray)
    library(SAIGEgds)

    # the genotype file in SeqArray GDS format (1000 Genomes Phase1, chromosome 22)
    (geno_fn <- seqExampleFileName("KG_Phase1"))

    ## [1] "C:/Users/biocbuild/bbs-3.14-bioc/R/library/SeqArray/extdata/1KG_phase1_release_v3_chr22.gds"

## 2.1 Preparing SNP data for genetic relationship matrix

    # open a SeqArray file in the package (1000 Genomes Phase1, chromosome 22)
    gds <- seqOpen(geno_fn)

The LD pruning is provided by snpgdsLDpruning() in the pacakge SNPRelate:

    library(SNPRelate)

    ## SNPRelate -- supported by Streaming SIMD Extensions 2 (SSE2)

    set.seed(1000)
    snpset <- snpgdsLDpruning(gds)

    ## SNV pruning based on LD:
    ## Calculating allele counts/frequencies ...
    ##
    [..................................................]  0%, ETC: ---
    [==================================================] 100%, completed, 1s
    ## # of selected variants: 19,651
    ## Excluding 122 SNVs (monomorphic: TRUE, MAF: NaN, missing rate: NaN)
    ##     # of samples: 1,092
    ##     # of SNVs: 19,651
    ##     using 1 thread
    ##     sliding window: 500,000 basepairs, Inf SNPs
    ##     |LD| threshold: 0.2
    ##     method: composite
    ## Chromosome 22: 37.70%, 7,455/19,773
    ## 7,455 markers are selected in total.

    str(snpset)

    ## List of 1
    ##  $ chr22: int [1:7455] 1 3 4 6 7 8 9 10 12 13 ...

    snpset.id <- unlist(snpset, use.names=FALSE)  # get the variant IDs of a LD-pruned set
    head(snpset.id)

    ## [1] 1 3 4 6 7 8

Create a genotype file for genetic relationship matrix (GRM) using the LD-pruned SNP set:

    grm_fn <- "grm_geno.gds"
    seqSetFilter(gds, variant.id=snpset.id)

    ## # of selected variants: 7,455

    # export to a GDS genotype file without annotation data
    seqExport(gds, grm_fn, info.var=character(), fmt.var=character(), samp.var=character())

    ## Export to 'grm_geno.gds'
    ##     sample.id (1,092)  [md5: bd2a93b49750ae227793ed23c575b620]
    ##     variant.id (7,455)  [md5: 67e7665f2ea9a4132b49a9f5bd84fbae]
    ##     position  [md5: a62a3752b030b2742f45dc3a5e751e51]
    ##     chromosome  [md5: 6cc8b780b298da2fb8237da650af7fe8]
    ##     allele  [md5: 3ff1146981d1807658f5b0243c2d47b0]
    ##     genotype  [md5: 5bd348af9af0c1f14bbb75c2b9a8b7e6]
    ##     phase  [md5: 922eca75d5098e1b684dcf0bae70e86b]
    ##     annotation/id  [md5: 7cfc098439330617251554e95fbb2f79]
    ##     annotation/qual  [md5: ce8ebcb77b11f0c53309778b928a7393]
    ##     annotation/filter  [md5: 11301697d2af5964436ec4eb2294906f]
    ## Done.
    ## Optimize the access efficiency ...
    ## Clean up the fragments of GDS file:
    ##     open the file 'grm_geno.gds' (412.9K)
    ##     # of fragments: 91
    ##     save to 'grm_geno.gds.tmp'
    ##     rename 'grm_geno.gds.tmp' (412.5K, reduced: 444B)
    ##     # of fragments: 54

If genotypes are split by chromosomes, `seqMerge()` in the SeqArray package can be used to combine the LD-pruned SNP sets.

    # close the file
    seqClose(gds)

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## 2.2 Fitting the null model

A simulated phenotype data is used to demonstrate the model fitting:

    set.seed(1000)
    sampid <- seqGetData(grm_fn, "sample.id")  # sample IDs in the genotype file

    pheno <- data.frame(
        sample.id = sampid,
        y  = sample(c(0, 1), length(sampid), replace=TRUE, prob=c(0.95, 0.05)),
        x1 = rnorm(length(sampid)),
        x2 = rnorm(length(sampid)),
        stringsAsFactors = FALSE)
    head(pheno)

    ##   sample.id y          x1         x2
    ## 1   HG00096 0  0.02329127 -0.1700062
    ## 2   HG00097 0 -1.38629108 -1.0058530
    ## 3   HG00099 0  0.56339700 -0.6989284
    ## 4   HG00100 0 -0.25703246 -1.5884806
    ## 5   HG00101 0  0.02926429  0.7645377
    ## 6   HG00102 0 -0.44480750  1.9964501

    grm_fn

    ## [1] "grm_geno.gds"

    # null model fitting using GRM from grm_fn
    glmm <- seqFitNullGLMM_SPA(y ~ x1 + x2, pheno, grm_fn, trait.type="binary", sample.col="sample.id")

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## 2.3 P-value calculations

    # genetic variants stored in the file geno_fn
    geno_fn

    ## [1] "C:/Users/biocbuild/bbs-3.14-bioc/R/library/SeqArray/extdata/1KG_phase1_release_v3_chr22.gds"

    # calculate, using 2 processes
    assoc <- seqAssocGLMM_SPA(geno_fn, glmm, mac=10, parallel=2)

    ## SAIGE association analysis:
    ##     open 'C:/Users/biocbuild/bbs-3.14-bioc/R/library/SeqArray/extdata/1KG_phase1_release_v3_chr22.gds'
    ##     # of samples: 1,092
    ##     # of variants: 19,773
    ##     MAF threshold: NaN
    ##     MAC threshold: 10
    ##     missing threshold for variants: 0.1
    ##     p-value threshold for SPA adjustment: 0.05
    ##     variance ratio for approximation: 1
    ##     # of processes: 2
    ## Distribute the model parameters to the 2 processes
    ##
    [..................................................]  0%, ETC: ---
    [==================================================] 100%, completed, 0s
    ## # of variants after filtering by MAF, MAC and missing thresholds: 9,371
    ## Done.

    head(assoc)

    ##   id chr      pos       rs.id                ref alt     AF.alt mac  num       beta        SE      pval
    ## 1  1  22 16051497 rs141578542                  A   G 0.30494505 666 1092  0.2214607 0.2194430 0.3128813
    ## 2  2  22 16059752 rs139717388                  G   A 0.05677656 124 1092  0.4227084 0.4240575 0.3188526
    ## 3  5  22 16060995   rs2843244                  G   A 0.06135531 134 1092  0.3044670 0.4100805 0.4578107
    ## 4  8  22 16166919             ATATTTTCTGCACATATT   A 0.01190476  26 1092 -1.0851502 0.8804521 0.2177653
    ## 5  9  22 16173887                             GT   G 0.03067766  67 1092 -0.8195449 0.5587294 0.1424302
    ## 6 10  22 16205515 rs144309057                  G   A 0.01098901  24 1092 -1.0826898 0.9340256 0.2463890
    ##      p.norm converged
    ## 1 0.3128813      TRUE
    ## 2 0.3188526      TRUE
    ## 3 0.4578107      TRUE
    ## 4 0.2177653      TRUE
    ## 5 0.1424302      TRUE
    ## 6 0.2463890      TRUE

    # filtering based on p-value
    assoc[assoc$pval < 5e-4, ]

    ##         id chr      pos      rs.id ref alt    AF.alt mac  num     beta        SE         pval       p.norm
    ## 8422 17856  22 48507315 rs57751251   T   C 0.1666667 364 1092 0.971123 0.2555021 1.442051e-04 5.999175e-05
    ## 8423 17857  22 48509092  rs7292083   C   T 0.1625458 355 1092 1.008351 0.2585365 9.610252e-05 3.358045e-05
    ## 8630 18282  22 49060987  rs4925399   A   G 0.5929487 889 1092 0.708757 0.1956659 2.920160e-04 2.363433e-04
    ##      converged
    ## 8422      TRUE
    ## 8423      TRUE
    ## 8630      TRUE

The output could be directly saved to an R object file or a GDS file:

    # save to 'assoc.gds'
    seqAssocGLMM_SPA(geno_fn, glmm, mac=10, parallel=2, res.savefn="assoc.gds")

    ## SAIGE association analysis:
    ##     open 'C:/Users/biocbuild/bbs-3.14-bioc/R/library/SeqArray/extdata/1KG_phase1_release_v3_chr22.gds'
    ##     # of samples: 1,092
    ##     # of variants: 19,773
    ##     MAF threshold: NaN
    ##     MAC threshold: 10
    ##     missing threshold for variants: 0.1
    ##     p-value threshold for SPA adjustment: 0.05
    ##     variance ratio for approximation: 1
    ##     # of processes: 2
    ## Distribute the model parameters to the 2 processes
    ##
    [..................................................]  0%, ETC: ---
    [==================================================] 100%, completed, 0s
    ## # of variants after filtering by MAF, MAC and missing thresholds: 9,371
    ## Save to 'assoc.gds' ...
    ## Done.

Open the output GDS file using the functions in the gdsfmt package:

    # open the GDS file
    (f <- openfn.gds("assoc.gds"))

    ## File: C:\Users\biocbuild\bbs-3.14-bioc\tmpdir\RtmpaE708v\Rbuild2af01e80447c\SAIGEgds\vignettes\assoc.gds (448.7K)
    ## +    [  ] *
    ## |--+ sample.id   { Str8 1092 LZMA(10.2%), 888B }
    ## |--+ id   { Int32 9371 LZMA(13.4%), 4.9K }
    ## |--+ chr   { Str8 9371 LZMA(0.46%), 128B }
    ## |--+ pos   { Int32 9371 LZMA(53.9%), 19.7K }
    ## |--+ rs.id   { Str8 9371 LZMA(35.7%), 34.0K }
    ## |--+ ref   { Str8 9371 LZMA(23.7%), 89.0K }
    ## |--+ alt   { Str8 9371 LZMA(21.0%), 4.0K }
    ## |--+ AF.alt   { Float64 9371 LZMA(20.1%), 14.7K }
    ## |--+ mac   { Float64 9371 LZMA(17.3%), 12.6K }
    ## |--+ num   { Int32 9371 LZMA(0.35%), 132B }
    ## |--+ beta   { Float64 9371 LZMA(92.9%), 68.0K }
    ## |--+ SE   { Float64 9371 LZMA(90.4%), 66.2K }
    ## |--+ pval   { Float64 9371 LZMA(89.7%), 65.7K }
    ## |--+ p.norm   { Float64 9371 LZMA(89.8%), 65.7K }
    ## \--+ converged   { Int32,logical 9371 LZMA(0.35%), 132B } *

    # get p-values
    pval <- read.gdsn(index.gdsn(f, "pval"))
    summary(pval)

    ##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.
    ## 0.0000961 0.2862278 0.4639086 0.4980417 0.7256674 0.9999449

    closefn.gds(f)

Load association results using the function `seqSAIGE_LoadPval()` in SAIGEgds:

    res <- seqSAIGE_LoadPval("assoc.gds")

    ## Loading 'assoc.gds' ...

    head(res)

    ##   id chr      pos       rs.id                ref alt     AF.alt mac  num       beta        SE      pval
    ## 1  1  22 16051497 rs141578542                  A   G 0.30494505 666 1092  0.2214607 0.2194430 0.3128813
    ## 2  2  22 16059752 rs139717388                  G   A 0.05677656 124 1092  0.4227084 0.4240575 0.3188526
    ## 3  5  22 16060995   rs2843244                  G   A 0.06135531 134 1092  0.3044670 0.4100805 0.4578107
    ## 4  8  22 16166919             ATATTTTCTGCACATATT   A 0.01190476  26 1092 -1.0851502 0.8804521 0.2177653
    ## 5  9  22 16173887                             GT   G 0.03067766  67 1092 -0.8195449 0.5587294 0.1424302
    ## 6 10  22 16205515 rs144309057                  G   A 0.01098901  24 1092 -1.0826898 0.9340256 0.2463890
    ##      p.norm converged
    ## 1 0.3128813      TRUE
    ## 2 0.3188526      TRUE
    ## 3 0.4578107      TRUE
    ## 4 0.2177653      TRUE
    ## 5 0.1424302      TRUE
    ## 6 0.2463890      TRUE

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## 2.4 QQ plot for p-values

    n <- nrow(assoc)
    # expected p-values
    exp.pval <- (1:n) / n
    # observed p-values
    obs.pval <- sort(assoc$pval)

    plot(-log10(exp.pval), -log10(obs.pval), xlab="-log10(expected P)", ylab="-log10(observed P)", cex=2/3)
    abline(0, 1, col="red", lty=2)

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# 3 Session Information

    sessionInfo()

    ## R version 4.1.3 (2022-03-10)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C                           LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] SNPRelate_1.28.0 SAIGEgds_1.8.1   Rcpp_1.0.8.3     SeqArray_1.34.0  gdsfmt_1.30.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] XVector_0.34.0         knitr_1.38             magrittr_2.0.3         zlibbioc_1.40.0
    ##  [5] GenomicRanges_1.46.1   BiocGenerics_0.40.0    IRanges_2.28.0         R6_2.5.1
    ##  [9] rlang_1.0.2            fastmap_1.1.0          highr_0.9              stringr_1.4.0
    ## [13] GenomeInfoDb_1.30.1    tools_4.1.3            parallel_4.1.3         xfun_0.30
    ## [17] cli_3.2.0              jquerylib_0.1.4        htmltools_0.5.2        RcppParallel_5.1.5
    ## [21] yaml_2.3.5             digest_0.6.29          crayon_1.5.1           GenomeInfoDbData_1.2.7
    ## [25] bitops_1.0-7           sass_0.4.1             S4Vectors_0.32.4       RCurl_1.98-1.6
    ## [29] evaluate_0.15          rmarkdown_2.13         stringi_1.7.6          compiler_4.1.3
    ## [33] bslib_0.3.1            Biostrings_2.62.0      stats4_4.1.3           jsonlite_1.8.0

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# 4 References

1. Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, LeFaive J, VandeHaar P, Gagliano SA, Gifford A, Bastarache LA, Wei WQ, Denny JC, Lin M, Hveem K, Kang HM, Abecasis GR, Willer CJ, Lee S. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet (2018). Sep;50(9):1335-1341. DOI: 10.1038/s41588-018-0184-y
2. Zheng X, Davis J.Wade, SAIGEgds – an efficient statistical tool for large-scale PheWAS with mixed models; (Abstract 1920276). The Annual Meeting of The American Society of Human Genetics (ASHG), Oct 15-19, 2019, Houston, US.
3. Zheng X, Gogarten S, Lawrence M, Stilp A, Conomos M, Weir BS, Laurie C, Levine D. SeqArray – A storage-efficient high-performance data format for WGS variant calls. Bioinformatics (2017). DOI: 10.1093/bioinformatics/btx145.

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# 5 See also

SeqArray: Data Management of Large-scale Whole-genome Sequence Variant Calls

SNPRelate: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data

gds2bgen: Format conversion from bgen to gds