# Using SNPknock with Genetic Data

#### Matteo Sesia (msesia@stanford.edu)

#### 2019-05-16

This vignette illustrates the basic usage of the `SNPknock` package in combination with the phasing software `fastphase` (Scheet and Stephens 2006) to create knockoffs of unphased genotypes or phased haplotypes (Sesia, Sabatti, and Candès 2019, Sesia et al. (2019)). Since `fastphase` is not available as an R package, this functionality of `SNPknock` requires the user to first obtain a copy of `fastphase`.

To learn more about the application of `SNPknock` to large genome-wide association studies (Sesia et al. 2019), visit: https://msesia.github.io/knockoffzoom/.

## Obtaining `fastphase`

`fastphase` is a phasing and imputation tool based on the hidden Markov model described in (Scheet and Stephens 2006).

Binary executables for Linux and Mac OS are available from http://scheet.org/software.html.

Before continuing with this tutorial, download the `fastphase` tarball from the above link and extract the `fastphase` executable file into a convenient directory (e.g. “~/bin/”).

## Knockoffs for unphased genotypes

### Fitting the hidden Markov model on genotype data

A small synthetic dataset of 1454 unphased genotype SNPs from 100 individuals can be found in the package installation directory. We can load it with:

    library(SNPknock)
    X_file = system.file("extdata", "genotypes.RData", package = "SNPknock")
    load(X_file)
    table(X)

    ## X
    ##      0      1      2
    ## 130548  90112  25540

Below, we show how to fit a hidden Markov model to this data, with the help of `fastphase`. Since `fastphase` takes as input genotype sequences in “.inp” format, we must first convert the X matrix by calling `writeXtoInp`. By default, this function will write onto a temporary file in the R temporary directory.

    # Convert X into the suitable fastphase input format, write it into a temporary file
    # and return the path to that file.
    Xinp_file = writeXtoInp(X)

Assuming that we have already downloaded `fastphase`, we can call it to fit the hidden Markov model to X.

    fp_path  = "~/bin/fastphase" # Path to the fastphase executable
    # Call fastphase and return the path to the parameter estimate files
    fp_outPath = runFastPhase(fp_path, Xinp_file, K = 12, numit = 15)

    ## ~/bin/fastphase -Pp -T1 -K12 -g -H-4 -C15 -S1 -o'/tmp/Rtmpd9jSUL/file142d20959abf' /tmp/Rtmpd9jSUL/file142d11a4dc0d.inp

Above, the `SNPknock` package could not find `fastphase` because we did not provide the correct path (we cannot include third-party executable files within this package). However, if you install `fastphase` separately and provide `SNPknock` with the correct path, this will work.

If the previous step worked for you, you can find the parameter estimates produced by `fastphase` in the following files:

    r_file = paste(fp_outPath, "_rhat.txt", sep="")
    alpha_file = paste(fp_outPath, "_alphahat.txt", sep="")
    theta_file = paste(fp_outPath, "_thetahat.txt", sep="")
    char_file  = paste(fp_outPath, "_origchars", sep="")

Otherwise, for the sake of this tutorial, you can use the example parameter files provided in the package installation directory:

    r_file = system.file("extdata", "genotypes_rhat.txt", package = "SNPknock")
    alpha_file = system.file("extdata", "genotypes_alphahat.txt", package = "SNPknock")
    theta_file = system.file("extdata", "genotypes_thetahat.txt", package = "SNPknock")
    char_file  = system.file("extdata", "genotypes_origchars", package = "SNPknock")

Then, we can construct the hidden Markov model with:

    hmm = loadHMM(r_file, alpha_file, theta_file, char_file)

## Generating knockoff genotypes

Finally, we can use the hidden Markov model created above to generate knockoffs.

    Xk = knockoffGenotypes(X, hmm$r, hmm$alpha, hmm$theta)
    table(Xk)

    ## Xk
    ##      0      1      2
    ## 130114  90466  25620

## Knockoffs for phased haplotypes

### Fitting the hidden Markov model on haplotype data

A small synthetic dataset of 1454 phased haplotype SNPs from 100 individuals can be found in the package installation directory. We can load it with:

    library(SNPknock)
    H_file = system.file("extdata", "haplotypes.RData", package = "SNPknock")
    load(H_file)
    table(H)

    ## H
    ##      0      1
    ## 351208 141192

Below, we show how to fit a hidden Markov model to this data, with the help of `fastphase`. Since `fastphase` takes as input haplotype sequences in “.inp” format, we must first convert the H matrix by calling `writeXtoInp`. By default, this function will write onto a temporary file in the R temporary directory.

    # Convert X into the suitable fastphase input format, write it into a temporary file
    # and return the path to that file.
    Hinp_file = writeXtoInp(H, phased = TRUE)

Assuming that we have already downloaded `fastphase`, we can call it to fit the hidden Markov model to X.

    fp_path  = "~/bin/fastphase" # Path to the fastphase executable
    # Call fastphase and return the path to the parameter estimate files
    fp_outPath = runFastPhase(fp_path, Hinp_file, K = 12, numit = 15, phased = TRUE)

    ## ~/bin/fastphase -Pp -T1 -K12 -g -H-4 -C15 -B -S1 -o'/tmp/Rtmpd9jSUL/file142d64b2bf9b' /tmp/Rtmpd9jSUL/file142d41a6e022.inp

Above, the `SNPknock` package could not find `fastphase` because we did not provide the correct path (we cannot include third-party executable files within this package). However, if you install `fastphase` separately and provide `SNPknock` with the correct path, this will work.

If the previous step worked for you, you can find the parameter estimates produced by `fastphase` in the following files:

    r_file = paste(fp_outPath, "_rhat.txt", sep="")
    alpha_file = paste(fp_outPath, "_alphahat.txt", sep="")
    theta_file = paste(fp_outPath, "_thetahat.txt", sep="")
    char_file  = paste(fp_outPath, "_origchars", sep="")

Otherwise, for the sake of this tutorial, you can use the example parameter files provided in the package installation directory:

    r_file = system.file("extdata", "haplotypes_rhat.txt", package = "SNPknock")
    alpha_file = system.file("extdata", "haplotypes_alphahat.txt", package = "SNPknock")
    theta_file = system.file("extdata", "haplotypes_thetahat.txt", package = "SNPknock")
    char_file  = system.file("extdata", "haplotypes_origchars", package = "SNPknock")

Then, we can construct the hidden Markov model with:

    hmm = loadHMM(r_file, alpha_file, theta_file, char_file)

### Generating knockoff haplotypes

Finally, we can use the hidden Markov model created above to generate knockoffs.

    Hk = knockoffHaplotypes(H, hmm$r, hmm$alpha, hmm$theta)
    table(Hk)

    ## Hk
    ##      0      1
    ## 351155 141245

## See also

If you want to see some basic usage of `SNPknock`, see the introductory vignette.

If you want to learn about `SNPknock` for large genome-wide association studies (Sesia et al. 2019), see https://msesia.github.io/knockoffzoom/.

Scheet, P., and M. Stephens. 2006. “A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase.” Am. J. Hum. Genet. 78:629–44. https://doi.org/10.1086/502802.

Sesia, M., E. Katsevich, S. Bates, E. Candès, and C. Sabatti. 2019. “Multi-Resolution Localization of Causal Variants Across the Genome.” bioRxiv. Cold Spring Harbor Laboratory. https://doi.org/10.1101/631390.

Sesia, M., C. Sabatti, and E. J. Candès. 2019. “Gene Hunting with Hidden Markov Model Knockoffs.” Biometrika 106:1–18. https://doi.org/10.1093/biomet/asy033.