BlueSNP

R package for highly scalable genome wide association studies using Hadoop clusters

Copyright IBM Corp. 2011, 2012

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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Download

Regular users who are not interested in the source code should download the following files from DOWNLOADS.

• BlueSNP R package
• BlueSNP Manual (installation instructions)
• BlueSNP Tutorial (usage instructions)
• Tutorial data (for following along)

BlueSNP_0.1.0 (current version) depends on RHIPE_0.69. Because RHIPE installation is non-trivial, we recommend trying BlueSNP using the RHIPE virtual machine (VM) made available by the authors of RHIPE. The BlueSNP Manual gives step-by-step instructions for using BlueSNP with the RHIPE VM.

Contact

• Contact rjprill@us.ibm.com for technical assistance and for academic collaborations related to large-scale bioinformatics.

How to cite

• H. Huang, S. Tata, and R. J. Prill. [BlueSNP: R package for highly scalable genome-wide association studies using Hadoop clusters]. Bioinformatics, 29(1):135–136, Jan 2013.

• http://bioinformatics.oxfordjournals.org/content/29/1/135

News

• 11/01/2012 BlueSNP source, package, and documentation posted.

Synopsis

Analyze many phenotypes (e.g., diseases)

library(BlueSNP)                         # GWAS functions using Hadoop

Import SNP data in PLINK tped format.

read.plink.tped(
  input.hdfs.path="/tped",               # where tped file(s) are located
  output.hdfs.path="/snps"               # where BlueSNP records will be written
)

Analyze two phenotypes reporting only those SNPs with small p-values.

gwas(
  genotype.hdfs.path="/snps",            # input
  phenotype.hdfs.path="/pheno.RData",    # input
  output.hdfs.path="/output",            # output
  phenotype.cols=c("pheno1", "pheno2"),  # selected phenotypes
  method="qt.linear.regression",         # association test
  pvalue.report.cutoff=1e-5              # only report SNPs with small p-values
)

P = gwas.results("/output", type="p.value")    

Output:

      type       rsid chr        bp       pheno1       pheno2
1  p.value  rs1898285   3 161531378 6.104797e-08          NaN
2  p.value rs10936248   3 161536436 6.701088e-08          NaN
3  p.value  rs4604153   5  26231934 4.240296e-07          NaN
4  p.value  rs6779918   3 161540640 1.274853e-06          NaN
5  p.value  rs1326419  13  89282652          NaN 5.023752e-07
6  p.value rs10848150  12 131057033          NaN 7.488903e-06
7  p.value rs11011036  10  19961205          NaN 9.903649e-06

Monte Carlo p-values

Any user-defined function of genotype and phenotype can be a test statistic.

my_custom_test.R

my.custom.test <- function(y, x) {
  # y is phenotype vector
  # x is genotype vector

  N = length(x)                          # number of individuals
  stat = cor(y, x)^2 * (N - 2)           # test statistic

  list(n.individuals=N, stat=stat)       # return a list of named entries
}

Estimate empirical p-values using the test statistic defined by my.custom.test.

gwas.adaptive.perm(
  genotype.hdfs.path="/snps",
  phenotype.hdfs.path="/pheno.RData",
  output.hdfs.path="/results-custom",
  n.permutations=1e7,
  user.code="/my_custom_test.R",
  mytest="my.custom.test",
  statistic.name="stat"
)

Fetch results.

results = gwas.results.perm("/results-custom")
subset(results, p.value<.0001)