RASQUAL (Robust Allele Specific QUAntification and quality controL) maps QTLs for sequenced based cellular traits by combining population and allele-specific signals.
Please make sure CLAPACK is installed in your environment (if you don't have it, then see below for installation tips). To build and install RASQUAL, firstly go to the source directory (src), then set environment variables appropriately to point to the CLAPACK library. Finally use "make" to build and install RASQUAL which will be installed in "$RASQUALDIR/bin".
RASQUALDIR=/path/to/rasqualdir/
cd $RASQUALDIR/src
export CFLAGS="-I/nfs/users/nfs_n/nk5/team170/Natsuhiko/CLAPACK-3.1.1.1/INCLUDE -I/nfs/users/nfs_n/nk5/team170/Natsuhiko/CLAPACK-3.1.1.1/F2CLIBS"
export LDFLAGS="-L/nfs/users/nfs_n/nk5/team170/Natsuhiko/CLAPACK-3.1.1.1 -L/nfs/users/nfs_n/nk5/team170/Natsuhiko/CLAPACK-3.1.1.1/F2CLIBS"
make
make install
RASQUAL needs two input data files:
- A fragment (read) count table, with sample specific offsets (such as library size)
- A VCF file with *phased* SNP genotypes and allele-specific counts.
An example of each of these files can be found in the data directory. In the usage example below, RASQUAL takes SNP information from a tabix-indexed VCF file as standard input, while the count table and sample specific offsets are binary files (Y.bin and K.bin, respectively). Tabix-indexing is not strictly necessary but allows for genotype and allelic count information to be accessed quickly from the command line. Using the example data files, you can use the following commands to map expression QTLs for two genes (C11orf21 and TSPAN32) using RASQUAL:
# make sure tabix is installed in your environment
cd $RASQUALDIR
tabix data/chr11.gz 11:2315000-2340000 | bin/rasqual -y data/Y.bin -k data/K.bin -n 24 -j 1 -l 409 -m 63 \
-s 2316875,2320655,2321750,2321914,2324112 -e 2319151,2320937,2321843,2323290,2324279 \
-t -f C11orf21 -z
tabix data/chr11.gz 11:2315000-2340000 | bin/rasqual -y data/Y.bin -k data/K.bin -n 24 -j 2 -l 409 -m 61 \
-s 2323227,2323938,2324640,2325337,2328175,2329966,2330551,2331219,2334884,2335715,2338574,2339093 \
-e 2323452,2324188,2324711,2325434,2328220,2330040,2330740,2331248,2334985,2337897,2338755,2339430 \
-t -f TSPAN32 -z
Sample size (in this example, N=24) is given by -n option, the feature ID is given by -j option (only two genes exist in this example, thereby j=1,2). You need to provide the number of testing SNPs and feature SNPs in the cis-window a priori (-l and -m, respectively). RASQUAL also requires the feature start and end positions (as comma separated values for more than one positions, e.g. such as for a union of exons in this example) as inputs (-s and -e, respectively). By default, RASQUAL outputs QTL mapping results for all tested SNPs, but you can also specify only the lead QTL SNP (-t option). In the output, you can also specify the feature name by -f option. To take account of genotype uncertainty, imputation quality score (R square value) are used in this example (-z option; see the section below).
On output, RASQUAL provides the following values for each tested SNP:
- Feature ID*
- rs ID*
- Chromosome*
- SNP position*
- Ref allele
- Alt allele
- Allele frequency (not MAF!)*
- HWE Chi-square statistic
- Imputation quality score (IA)
- Imputation quality score (R-square)
- Chi square statistic (2 x log Likelihood ratio)*
- Effect size (Pi)
- Reference allele mapping bias (Phi)
- Sequencing/mapping error rate (Delta)
- Overdispersion
- SNP ID within the region
- No. of feature SNPs
- No. of tested SNPs*
- No. of iterations for null hypothesis
- No. of iterations for alternative hypothesis
- Random location of ties (tie lead SNP; only useful with -t option)
- likelihood for null hypothesis
- Convergence status (0=success)
- Squared correlation between prior and posterior genotypes (fSNPs)
- Squared correlation between prior and posterior genotypes (rSNP)
You may need columns with (*) for the downstream analysis.
You can find an example expression data for C11orf21 and TSPAN32 genes in the data directory. There are two files: a read/fragment count table (Y.txt) and sample specific offset data (K.txt), both have to be organised in feature by sample format (i.e., row: gene; col: sample). We have included an R script to allow you to convert the count and offset files (text) into binary format for us by RASQUAL. The script converts a table data into a vector of double precision values.
cd $RASQUALDIR
RHOME=/software/R-3.0.0/bin/
$RHOME/R --vanilla --quiet --args data/Y.txt data/K.txt < R/txt2bin.R > log
You will also need to prepare custom VCF files containing the allele specific counts of your target cellular trait at all SNPs. The files have to contain an additional subfield, "AS", located within the genotype field consisting of two integers, the reference and alternative allele counts, separated by a comma. For example, sample i is heterozygous at a SNP and has 1 and 10 reads overlapping the reference and alternative alleles respectively, the genotype field for the sample becomes
... FORMAT ... Sample_i ...
... GL:AS ... 0|1:1,10 ...
An example VCF file (chr11.gz) can be found in the data directory. Note that, phased genotypes are required for QTL mapping with RASQUAL. Currently, SNP genotypes are only used to map QTLs, but short INDELs and some form of structural variations will be able to use shortly.
To maximise the ability of RASQUAL, we recommend to incorporate uncertainty in imputed genotypes. There are 4 options:
-
Allelic probability (AP)
Allelic probability can be obtained from the standard 2-step imputation scheme, where genotypes are first phased then imputed on haplotype-by-haplotype basis. The custom AP field consists of the allelic probabilities (in Log10 scale) of two haplotypes from one individual, separated by a comma:
... FORMAT ... Sample_i ... ... AP:AS ... 0.0,-5.0:1,10 ... -
Genotype likelihood (GL)
You may also use genotype likelihood from conventional genotype imputation in conjunction with phased genotype data:
... FORMAT ... Sample_i ... ... GT:GL:AS ... 0|1:-4.0,-0.1,-0.6:1,10 ... -
Dosage (DS)
Genotype dosage can also be utilised to take account of genotype uncertainty:
... FORMAT ... Sample_i ... ... GT:DS:AS ... 0|1:1.1:1,10 ... -
Imputation quality score (R square value; RSQ)
Imputation methods often provide a quality score for each SNP locus that approximates squared correlation coefficient between true and observed genotypes (e.g., R^2 from MaCH or Beagle; I^2 from IMPUTE2 ). RASQUAL can convert the score into genotyping error rate to handle uncertainly:
... INFO FORMAT ... Sample_i ... ... ...;RSQ=0.9;... GL:AS ... 0|1:1,10 ...If you want to prioritise RSQ, you need to specify -z option for RASQUAL (see above example).
-
Population allele frequency
If genotype information is not available, RASQUAL can run in SNP free mode (--population-allele-frequency option). In this case, you just need to provide the population allele frequency (not minor allele frequency!) for the SNP in VCF files. Note that, this mode is only feasible when the causal SNP is a feature SNP (e.g., ChIP-seq QTL).
... INFO FORMAT ... Sample_i ... ... ...;AF=0.4;... AS ... 1,10 ...
We strongly recommend using the AP field for QTL mapping. If there are multiple subfields of AP, GL and DS, AP is prioritised over both GL and DS, and GL is prioritised over DS. If neither of GL, DS nor AP is provided, GL is used as AP.
Sample specific offset terms can be calculated from the count table. See the script makeOffset.R in the R directory. The usage is:
# Not run!
$RHOME/R --vanilla --quiet --args data/your.Y.txt < R/makeOffset.R > log
# With GC content; not run!
$RHOME/R --vanilla --quiet --args data/your.Y.txt data/gcc.txt < R/makeOffset.R > log
Note that you need to prepare a GC content file (gcc.txt in this example) to apply GC correction for the read count. The file is a vector of GC% values as a text file (separated by either, a comma, a tab or a line break).
Real data is usually affected by hidden confounding factors, such as sequencing batch, sample preparation date etc, that can reduce power to detect QTLs. RASQUAL handles covariates as an input (-x option). The following is the same eQTL mapping example above, but with covariates:
tabix data/chr11.gz 11:2315000-2340000 | bin/rasqual -y data/Y.bin -k data/K.bin -n 24 -j 1 -l 409 -m 63 \
-s 2316875,2320655,2321750,2321914,2324112 -e 2319151,2320937,2321843,2323290,2324279 \
-z -t -f C11orf21 \
-x data/X.bin
The covariate file is based on a sample-by-variable table (see X.txt in the data directory). You can convert the file into binary format for RASQUAL by using the same R script:
$RHOME/R --vanilla --quiet --args data/Y.txt data/K.txt data/X.txt < R/txt2bin.R > log
Those confounding factors are not often observed but can be captured by principal component analysis (PCA). In our example, we applied PCA onto log FPKMs with and without permutation and picked the first N components whose explained variances are greater than those from permutation result as covariates for subsequent analyses. A sample code is also available in the R directory:
# Not run!
$RHOME/R --vanilla --quiet --args data/your.Y.txt data/your.K.txt < R/makeCovariates.R > log
Note that, the result of PCA is always sensitive to few outliers (just one or two), which explain almost all variance in the data. Using those PCs as covariates hurts your QTL mapping result. We strongly recommend to spend some time to explore and clean up your data first.
You first need to get the latest library from http://www.netlib.org/clapack/. Then, compile it like
tar zxvf clapack.tgz
cd CLAPACK-3.2.1
mv make.inc.example make.inc
make
When it has been done, you will find three archive files in the directory which need to be either linked or renamed, such as
ln -s lapack_LINUX.a liblapack.a
ln -s tmglib_LINUX.a libtmglib.a
ln -s blas_LINUX.a libblas.a
before compiling RASQUAL.