Bulked segregant analysis, as implemented in QTL-seq (Takagi et al., 2013), is a powerful and efficient method to identify agronomically important loci in crop plants. QTL-seq was adapted from MutMap to identify quantitative trait loci. It utilizes sequences pooled from two segregating progeny populations with extreme opposite traits (e.g. resistant vs susceptible) and a single whole-genome resequencing of either of the parental cultivars. While the original QTL-seq algorithm did not assume a highly heterozygous genome, a “modified QTL-seq” has been developed to handle this situation using high resolution mapping (Itoh et al., 2019).
- Hiroki Takagi, Akira Abe, Kentaro Yoshida, Shunichi Kosugi, Satoshi Natsume, Chikako Mitsuoka, Aiko Uemura, Hiroe Utsushi, Muluneh Tamiru, Shohei Takuno, Hideki Innan, Liliana M. Cano, Sophien Kamoun, Ryohei Terauchi (2013). QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. Plant journal 74:174-183. [URL]
- Yu Sugihara, Lester Young, Hiroki Yaegashi, Satoshi Natsume, Daniel J. Shea, Hiroki Takagi, Helen Booker, Ryohei Terauchi, Akira Abe (in preparation). High performance pipeline for MutMap and QTL-seq.
- matplotlib
- numpy
- pandas
- seaborn (optional)
You can install QTL-seq using bioconda.
$ conda install -c bioconda qtlseq
If you got a error during installation, you can install QTL-seq, manually.
$ git clone https://github.com/YuSugihara/QTL-seq.git
$ cd QTL-seq
$ pip install -e .
Then you have to install other dependencies by yourself. We highly recommend you to install SnpEff and Trimmomatic using bioconda.
$ conda install -c bioconda snpeff
$ conda install -c bioconda triimomatic
After installation, please check whether SnpEff and Trimmomatic work through the commands like below.
$ snpEff --help
$ trimmomatic --help
$ qtlseq -h
usage: qtlseq -r <FASTA> -p <BAM|FASTQ> -b1 <BAM|FASTQ>
-b2 <BAM|FASTQ> -n1 <INT> -n2 <INT> -o <OUT_DIR>
[-F <INT>] [-T] [-e <DATABASE>]
QTL-seq version 2.0.7
optional arguments:
-h, --help show this help message and exit
-r , --ref Reference fasta.
-p , --parent fastq or bam of parent. If you specify
fastq, please separate pairs by commma,
e.g. -p fastq1,fastq2. You can use this
optiion multiple times
-b1 , --bulk1 fastq or bam of bulk 1. If you specify
fastq, please separate pairs by commma,
e.g. -b1 fastq1,fastq2. You can use this
optiion multiple times
-b2 , --bulk2 fastq or bam of bulk 2. If you specify
fastq, please separate pairs by commma,
e.g. -b2 fastq1,fastq2. You can use this
optiion multiple times
-n1 , --N-bulk1 Number of individuals in bulk 1.
-n2 , --N-bulk2 Number of individuals in bulk 2.
-o , --out Output directory. Specified name must not
exist.
-F , --filial Filial generation. This parameter must be
more than 1. [2]
-t , --threads Number of threads. If you specify the number
below one, then QTL-seq will use the threads
as many as possible. [2]
-w , --window Window size (kb). [2000]
-s , --step Step size (kb). [100]
-D , --max-depth Maximum depth of variants which will be used. [250]
-d , --min-depth Minimum depth of variants which will be used. [8]
-N , --N-rep Number of replicates for simulation to make
null distribution. [5000]
-T, --trim Trim fastq using trimmomatic.
-a , --adapter FASTA of adapter sequences. This will be used
when you specify "-T" for trimming.
--trim-params Parameters for trimmomatic. Input parameters
must be separated by comma with following
order: phred, ILLUMINACLIP, LEADING, TRAILING,
SLIDINGWINDOW, MINLEN. If you want to remove
adapters of illumina, please specify FASTA of
the adapter sequences with "--adapter". Specified
name will be inserted into <ADAPTER_FASTA>. If you
don't specify it, adapter trimming will be skipped.
[33,<ADAPTER_FASTA>:2:30:10,20,20,4:15,75]
-e , --snpEff Predict causal variant using SnpEff. Please
check available databases in SnpEff.
--mem Maximum memory per thread when bam sorted;
suffix K/M/G recognized. [1G]
-q , --min-MQ Minimum mapping quality in mpileup. [40]
-Q , --min-BQ Minimum base quality in mpileup. [18]
-C , --adjust-MQ "adjust-MQ" in mpileup. Default parameter
is suited for BWA. [50]
-v, --version show program's version number and exit
QTL-seq can run from FASTQ (without or with trimming) and BAM. If you want to run QTL-seq from VCF, please use QTL-plot (example 5). Once you run QTL-seq, QTL-seq automatically complete the subprocesses.
- Example 1 : run QTL-seq from FASTQ without trimming
- Example 2 : run QTL-seq from FASTQ with trimming
- Example 3 : run QTL-seq from BAM
- Example 4 : run QTL-seq from multiple FASTQs and BAMs
- Example 5 : run QTL-plot from VCF
$ qtlseq -r reference.fasta \
-p parent.1.fastq,parent.2.fastq \
-b1 bulk_1.1.fastq,bulk_1.2.fastq \
-b2 bulk_2.1.fastq,bulk_2.2.fastq \
-n1 20 \
-n2 20 \
-o example_dir
-r : reference fasta
-p : FASTQs of parent. Please input pair-end reads separated by comma. FASTQs can be gzipped.
-b1 : FASTQs of bulk 1. Please input pair-end reads separated by comma. FASTQs can be gzipped.
-b2 : FASTQs of bulk 2. Please input pair-end reads separated by comma. FASTQs can be gzipped.
-n1 : number of individuals in bulk 1.
-n2 : number of individuals in bulk 2.
-o : name of output directory. Specified name cannot exist.
$ qtlseq -r reference.fasta \
-p parent.1.fastq,parent.2.fastq \
-b1 bulk_1.1.fastq,bulk_1.2.fastq \
-b2 bulk_2.1.fastq,bulk_2.2.fastq \
-n1 20 \
-n2 20 \
-o example_dir \
-T
-r : reference fasta
-p : FASTQs of parent. Please input pair-end reads separated by comma. FASTQs can be gzipped.
-b1 : FASTQs of bulk 1. Please input pair-end reads separated by comma. FASTQs can be gzipped.
-b2 : FASTQs of bulk 1. Please input pair-end reads separated by comma. FASTQs can be gzipped.
-n1 : number of individuals in bulk 1.
-n2 : number of individuals in bulk 2.
-o : name of output directory. Specified name cannot exist.
-T : trim your reads using triimomatic.
$ qtlseq -r reference.fasta \
-p parent.bam \
-b1 bulk_1.bam \
-b2 bulk_2.bam \
-n1 20 \
-n2 20 \
-o example_dir
-r : reference fasta
-p : BAM of parent.
-b1 : BAM of bulk 1.
-b2 : BAM of bulk 2.
-n1 : number of individuals in bulk 1.
-n2 : number of individuals in bulk 2.
-o : name of output directory. Specified name cannot exist.
$ qtlseq -r reference.fasta \
-p parent_1.1.fastq,parent_1.2.fastq \
-p parent_1.bam \
-b1 bulk_11.1.fastq,bulk_11.2.fastq \
-b1 bulk_12.bam \
-b1 bulk_13.bam \
-b2 bulk_21.1.fastq,bulk_21.2.fastq \
-b2 bulk_22.bam \
-b2 bulk_23.bam \
-n1 20 \
-n2 20 \
-o example_dir
QTL-seq can automatically merge multiple FASTQs and BAMs. Of course, you can merge FASTQs or BAMs using cat or samtools merge before input them to QTL-seq. If you specify -p multiple times, please make sure that those files include only 1 individual. On the other hand, -b1 and -b2 can include more than 1 individuals because those are bulked samples. QTL-seq can automatically classify FASTQs and BAMs from whether comma exits or not.
$ qtlplot -h
usage: qtlplot -v <VCF> -n1 <INT> -n2 <INT> -o <OUT_DIR>
[-F <INT>] [-t <INT>] [-w <INT>] [-s <INT>] [-D <INT>]
[-d <INT>] [-N <INT>] [-m <FLOAT>] [-S <INT>] [-e <DATABASE>]
[--igv] [--indel]
QTL-plot version 2.0.7
optional arguments:
-h, --help show this help message and exit
-v , --vcf VCF which contains parent, bulk1 and bulk2
in this order.
-n1 , --N-bulk1 Number of individuals in bulk 1.
-n2 , --N-bulk2 Number of individuals in bulk 2.
-o , --out Output directory. Specified name can exist.
-F , --filial Filial generation. This parameter must be
more than 1. [2]
-t , --threads Number of threads. If you specify the number
below one, then QTL-plot will use the threads
as many as possible. [2]
-w , --window Window size (kb). [2000]
-s , --step Step size (kb). [100]
-D , --max-depth Maximum depth of variants which will be used. [250]
-d , --min-depth Minimum depth of variants which will be used. [8]
-N , --N-rep Number of replicates for simulation to make
null distribution. [5000]
-m , --min-SNPindex Cutoff of minimum SNP-index for clear results. [0.3]
-S , --strand-bias Filter spurious homo genotypes in cultivar using
strand bias. If ADF (or ADR) is higher than this
cutoff when ADR (or ADF) is 0, that SNP will be
filtered out. If you want to supress this filtering,
please set this cutoff to 0. [7]
-e , --snpEff Predict causal variant using SnpEff. Please
check available databases in SnpEff.
--igv Output IGV format file to check results on IGV.
--indel Plot SNP-index with INDEL.
--fig-width Width allocated in chromosome figure. [7.5]
--fig-height Height allocated in chromosome figure. [4.0]
--white-space White space between figures. (This option
only affect vertical direction.) [0.6]
--version show program's version number and exit
QTL-plot is included in QTL-seq. QTL-seq run QTL-plot after making VCF. Then, QTL-plot will work with default parameters. If you want to change some parameters, you can use VCF inside of (OUT_DIR/30_vcf/QTL-seq.vcf.gz) to retry plotting process like below.
$ qtlplot -v OUT_DIR/30_vcf/QTL-seq.vcf.gz \
-o ANOTHER_DIR_NAME \
-n1 20 \
-n2 20 \
-w 2000 \
-s 100
In this case, please make sure that:
- Your VCF include AD format.
- Your VCF include three columns of parent, bulk1 and bulk2 in this order.
If you got a error, please try to run QTL-seq from FASTQ or BAM before asking in issues.
Inside of OUT_DIR is like below.
├── 10_ref
│ ├── IRGSP-1.0_genome.fasta
│ ├── IRGSP-1.0_genome.fasta.amb
│ ├── IRGSP-1.0_genome.fasta.ann
│ ├── IRGSP-1.0_genome.fasta.bwt
│ ├── IRGSP-1.0_genome.fasta.fai
│ ├── IRGSP-1.0_genome.fasta.pac
│ └── IRGSP-1.0_genome.fasta.sa
├── 20_bam
│ ├── bulk1.filt.bam
│ ├── bulk1.filt.bam.bai
│ ├── bulk2.filt.bam
│ ├── bulk2.filt.bam.bai
│ ├── parent.filt.bam
│ └── parent.filt.bam.bai
├── 30_vcf
│ ├── qtlseq.vcf.gz
│ └── qtlseq.vcf.gz.tbi
├── 40_qtlseq
│ ├── bulk1_SNPindex.png
│ ├── bulk2_SNPindex.png
│ ├── delta_SNPindex.png
│ ├── sliding_window.tsv
│ └── snp_index.tsv
└── log
├── bcftools.log
├── bgzip.log
├── bwa.log
├── samtools.log
└── tabix.log
- If you run QTL-seq with trimming, you will get the directory of
00_fastqwhich includes FASTQs after trimming. - You can check the results in
40_QTL-seq.snp_index.tsv: columns in this order.- CHROM : chromosome name
- POSI : position in chromosome
- VARIANT : SNP or INDEL
- DEPTH 1 : depth of bulk 1
- DEPTH 2 : depth of bulk 2
- p99 : 99% confidence interval of simulated delta SNP-index (absolute value)
- p95 : 95% confidence interval of simulated delta SNP-index (absolute value)
- SNP-index 1 : real SNP-index of bulk 1
- SNP-index 2 : real SNP-index of bulk 2
- DELTA SNP-index : real delta SNP-index (bulk2 - bulk1)
sliding_window.tsv: columns in this order.- CHROM : chromosome name
- POSI : central position of window
- MEAN p99 : mean of p99
- MEAN p95 : mean of p95
- MEAN SNP-index 1 : mean SNP-index of bulk 1 (absolute value)
- MEAN SNP-index 2 : mean SNP-index of bulk 2 (absolute value)
- MEAN DELTA SNP-index : mean delta SNP-index
QTL-seq_plot.png: resulting plot (like below)- BLUE dot : variant
- RED line : mean SNP-index
- ORANGE line : mean p99
- GREEN line : mean p95
