This study evaluated the use of double digest restriction site associated DNA sequencing (ddRAD) and restriction site associated DNA sequencing (RAD) to genotype near-isogenic lines of pearl millet for a drought tolerance quantitative trait locus on linkage group 2. The ddRAD approach generated more data (>400 Mb), clustered reads, aligned reads to the reference genome, and identified more SNPs and INDELs than the RAD approach. Based on these results, ddRAD is an effective technique for SNP and INDEL discovery in pearl millet and will be used to further fine map the drought tolerance QTL.

1. January2016
Genotyping-by-Sequencing Using Double Digest
Restriction Associated DNA (ddRAD) Approach for Fine
Mapping LG2 Drought Tolerance QTL in Pearl Millet
Rakesh K. Srivastava1
*, Vijaya B. Reddy Lachagari2
, Vincent Vadez1
, Jana Kholova1
, Sivarama P. Lekkala2
and Eduardo Blumwald3
1
ICRISAT, Patancheru, Hyderabad, India; 2
SciGenom Labs Pvt Ltd, Kochi, India; 3
Department of Plant Sciences, University of California, Davis, CA, USA
* Corresponding author: r.k.srivastava@cgiar.org
Introduction
▪▪ Pearl millet [Pennisetum glaucum (L.) R. Br.] is widely
cultivated for both grain and fodder in semi-arid and
arid sub-Saharan Africa and South Asia.
▪▪ Drought is an important abiotic constraint for pearl
millet production (Yadav et al., 2002. Theor Appl
Genet, 104: 67–83).
▪▪ Previously identified and validated (Bidinger et
al., 2005. Field Crops Research, 94:14–32) major
Linkage Group 2 (LG2) drought tolerance (DT) QTL
contributing to hybrid grain and stover yield potential
to terminal drought stress is being fine mapped.
▪▪ We assessed ddRAD (Peterson et al., 2012. PLoS ONE
7(5): e37135) sequencing over RAD-sequencing for a
pair of near-isogenic lines (NILs) for LG2 DT QTL.
Figure 1. Bioanalyzer profile of RAD (left) and ddRAD (right) libraries showing the selected size
ranges and library profiles.
Figure 2. Bioinformatic pipeline used for data analysis.
Figure 4. Clustering and alignment of reads generated on Illumina HiSeq 2500 for the NILs.
Figure 5. SNPs (left) and INDELs (right) identified among the samples with various read depth on
Illumina HiSeq 2500 for the NILs.
Materials and methods
▪▪ Different RADTag approaches were evaluated for application in pearl millet NILs (H77/833-
2-P10 & ICMR 01029-P1) using single (ApeKI) and double enzyme (SphI and MluCI) protocols
for RADTag generation on Illumina HiSeq2500.
▪▪ Clean-up of the digested product using Ampure beads and ligated P1 (barcoded) and P2
adaptors was done using T4 DNA ligase.
▪▪ PCR amplification was performed to enrich and add the Illumina specific adapters and flowcell
annealing sequences.
▪▪ Final pooling and sequencing was performed after QC check on bioanalyzer (Figure 1).
▪▪ Bioinformatic analysis was done using pipelinse Uclust (ver. 1.2), Bowtie2 (ver. 2.1.0),
Samtools (ver. 0.1.18), as described in Figure 2.
Results
▪▪ We generated ~400
Mb data (Table 1
and Figure 3)
for each sample
using both RAD and
ddRAD techniques
for NILs.
▪▪ Data were
analyzed using
custom scripts
for polymorphic
markers in de-
novo approach
using Uclust and
Samtools (Figure 2).
Table 1: RADTag and ddRADTag data summary generated on Illumina HiSeq 2500 for the NILs.
Sample Name No. of raw reads No. of bases (Mb) GC (%) % of data >= Q30 Raw read length (bp)
P1 ddRAD 3,018,210 301.82 48.34 88.31 100 × 2
P2 ddRAD 3,990,900 399.09 48.75 89.26 100 × 2
P1 RAD 4,886,676 488.66 47.07 84.01 100 × 2
P2 RAD 4,665,114 466.51 48.25 83.88 100 × 2
▪▪ More than 98% of the reads qualified QC in both the samples captured with
respective RadTags indexes, clustered in to 0.3 million clusters in ddRAD data and 0.6
million in RAD samples (Figure 4) with ~91% reads aligned to the reference in ddRAD
and 78-86% aligned in RAD sequence data.
▪▪ A total of 14,294 SNPs and 188 INDELs were identified with read depth of 10
in ddRAD data, and 3,465 SNPs and 26 INDELs from RAD data, demonstrating
effectiveness of ddRAD technique over RAD technique (Figure 5).
▪▪ Further analysis of homozygous polymorphic markers between parental lines
revealed 84 markers for ddRAD and 33 markers for RAD technique in the NILs.
▪▪ A total of 150 Gb data is generated and is being utilized for fine mapping.
▪▪ Based on this evaluation, ddRAD technique is being further employed for genotyping
NIL fine mapping population segregating for LG2 drought tolerance QTL .
Conclusions
▪▪ A total of 14,294 SNPs and 188 INDELs were identified with read depth of 10 in
ddRAD data (using SphI and MluCI enzyme combination), and 3,465 SNPs and 26
INDELs from RAD data (using ApeKI) between the NIL pair for LG2 DT QTL .
▪▪ This study demonstrated effectiveness of ddRAD technique over RAD technique for
SNP and INDEL discovery in pearl millet.
Acknowledgements
▪▪ This paper presents result from a commissioned project supported by the USAID,
entitled “Development of Abiotic Stress Tolerant Millet for Africa and South Asia”.
▪▪ This work has been published as part of the CGIAR Research Program on Dryland
Cereals. ICRISAT is a member of CGIAR consortium.
0
20,000
40,000
60,000
80,000
100,000
120,000
P1 (ddRAD) P1 (RAD)P2 (ddRAD) P2 (RAD)
Depth=2
Depth=5
Depth=10
0
200
100
400
300
500
600
700
800
P1 (ddRAD) P1 (RAD)P2 (ddRAD) P2 (RAD)
Depth=2
Depth=5
Depth=10
Total number of reads
Number of clustered reads
Number of clustered reads align to reference
Number of unique aligned reads
Number of unaligned reads
P1 (ddRAD) P1 (RAD)P2 (ddRAD) P2 (RAD)
6,000,000
5,000,000
4,000,000
3,000,000
2,000,000
1,000,000
0
Figure 3. Summary of reads generated on Illumina HiSeq 2500 for the NILs.
1,000,0000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000
Total reads
Unique reads
Number of reads
Samples
P2 (RAD)
P1 (RAD)
P1 (ddRAD)
P2 (ddRAD)
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This work has
been undertaken
as part of the