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 Genotyping-by-Sequencing using double digest Restriction Associated DNA (ddRAD) approach for fine mapping LG2 drought tolerance QTL in pearl millet
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Genotyping-by-Sequencing using double digest Restriction Associated DNA (ddRAD) approach for fine mapping LG2 drought tolerance QTL in pearl millet

  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) ICRISAT is a member of the CGIAR Consortium About ICRISAT: www.icrisat.org ICRISAT’s scientific information: EXPLOREit.icrisat.org This work has been undertaken as part of the
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