QTL-BSA: A Bulked Segregant Analysis and Visualization Pipeline for QTL-seq

  • Sanling WuEmail author
  • Jie Qiu
  • Qikang Gao
Original research article


In recent years, the application of Whole Genome Sequencing (WGS) on plants has generated sufficient data for the identification of trait-associated genomic loci or genes. A high-throughput genome-assisted QTL-seq strategy, combined with bulked-segregant analysis and WGS of two bulked populations from a segregating progeny with opposite phenotypic trait values, has gained increasing popularities in research community. However, there is no publicly available user friendly software for the identification and visualization. Hence, we developed a tool named QTL-BSA (QTL-bulked segregant analysis and visualization pipeline), which could facilitate the rapid identification and visualization of candidate QTLs from QTL-seq. As a proof-of-concept study, we have applied the tool for the rapid discovery and the identification of genes related with the partial blast resistance in rice. Genomic region of the major QTL identified on chromosome 6, is located between 1.52 and 4.32 Mb, which is consistent with previous studies (2.39–4.39 Mb). We also derived the gene and QTLs functional annotation of this region. QTL-BSA offers a comprehensive solution to facilitate a wide range of programming and visualization tasks in QTL-seq analysis, is expected to be used widely by the research community.


Quantitative trait loci QTL-seq Bioinformatics tools 



We gratefully acknowledge the support of Dr. Gulei Jin (Guhe Information Inco. Ltd.).

Supplementary material

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  1. 1.
    Eizenga GC, Prasad B, Jackson AK, Jia MH (2013) Identification of rice sheath blight and blast quantitative trait loci in two different O. sativa/O. nivara Advanced backcross populations. Mol Breed. 31(4):889–907CrossRefGoogle Scholar
  2. 2.
    Liang YS, Gao ZQ, Shen XH, Zhan XD, Zhang YX, Wu WM, Cao LY, Cheng SH (2011) Mapping and comparative analysis of QTL for rice plant height based on different sample sizes within a single line in RIL population. Rice Sci. 18(4):265–272CrossRefGoogle Scholar
  3. 3.
    Ashikari M, Sakakibara H, Lin SY, Yamamoto T, Takashi T, Nishimura A, Angeles ER, Qian Q, Kitano H, Matsuoka M (2005) Cytokinin oxidase regulates rice grain production. Science 309(5735):741–745PubMedCrossRefGoogle Scholar
  4. 4.
    Ashikari M, Matsuoka M (2006) Identification, isolation and pyramiding of quantitative trait loci for rice breeding. Trends Plant Sci. 11:344–350PubMedCrossRefGoogle Scholar
  5. 5.
    Konishi S, Izawa T, Lin SY, Ebana K, Fukuta Y, Sasaki T, Yano M (2006) An SNP caused loss of seed shattering during rice domestication. Science 312(5578):1392–1396PubMedCrossRefGoogle Scholar
  6. 6.
    Lin HX (1995) RFLP mapping of QTLs for grain shape traits in indica rice (Oryza sativa L. subsp. indica). Sci Agric. 28:1–7Google Scholar
  7. 7.
    Lin HX, Qian HR, Zhang JY, Zheng KL (1996) RFLP mapping of QTLs for yield and related characters in rice (Oryza sativa L.). Theor Appl Genet. 92:920–927PubMedCrossRefGoogle Scholar
  8. 8.
    Thomson MJ, Tai TH, McClung AM, Lai XH, Hinga ME, Lobos KB, Xu Y, Martinez CP, McCouch SR (2003) Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson. Theor Appl Genet. 107:479–493PubMedCrossRefGoogle Scholar
  9. 9.
    Yoon DB, Kang KH, Kim HJ, Ju HG, Kwon SJ, Suh JP, Jeong OY, Ahn SN (2006) Mapping quantitative trait loci for yield components and morphological traits in an advanced backcross population between Oryza grandiglumis and the O. sativa Japonica cultivar Hwaseongbyeo. Theor Appl Genet. 112:1052–1062PubMedCrossRefGoogle Scholar
  10. 10.
    Hori K, Sato K, Nankaku N, Takeda K (2005) QTL analysis in recombinant chromosome substitution lines and doubled haploid lines derived from across between Hordeum vulgare ssp. vulgare and Hordeum vulgare ssp. spontaneum. Mol Breed. 16:295–311CrossRefGoogle Scholar
  11. 11.
    Sato K, Matsumoto T, Ooe N, Takeda K (2009) Genetic analysis of seed dormancy QTL in barley. Breed Sci. 59:645–650CrossRefGoogle Scholar
  12. 12.
    Grewal TS, Rossnagel BG, Pozniak CJ, Scoles GJ (2007) Mapping quantitative trait loci associated with barley net blotch resistance. Theor Appl Genet. 116:529–539PubMedCrossRefGoogle Scholar
  13. 13.
    Cho S, Kumar J, Shultz JF, Anupama K, Tefera F, Muehlbauer FJ (2002) Mapping genes for double podding and other morphological traits in chickpea. Euphytica. 125:285–292CrossRefGoogle Scholar
  14. 14.
    Rakshit S, Winter P, Tekeoglu M, Munoz JJ, Pfaff T, Benko-Iseppon AM, Muehlbauer FJ, Kahl G (2003) DAF marker tightly linked to a major locus for Ascochyta blight resistance in chickpea (Cicer arietinum L.). Euphytica. 132:23–30CrossRefGoogle Scholar
  15. 15.
    Cobos MJ, Winter P, Kharrat M, Cubero JI, Gil J, Millan T, Rubio J (2009) Genetic analysis of agro-nomic traits in a wide cross of chickpea. Field Crop Res. 111:130–136CrossRefGoogle Scholar
  16. 16.
    Vadez V, Krishnamurthy L, Thudi M, Anuradha C, Colmer TD, Turner NC, Siddique KHM, Gaur PM, Varshney RK (2012) Assessment of ICCV 2 × JG 62 chickpea progenies shows sensitivity of reproduction to salt stress and reveals QTLs for seed yield and yield components. Mol Breed. 30:9–21CrossRefGoogle Scholar
  17. 17.
    Knoll J, Gunaratna N, Ejeta G (2008) QTL analysis of early-season cold tolerance in Sorghum. Theor Appl Genet. 116(4):577–587PubMedCrossRefGoogle Scholar
  18. 18.
    Shehzad T, Okuno K (2015) QTL mapping for yield and yield-contributing traits in Sorghum (Sorghum bicolor (L.) Moench) with genome-based SSR markers. Euphytica. 203(1):17–31CrossRefGoogle Scholar
  19. 19.
    Wassom J, Wong JC, Martinez E, King JJ, DeBaene J, Hotchkiss JR, Mikkilineni V, Bohn MO, Rocheford TR (2008) QTL associated with maize kernel oil, protein, and starch concentrations; kernel mass; and grain yield in Illinois high oil × B73 backcross-derived lines. Crop Sci. 48(1):243–252CrossRefGoogle Scholar
  20. 20.
    Nikolic A, Andjelkovic V, Dodig D, Mladenovicdrinic S, Kravic N, Ignjatovic-Micic D (2013) Identification of QTLs for drought tolerance in maize: II: yield and yield components. Genetika. 45(2):341–350CrossRefGoogle Scholar
  21. 21.
    Breseghello F, Sorrells ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics. 172:1165–1177PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Ni J, Pujar A, Youens-Clark K, Yap I, Jaiswal P, Tecle I, McCouch S (2009) Gramene QTL database: development, content and applications. Database. CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Yonemaru JI, Yamamoto T, Fukuoka S, Uga Y, Hori K, Yano M (2010) Q-TARO: QTL annotation rice online database. Rice. 3(2):194–203CrossRefGoogle Scholar
  24. 24.
    Kim CK, Yoon UH, Lee GS, Lee HK, Kim YH, Hahn JH (2009) Rice genetic marker database: an identification of single nucleotide polymorphism (SNP) and quantitative trait loci (QTL) markers. Afr J Biotech. 8(13):2963–2967Google Scholar
  25. 25.
    Fukuoka S, Ebana K, Yamamoto T, Yano M (2010) Integration of genomics into rice breeding. Rice. 3:131–137CrossRefGoogle Scholar
  26. 26.
    Takaji H, Abe A, Yoshida K, Kosugi S, Natsume S, Mitsuoka C, Uemura A, Utsushi H, Tamiru M, Takuno S, Innan H, Cano LM, Kamoun S, Terauchi R (2013) QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. Plant J. 74:174–183CrossRefGoogle Scholar
  27. 27.
    Lu HF, Lin T, Klein J, Wang SH, Qi JJ, Zhou Q, Sun JJ, Zhang ZH, Weng YQ, Huang SW (2014) QTL-seq identifies early flowering QTL located near Flowering Locus T in cucumber. Theor Appl Genet. 127:1491–1499PubMedCrossRefGoogle Scholar
  28. 28.
    Xu FF, Sun X, Chen YL, Huang Y, Tong C, Bao JS (2015) Rapid identification of major QTLs associated with rice grain weight and their utilization. PLoS One. 10(3):e0122206PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Das S, Upadhyaya HD, Bajaj D, Kujur A, Badoni S, Laxmi Kumar V, Tripathi S, Gowda CLL, Sharma S, Singh S, Tyagi AK, Parida SK (2015) Deploying QTL-seq for rapid delineation of a potential candidate gene underlying major trait-associated QTL in chickpea. DNA Res 22:193–203PubMedPubMedCentralCrossRefGoogle Scholar
  30. 30.
    Hisano H, Sakamoto K, Takagi H, Terauchi R, Sato K (2017) Exome QTL-seq maps monogenic locus and QTLs in barley. BMC Genomics. 18:125PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 25:1754–1760PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 genome project data processing subgroup (2009) The sequence alignment/map (SAM) format and SAMtools. Bioinformatics. 25:2078–2079PubMedPubMedCentralCrossRefGoogle Scholar
  33. 33.
    Kosugi S, Natsume S, Yoshida K, MacLean D, Cano L, Kamoun S, Terauchi R (2013) Coval: improving alignment quality and variant calling accuracy for next-generation sequencing data. PLoS One. 8(10):1–11CrossRefGoogle Scholar
  34. 34.
    Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10(3):R251–R310CrossRefGoogle Scholar
  35. 35.
    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M et al (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20:1297–1303PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Angel GD, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella KV, Altshuler D, Gabriel S, DePristo MA (2013) From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline. Curr Protoc Bioinform. 11(1110):11.10.1–11.10.33Google Scholar
  37. 37.
    DePristo MA, Banks E, Poplin RE, Garimella KV, Maguire JR, Philippakis AA, Angel G, Rivas MA, Hanna M, McKenna A et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. NatGenet. 43(5):491–498Google Scholar
  38. 38.
    Milne I, Stephen G, Bayer M, Cock PJA, Pritchard L, Cardle L, Shaw PD, Marshall D (2013) Using Tablet for visual exploration of second-generation sequencing data. Brief Bioinform. 14(2):193–202PubMedCrossRefGoogle Scholar
  39. 39.
    James TR, Helga T, Wendy W, Mitchell G, Eric SL, Gad G, Jill PM (2011) Integrative genomics viewer. Nat Biotechnol. 29:24–26CrossRefGoogle Scholar
  40. 40.
    Helga T, James TR, Jill PM (2013) Integrative genomics viewer (Igv): high-performance genomics data visualization and exploration. Briefings Bioinform. 14:178–192CrossRefGoogle Scholar
  41. 41.
    Ryu HS, Han M, Lee SK, Cho JI, Ryoo N, Heu S, Lee YH, Bhoo SH, Wang GL, Hahn TR, Jeon JS (2006) A comprehensive expression analysis of the WRKY gene superfamily in rice plants during defense response. Plant Cell Rep. 25(8):836–847PubMedCrossRefGoogle Scholar
  42. 42.
    Berri S, Abbruscato P, Faivre-Rampant O, Brasileiro AC, Fumasoni I, Satoh K, Kikuchi S, Mizzi L, Morandini P, Pe ME et al (2009) Characterization of WRKY co-regulatory networks in rice and Arabidopsis. BMC Plant Biol. 9:120PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Duan Y, Jiang Y, Ye S, Karim A, Ling Z, He Y, Yang S, Luo K (2015) PtrWRKY73, asalicylic acid-inducible poplar WRKY transcription factor, is involved in disease resistance in Arabidopsis thaliana. Plant Cell Rep. 34(5):831–841PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Yoshida K, Mitsuoka C, Tamiru M et al (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol. 30:174–178PubMedCrossRefGoogle Scholar
  45. 45.
    Luo X, Ji SD, Yuan PR, Lee HS, Kim DM, Balkunde S, Kang JW, Ahn SN (2013) QTL mapping reveals a tight linkage between QTLs for grain weight and panicle spikelet number in rice. Rice 6:33PubMedPubMedCentralCrossRefGoogle Scholar
  46. 46.
    Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G, Linsley PS, Mao M, Stoughton RB, Friend SH (2003) Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297–302PubMedCrossRefGoogle Scholar
  47. 47.
    Damerval C, Maurice A, Josse JM, de Vienne D (1994) Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics 137:289–301PubMedPubMedCentralGoogle Scholar
  48. 48.
    Meena RK, Shome S, Thakur S (2017) Prediction of phenotypic effects of variants observed in LOC_Oso4g36720 of FRO1 gene in rice (Oryza sativa L.). Interdiscip Sci: Comput Life Sci. 9(2):304–308CrossRefGoogle Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  1. 1.Analysis Center of Agrobiology and Environmental Sciences, Faculty of Agriculture, Life and Environment SciencesZhejiang UniversityHangzhouChina
  2. 2.Department of Agronomy and James D Watson Institute of Genome ScienceZhejiang UniversityHangzhouChina

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