Skip to main content

Advertisement

SpringerLink
Log in

QTL mapping for combining ability in different population-based NCII designs: a simulation study

  • Research Article
  • Published:
Journal of Genetics Aims and scope Submit manuscript

Abstract

The NCII design (North Carolina mating design II) has been widely applied in studies of combining ability and heterosis. The objective of our research was to estimate how different base populations, sample sizes, testcross numbers and heritability influence QTL analyses of combining ability and heterosis. A series of Monte Carlo simulation experiments with QTL mapping were then conducted for the base population performance, testcross population phenotypic values and the general combining ability (GCA), specific combining ability (SCA) and Hmp (midparental heterosis) datasets. The results indicated that: (i) increasing the number of testers did not necessarily enhance the QTL detection power for GCA, but it was significantly related to the QTL effect. (ii) The QTLs identified in the base population may be different from those from GCA dataset. Similar phenomena can be seen from QTL detected in SCA and Hmp datasets. (iii) The QTL detection power for GCA ranked in the order of DH(RIL) based > F2 based > BC based NCII design, when the heritability was low. The recombinant inbred lines (RILs) (or DHs) allows more recombination and offers higher mapping resolution than other populations. Further, their testcross progeny can be repeatedly generated and phenotyped. Thus, RIL based (or DH based) NCII design was highly recommend for combining ability QTL analysis. Our results expect to facilitate selecting elite parental lines with high combining ability and for geneticists to research the genetic basis of combining ability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1

Similar content being viewed by others

References

  • Berg B. A. 2004 Markov Chain Monte Carlo simulations and their statistical analysis (with web-based Fortran code). World Scientific, Hackensack, USA.

    Book  Google Scholar 

  • Basbag S., Ekinci R. and Gencer O. 2007 Combining ability and heterosis for earliness characters in line6tester population of Gossypium hirsutum L. Hereditas 144, 185–190.

    Article  PubMed  Google Scholar 

  • Dhillon B. S. and Singh J. 1978 Evaluation of circulant partial diallel crosses in maize. Theor. Appl. Genet. 52, 29–37.

    Article  CAS  PubMed  Google Scholar 

  • Fishman G. S. 1995 Monte Carlo: concepts, algorithms, and applications. Springer, New York, USA.

    Google Scholar 

  • Frascaroli E., Cane M. A., Landi P., Pea G. G., Villa M., Morgante M. and Pè M. E. 2007 Classical genetic and quantitative trait loci analyses of heterosis in a maize hybrid between two elite inbred lines. Genetics 176, 625-644.

    Article  CAS  PubMed  Google Scholar 

  • Frascaroli E., Canè M. A., Pè M. E., Pea G., Morgante M. and Landi P. 2009 QTL detection in maize testcross progenies as affected by related and unrelated testers. Theor. Appl. Genet. 118, 993–1004.

    Article  PubMed  Google Scholar 

  • Garcia A. A. F., Wang S., Melchinger A. E. and Zeng Z. B. 2008 Quantitative trait loci mapping and the genetic basis of heterosis in maize and rice. Genetics 180, 1707–1724.

    Article  PubMed  Google Scholar 

  • Griffing B. 1956 Concept of general and specific combining ability in relation to diallel crossing system. Aust. J. Biol. Sci. 9, 463–493.

    Google Scholar 

  • Groh S., Gonzáles-de-León D., Khairallah M. M., Jiang C., Bergvinson D., Bohn M. et al. 1998 QTL mapping in tropical maize: III. Genomic regions for resistance to Diatraea spp. and associated traits in two RIL populations. Crop Sci. 38, 1062–1072.

    Article  Google Scholar 

  • He X. H., Hu Z. L. and Zhang Y. M. 2012 Genome-wide mapping of QTL associated with heterosis in the RIL-based NCIII design. Chin. Sci. Bull. 57, 2655–2665.

    Article  Google Scholar 

  • He X. H., Xu C. W., Kuai J. M., Gu S. L. and Li T. 2001 Principal factors affecting the power of detection and accuracy of QTL mapping. Acta Agron. Sin. 27, 469–475.

    Google Scholar 

  • He X. H. and Zhang Y. M. 2011 A complete solution for dissecting pure main and epistatic effects of QTL in triple testcross design. PLoS One 6, e24575.

    Article  Google Scholar 

  • Hu Z. L., Xie C., McDaniel G. R., Kuhler D. L. and Zhang X. 1995 A correlation method for detecting and estimating linkage between a marker locus and a quantitative trait locus using inbred lines. Theor. Appl. Genet. 90, 1074–1078.

    CAS  PubMed  Google Scholar 

  • Kempthorne O. and Curnow R. N. 1961 The partial diallel cross. Biometrics 17, 229–250.

    Article  Google Scholar 

  • Kusterer B., Piepho H. P., Utz H. F., Schön C. C., Muminovic J., Meyer R. C. et al. 2007 Heterosis for biomass-related traits in Arabidopsis investigated by quantitative trait loci analysis of the triple testcross design with recombinant inbred lines. Genetics 177, 1839–1850.

    Article  CAS  PubMed  Google Scholar 

  • Li L. Z., Lu K. Y., Chen Z. M., Mou T. M., Hu Z. L. and Li X. Q. 2010 Gene actions at loci underlying several quantitative traits in two elite rice hybrids. Mol. Genet. Genomics 284, 383–397.

    Article  CAS  PubMed  Google Scholar 

  • Li L. Z., Lu K. Y., Mou T. M., Hu Z. L. and Li X. Q. 2008 Dominance, overdominance and epistasis condition the heterosis in two heterotic rice hybrids. Genetics 180, 1725–1742.

    Article  PubMed  Google Scholar 

  • Melchinger A. E., Piepho H. P., Utz H. F., Muminovic J., Wegenast T., Törjék O. et al. 2007 Genetic basis of heterosis for growth-related traits in Arabidopsis investigated by testcross progenies of near-Isogenic lines reveals a significant role of epistasis. Genetics 177, 1827–1837.

    Article  PubMed  Google Scholar 

  • Qi H., Huang J., Zheng Q., Huang Y., Shao R., Zhu L. et al. 2012 Identification of combining ability loci for five yield-related traits in maize using a set of testcrosses with introgression lines. Theor. Appl. Genet. 126, 369–377.

    Article  PubMed  Google Scholar 

  • Qu Z., Li L. Z., Luo J. Y., Wang P., Yu S. B., Mou T. M. et al. 2012 QTL Mapping of combining ability and heterosis of agronomic traits in rice backcross recombinant inbred lines and hybrid crosses. PLoS One 7, e28463.

    Article  Google Scholar 

  • Rao S. and Li X. 2000 Strategies for genetic mapping of categorical traits. Genetica 109, 183–197.

    Article  CAS  PubMed  Google Scholar 

  • Reif J. C., Kusterer B., Piepho H. P., Meyer R. C., Altmann T., Schön C. C. and Melchinger A. E. 2009 Unraveling epistasis with triple testcross progenies of near-isogenic lines. Genetics 181, 247–251.

    Article  PubMed  Google Scholar 

  • Riedelsheimer C., Czedik-Eysenberg A., Grieder C., Lisec J., Technow F., Sulpice R. et al. 2012 Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nat. Genet. 44, 217–220.

    Article  CAS  PubMed  Google Scholar 

  • Schön C. C., Dhillon B. S., Utz H. F. and Melchinger A E. 2010. High congruency of QTL positions for heterosis of grain yield in three crosses of maize. Theor. Appl. Genet. 120, 321–332.

    Article  PubMed  Google Scholar 

  • Shukla S. K. and Pandey M. P. 2008 Combining ability and heterosis over environments for yield and yield components in two-line hybrids involving thermosensitive genic male sterile lines in rice Oryza sativa L. Plant Breed. 127, 28–32.

    Google Scholar 

  • Su C. F., Zhao T. J. and Ga J. Y. 2010 Simulation comparisons of effectiveness among QTL mapping procedures of different statistical genetic models. Acta Agron. Sin. 36, 1100–1107.

    Article  CAS  Google Scholar 

  • Verhoeven K. J. F., Jannink J. L. and McIntyre L. M. 2006 Using mating designs to uncover QTL and the genetic architecture of complex traits. Heredity 96, 139–149.

    Article  CAS  PubMed  Google Scholar 

  • Wu X. L. and Jannink J. L. 2004 Optimal sampling of a population to determine QTL location, variance, and allelic number. Theor. Appl. Genet. 108, 1434–1442.

    Article  PubMed  Google Scholar 

  • Xiao J., Li J., Yuan L. and Tanksley S. D. 1995 Dominance is the major genetic basis of heterosis in rice as revealed by QTL analysis using molecular markers. Genetics 140, 745–754.

    CAS  PubMed  Google Scholar 

  • Xie C. Q., Gessler D. D. G. and Xu S. Z. 1998 Combining different line crosses for mapping quantitative trait loci using the identical by descent-based variance component method. Genetics 149, 1139–1146.

    CAS  PubMed  Google Scholar 

  • Xu S. 1998 Mapping quantitative trait loci using multiple families of line crosses. Genetics 148, 517–524.

    CAS  PubMed  Google Scholar 

  • Yang J., Hu C. C., Yu R. D., Xia Z., Ye X. Z. and Zhu J. 2008 QTLNetwork, mapping and visualizing genetic architecture of complex traits in experimental populations. Bioinformatics 24, 721–723.

    Article  PubMed  Google Scholar 

  • Zhu Y. P. and He F. M. 2001 Studies on inbred lines of sweet corn as testers and its number. Southwest Chin. J. Agric. Sci. 14, 25–28.

    Google Scholar 

Download references

Acknowledgements

This work was financially supported by the 973 program (no. 2011CB100102), the National Natural Science Foundation of China (no. 31000666), the China Postdoctoral Science foundation (no. 2012M511722) and Hunan Province Postdoctoral Science foundation (no. 2012RS4039).

Author information

Authors and Affiliations

  1. Hunan Provincial Key Laboratory for Biology and Control of Plant Disease and Insect Pests, College of Bio-Safety Science and Technology, Hunan Agricultural University, 410128, Changsha, People’s Republic of China

    LANZHI LI, CONGWEI SUN, YUAN CHEN & ZHIJUN DAI

  2. State Key Laboratory of Hybrid Rice, College of Life Science, Wuhan University, 430072, Wuhan, People’s Republic of China

    ZHEN QU, XINGFEI ZHENG & ZHONGLI HU

  3. National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 430070, Wuhan, People’s Republic of China

    SIBIN YU & TONGMIN MOU

  4. Division of Biostatistics and Quantitative Genetics, College of Agricultural Science, Yangzhou University, 225009, Yangzhou, People’s Republic of China

    CHENWU XU

Authors
  1. LANZHI LI
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. CONGWEI SUN
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YUAN CHEN
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. ZHIJUN DAI
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. ZHEN QU
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. XINGFEI ZHENG
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. SIBIN YU
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. TONGMIN MOU
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. CHENWU XU
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. ZHONGLI HU
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to CHENWU XU or ZHONGLI HU.

Additional information

[Li L., Sun C., Chen Y., Dai Z., Qu Z., Zheng X., Yu S., Mou T., Xu C. and Hu Z. 2013 QTL mapping for combining ability in different population-based NCII designs by a simulation study. J. Genet. 92, xx–xx]

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

(PDF 609 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

LI, L., SUN, C., CHEN, Y. et al. QTL mapping for combining ability in different population-based NCII designs: a simulation study. J Genet 92, 529–543 (2013). https://doi.org/10.1007/s12041-013-0311-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12041-013-0311-6

Keywords

Search

Navigation