Advertisement

Theoretical and Applied Genetics

, Volume 125, Issue 7, pp 1487–1501 | Cite as

Quality control genotyping for assessment of genetic identity and purity in diverse tropical maize inbred lines

  • Kassa Semagn
  • Yoseph Beyene
  • Dan Makumbi
  • Stephen Mugo
  • B. M. Prasanna
  • Cosmos Magorokosho
  • Gary Atlin
Original Paper

Abstract

Quality control (QC) genotyping is an important component in breeding, but to our knowledge there are not well established protocols for its implementation in practical breeding programs. The objectives of our study were to (a) ascertain genetic identity among 2–4 seed sources of the same inbred line, (b) evaluate the extent of genetic homogeneity within inbred lines, and (c) identify a subset of highly informative single-nucleotide polymorphism (SNP) markers for routine and low-cost QC genotyping and suggest guidelines for data interpretation. We used a total of 28 maize inbred lines to study genetic identity among different seed sources by genotyping them with 532 and 1,065 SNPs using the KASPar and GoldenGate platforms, respectively. An additional set of 544 inbred lines was used for studying genetic homogeneity. The proportion of alleles that differed between seed sources of the same inbred line varied from 0.1 to 42.3 %. Seed sources exhibiting high levels of genetic distance are mis-labeled, while those with lower levels of difference are contaminated or still segregating. Genetic homogeneity varied from 68.7 to 100 % with 71.3 % of the inbred lines considered to be homogenous. Based on the data sets obtained for a wide range of sample sizes and diverse genetic backgrounds, we recommended a subset of 50–100 SNPs for routine and low-cost QC genotyping, verified them in a different set of double haploid and inbred lines, and outlined a protocol that could be used to minimize errors in genetic analyses and breeding.

Keywords

Inbred Line Simple Sequence Repeat Marker Double Haploid Seed Source Cetyl Trimethyl Ammonium Bromide 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We thank Veronica Ogugo for sample preparation and DNA extraction. This work was carried out under the Drought Tolerant Maize for Africa (DTMA) and Water Efficient Maize for Africa (WEMA) projects undertaken by CIMMYT and national partners in Africa, and funded by the Bill and Melinda Gates Foundation.

Supplementary material

122_2012_1928_MOESM1_ESM.doc (492 kb)
Supplementary material 1 (DOC 492 kb)

References

  1. Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331PubMedGoogle Scholar
  2. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A Robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e19379PubMedCrossRefGoogle Scholar
  3. Fan JB, Gunderson KL, Bibikova M, Yeakley JM, Chen J, Wickham GE, Lebruska LL, Laurent M, Shen R, Barker D (2006) Illumina universal bead arrays. Methods Enzymol 410:57–73PubMedCrossRefGoogle Scholar
  4. Fleming AA, Kozelnicky GM, Browne EB (1964) Variations between stocks within long-time inbred lines of maize (Zea mays L.) 1. Crop Sci 4:291–295CrossRefGoogle Scholar
  5. Gethi JG, Labate JA, Lamkey KR, Smith ME, Kresovich S (2002) SSR variation in important US maize inbred lines. Crop Sci 42:951–957CrossRefGoogle Scholar
  6. Gupta K, Balyan S, Edwards J, Isaac P, Korzun V, Rodër M, Gautier MF, Joudrier P, Schlatter R, Dubcovsky J, De La Pena C, Khairallah M, Penner G, Hayden J, Sharp P, Keller B, Wang C, Hardouin P, Jack P, Leroy P (2002) Genetic mapping of 66 new microsatellite (SSR) loci in bread wheat. Theor Appl Genet 105:413–422PubMedCrossRefGoogle Scholar
  7. Hamblin MT, Warburton ML, Buckler ES (2007) Empirical comparison of simple sequence repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness. PLoS One 2:e1367PubMedCrossRefGoogle Scholar
  8. Heckenberger M, Bohn M, Ziegle JS, Joe LK, Hauser JD, Hutton M, Melchinger AE (2002) Variation of DNA fingerprints among accessions within maize inbred lines and implications for identification of essentially derived varieties. I. Genetic and technical sources of variation in SSR data. Mol Breed 10:181–191CrossRefGoogle Scholar
  9. Heckenberger M, Voort JR, Melchinger AE, Peleman J, Bohn M (2003) Variation of DNA fingerprints among accessions within maize inbred lines and implications for identification of essentially derived varieties. II. Genetic and technical sources of variation in AFLP data and comparison with SSR data. Mol Breed 12:97–106CrossRefGoogle Scholar
  10. Heckenberger M, Bohn M, Frisch M, Maurer HP, Melchinger AE (2005) Identification of essentially derived varieties with molecular markers: an approach based on statistical test theory and computer simulations. Theor Appl Genet 111:598–608PubMedCrossRefGoogle Scholar
  11. Heckenberger M, MuminovicÂ′ J, Voort JR, Peleman J, Bohn M, Melchinger AE (2006) Identification of essentially derived varieties obtained from biparental crosses of homozygous lines. III. AFLP data from maize inbreds and comparison with SSR data. Mol Breed 17:111–125CrossRefGoogle Scholar
  12. Jones DF (1945) Heterosis resulting from degenerative changes. Genetics 30:527–542Google Scholar
  13. Liu K, Muse SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129PubMedCrossRefGoogle Scholar
  14. Liu K, Goodman M, Muse S, Smith JS, Buckler E, Doebley J (2003) Genetic structure and diversity among maize inbred lines as inferred from DNA microsatellites. Genetics 165:2117–2128PubMedGoogle Scholar
  15. Low YL, Wedrén S, Liu J (2006) High-throughput genomic technology in research and clinical management of breast cancer. Evolving landscape of genetic epidemiological studies. Breast Cancer Res 8:209PubMedCrossRefGoogle Scholar
  16. Lu Y, Yan J, Guimarães CT, Taba S, Hao Z, Gao S, Chen S, Li J, Zhang S, Bindiganavile SV et al (2009) Molecular characterization of global maize breeding germplasm based on genome-wide single nucleotide polymorphisms. Theor Appl Genet 120: 93–115.PubMedCrossRefGoogle Scholar
  17. Mace EM, Buhariwalla HK, Crouch JH (2003) A high-throughput DNA extraction protocol for tropical molecular breeding programs. Plant Mol Biol Report 21:459a–459hCrossRefGoogle Scholar
  18. Mantel N (1967) The detection of disease clustering and a generalized regression approach. Cancer Res 27:209–220PubMedGoogle Scholar
  19. Prasanna B, Pixley K, Warburton M, Xie CX (2010) Molecular marker-assisted breeding options for maize improvement in Asia. Mol Breed 26:339–356CrossRefGoogle Scholar
  20. Revilla P, AbuÃn MC, Malvar RA, Soengas P, Ordas B, Ordas A (2005) Genetic variation between Spanish and American versions of sweet corn inbred lines. Plant Breed 124:268–271CrossRefGoogle Scholar
  21. Rholf FJ (1993) NTSYS-pc, numerical taxonomy and multivariate analysis system. Exeter software, New YorkGoogle Scholar
  22. Rogers JS (1972) Measures of genetic similarity and genetic distance. Stud Genet VII Univ Texas Publ 7213:145–153Google Scholar
  23. Russell WA, Vega UA (1973) Genetic stability of quantitative characters in successive generations in maize inbred lines. Euphytica 22:172–180CrossRefGoogle Scholar
  24. Russell WA, Sprague GF, Penny LH (1963) Mutations affecting quantitative characters in long-time inbred lines of maize1. Crop Sci 3:175–178CrossRefGoogle Scholar
  25. Schuler JF (1954) Natural mutations in inbred lines of maize and their heterotic effect. I. Comparison of parent, mutant and their F1 hybrid in a highly inbred background. Genetics 39:908–922PubMedGoogle Scholar
  26. Semagn K, Magorokosho C, Vivek BS, Makumbi D, Beyene Y, Mugo S, Prasanna BM, Warburton ML (2012) Molecular characterization of diverse CIMMYT maize inbred lines from eastern and southern Africa using single nucleotide polymorphic markers. BMC Genomics 13:113. doi: 10.1186/1471-2164-13-113 PubMedCrossRefGoogle Scholar
  27. Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol 24:1596–1599PubMedCrossRefGoogle Scholar
  28. Warburton ML, Setimela P, Franco J, Cordova H, Pixley K, Banziger M, Dreisigacker S, Bedoya C, MacRobert J (2010) Toward a cost-effective fingerprinting methodology to distinguish maize open-pollinated varieties. Crop Sci 50:467–477CrossRefGoogle Scholar
  29. Yan J, Shah T, Warburton ML, Buckler ES, McMullen MD, Crouch J (2009) Genetic characterization and linkage disequilibrium estimation of a global maize collection using SNP markers. PLoS One 4:e8451PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Kassa Semagn
    • 1
  • Yoseph Beyene
    • 1
  • Dan Makumbi
    • 1
  • Stephen Mugo
    • 1
  • B. M. Prasanna
    • 1
  • Cosmos Magorokosho
    • 2
  • Gary Atlin
    • 3
  1. 1.International Maize and Wheat Improvement Center (CIMMYT)NairobiKenya
  2. 2.International Maize and Wheat Improvement Center (CIMMYT)HarareZimbabwe
  3. 3.International Maize and Wheat Improvement Center (CIMMYT)Mexico D.F.Mexico

Personalised recommendations