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Parent Selection – Usefulness and Prediction of Hybrid Performance

  • Adel H. Abdel-Ghani
  • T. Lübberstedt
Chapter

Abstract

Efficiency of breeding programs depends on choice of optimal parental combinations. The ability to identify parental combinations that will result in greater genetic variance of progeny would help to maximize genetic gain from selection in plant breeding programs. Substantial efforts have been invested to study the possible correlations between various predictors based on parental information with population mean \( ({\mu_m}) \) and genetic variance of segregating population \( \left( {\Delta {\sigma_{g(m) }}} \right) \). With the development of DNA-based markers, it became possible to determine genetic distances \( (G{\overset{\lower0.0em\hbox{$\smash{\scriptscriptstyle\frown}$}}{D}}) \) among parental lines. However, prediction \( \Delta {\sigma_{g(m) }} G{\overset{\lower0.0em\hbox{$\smash{\scriptscriptstyle\frown}$}}{D}} \) based on DNA marker data remains a challenging issue. In view of the weakness of traditional marker-assisted selection in prediction potential \( \Delta {\sigma_{g(m) }}, \) the use of genomic selection (GS) is considered as promising approach. Rather than seeking to identify individual markers significantly associated with a quantitative trait, GS could use all marker data as predictors of parental line performance and contributes to accurate predictions of the usefulness of parental combinations.

The ability to predict hybrid performance (HP) of breeding lines based on molecular-based genetic data would greatly enhance the efficiency of hybrid breeding programs. Therefore, the relationship between genetic distance \( G{\overset{\lower0.0em\hbox{$\smash{\scriptscriptstyle\frown}$}}{D}} \) and HP was intensively studied by many authors, predominantly by allogamous crop breeders. Prediction of HP without having to produce and assess hundreds of single-cross hybrids would reduce the time and effort required to identify promising combinations. Earlier studies indicated that the success of predicting HP based on \( G{\overset{\lower0.0em\hbox{$\smash{\scriptscriptstyle\frown}$}}{D}} \) of parental information was not encouraging and at best inconsistent. However, recent marker-based prediction approaches based on expression profiling and those based on multiple linear regressions gave results often superior or at least similar to phenotypic and pedigree data approaches.

Keywords

Quantitative Trait Locus Inbred Line Genomic Selection General Combine Ability Specific Combine Ability 
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

Acknowledgements

This book chapter was prepared while Dr. Adel Abdel-Ghani was a visiting Fulbright Postdoctoral Fellow and during the sabbatical leave granted to Dr. Adel Abdel-Ghani from Mu’tah University, Jordan during the academic year 2011–2012 at Iowa State University, Ames, USA.

References

  1. Ajmone MP, Castiglioni P, Fusari F, Kuiper M, Motto M (1998) Genetic diversity and its relationship to hybrid performance in maize as revealed by RFLP and AFLP markers. Theor Appl Genet 96:219–227CrossRefGoogle Scholar
  2. Barbosa-Neto JF, Sorrells ME, Cisar G (1996) Prediction of heterosis in wheat usingcoefficient of parentage and RFLP-basedestimates of genetic relationship. Genome 39:1142–1149PubMedCrossRefGoogle Scholar
  3. Benchimol LL, De Souza JR, Garcia AAF, Kono PMS, Mangolin CA, Barbosa AMM, Coelho ASG, De Souza AP (2000) Genetic diversity in tropical maize inbred lines: heterotic group assignment and hybrid performance determined by RFLP markers. Plant Breed 119:491–496CrossRefGoogle Scholar
  4. Bernardo R (1992) Relationship between single-cross performance and molecular marker heterozygosity. Theor Appl Genet 83:628–634CrossRefGoogle Scholar
  5. Bernardo R (1994) Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci 34:20–25CrossRefGoogle Scholar
  6. Bernardo R, Yu J (2007) Prospects for genome-wide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  7. Betrán FJ, Ribaut JM, Beck D, Gonzalez de Leon D (2003) Genetic diversity, specific combining ability, and heterosis in tropical maize under stress and nonstress environments. Crop Sci 43:797–806CrossRefGoogle Scholar
  8. Bohn M, Utz HF, Melchinger AE (1999) Genetic diversity among winter wheat cultivars determined on the basis of RFLPs, AFLPs, and SSRs and their use for predicting progeny variance. Crop Sci 39:228–237CrossRefGoogle Scholar
  9. Boppenmaier J, Melchinger AE, Brunklaus-Jung E, Geiger HH, Herrmann RG (1992) Genetic diversity for RFLPs in European maize inbreds: I. Relation to performance of Flint ✕ Dent croses for forage traits. Crop Sci 32:895–902CrossRefGoogle Scholar
  10. Boppenmaier J, Melchinger AE, Seiltz G, Geiger HH, Herrmann RG (1993) Genetic diversity for RFLPs in European maize inbreds: III. Performance of crosses within versus between heterotic groups for grain traits. Plant Breed 11:217–226CrossRefGoogle Scholar
  11. Burkhamer RL (1998) Predicting progeny variance from parental divergence in hard red spring wheat. Crop Sci 38:243–248CrossRefGoogle Scholar
  12. Busch RH, Lucken KA, Frohberg RC (1971) F1 hybrids versus random F5 line performance and estimates of genetic effects in spring wheat. 11:357--316 Google Scholar
  13. Burstin J, Charcosset A, Barrière Y, Hébert Y, Devienne D, Damerval C (1995) Molecular markers and protein quantities as genetic descriptors in maize. II. Prediction of performance of hybrids for forage traits. Plant Breed 114:427–433Google Scholar
  14. Ceccarelli S (2009) Main stages of a plant breeding program. In: Ceccarelli S, Guimarães EP, Weltzien E (eds) Plant breeding and farmer participation. Food and Agriculture Organization (FAO), Rome, pp 63–74Google Scholar
  15. Cheres MT, Miller JF, Crane JM, Knapp SJ (2000) Genetic distance as a predictor of heterosis and hybrid performance within and between heterotic groups in sunflower. Theor Appl Genet 100:889–894CrossRefGoogle Scholar
  16. Cox TS, Lookhart GL, Walker DE, Harrell LG, Albers LD, Rogers DM (1985) Genetic relationships among hard red winter wheat cultivars as evaluated by pedigree analysis and gliadin polyacrylamide gel-electrophoretic patterns. Crop Sci 25:1058–1063CrossRefGoogle Scholar
  17. De Paepe A, Vuylsteke M, Van Hummelen P, Zabeau M, Van Der Straeten D (2004) Transcriptional profiling by cDNA AFLP and microarray analysis reveals novel insights into the early response to ethylene in Arabidopsis. Plant J 39:537–559PubMedCrossRefGoogle Scholar
  18. Dhliwayo T, Pixley K, Menkir A, Warburton M (2010) Combining ability, genetic distances, and heterosis among elite CIMMYT and IITA tropical maize inbred lines. Crop Sci 49:1201–1210CrossRefGoogle Scholar
  19. Dreisigacker S, Melchinger AE, Zhang P, Ammar K, Flachenecker C, Hoisington D, Warburton ML (2005) Hybrid performance and heterosis in spring bread wheat, and their relations to SSR-based genetic distances and coefficient of parentage. Euphytica 144:51–59CrossRefGoogle Scholar
  20. Duvick DN, Smith JSC, Cooper M (2004) Long-term selection in a commercial hybrid maize breeding program. In: Janick J (ed) Wiley, Engelwood Cliffs. Plant Breed Rev 24:109–51Google Scholar
  21. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longmans Green, HarlowGoogle Scholar
  22. Fehr WR (1993) Principles of cultivar development, 1st edn. Macmillian Publishing Compant, New YorkGoogle Scholar
  23. Frascaroli E, Cane MA, Landi P, Pea G, Gianfranceschi L, Villa M, Morgante M, Pe ME (2007) Classical genetic and quantitative trait loci analyses of heterosis in a maize hybrid between two elite inbred lines. Genetics 176:625–644PubMedCrossRefGoogle Scholar
  24. Frei OM, Stuber CW, Goodman MM (1986) Use of allozymes as genetic markers for predicting performance in maize single cross hybrids. Crop Sci 26:37–42CrossRefGoogle Scholar
  25. Frisch M, Thiemann A, Tobias JF, Schrag A, Scholten S, Melchinger AE (2010) Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize. Theor Appl Genet 120:441–450PubMedCrossRefGoogle Scholar
  26. Fu H, Dooner HK (2002) Intraspecific violation of genetic colinearity and its implications in maize. Proc Natl Acad Sci U S A 99:9573–9578PubMedCrossRefGoogle Scholar
  27. Gallais A (1979) The concept of varietal ability in plant breeding. Euphytica 28:811–823CrossRefGoogle Scholar
  28. Garay G, Igartua E, Alvarez A (1996) Response to S1 selection in flint and dent synthetic maize populations. Crop Sci 36:1129–1134CrossRefGoogle Scholar
  29. Gibson G, Dworkin I (2004) Uncovering cryptic genetic variation. Nat Rev Genet 5:681–690PubMedCrossRefGoogle Scholar
  30. Griffing B (1956) Concept of general combining ability and specific combining ability to diallele cross system. Aust J Bio Sci 9:463–493Google Scholar
  31. Gumber RK, Schill B, Link W, Kittlitz EV, Melchinger AE (1999) Mean, genetic variance, and usefulness of selfing progenies from intra- and inter-pool crosses in faba beans (Vicia faba L.) and their prediction from parental parameters. Theor Appl Genet 98:569–580CrossRefGoogle Scholar
  32. Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690CrossRefGoogle Scholar
  33. Heffner EL, Sorrells ME, Jannink J (2009) Genomic selection for crop improvement. Crop Sci 49:1–12CrossRefGoogle Scholar
  34. Hochholdinger F, Hoecker N (2007) Towards the molecular basis of heterosis. Trends Plant Sci 12:427–432PubMedCrossRefGoogle Scholar
  35. Jannink J, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177PubMedCrossRefGoogle Scholar
  36. Jinks JL, Pooni HS (1976) Predicting the properties of recombinant inbred lines derived by single seed descent. Heredity 36:253–266CrossRefGoogle Scholar
  37. Jordan DR, Tao Y, Godwin ID, Henzell RG, Cooper M, McIntyre CL (2003) Prediction of hybrid performance in grain sorghum using RFLP markers. Theor Appl Genet 106:559–567PubMedGoogle Scholar
  38. Joshi SP, Bhave SG, Chowdari KV, Apte GS, Dhonukshe BL, Lalitha K, Ranjekar PK, Gupta VS (2001) Use of DNA markers in prediction of hybrid performance and heterosis for a three-line hybrid system in rice. Biochem Genet 39:179–200PubMedCrossRefGoogle Scholar
  39. Kearsey M, Pooni HS (1996) The genetical analysis of quantitative traits. Chapman & Hall, LondonGoogle Scholar
  40. Kim BH, Arnim AG (2006) The early dark-response in Arabidopsis thaliana revealed by cDNA microarray analysis. Plant Mol Biol 60:321–342PubMedCrossRefGoogle Scholar
  41. Kisha TJ, Sneller CH, Diers BW (1997) Relationship between genetic distance among parents and genetic variance in populations of soybean. Crop Sci 37:1317–1325CrossRefGoogle Scholar
  42. Kotzamanidis ST, Lithourgidisb AS, Mavromatisc AG, Chasiotic DI, Roupakias DG (2008) Prediction criteria of promising F3 populations in durum wheat: a comparative study. Field Crop Res 107:257–264CrossRefGoogle Scholar
  43. Kuczyńska A, Surma M, Kaczmarek Z, Adamski T (2007) Relationship between phenotypic and genetic diversity of parental genotypes and the frequency of transgression effects in barley (Hordeum vulgare L.). Plant Breed 126:361–368CrossRefGoogle Scholar
  44. Lai J, Li R, Xu X, Jin W, Xu M (2010) Genome-wide patterns of genetic variation among elite maize inbred lines. Nat Genet 42:1027–1030PubMedCrossRefGoogle Scholar
  45. Lanza LLB, Souza Júnior CL, Ottoboni LMM, Vieira MLC, de Souza AP (1997) Genetic distance of inbred lines and prediction of maize single-cross performance using RAPD markers. Theor Appl Genet 94:1023–1030CrossRefGoogle Scholar
  46. Lee EA, Ash MJ, Good B (2007) Re-examining the relationship between degree of relatedness, genetic effects, and heterosis in maize. Crop Sci 47:629–635CrossRefGoogle Scholar
  47. Legesse BW, Myburg AA, Pixley KV, Twumasi-Afriyie S, Botha AM (2008) Relationship between hybrid performance and AFLP based genetic distance in highland maize inbred lines. Euphytica 162:313–323CrossRefGoogle Scholar
  48. Lian X, Wang S, Zhang J, Feng Q, Zhang L, Fan D, Li X, Yuan D, Han B, Zhang Q (2006) Expression profiles of 10,422 genes at early stage of low nitrogen stress in rice assayed using a cDNA microarray. Plant Mol Biol 60:617–631PubMedCrossRefGoogle Scholar
  49. Link W, Schill B, Barbera AC, Cubero JI, Filippetti A, Stringi L, Kittlitz EV, Melchinger AE (1996) Comparison of intra- and inter-pool crosses in fababean (ViciafabaL.): I. Hybrid performance and heterosis of crosses in Mediterranean and German environments. Plant Breed 115:352–360CrossRefGoogle Scholar
  50. Lonnquist JH (1967) Genetic variability in maize and indicated procedures for its maximum procedures for its maximum utilization. Sciencia y Cultura 19:135–144Google Scholar
  51. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith KP, Sorrells ME, Jannink J (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:78–109Google Scholar
  52. Lorenzana RE, Bernardo R (2009) Accuracy of genotypic value predictions for marker-based selection in biparental plant populations. Theor Appl Genet 120:151–161PubMedCrossRefGoogle Scholar
  53. Malécot G (1948) Les mathématiques de l’hérédité. Masson et Cie, ParisGoogle Scholar
  54. Malik SI, Malik HN, Minhas NM, Munir M (2004) General and specific combining ability studies in maize diallelcrosses. Int J AgrBiol 6:856–859Google Scholar
  55. Manjarrez-Sandoval P, Carter TE Jr, Webb DM, Burton JW (1997) RFLP genetic similarity estimates and coefficient of parentage as genetic variance predictors for soybean yield. Crop Sci 37:698–703CrossRefGoogle Scholar
  56. Marsan AP, Castiglioni P, Fusari F, Kuiper M, Motto M (1998) Genetic diversity and its relationship to hybrid performance in maize, as revealed by RFLP and AFLP markers. Theor Appl Genet 96:219–227CrossRefGoogle Scholar
  57. Martinez OJ, Goodman MM, Timothy DH (1983) Measuring racial differentiation in maize using multivariate distance measures standardized by variation in F2 populations. Crop Sci 23:775–781CrossRefGoogle Scholar
  58. Melchinger AE (1993) Use of RFLP markers for analyses of genetic relationships among breeding materials and prediction of hybrid performance. In: Proceedings of the First International Crop Science CongressGoogle Scholar
  59. Melchinger AE (1999) Genetic diversity and heterosis. In: Coors JG, Pandey S (eds) The genetics and exploitation of heterosis. ASA, CSSA, and SSSA, Madison, pp 99–118Google Scholar
  60. Melchinger AE, Gumber RK (1998) Overview of heterosis and heterotic groups in agronomic crops. In: Lamkey KR, Staub JE (eds) Concepts and breeding of heterosis in crop plants. CSSA, MadisonGoogle Scholar
  61. Melchinger AE, Lee M, Lamkey KR, Woodman WL (1990) Genetic diversity for restriction fragment length polymorphisms: Relation to estimated genetic effects in maize inbreds. Crop Sci 30:1033–1040CrossRefGoogle Scholar
  62. Melchinger AE, Gumber RK, Leipert RB, Vuylsteke M, Kuiper M (1998) Prediction of test cross means and variances among F3 progenies of F1 crosses from test cross means and genetic distances of their parents in maize. Theor Appl Genet 96:503–512CrossRefGoogle Scholar
  63. Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  64. Miedaner T, Schneider B, Oettler G (2006) Means and variances for Fusarium head blight resistance of F2-derived bulks from winter triticale and winter wheat crosses. Euphytica 152:405–411CrossRefGoogle Scholar
  65. Moser H, Lee M (1994) RFLP variation and genealogical distance, multivariate distance, heterosis, and genetic variation in oats. TheorAppl Genet 87:947–956Google Scholar
  66. Munhoz REF, Prioli AJ, Amaral Júnior AT, Scapim CA, Simon GA (2009) Genetic distances between popcorn populations based on molecular markers and correlations with heterosis estimates made by diallel analysis of hybrids. Genet Mol Res 8:951–962PubMedGoogle Scholar
  67. Nei M, Li WH (1979) Mathematical model for studying genetic variation in terms of restriction endonucleases. Proc Natl Acad Sci U S A 76:5269–5273PubMedCrossRefGoogle Scholar
  68. Ni XL, Zhang T, Jiang KF, Yang L, Yang QH, Cao CY, Wen CY, Zheng JK (2009) Correlations between specific combining ability, heterosis and genetic distance in hybrid rice. Yi Chuan 31:849–854PubMedCrossRefGoogle Scholar
  69. Qi X, Kimatu JN, Li Z, Jiang L, Cui Y, Liu B (2010a) Heterotic analysis using AFLP markers reveals moderate correlations between specific combining ability and genetic distance in maize inbred lines. Afr J Biot 9:1568–1572Google Scholar
  70. Qi X, Li ZH, Jiang LL, Yu XM, Ngezahayo F, Liu B (2010b) Grain-yield heterosis in Zea mays L. shows positive correlation with parental difference in CHG methylation. Crop Sci 50:2338–2346CrossRefGoogle Scholar
  71. Quinby JR (1963) Manifestation of hybrid vigor in sorghum. Crop Sci 3:288–291CrossRefGoogle Scholar
  72. Reif JC, Melchinger AE, Xia XC, Warburton ML, Hoisington DA, Vasal SK, Beck D, Bohn M, Frisch M (2003a) Use of SSRs for establishing heterotic groups in subtropical maize. Theor Appl Genet 83:628–634Google Scholar
  73. Reif JC, Melchinger AE, Xia XC, Warburton ML, Hoisington DA, Vasal SK, Srinivasan G, Bohn M, Frisch M (2003b) Genetic distance based on simple sequence repeats and heterosis in tropical maize populations. Crop Sci 43:1275–1282CrossRefGoogle Scholar
  74. Reif JC, Melchinger AE, Xia XC, Warburton ML (2003c) Use of SSRs for establishing heterotic groups in subtropical maize. Theor Appl Genet 107:947–957PubMedCrossRefGoogle Scholar
  75. Reif JC, Melchinger AE, Frisch M (2005) Genetical and mathematical properties of similarity and dissimilarity coefficients applied in plant breeding and seed bank management. Crop Sci 41:1–7CrossRefGoogle Scholar
  76. Riday H, Brummer EC, Campbell TA, Luth D, Cazcarro PM (2003) Comparisons of genetic and morphological distance with heterosis between Medicago sativa subsp. sativa and subsp. falcata. Euphytica 131:37–45CrossRefGoogle Scholar
  77. Robinson HF, Harvey PH (1955) Genetic variances in open-pollinated crops varieties of corn. Genetics 40:45–60PubMedGoogle Scholar
  78. Rojas BA, Sprague GF (1952) A comparison of variance components in corn yield trials: III. General and specific combining ability and their interaction with locations and years. Agron J 44:462–466CrossRefGoogle Scholar
  79. Saghai-Maroof MA, Yang GP, Zhang Q, Gravois KA (1997) Correlation between molecular marker distance and hybrid performance in U.S. Southern long grain rice. Crop Sci 37:145–150CrossRefGoogle Scholar
  80. Sarawat P, Stoddard FL, Marshall DR, Ali SM (1994) Heterosis for yield and related characters in pea. Euphytica 80:39–48CrossRefGoogle Scholar
  81. Schnell FW (1982) A synoptic study of the methods and categories of plant breeding. Z Pflanzenzücht 89:1–18Google Scholar
  82. Schnell FW, Utz HF (1975) F1-Leistung und Elternwahl in der Züchtung von Selbstbefruchtern. Berichtüber die Arbeitstagung der Vereinigungösterreichischer. Z Pflanzenzüchter 243–248Google Scholar
  83. Schrag TA, Maurer HP, Melchinger AE, Piepho H-P, Peleman J, Frisch M (2007) Prediction of single-cross hybrid performance in maize using haplotype blocks associated with QTL for grain yield. Theor Appl Genet 114:1345–1355PubMedCrossRefGoogle Scholar
  84. Schrag TA, Möhring J, Kusterer B, Melchinger AE, Dhillon BS, Piepho H, Frisch M (2010) Prediction of hybrid performance in maize using molecular markers and joint analyses of hybrids and parental inbreds. Theor Appl Genet 120:451–461PubMedCrossRefGoogle Scholar
  85. Selvaraj I, Nagarajan P, Thiyagarajan K, Bharathi M (2010) Predicting the relationship between molecular marker heterozygosity and hybrid performance using RAPD markers in rice (Oryza sativa L.). Afr J Biot 9:7641–7653Google Scholar
  86. Sneath PHA, Sokal RR (1973) Numerical taxonomy. W H Freeman & Co, San Francisco, 573 ppGoogle Scholar
  87. Song S, Qu H, Chen C, Hu S, Yu J (2007) Differential gene expression in an elite hybrid rice cultivar (Oryzasativa, L) and its parental lines based on SAGE data. Plant Biol 7:1–15Google Scholar
  88. Souza E, Sorrells ME (1989) Pedigree analysis of North American oat cultivars released from 1951 to 1985. Crop Sci 29:595–601CrossRefGoogle Scholar
  89. Souza E, Sorrells ME (1991) Relationships among 70 North American oat germplasms. I. Cluster analysis using quantitative characters. Crop Sci 31:599–605CrossRefGoogle Scholar
  90. Springer NM, Stupar RM (2007) Allelic variation and heterosis in maize: how do two halves make more than a whole? Genome Res 17:264–275PubMedCrossRefGoogle Scholar
  91. Springer NM, Stupar RM (2011) Allelic variation and heterosis in maize: how do two halves make more than a whole? Genome Res 17:264–275CrossRefGoogle Scholar
  92. Sun GL, William M, Liu J, Kasha KJ, Pauls KP (2004) Microsatellite and RAPD polymorphisms in Ontario corn hybrids are related to the commercial sources and maturity ratings. Mol Breed 7:13–24CrossRefGoogle Scholar
  93. Tams SH, Bauer E, Oettler G, Melchinger AE, Schön CC (2006) Prospects for hybrid breeding in winter triticale: II. Relationship between parental genetic distance and specific combining ability. Plant Breed 125:331–336CrossRefGoogle Scholar
  94. Tao Z, Xian-lin N, Kai-Feng J, Qianhua Y, Li Y, Xian-Qi W, Yingjiang C, Jiakui Z (2010) Correlation between heterosis and genetic distance based on molecular markers of functional genes in rice. Rice Sci 17:288–295CrossRefGoogle Scholar
  95. Thiemann A, Fu J, Schrag TA, Melchinger AE, Frisch M, Scholten S (2010) Correlation between parental transcriptome and field data for the characterization of heterosis in Zea mays L. Theor Appl Genet 120:401–413PubMedCrossRefGoogle Scholar
  96. Utz HF, Bohn M, Melchinger AE (2001) Predicting progeny means and variances of winter wheat crosses from phenotypic values of their parents. Crop Sci 41:1470–1478CrossRefGoogle Scholar
  97. Wong CK, Bernardo R (2008) Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theor Appl Genet 116:815–824PubMedCrossRefGoogle Scholar
  98. Wright AJ (1974) A genetic theory of general varietal ability for diploid crops. Theor Appl Genet 45:163–169Google Scholar
  99. Xiao J, Li J, Yuan L, McCouch SR, Tanksley SD (1996) Genetic diversity and its relationships to hybrid performance and heterosis in rice as revealed by PCR-based markers. Theor Appl Genet 92:637–643CrossRefGoogle Scholar
  100. Xin Q, Kimatu JN, Li Z, Jiang L, Cui Y, Liu B (2010) Heterotic analysis using AFLP markers reveals moderate correlations between specific combining ability and genetic distance in maize inbred lines. Afr J Biotechnol 9:1568–1572Google Scholar
  101. Yang GX, Jan A, Shen SH, Yazaki J, Ishikawa M, Shimatani Z, Kishimoto N, Kikuchi S, Matsumoto H, Komatsu S (2004) Microarray analysis of brassinosteroids- and gibberellin regulated gene expression in rice seedlings. Mol Genet Genomics 271:468–478PubMedCrossRefGoogle Scholar
  102. Zhang PJ, Cai HW, Li HC, Yang LS, Bai YS, Hu XM, Xu CW (2000) RAPD molecular markers of rice genetic distance and its relationship with heterosis. J Anhui Agric Univ 28:697–700 (in Chinese with English abstract)Google Scholar
  103. Zheng D, Van K, Wang L, Lee S (2008) Molecular genetic distance and hybrid performance between Chinese and American maize (Zea mays L.) inbreds. Aust J Agri Res 59:1010–1020CrossRefGoogle Scholar
  104. Zhong S, Jannink J (2007) Using quantitative trait loci results to discriminate among crosses on the basis of their progeny mean and variance. Genetics 177:567–576PubMedCrossRefGoogle Scholar
  105. Zhong S, Dekkers JCM, Fernando RL, Jannink JL (2009) Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: a barley case study. Genetics 182:355–364PubMedCrossRefGoogle Scholar

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© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Plant Production, Faculty of AgricultureMu’tah UniversityKarakJordan
  2. 2.Department of AgronomyIowa State UniversityAmesUSA

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