Designing an optimal marker-based pedigree selection strategy for parent building in barley in the presence of repulsion linkage, using computer simulation
G. Ye A C , D. Moody B , L. Emebiri B and M. van Ginkel BA Primary Industries Research Victoria, Department of Primary Industries, Molecular Plant Breeding Cooperative Research Center, 1 Park Drive, Bundoora, Vic. 3086, Australia.
B Primary Industries Research Victoria, Department of Primary Industries, Horsham, Vic. 3401, Australia.
C Corresponding author. Email: guoyou.ye@dpi.vic.gov.au
Australian Journal of Agricultural Research 58(3) 243-251 https://doi.org/10.1071/AR06177
Submitted: 29 May 2006 Accepted: 11 January 2007 Published: 16 March 2007
Abstract
Pyramiding multiple desirable genes is an important method for the development of improved breeding materials and/or new cultivars. When the number of genes to be pyramided is many, or the genes are tightly linked in repulsion, it is practically impossible to recover the desirable recombinants in a single generation using a realistic population size, and repeated selection at several generations is required. The availability of markers tightly linked to the desirable genes makes it possible to conduct effective individual selection at early generations. This reduces the number of lines tested in the later generations and increases the desirable genotype frequency in the selected progeny. Computer simulation was used to develop such a marker-based pedigree selection strategy for the development of a barley line that contains 6 desired genes from 3 parental breeding lines (HS078 (H): 221222; PI366444 (P): 212222; Sloop Vic. (S): 122111; with 1 and 2 representing desirable and undesirable alleles, respectively), using the top cross H/P//S. The 6 genes targetted contribute to photoperiod sensitivity, Russian wheat aphid resistance, leaf rust resistance, boron tolerance, earliness per se, and cereal cyst nematode resistance. Under the assumption that perfect markers were available for all the 6 genes, a TC1 population of 300 plants was required to obtain 3 or more lines of the best genotype ‘211222/122111’, in which 3 loci were fixed for the desirable alleles, while the remaining 3 were kept as heterozygous. When single seed descent was used from the TC2 generation until complete homozygosity, the probability of obtaining lines of the desirable genotype (fixed for the desirable alleles at all 6 loci) was low due to the tight repulsion linkage between some of the genes. About 4000 individuals would be required to ensure with 99% probability the recovery of at least 1 line with the desirable genotype. The total number of lines that would need to be genotyped would be at least 5000. When the pedigree method was used in all test-cross generations, many schemes resulted in more lines of the fixed desirable genotype by genotyping fewer lines. The various options were compared using the genetic simulation software module QuLine, based on the QU-GENE simulation platform. The optimum scheme in terms of high success rate and relatively low genotyping costs consisted of the following steps: (1) in TC1 genotyping of 300 individuals allows for 3 or more individuals with the genotype ‘211222/122111’ to be identified; (2) in the TC2 individuals that are fixed for 3 loci and segregating for the remaining 3, loci can be selected from among 500 TC2 plants; (3) in the TC3, 50 or more individuals per TC3 line are genotyped for the 3 segregating loci, and individuals fixed for 5 loci and segregating for the 6th locus can be detected (genotyping is only needed for the segregating loci); (4) 25 individuals per TC4 line are genotyped for the single remaining segregating locus and several individuals of the desirable genotype (111111/111111) are finally selected. The desirable line is then obtained by collecting selfed seed from the selected TC4 plants. Using this scheme, on average, 320 desired TC5 lines were obtained by genotyping fewer than 2000 lines. When markers were tightly linked to the target genes but not diagnostic (perfect), not only was more genotyping required, but also appropriate phenotyping at the end of the marker selection process was necessary to confirm the presence of all the target genes. Under the assumption that recombination between marker and target gene was 5%, the best selection scheme identified, on average, 30 fixed desirable lines by genotyping 8000 lines and phenotyping 700 TC5 lines. If double haploid lines were produced from the F1 generation between H and P, and marker and phenotypic screening were conducted, followed by crossing of the individual with the target 2 loci in desired homozygous allelic status with parent S, the total amount of genotyping and phenotyping could be halved. This study showed that genetic simulation allows for numerous strategies to be compared using real data, and to develop an optimal crossing and selection strategy to combine desired alleles in the most effective and efficient way. This approach could likewise be used in other marker-assisted breeding programs.
Additional keyword: marker-assisted selection.
Dekkers JCM, Hospital F
(2002) The use of molecular genetics in the improvement of agricultural populations. Nature Reviews Genetics 3, 22–32.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Dubcovsky J
(2004) Marker assisted selection in public breeding programs: the wheat experience. Crop Science 44, 1895–1898.
Eagles HA,
Bariana HS,
Ogbonnaya FC,
Rebetzke GJ,
Hollamby GJ,
Henry RJ,
Henschke PH, Carter M
(2001) Implementation of markers in Australian wheat breeding. Australian Journal of Agricultural Research 52, 1349–1356.
| Crossref | GoogleScholarGoogle Scholar |
Frisch M, Melchinger AE
(2005) Selection theory for marker-assisted backcrossing. Genetics 170, 909–917.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Hospital F
(2005) Selection in backcross programmes. Philosophical Transactions: Biological Sciences 360, 1503–1511.
| Crossref |
Hospital F,
Chevalet C, Mulsant P
(1992) Using markers in gene introgression breeding programs. Genetics 132, 1199–1210.
| PubMed |
Huang N,
Angeles ER,
Domingo J,
Magpantay G,
Singh S,
Zhang G,
Kumaravadivel N,
Bennet J, Khush GS
(1997) Pyramiding of bacterial blight resistance genes in rice: marker-assisted selection using RFLP and PCR. Theoretical and Applied Genetics 95, 313–320.
| Crossref | GoogleScholarGoogle Scholar |
Kuchel H,
Ye G,
Fox R, Jefferies S
(2005) Genetic and genomic analysis of a targeted marker-assisted wheat breeding strategy. Molecular Breeding 16, 67–78.
| Crossref | GoogleScholarGoogle Scholar |
Langridge P, Barr A
(2003) Preface to ‘Better barley faster: the role of marker assisted selection’. Australian Journal of Agricultural Research 54, i–iv.
| Crossref | GoogleScholarGoogle Scholar |
Paterson AH,
Tanksley SD, Sorrells ME
(1991) DNA markers in plant improvement. Advances in Agronomy 46, 39–90.
Podlich DW, Cooper M
(1998) QU-GENE: a platform for quantitative analysis of genetic models. Bioinformatics (Oxford, England) 14, 632–653.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Ribaut JM,
Jiang C, Hoisington D
(2002) Simulation experiments on efficiencies of gene introgression by backcrossing. Crop Science 42, 557–565.
Servin B,
Martin OC,
Mezard M, Hospital F
(2004) Toward a theory of marker-assisted gene pyramiding. Genetics 168, 513–523.
| Crossref | GoogleScholarGoogle Scholar | PubMed |
Stuber CW,
Polacco M, Senior ML
(1999) Synergy of empirical breeding, marker-assisted selection, and genomics to increase crop yield potential. Crop Science 39, 1571–1583.
Toojinda T,
Baird E,
Booth A,
Broers L,
Hayes P,
Powell W,
Thomas W,
Vivar H, Young G
(1998) Introgression of quantitative trait loci (QTLs) determining stripe rust resistance in barley: an example of marker-assisted line development. Theoretical and Applied Genetics 96, 123–131.
| Crossref | GoogleScholarGoogle Scholar |
Wang J,
Eagles HA,
Trethowan R, van Ginkel M
(2005) Using computer simulation of the selection process and known gene information to assist in parental selection in wheat quality breeding. Australian Journal of Agricultural Research 56, 465–473.
| Crossref | GoogleScholarGoogle Scholar |
Wang J,
van Ginkel M,
Podlich D,
Ye G,
Trethowan R,
Pfeiffer W,
DeLacy IH,
Cooper M, Rajaram S
(2003) Comparison of two breeding strategies by computer simulation. Crop Science 43, 1764–1773.