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Mapping As You Go

An Effective Approach for Marker-Assisted Selection of Complex Traits

Pioneer Hi-Bred International, 7250 NW 62nd Ave., P.O. Box 552, Johnston, IA 50131-0552

View larger version (22K): [in a new window]   Fig. 1. A schematic representation of the (a) Mapping Start Only and (b) Mapping As You Go approaches to marker-assisted selection. A solid circle on the timeline indicates a mapping event. QTL = quantitative trait loci.

View larger version (17K): [in a new window]   Fig. 2. The performance of the Mapping Start Only (MSO) and three versions of the Mapping As You Go approach averaged across all genetic models considered in the experiment. (a) The performance is shown in terms of trait value normalized between 0 and 100%. (b) The response shown in (a) is represented as the difference in response between a given breeding strategy and the MSO method. Positive values indicate the breeding strategy had a higher response than the MSO method, and negative values indicate the breeding strategy had a lower response than the MSO method. The performance differences are expressed in terms of normalized trait value.

View larger version (35K): [in a new window]   Fig. 3. The relative performance of the Mapping Start Only (MSO) and three versions of the Mapping As You Go approach for nine general classes of genetic models (Table 1). E = the number of different environment types conditioning gene-by-environment interactions in the target population of environments, and K = level of epistasis. Additive genetic models: E = 1, K = 0; Epistatic effects models: E = 1, K = 1, 2; Gene-by-environment effects models: K = 0, E = 5, 10; Epistasis and gene-by-environment effects models: E = 5, 10, K = 1, 2. In all cases, performance is represented as the difference in response between a given breeding strategy and the MSO method. Positive values indicate the breeding strategy had a higher response than the MSO method, and negative values indicate the breeding strategy had a lower response than the MSO method. The performance differences are expressed in terms of normalized trait value.

View larger version (34K): [in a new window]   Fig. 4. The standard deviation of performance (Fig. 3) for nine general classes of genetic models (Table 1). E = the number of different environment types conditioning gene-by-environment interactions in the target population of environments, and K = level of epistasis. Additive genetic models: E = 1, K = 0; Epistatic effects models: E = 1, K = 1, 2; Gene-by-environment effects models: K = 0, E = 5, 10; Epistasis and gene-by-environment effects models: E = 5, 10, K = 1, 2.

View larger version (26K): [in a new window]   Fig. 5. The performance of the Mapping Start Only and Mapping As You Go (update = every cycle) approaches to marker-assisted selection at Cycles 10 and 20 of the breeding program. Each point represents the response for a single genetic model and a single run of the breeding program. All nine classes of the genetic models (Table 1) are shown (250 points per class).

View larger version (21K): [in a new window]   Fig. 6. The effect of genotype combinations (line plots; referred to as physiological epistasis by Cheverud and Routman, 1995) and average genotype effect of gene A across the genetic background (vertical bars; statistical estimation of the average genotype value for the aa and AA genotypes of gene A) for three hypothetical genetic models; (a) a single independent additive gene (gene A); K = 0, (b) a digenic network where gene A interacts with gene B; K = 1, and (c) a trigenic network where gene A interacts with genes B and C; K = 2. K = level of epistasis. Values of the vertical bars show the effect of the two homozygous genotype classes for gene A averaged across all background genotype combinations in the network.

View larger version (61K): [in a new window]   Fig. 7. The effect of gene A in different populations of individuals. The three genetic models used in the construction of this figure are an extension of those presented in Fig. 6. In all cases, the genetic models had 10 genes. Genes not represented in Fig. 6 were defined as having additive effects (i.e., equivalent to Fig. 6a). For example, for genetic models (K = 2), the first three genes were defined as in Fig. 6c and the remaining seven genes were defined to have additive independent effects. Figure 7a shows the distribution of allele effect size for gene A estimated in 10000 independent populations. Figure 7b shows the estimated allele effect for gene A across 10 cycles of selection for 30 different runs of selection. A positive effect size indicates that genotype class AA was favorable and a negative effect size indicates that genotype class aa was favorable. K = level of epistasis.

View larger version (28K): [in a new window]   Fig. 8. The relative performance of five breeding strategies for six general classes of genetic model. In all cases, performance is represented as the difference in response between a given breeding strategy and the Mapping Start Only (MSO) method. Positive values indicate the breeding strategy had a higher response than the MSO method, and negative values indicate the breeding strategy had a lower response than the MSO method. The performance differences are expressed in terms of normalized trait value. Each line represents average performance across 20000 runs of the breeding program (600000 runs in total). A categorization of the E(NK) models considered is given in Table 1; E = the number of different environment types conditioning gene-by-environment interactions in the target population of environments, and K = level of epistasis.

View larger version (25K): [in a new window]   Fig. 9. The relative performance of the Mapping Start Only (MSO) and Mapping As You Go methods for six general classes of genetic model. In all cases, performance is represented as the difference in response between a given breeding strategy and the MSO method. The performance differences are expressed in terms of normalized trait value. Each line represents average performance across 1000 runs of the breeding program (24000 runs total). A categorization of the E(NK) models considered is given in Table 1; E = the number of different environment types conditioning gene-by-environment interactions in the target population of environments, and K = level of epistasis.

View larger version (26K): [in a new window]   Fig. 10. The relative performance of the Mapping Start Only (MSO) and Mapping As You Go methods for three general classes of genetic models (E = 10; K = 0, 1, 2) for two different starting population configurations (low and high linkage disequilibrium [LD] among markers). In all cases, performance is represented as the difference in response between a given breeding strategy and the MSO method. The performance differences are expressed in terms of normalized trait value. Each line represents average performance across 1000 runs of the breeding program (24000 runs in total). A categorization of the E(NK) models considered is given in Table 1; E = the number of different environment types conditioning gene-by-environment interactions in the target population of environments, and K = level of epistasis.