Phenotypic Selection Index Research Articles

Genomic and phenotypic selection indices for decreased birth weight, shortening fattening period, and regulating total weight gain in beef cattle.

To develop genomic and phenotypic indices for beef cattle selection that afford progeny with reduced birth weight and fattening period. The indices should also allow regulating total weight gain during the fattening period and avoid marked increases of inbreeding. Whether addition of constraint on total weight gain to constraints on body weight gain at up to four specific time points during the fattening process was effective for the weight gain until the end of the fattening period was examined in two selection indices with the selection trait being a phenotypic or a genomic breeding value random regression coefficient. Both indices afforded cattle with desired weight gains at specific time points and desired total weight gains. One cycle of index-based selection made it possible to shorten the fattening period two weeks compared with that before selection while maintaining the same final fattening weight as before selection. The impact of constraining total weight gain was smallest when the number of weight gain constraints at specific time points was three or four. However, constraining total weight gain was necessary to avoid poor total weight gain when the number of weight gain constraints at specific time points was only one or two. The developed indices make it possible to regulate total weight gain during the fattening process and to achieve desired weight gains at specific time points. Importantly, the indices bring about genetic improvement without an excessive increase of inbreeding. Thus, we expect that these indices will contribute to sustainable genetic improvement in cattle while maintaining genetic diversity.

Estratégias de seleção simultânea de características aplicadas à seleção genômica ampla para identificação de genótipos superiores

Abstract The objective of this work was to evaluate the efficiency of direct and indirect selection in genome-wide selection (GWS) and to compare multivariate strategies applied to GWS via selection indices. Ten F2 populations with 800 individuals were simulated, considering four traits with different heritabilities. The simulated data were subjected to genomic selection analyses. The five following strategies of selection indices were developed and applied to GWS: weighting of marker effects by residual variance; coding and standardization of marker effects; application of the average to the marker effects; application of the Mulamba & Mock index to genomic genetic values; and coding and standardization of phenotypic values before the GWS analyses. The GWS methods were more efficient than phenotypic selection. The multivariate strategies provide a higher selection efficiency than the direct and indirect phenotypic selections and than the direct and indirect selections based on genomic genetic values and on the phenotypic selection index.

A linear profit function for economic weights of linear phenotypic selection indices in plant breeding

AbstractThe profit function (net returns minus costs) allows breeders to derive trait economic weights to predict the net genetic merit (H) using the linear phenotypic selection index (LPSI). Economic weight is the increase in profit achieved by improving a particular trait by one unit and should reflect the market situation and not only preferences or arbitrary values. In maize (Zea mays L.) and wheat (Triticum aestivum) breeding programs, only grain yield has a specific market price, which makes application of a profit function difficult. Assuming the traits’ phenotypic values have multivariate normal distribution, we used the market price of grain yield and its conditional expectation given all the traits of interest to construct a profit function and derive trait economic weights in maize and wheat breeding. Using simulated and real maize and wheat datasets, we validated the profit function by comparing its results with the results obtained from a set of economic weights from the literature. The criteria to validate the function were the estimated values of the LPSI selection response and the correlation between LPSI and H. For our approach, the maize and wheat selection responses were 1,567.13 and 1,291.5, whereas the correlations were .87 and .85, respectively. For the other economic weights, the selection responses were 0.79 and 2.67, whereas the correlations were .58 and .82, respectively. The simulated dataset results were similar. Thus, the profit function is a good option to assign economic weights in plant breeding.

Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat.

The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p < 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future.

Identification of quantitative trait loci for growth-related traits in the blood clam (Tegillarca granosa)

The blood clam (Tegillarca granosa) is an economically important marine shellfish in the southeast coastal area of China. In T. granosa breeding practice, growth-related traits are most important phenotypic selection index. In order to increase the breeding efficiency, we used a previously published genetic linkage map of T. granosa to map quantitative trait loci (QTL) for growth-related traits in a single full sibling F1 family. Eight growth-related traits including shell length (SL), shell height (SH), shell width (SW), shell weight (Swi), shell umbo width (SUW), as well as outer ligament length (OLL), meat weight (MW) and total weight (TW) of T. granosa were measured for QTL mapping. The estimated phenotypic correlations between any 2 of the 8 growth-related traits were significant (P < 0.001). Twenty-nine QTL were identified for growth-related traits, including 13 QTL with the phenotypic variance of 4.20–15.63% in the female map and 16 QTL of 3.99%–14.58% in the male map. Thirteen amplification fragment length polymorphism (AFLP) markers were validated to closely link to seven QTL except the shell height. This is the first report of QTL mapping on growth-related traits in T. granosa. These QTL and their tightly linked markers present the initial step towards the goal of marker-assisted selection in T. granosa.

Identification of Spring Wheat with Superior Agronomic Performance under Contrasting Nitrogen Managements Using Linear Phenotypic Selection Indices.

Both the Linear Phenotypic Selection Index (LPSI) and the Restrictive Linear Phenotypic Selection Index (RLPSI) have been widely used to select parents and progenies, but the effect of economic weights on the selection parameters (the expected genetic gain, response to selection, and the correlation between the indices and genetic merits) have not been investigated in detail. Here, we (i) assessed combinations of 2304 economic weights using four traits (maturity, plant height, grain yield and grain protein content) recorded under four organically (low nitrogen) and five conventionally (high nitrogen) managed environments, (ii) compared single-trait and multi-trait selection indices (LPSI vs. RLPSI by imposing restrictions to the expected genetic gain of either yield or grain protein content), and (iii) selected a subset of about 10% spring wheat cultivars that performed very well under organic and/or conventional management systems. The multi-trait selection indices, with and without imposing restrictions, were superior to single trait selection. However, the selection parameters differed quite a lot depending on the economic weights, which suggests the need for optimizing the weights. Twenty-two of the 196 cultivars that showed superior performance under organic and/or conventional management systems were consistently selected using all five of the selected economic weights, and at least two of the selection scenarios. The selected cultivars belonged to the Canada Western Red Spring (16 cultivars), the Canada Northern Hard Red (3), and the Canada Prairie Spring Red (3), and required 83–93 days to maturity, were 72–100 cm tall, and produced from 4.0 to 6.2 t ha−1 grain yield with 14.6–17.7% GPC. The selected cultivars would be highly useful, not only as potential trait donors for breeding under an organic management system, but also for other studies, including nitrogen use efficiency.

Efficiency of a Constrained Linear Genomic Selection Index To Predict the Net Genetic Merit in Plants.

The constrained linear genomic selection index (CLGSI) is a linear combination of genomic estimated breeding values useful for predicting the net genetic merit, which in turn is a linear combination of true unobservable breeding values of the traits weighted by their respective economic values. The CLGSI is the most general genomic index and allows imposing constraints on the expected genetic gain per trait to make some traits change their mean values based on a predetermined level, while the rest of them remain without restrictions. In addition, it includes the unconstrained linear genomic index as a particular case. Using two real datasets and simulated data for seven selection cycles, we compared the theoretical results of the CLGSI with the theoretical results of the constrained linear phenotypic selection index (CLPSI). The criteria used to compare CLGSI vs. CLPSI efficiency were the estimated expected genetic gain per trait values, the selection response, and the interval between selection cycles. The results indicated that because the interval between selection cycles is shorter for the CLGSI than for the CLPSI, CLGSI is more efficient than CLPSI per unit of time, but its efficiency could be lower per selection cycle. Thus, CLGSI is a good option for performing genomic selection when there are genotyped candidates for selection.

Optimum and Decorrelated Constrained Multistage Linear Phenotypic Selection Indices Theory.

Some authors have evaluated the unconstrained optimum and decorrelated multistage linear phenotypic selection indices (OMLPSI and DMLPSI, respectively) theory. We extended this index theory to the constrained multistage linear phenotypic selection index context, where we denoted OMLPSI and DMLPSI as OCMLPSI and DCMLPSI, respectively. The OCMLPSI (DCMLPSI) is the most general multistage index and includes the OMLPSI (DMLPSI) as a particular case. The OCMLPSI (DCMLPSI) predicts the individual net genetic merit at different individual ages and allows imposing constraints on the genetic gains to make some traits change their mean values based on a predetermined level, while the rest of them remain without restrictions. The OCMLPSI takes into consideration the index correlation values among stages, whereas the DCMLPSI imposes the restriction that the index correlation values among stages be null. The criteria to evaluate OCMLPSI efficiency vs. DCMLPSI efficiency were that the total response of each index must be lower than or equal to the single-stage constrained linear phenotypic selection index response and that the expected genetic gain per trait values should be similar to the constraints imposed by the breeder. We used one real and one simulated dataset to validate the efficiency of the indices. The results indicated that OCMLPSI accuracy when predicting the selection response and expected genetic gain per trait was higher than DCMLPSI accuracy when predicting them. Thus, breeders should use the OCMLPSI when making a phenotypic selection.

The Relative Efficiency of Two Multistage Linear Phenotypic Selection Indices to Predict the Net Genetic Merit

Multistage linear phenotypic selection indices predict the individual net genetic merit at different individual ages or stages and are cost‐saving strategies for improving several traits. In a two‐stage context, we compared the relative efficiency of the optimum multistage linear phenotypic selection index (OMLPSI) and the decorrelated multistage linear phenotypic selection index (DMLPSI) theory to predict the individual net genetic merit and selection response using a real and a simulated dataset. In addition, we described a method for obtaining the OMLPSI selection intensity in a two‐stage context. The criteria used to compare the relative efficiency of both indices were that the total selection response of each index must be lower than or equal to the single‐stage linear phenotypic selection index (LPSI) selection response, similar to the accuracy of each index to predict the net genetic merit. Using four different total proportions (p = 0.05, 0.10, 0.20, and 0.30) for the real dataset, the total DMLPSI selection response was 22.80% higher than the estimated single‐stage LPSI selection response, whereas the total OMLPSI selection response was only 2.21% higher than the estimated single‐stage LPSI selection response. In addition, at Stage 2, OMLPSI accuracy was 62.24% higher than the DMLPSI accuracy for predicting the net genetic merit. We found similar results for the simulated data. Thus, we recommend using OMLPSI when performing the multistage phenotypic selection.

Comparison of genome-wide and phenotypic selection indices in maize

The objective of this study was to compare the selection of maize lines by genomic selection and phenotypic selection, performed on the basis of selection indices rather than on each trait separately. To achieve this, 256 plants of an F2 population (S1 lines) were genotyped with 177 microsatellite molecular markers. These lines were evaluated in experiments with replications, considering different traits, phenotypic means (PM) and means predicted from the effects of the molecular markers (GM) were obtained and, from these, different selection indices were estimated for each line. The results showed that the GM were estimated with good precision, with high estimates for correlations, coefficients of determination and accuracy between the GM and the PM. The estimates of the correlation coefficients varied from medium to high between the selection indices obtained from PM and GM, with PM and GM generated values showing the closest agreement for the index proposed by Mulamba and Mock. The occurrence of superior genotype coincidences was not high between PM and GM based selections for the different selection indices, but values close to the observed phenotypic values were found. The evaluation of the populations selection for the different strategies confirmed that there were differences among the selection indices used for both PM and GM for most of the traits evaluated, that in average their means did not differ for most of the traits, and both differed from the control population, confirming the efficiency of the genomic selection performed on the basis of selection indices.

Selecting sugarcane genotypes by the selection index reveals high gain for technological quality traits.

Sugarcane (Saccharum sp) is one of the most promising crops and researchers have sought for renewable alternative energy sources to reduce CO2 emission. The study of strategies, which allow breeders in the selection of superior genotypes for many traits simultaneously, is important. Therefore, the objectives of this study were: i) to apply path analysis to better understand the relationship between the lignocellulosic traits and technological quality traits with total recoverable sugars (TRS) and ii) to use several multivariate selection indexes to predict the genetic gain and to select superior genotypes in the sugarcane breeding. A total of 40 sugarcane genotypes were evaluated in an experimental design using incomplete blocks with two replicates. The follow traits were evaluated: dry matter (DM), total soluble solids (BRIX), apparent sucrose content in the juice (POL), apparent sucrose content in sugarcane (POLS), fiber content (FIB), purity (PUR), TRS, lignin content (LC), cellulose content (CC), hemicellulose content (HC), and ash content (AC). These traits were analyzed by analysis of variance, phenotypic correlation network, path analysis, and selection index. The highest direct effect on TRS was obtained by POLS (0.337), POL (0.299), BRIX (0.227), and FIB (-0.146). The estimates of phenotypic correlation between these characters and TRS were in the same direction, which demonstrated a cause-and-effect relationship. The highest indirect effect was of POL via POLS (0.331) followed by POLS via POL (0.294). BRIX presented high indirect effects via POLS (0.266) and via POL (0.246). On the other hand, FIB presented negative indirect effects via POLS (-0.169) and POL (-0.103). In conclusion, path analysis and index selection are useful strategies to help breeders in the selection of superior genotypes in sugarcane.

A Predetermined Proportional Gains Eigen Selection Index Method

The most general linear phenotypic selection index (PSI) is the predetermined proportional gains phenotypic selection index (PPG‐PSI) that allows imposing restrictions on the trait expected genetic gain values to make some traits change their mean values based on a predetermined level, while the rest of the traits remain without restrictions. However, due to the increasing number of restricted traits: (i) PPG‐PSI accuracy decreases; (ii) the proportional constant associated with this index can be negative, in which case, its results have no meaning in practice; and (iii) the PPG‐PSI can shift the population means in the opposite direction to the predetermined desired direction. Based on the eigen selection index method (ESIM), we propose a PPG‐ESIM that does not require a proportional constant, and due to the properties associated with eigen analysis, it is possible to use the theory of similar matrices to change the direction of the eigenvector values without affecting PPG‐ESIM accuracy, which helps to eliminate the problem indicated in the third point above, associated with the standard PPG‐PSI. The PPG‐ESIM uses the first eigenvector as its vector of coefficients, and the first eigenvalue in the selection response. Two simulated and one real data set, each with four traits, were used to validate PPG‐ESIM efficiency vs. PPG‐PSI efficiency; the simulated and real results indicated that PPG‐ESIM efficiency was higher than PPG‐PSI efficiency. We concluded that PPG‐ESIM is an efficient selection index that can be used in any selection program as a good alternative to PPG‐PSI.

Statistical Sampling Properties of the Coefficients of Three Phenotypic Selection Indices

ABSTRACTThe aim of the Smith phenotypic selection index (SPSI), the restricted phenotypic selection index (RPSI), and the predetermined proportional gains phenotypic selection index (PPG‐PSI) is to maximize the response to selection and provide the breeder with an objective rule for evaluating and selecting several traits. When the phenotypic and genotypic variances and covariances are known, these three indices are the best linear predictors. When these parameters are estimated, the three indices will be optimal only if the estimators of the index weights are unbiased and have minimal variance. There are many methods for determining the sampling properties of the SPSI but there is no method for determining the sampling properties of RPSI and PPG‐PSI coefficients. Using the canonical correlation theory, we proposed an asymptotic method for determining the statistical sampling properties of the estimators of the coefficients of the three phenotypic selection indices. We showed that under some assumptions, the sampling properties of the RPSI and PPG‐PSI coefficient estimators could be obtained using the sampling properties of the SPSI coefficient estimator. We validated the theoretical results using two real datasets. The theoretical and numerical results indicated that the three estimators of the weights for the three indices were unbiased with minimal variances. We concluded that when the number of genotypes is large, the proposed method could be used to find the sampling properties of the coefficient of the three indices.

A Genomic Selection Index Applied to Simulated and Real Data.

A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory of a GSI and apply it to two simulated and four real data sets with four traits. Also, we numerically compare its efficiency with that of the phenotypic selection index (PSI) by using the ratio of the GSI response over the PSI response, and the PSI and GSI expected genetic gain per selection cycle for observed and unobserved traits, respectively. In addition, we used the Technow inequality to compare GSI vs. PSI efficiency. Results from the simulated data were confirmed by the real data, indicating that GSI was more efficient than PSI per unit of time.

Genetic Gains in Grain Yield Through Genomic Selection in Eight Bi‐parental Maize Populations under Drought Stress

ABSTRACTGenomic selection incorporates all the available marker information into a model to predict genetic values of breeding progenies for selection. The objective of this study was to estimate genetic gains in grain yield from genomic selection (GS) in eight bi‐parental maize populations under managed drought stress environments. In each population, 148 to 300 F2:3 (C0) progenies were derived and crossed to a single‐cross tester from a complementary heterotic group. The resulting testcrosses of each population were evaluated under two to four managed drought stress and three to four well‐watered conditions in different locations and genotyped with 191 to 286 single nucleotide polymorphism (SNP) markers. The top 10% families were selected from C0 using a phenotypic selection index and were intermated to form C1. Selections both at C1 and C2 were based on genomic estimated breeding values (GEBVs). The best lines from C0 were also advanced using a pedigree selection scheme. For genetic gain studies, a total of 55 entries representing the eight populations were crossed to a single‐cross tester, and evaluated in four managed drought stress environments. Each population was represented by bulk seed containing equal amounts of seed of C0, C1, C2, C3, parents, F1s, and lines developed via pedigree selection. Five commercial checks were included for comparison. The average gain from genomic selection per cycle across eight populations was 0.086 Mg ha–1. The average grain yield of C3–derived hybrids was significantly higher than that of hybrids derived from C0. Hybrids derived from C3 produced 7.3% (0.176 Mg ha–1) higher grain yield than those developed through the conventional pedigree breeding method. The study demonstrated that genomic selection is more effective than pedigree‐based conventional phenotypic selection for increasing genetic gains in grain yield under drought stress in tropical maize.

A Canonical Correlation Analysis of Relationships Between Growth, Compositional Traits and Longevity, Lifetime Productivity and Efficiency in Polish Landrace Sows

Abstract Relationships between performance test traits (growth rate, backfat thickness, loin depth, lean meat percentage, exterior, phenotypic selection index) and longevity traits (length of productive life, number of litters, total number of weaned pigs, number of weaned piglets per year, number of litters per year) in Landrace sows were evaluated using canonical correlation analysis. The data set consisted of 23,012 purebred sows that farrowed from 1994 to 2011 in 161 herds. The first three canonical correlations (0.37, 0.25, 0.07) were highly significant (P&lt;0.0001). Correlations of the first canonical variate with the original measured variables indicated that sows with high values for this variate had lower growth rate (r=-0.31) and loin depth (r=-0.43), greater backfat thickness (r=0.23), as well as being older at birth of their last litter (r=0.98). These sows also had a greater number of litters (r=0.94) and better lifetime efficiency (r=0.61 and r=0.70 for number of weaned piglets per year and number of litters per year, respectively). Canonical loadings for the second canonical function indicate that sows with high values for the second set of variates had high growth rate (r=0.79) and phenotypic selection index (r=0.83), excellent conformation (r=0.62), as well as better efficiency in pig production (r=0.67). The squared multiple correlations show that the first canonical variate of the performance traits is a poor predictor of longevity (0.13) and nearly useless for predicting efficiency traits (0.07). Performance test traits explain 11% of the variance in the variables of longevity and lifetime productivity, whereas dependent variables explain only 3% of the variance in performance test traits. The relationships between performance test data and subsequent lifetime productivity or longevity were significant and unfavourable but low for Polish Landrace population

Relationships between longevity, lifetime productivity, carcass traits and conformation in Polish maternal pig breeds

The objective of this study was to obtain heritability estimates for longevity (length of life, length of productive life, number of litters) and lifetime productivity traits (lifetime pig production, lifetime pig efficiency, lifetime litter efficiency) and genetic correlation between them and litter size at first farrowing, growth (ADG), backfat thickness (BF), loin depth, lean meat percentage (LMP), phenotypic selection index (PSI), and exterior in 19423 Polish Landrace (L) and 16049 Polish Large White (LW) sows. Heritabilities for longevity and lifetime productivity traits were 0.10-0.13 for L sows and 0.09-0.11 for LW sows depending on the trait definition. The genetic correlations among these traits were all high and positive, ranging from 0.76 to 0.99. Antagonistic genetic correlations (-0.21 to -0.26) were found between longevity traits and PSI and LMP in LW sows, while in L sows the respective parameters were lower and not significant for length of productive life. The number of live-born piglets in the first litter was positively correlated with lifetime pig production and lifetime pig efficiency in both breeds. The genetic correlations of longevity and lifetime pig production with ADG, BF, loin depth and exterior were small, and in most cases, not significant.

QTL mapping for frond length and width in Laminaria japonica aresch (Laminarales, Phaeophyta) using AFLP and SSR markers.

In Laminaria japonica Aresch breeding practice, two quantitative traits, frond length (FL) and frond width (FW), are the most important phenotypic selection index. In order to increase the breeding efficiency by integrating phenotypic selection and marker-assisted selection, the first set of QTL controlling the two traits were determined in F(2) family using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers. Two prominent L. japonicas inbred lines, one with "broad and thin blade" characteristics and another with "long and narrow blade" characteristics, were applied in the hybridization to yield the F(2) mapping population with 92 individuals. A total of 287 AFLP markers and 11 SSR markers were used to construct a L. japonica genetic map. The yielded map was consisted of 28 linkage groups (LG) named LG1 to LG28, spanning 1,811.1 cM with an average interval of 6.7 cM and covering the 82.8% of the estimated genome 2,186.7 cM. While three genome-wide significant QTL were detected on LG1 (two QTL) and LG4 for "FL," explaining in total 42.36% of the phenotypic variance, two QTL were identified on LG3 and LG5 for the trait "FW," accounting for the total of 36.39% of the phenotypic variance. The gene action of these QTL was additive and partially dominant. The yielded linkage map and the detected QTL can provide a tool for further genetic analysis of two traits and be potential for maker-assisted selection in L. japonica breeding.

COMPARISON OF THE EFFICIENCIES OF THE DIFFERENT SELECTION PROCEDURES IN THREE POPULATIONS OF EGYPTIAN COTTONS (Gossypium barbadense L.)

Different selection methods i e phenotypic individual trait selection, selection index and recurrent selection were used to improve lint yield in three populations of Egyptian cotton, G. 45 x G. 75, G. 87 x G. 89 and G. 86 variety grown in open-pollinated bulks for four years. In population I (G. 45 x G. 75), the direct phenotypic trait selection procedure in F4 generation for lint yield (In.,) was superior to the other selection procedures. Concerning population II (G. 87 x G. 89), the recurrent selection and phenotypic trait selection for bolls/plant (I4) in F4 generation were more efficient than the other selection procedures. Regarding population III (G. 86 open-pollinated cultivar), both recurrent selection and index I, in S2 generation were superior compared with the other selection procedures.

Comparison of Phenotype and Combined Index Selection at Optimal Breeding Population Size Considering Gain and Gene Diversity

Abstract A breeding program was simulated in this study. Two alternative ways of selecting the breeding population for the following generation was compared. Phenotypic selection, which means to select just on the individual performance, and combined index selection, which means selection on predicted breeding value for each individual obtained by weighting family average and individual phenotype, were compared. The plant number (testing resource) and gene diversity (status number, Ns) were kept constant, but the breeding population size was variable and chosen for maximizing gain for the particular breeding scenario. At low and medium heritability phenotypic selection was inferior to combined index selection. Only when heritability was high phenotypic selection was as efficient (generation 1) as or more efficient (generation 5) than combined index selection. This contrasts to earlier studies done under constant breeding population size, where selection methods appeared similar. The advantage in gain of combined index selection is usually at a larger breeding population size. At limited heritability and breeding population size the difference is considerable. When breeding population size was kept rather small (&lt;100), and the heritability limited, combined index selection can result in slightly higher gain than phenotypic selection at the same gene diversity, but this was at the cost of a much larger breeding population. Phenotypic selection and combined index selection appears as rather similar for many cases in this simple model used in this study. Considering other advantages with phenotypic selection, it may often be regarded as a competitive alternative.