Estimation of genetic parameters and prediction of breeding values in an autotetraploid blueberry breeding population with extensive pedigree data

The strategy for breeding blueberries has been based on phenotypic selection without accounting for pedigree correlation information. A central premise of classical quantitative genetics is that through knowledge of the relationships among individuals in a population, we can make inferences about breeding values (BVs), estimate genetic parameters, and support phenotypic selection. Genetic evaluations using best linear unbiased prediction (BLUP) and restricted maximum likelihood (REML) are founded on pedigree information and have been the standard method used in livestock and forest breeding. Despite theoretical and practical benefits, their application in blueberry breeding programs remains unexploited. The objective of this study was to evaluate the use of REML/BLUP in the University of Florida blueberry breeding program in order to estimate genetic parameters and predict BVs of primary selection traits. To do this, we collected phenotypic data for eight selectable traits (yield, flower bud density, fruit weight, fruit firmness, fruit diameter, fruit soluble solids, fruit pH, and fruit scar diameter) from 1996 individuals. The highest narrow-sense heritabilities were found for fruit weight, scar diameter, and yield. The majority of genetic correlations between traits were under 0.20. The largest genetic gain was achieved when selections from early stages were used as parents and the top 5% of the population was selected. Compared to traditional phenotypic selection, our results are evidencing of the importance of implementing REML/BLUP to estimate genetic parameters that help and support the breeding strategy and maximize genetic gains.     

Ranking parents by best linear unbiased prediction (BLUP) breeding values in oil palm

Summary Breeding values obtained from best linear unbiased prediction (BLUP) were used to rank nine pisifera (P) male parents belonging to two different but related genetic groups, utilising highly unbalanced data from three D (Dura) × P progeny-test trials of oil palm. The traits studied were fresh fruit bunch yield (FFB), total number of bunches (BNo), average bunch weight (BW), oil to bunch percentage (OB) and average annual height increment (HINC). Four P's from the AVROS genetic group had higher breeding values for FFB, BNo and OB while five P's from the Dumpy-AVROS group were superior in breeding values for BW and HINC. Selection of parents based on the average of the rankings in breeding value for BNo, OB and HINC for each parent tended to favour AVROS P's. However, selection of parents based on the aggregate genetic worth function involving relative economic values, tended to favour Dumpy-AVROS P's. The latter method is preferred to the average ranking method because of its emphasis on relative economic values and the selection index approach.It is suggested that the BLUP technique may prove to be a very useful tool in oil palm breeding.     

Sustainable plant breeding

Plant breeders disrupt Hardy–Weinberg equilibrium through selection, non‐random mating, drift, migration and mutation. Sustainable plant breeding can be defined as productive and competitive breeding that is achieved without loss of genetic diversity in the elite breeding population during the professional career of the breeder. Breeding is often productive but not sustainable. From 1974 to 2000, the animal breeding programme Meatlinc in the United Kingdom had effective population size of 95, population inbreeding of 0.19% per year and generation interval of 2.15 years. Genetic progress in Meatlinc tripled in the 8 years following introduction of best linear unbiased prediction (BLUP) selection (based on the information from relatives) in 1992. Canola breeding in Australia from 1970 to 2000 had longer generation interval (6 years), smaller effective population size (<11) and higher rates of inbreeding (>0.7% per year). BLUP selection in canola was first reported in 2010. Neither programme replaced genetic diversity lost through selection and drift. Most breeding programmes violate conditions of the infinitesimal model, thereby reducing predictability of selection, but breeders can minimize these limitations to sustainable plant breeding.     

Comparison of predicted and observed response to selection in two breeding populations of perennial ryegrass

To predict any genetic response to selection, two breeding populations of perennial ryegrass, consisting of half-sib families, were evaluated as spaced plants from 1987 to 1989 for several agronomical traits. In each population, the best plants selected through a multitrait Smith and Hazel's index were planted in a polycross to produce the second generation half-sib families. Both generations were evaluated together from 1991 to 1993 as spaced plants. The difference between generations was the observed genetic response, which was compared to that predicted. Rust tolerance, aftermath heading and persistence were significantly improved in both populations. In trial plots used to assess the yield, no effect of mass selection was noticed. Thus, it is concluded that mass selection failed to increase productivity but produced some improvements in disease tolerance and aftermath heading.     

Applications of multi-trait selection in common bean using real and simulated experiments

The analysis of multiples traits has been largely employed in animal and forestry crop breeding; however, in annual crops such as common bean this approach is practically lacking. This study examined the selective accuracy and estimates of genetic parameters in common bean using single- and multi-trait analyses in several designs of genetic correlations and heritabilities by means of simulated- and real-traits. We also compared the efficiency of the Bayesian and REML methods as regarding its selective accuracy and estimates of genetic parameters. In real data experiments, 100 endogamic families of common bean were assessed for grain yield (GY) and grain type (GT) in three environments using a 10?×?10 triple lattice design. Single- and multi-trait analyses were applied for these traits using REML-based BLUP analysis and Bayesian methods. In simulated experiments, traits corresponding to GY and yield components in common bean were simulated under several levels of genetic correlations and evaluated under different levels of heritability in order to verify in which genetic/experimental design it is advantageous to apply single- or multi-trait analyses. The results obtained in this study indicated that GY and GT present low genetic correlation and, therefore, multi-trait analysis may be unjustifiable for these traits. The Bayesian and REML multi-trait analyses were equivalent in point and interval estimates of genetic parameters and predictive accuracy, both for the field and simulated experiments. In addition, it was found that multi-trait analysis is justified in situations where the traits present low/moderate heritability and medium/high genetic correlations among them. It also was found that GY in common bean may be outstandingly favored in accuracy when evaluated jointly with its components. Thus, one can conclude that the simultaneous analysis of GY and yield components in common bean may provide superior gains accuracy and selection efficiency, being the response to selection better obtained by use of the multi-trait heritability.     

Modified full-sib selection and best linear unbiased prediction of progeny performance in a European F2 maize population

Four cycles of modified recurrent full‐sib (FS) selection were conducted in an intermated F₂ population of European flint maize. The objectives of our study were to monitor trends across selection cycles in the estimates of population mean, inbreeding coefficients and variance components, and to investigate the usefulness of best linear unbiased prediction (BLUP) of progeny performance under the recurrent FS selection scheme applied. We used a selection rate of 25% for a selection index, based on grain yield and dry matter content. A pseudo‐factorial mating scheme was used for recombination. In this scheme, the selected FS families were divided into an upper‐ranking group of parents mated to the lower‐ranking group. Variance components were estimated with restricted maximum likelihood (REML). Average grain yield increased 1.2 t/ha per cycle, average grain moisture decreased 20.1 g/kg per cycle, and the selection index relative to the F₂ check entries decreased 0.3% per cycle. For a more precise calculation of selection response, the four cycles should be tested together in multi‐environmental trials. We observed a significant decrease in additive variance in the selection index, suggesting smaller future selection response. Predictions of FS family performance in Cn + 1 based on mean performance of parental FS families in Cn were of equal precision as those based on the mean additive genetic BLUP of their parents, and corresponding correlations were of moderate size for grain moisture and selection index.     

Potential of genomic selection in rapeseed (Brassica napus L.) breeding

Genomic selection employs genome‐wide marker data to predict genomic breeding values. In this study, a population consisting of 391 lines of elite winter oilseed rape derived from nine families was used to evaluate the prospects of genomic selection in rapeseed breeding. All lines have been phenotyped for six morphological, quality‐ and yield‐related traits and genotyped with genome‐wide SNP markers. We used ridge regression best linear unbiased prediction in combination with cross‐validation and obtained medium to high prediction accuracies for the studied traits. Our results illustrate that among‐family variance contributes to the prediction accuracy and can lead to an overestimation of the prospects of genomic selection within single segregating families. We also tested a scenario where estimation of effects was carried out without individuals from the family in which breeding values were predicted, which yielded lower but nevertheless attractive prediction accuracies. Taken together, our results suggest that genomic selection can be a valuable genomic approach for complex agronomic traits towards a knowledge‐based breeding in rapeseed.     

Introgression of temperate germplasm to improve an elite tropical maize population

The effect of introgression of temperate germplasm into an elite tropical maize population, EV8443SR, was studied for different traits. Two temperate lines, FS14 and DEA, were crossed with EV8443SR. The F1 of each cross was backcrossed to EV8443SR to produce the BC1. F1 and BC1 were subjected to two cycles of random mating. Random sets of S1 families were derived from the tropical-temperate populations and the tropical parent for evaluation in two tropical locations. Introgression of FS14 and DEA into EV8443SR significantly increased earliness, and reduced plant height, number of grains per ear, 1000 grains weight and grain yield. Significant harvest index increases were observed. The effect of introgression on genetic variance of EV8443SR varied with trait and sometimes with cross and location. The highest expected genetic progress was obtained in a tropical-temperate population for most of the traits studied. EV8443SR appeared better than, or pratically equal to, the tropical-temperate populations as a foundation population to improve grain yield and husk cover. This revised version was published online in July 2006 with corrections to the Cover Date.     

Repeatability of full-sib sugarcane families across harvests and the efficiency of early selection

This study was designed to estimate the repeatability coefficient of traits of the plant-cane and first rattoon in a large number of full-sib sugarcane families and to select the superior families based on their predicted genotypic values. The population used in this study consisted of 190 families, and crossing was performed at the Federal University of Alagoas, in Murici, Alagoas, Brazil, in 2007. Five experiments, each with 22 families, took place at the Centro de Pesquisa e Melhoramento da Cana-de-Açúcar (CECA), in Oratórios, Minas Gerais, while four experiments, each with 20 families, took place at the Volta Grande mill (VGM), in Conceição das Alagoas, Minas Gerais. Each experiment took the form of a randomized complete block design with six replications. The following traits were determined from the plant-cane and first rattoon: percentage of soluble solids w/w in the juice (Brix), tons of stalks per hectare (TSH) and tons of brix per hectare (TBH). Statistical analyses were performed using the mixed model methodology. Variance components were estimated by restricted maximum likelihood (REML) and the genotypic values of families were predicted by best linear unbiased prediction (BLUP). The moderately high estimates of repeatability for TSH and TBH and the high rates of coincidences of the families selected in both plant-cane and first rattoon indicate that selection of families using repeated measures across the first two harvests combined with individual clone selection at the rattoon stage may be efficient in sugarcane breeding programs, increasing the efficiency of obtaining new cultivars.     

BLUP for phenotypic selection in plant breeding and variety testing

Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non-genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance–covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedigree information is often included via the so-called numerator relationship matrix $({\user2{A}})Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non-genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance–covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedigree information is often included via the so-called numerator relationship matrix , we stress that it is frequently straightforward to exploit the same information by a simple mixed model without explicit reference to the -matrix. This paper is dedicated to Prof. Dr. Wolfgang K?hler (University of Giessen, Germany) on the occasion of his 65th birthday.     

高丹草重组自交系群体的遗传变异与高产种质的创新

高丹草是一年生禾本科新型饲用牧草,提高其饲草产量是高丹草育种的重要目标.以高粱314A×棕壳苏丹草的F2:3遗传作图群体的后代材料建立的重组自交系(Recombinant inbred line,RIL)群体为材料, 对后代进行遗传变异和DNA多样性分析.结果表明:通过采用两种方法(性状和分子标记)进行同时选择,获得了9个高产的超亲重组后代.通过SSR标记分析RIL群体的DNA多样性,可知家系间存在较大的遗传变异,杂交重组产生的超亲高产种质分布在不同类群中.鉴定获得了2组SSR遗传距离接近0而且在主要农艺性状和产量等方面相似,只在抗性方面有差异的不同家系.农艺性状和抗性等鉴定试验筛选了4份性状优良的高丹草新种质并已有2份进入区域试验.     

An improved breeding strategy for autotetraploid alfalfa (Medicago sativa L.)

Alfalfa cultivar development will be enhanced by breeding strategies whichutilize the full potential of autotetraploid population genetic structures. Thisstudy evaluates the effectiveness of an allelic selection scheme, which wasdeveloped to overcome limitations of inbreeding depression and to exploitgeneral and specific combining ability effects in autotetraploid populations.Allelic selection entails the minimization of non-additive genetic effects byselecting among full-sib families (F1) which are at uniform levels ofheterozygosity. Such F1 lines are developed by crossing individuals fromtwo unrelated random mating populations. Selected F1 lines wereintercrossed to form an improved population. Eight random matingpopulations of alfalfa were developed to study the effectiveness of allelicselection. Selection for increased dry matter yield resulted in alfalfapopulations with 38 percent greater yield than the parent populations. Twocycles of intercrossing, among selected F1 lines, did not dissipate the gainfrom selection. This result has important implications for synthetic cultivardevelopment in which a major limitation is the decline in productivity withadvancing generations of seed increase. A positively correlated response toselection for dry matter yield was observed for plant height and stemdiameter. The results of this research indicate that continued testing of theallelic selection scheme is warranted and could have a significant impact onthe breeding of autotetraploid alfalfa, particularly for synthetic cultivardevelopment.     

Turnip (Brassica rapa L. ssp. rapifera Metzg.) population improvement and cultivar production

In 1977, a fodder turnip breeding programme was started from seven cultivars with the primary aim of increasing dry‐matter yield. The breeding method chosen was population improvement by half‐sib family selection on a biennial cycle. Seed production in polythene tunnels with blowflies as pollinators was followed by assessing the resulting progenies in replicated yield trials and observation plots from which plants were selected for the next cycle. Six generations of selection resulted in a population with a yield that was 25% higher than the mean of the initial seven cultivars. This was remarkably close to the predicted superiority of the population, despite a significant discrepancy in one generation. It is concluded that the greatest response to selection per year would be achieved by selecting eight families from 128 assessed for 1 year in trials at two or three sites with an overall total of six replicates, given a resource limit of 800 plots.     

Expected levels of heterozygosity in autotetraploid progeny populations

Summary A breeding program aimed at improving the seed yield in bean for processing was started. Six parents divergent in a range of agronomical characters were crossed according to diallel mating design. Cluster analysis performed on the composite cross population evidenced divergence between F1 hybrids and to maximize genetic recombination, the most different F1 hybrids were intercrossed. A Factor analysis was performed on 7 morphological and yield-determining traits of 15 hybrids F1 and parents. Three Factors, representing patterns of variables interpreted as productivity per plant, growth factor, and branching-productivity, were extracted. These results evidenced the inverse relationship between seed yield components (seeds/pod, pods/plant, and hundred seed weight): i.e. the selection for either variable is detrimental to the other. It may be possible to break this association if the plant construct assures high sink to satisfy all plant requirements: tall and large plants bearing numerous nodes, leaves and reproductive structures. Moreover, the selection practiced simultaneously on the yield components positively affected the seed yield trait. An index of selection and response to selection were estimated on the segregant populations. The realized gain in seed/yield reached after one cycle of selection (C1) was 6.5 g, about 20% over the base population (C0). The expected gain from selection based on the superior 5% plants in C2 families was 25.5% and approached the realized gain. The result of this paper gives evidence to support that the seed yield can be improved by an adequate selection intensity and selection index can be very useful when it is faced with the improvement of several traits at the same time.     

低酚棉产量、纤维品质性状的遗传分析

本文以32个低酚棉品种（系）为材料,对10个产量、纤维品质性状进行的遗传分析结果表明：低酚棉多数产量构成因素与皮棉产量为正相关,提高衣指、降低籽指虽可提高衣分和单株结铃数,但不利于增加单铃重;高产、优质的矛盾主要表现在纤维长度、比强度与衣分和单株结铃数有较强的负相关。在较大群体的基础上,通过早代的正向选     

玉米籽粒产量与产量构成因子的关系及条件QTL分析

以我国玉米育种的骨干亲本齐319和黄早四构建的230个F2:3家系群体为材料,通过条件分析结合QTL定位方法探讨了单株产量(GYPP)与单株粒数(KNPP)和百粒重(KWEI)的遗传关系。结果表明,百粒重比单株粒数和单株产量遗传力高,受环境影响小。相关分析表明,单株粒数和百粒重与单株产量均呈现显著正相关,单株粒数与单株产量的相关系数更高。单株产量非条件QTL分析共定位到5个遗传主效应QTL和5对上位性位点,其中4个是控制3个性状(单株产量、单株粒数和百粒重)的一因多效性位点,1个是控制2个性状(单株产量和单株粒数)的一因多效性位点,全部5对上位性位点都与单株粒数和百粒重有关。条件QTL分析还检测到14个QTL位点及10对上位性位点,这些位点在非条件QTL分析中未被检测到,其效应较小。因此,单株粒数和百粒重与单株产量密切相关,通过改良单株粒数和百粒重可有效提高产量;条件QTL分析方法在单个QTL水平上证实了单株产量与单株粒数、百粒重较强的遗传相关性,并且能够检测到更多效应较小的QTL;发掘的两个效应较大的一因多效位点可为玉米高产分子育种和进一步精细定位提供理论参考。     

Genetic diversity, hybrid performance, and combining ability for yield in Musa germplasm

Genome size variation occurs within and across generations in Musa spp., which reduces the predictive accuracy of parental performance on progeny value for yield and other traits with complex inheritance. Parental selection through progeny testing of prospective parents is required to achieve further genetic gains. This was carried out in this study, using a factorial mating design involving five 4x females and five 2x males. Genetic differences among offspring families were essentially due to differences in additive effects of the parents. Thus, little recombinative heterosis can be expected upon 4x-2x cross-breeding, and breeding strategies should target the development of 4x and 2x cultivars by accumulation of favorable alleles through recurrent selection within each ploidy pool. Offspring yield was positively correlated with parental GCAs but not with mid-parent values. Hybrid performance was also associated but not significantly correlated with genetic similarity indices based on both pedigree and molecular data. This study further suggests that current genetic models may not be adequate for populations with intergeneration genome size polymorphism. This revised version was published online in July 2006 with corrections to the Cover Date.     

Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations

Genomic selection (GS) is a promising alternative to marker‐assisted selection particularly for quantitative traits. In this study, we examined the prediction accuracy of genomic breeding values by using ridge regression best linear unbiased prediction in combination with fivefold cross‐validation based on empirical data of a commercial maize breeding programme. The empirical data is composed of 930 testcross progenies derived from 11 segregating families evaluated at six environments for grain yield and grain moisture. Accuracy to predict genomic breeding values was affected by the choice of the shrinkage parameter λ², by unbalanced family size, by size of the training population and to a lower extent by the number of markers. Accuracy of genomic breeding values was high suggesting that the selection gain can be improved implementing GS in elite maize breeding programmes.     

Accuracy of within‐ and among‐family genomic prediction in triticale

Genomic prediction has emerged as a powerful genomic tool to assist breeding of complex traits. In this study, we employed a population of 647 triticale doubled haploid lines derived from four families to assess the potential of this approach for triticale breeding. All lines were phenotyped for grain yield, thousand‐kernel weight, biomass yield, plant height, frost tolerance and Fusarium head blight resistance. The obtained prediction accuracies were moderate to high and consisted to varying degrees of within‐ and among‐family variance, in line with the different degrees of phenotypic differences between family means. The prediction accuracy within individual families also varied with the genetic complexity of the traits and was generally highest based on effect estimation with lines from the respective family, whereas the prediction accuracy decreased with decreasing relatedness among the families. Taken together, our results illustrate the potential of genomic prediction to increase selection gain in triticale breeding, but the composition of the training set is of utmost importance, and consequently, the implementation of this approach in applied breeding programmes is not straightforward.