Accuracy of genotypic value predictions for marker-based selection in biparental plant populations

The availability of cheap and abundant molecular markers has led to plant-breeding methods that rely on the prediction of genotypic value from marker data, but published information is lacking on the accuracy of genotypic value predictions with empirical data in plants. Our objectives were to (1) determine the accuracy of genotypic value predictions from multiple linear regression (MLR) and genomewide selection via best linear unbiased prediction (BLUP) in biparental plant populations; (2) assess the accuracy of predictions for different numbers of markers (N _M) and progenies (N _P) used in estimation; and (3) determine if an empirical Bayes approach for modeling of the variances of individual markers and of epistatic effects leads to more accurate predictions in empirical data. We divided each of four maize (Zea mays L.) datasets, one Arabidopsis dataset, and two barley (Hordeum vulgare L.) datasets into an estimation set, where marker effects were calculated, and a test set, where genotypic values were predicted based on markers. Predictions were more accurate with BLUP than with MLR. Predictions became more accurate as N _P and N _M increased, until sufficient genome coverage was reached. Modeling marker variances with the empirical Bayes method sometimes led to slightly better predictions, but the accuracy with different variants of the empirical Bayes method was often inconsistent. In nearly all cases, the accuracy with BLUP was not significantly different from the highest accuracy across all methods. Accounting for epistasis in the empirical Bayes procedure led to poorer predictions. We concluded that among the methods considered, the quick and simple BLUP approach is the method of choice for predicting genotypic value in biparental plant populations.     

Accuracy of genomic selection in European maize elite breeding populations

Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3–4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.     

Potential for improvement by selection for reducing sugar content after cold storage for three potato populations

The objectives of this study were to examine the expected response to selection for reducing-sugar content after cold storage in three hybrid populations, to determine whether these populations included clones low in reducing sugars, and to investigate the effectiveness of indirect selection for chip colour based on selection of sugar content after cold storage. The three hybrid populations included: a random sample of 39 clones of Population 1, which was derived from crossing ND860-2 (a clone low in reducing sugars) with F58089 (a clone intermediate in reducing sugars); 40 clones of Population 2, which was obtained from crossing ND860-2 with Russette (a clone high in reducing sugars); and 40 clones of Population 3, which was derived from crossing Russette with F58089. Sugar content and chip colour were assessed in tubers stored for 2 months at 4 °C at Cambridge, Ontario, and at 3 °C at Benton Ridge, New Brunswick. Population 1 had a slightly greater predicted response to selection for reduction in glucose and total reducing sugars than the other two populations. This could be attributed to higher heritability estimates for Population 1, which was a reflection of smaller clone × environment interaction mean squares. The greater potential advance by selection for fructose, glucose, and total reducing sugars, was a direct consequence of its lower means for these traits. Low reducing-sugar clones were found in all three populations, indicating their potential use for the selection of low reducing sugars. Populations 2 and 3, however, would require stronger selection pressures and, therefore, large population sizes. Expected correlated responses for chip colour by selection for fructose and glucose were similar to, and sometimes exceeded, the expected direct responses in all three populations. Indirect responses for chip colour by selection for sucrose, however, were lower than direct selection responses. These results indicate that indirect selection for chip colour, by selection for either fructose or glucose content after cold storage, is as effective as direct selection for chip colour.     

Size of breeding populations required for selection programs

The minimum population size required for selection in order to reduce the effect of genetic drift to a particular level has been considered. The model of Nicholas was extended to include the measurement-error variance in the response variance. Situations where the sex ratios among scored and breeding individuals are unequal are also considered. When the duration of a selection experiment is relatively long, Nicholas' approximation (i.e., assuming that measurement error is negligible relative to drift) is useful in determining the minimum effective population size required. However, the measurement-error variance becomes an important source of variation in short-term ( 5 generations) selection experiments, and should not be ignored.     

Edaphic control of habitat selection by kangaroo mice (Microdipodops) in three Nevadan populations

The influence of soil texture on habitat selection by kangaroo mice was evaluted at three areas — Lincoln, Nye, and Pershing Counties, Nevada, where Microdipodops megacephalus and M. pallidus are sympatric.All rodents captured on surveyed grids were marked by toe-clipping and released where they were caught. Soils were sampled chiefly at the positions of traps which had captured kangaroo mice. A soil was classified as sand if 95% (by weight) or more of a sample passed a 2.00-mm sieve, and as gravel if less material passed the sieve. In the areas investigated, a Sarcobatus baileyi-Atriplex confertifolia community predominated.Capture-recapture data from the three study areas were both evaluted individually for each plot and pooled. Although the distribution of M. pallidus was shown to be random with regard to soil type, M. megacephalus was captured almost exclusively on gravel. Comparison of the species using pooled data showed a highly significant difference (p=0.001) in the types of soil on which they were captured. Theoretical implications of the study are discussed.     

Opportunity for natural selection among the Indian populations

A critical review of data on opportunity for natural selection among the Indian populations has been made. These data on 96 populations were analysed according to regional, habitat, and socioeconomic backgrounds. The trends observed on the basis of these Indian data have been compared with worldwide data and data from industrialized nations. As in the industrialized nations, a gradual decrease in I(m) and I(t) is observed with improving socioeconomic and technological status in the Indian populations. The Indian situation is similar to that of the first phase in the modern demographic transition among the industrialized nations.     

Potential of genotyping-by-sequencing for genomic selection in livestock populations

Background

Next-generation sequencing techniques, such as genotyping-by-sequencing (GBS), provide alternatives to single nucleotide polymorphism (SNP) arrays. The aim of this work was to evaluate the potential of GBS compared to SNP array genotyping for genomic selection in livestock populations.

Methods

The value of GBS was quantified by simulation analyses in which three parameters were varied: (i) genome-wide sequence read depth (x) per individual from 0.01x to 20x or using SNP array genotyping; (ii) number of genotyped markers from 3000 to 300 000; and (iii) size of training and prediction sets from 500 to 50 000 individuals. The latter was achieved by distributing the total available x of 1000x, 5000x, or 10 000x per genotyped locus among the varying number of individuals. With SNP arrays, genotypes were called from sequence data directly. With GBS, genotypes were called from sequence reads that varied between loci and individuals according to a Poisson distribution with mean equal to x. Simulated data were analyzed with ridge regression and the accuracy and bias of genomic predictions and response to selection were quantified under the different scenarios.

Results

Accuracies of genomic predictions using GBS data or SNP array data were comparable when large numbers of markers were used and x per individual was ~1x or higher. The bias of genomic predictions was very high at a very low x. When the total available x was distributed among the training individuals, the accuracy of prediction was maximized when a large number of individuals was used that had GBS data with low x for a large number of markers. Similarly, response to selection was maximized under the same conditions due to increasing both accuracy and selection intensity.

Conclusions

GBS offers great potential for developing genomic selection in livestock populations because it makes it possible to cover large fractions of the genome and to vary the sequence read depth per individual. Thus, the accuracy of predictions is improved by increasing the size of training populations and the intensity of selection is increased by genotyping a larger number of selection candidates.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0102-z) contains supplementary material, which is available to authorized users.     

Accuracy of marker-assisted selection with auxiliary traits

Genetic information on molecular markers is increasingly being used in plant and animal improvement programmes particularly as indirect means to improve a metric trait by selection either on an individual basis or on the basis of an index incorporating such information. This paper examines the utility of an index of selection that not only combines phenotypic and molecular information on the trait under improvement but also combines similar information on one or more auxiliary traits. The accuracy of such a selection procedure has been theoretically studied for sufficiently large populations so that the effects of detected quantitative trait loci can be perfectly estimated. The theory is illustrated numerically by considering one auxiliary trait. It is shown that the use of an auxiliary trait improves the selection accuracy; and, hence, the relative efficiency of index selection compared to individual selection which is based on the same intensity of selection. This is particularly so for higher magnitudes of residual genetic correlation and environmental correlation having opposite signs, lower values of the proportion of genetic variation in the main trait associated with the markers, negligible proportion of genetic variation in the auxiliary trait associated with the markers, and lower values of the heritability of the main trait but higher values of the heritability of the auxiliary trait.     

Dermatoglyphics of three Hungarian populations

The dermatoglyphics of 709 individuals from three Hungarian populations living near each other but of different origin were analyzed. In dermatoglyphic traits the three populations present but slight differences from one another.     

Accuracy of a routine real-time PCR assay for the diagnosis of Pneumocystis jirovecii pneumonia

Pneumocystis jirovecii is a common cause of life-threatening pneumonia among immunocompromised patients. Using 400 fresh bronchoalveolar lavage samples, we compared prospectively routine direct immunofluorescence assay (DFA) and a real-time PCR assay, performed on a LightCycler system, for the detection of P. jirovecii. Among the 66 PCR positive samples, 31 were positive by DFA. No patient was found as having the pattern "PCR--ve/DFA+ve". The semi-quantification of the P. jirovecii DNA was represented by the cycle threshold (Ct). Using DFA as the gold standard, the sensitivity of the PCR was 100% for Ct>/=28 and the specificity was 100% for Ct<22. Between these two points, the results could be discrepant. The patients of the "22/=28" group, and presented less frequently with HIV-infection and elevated lactate dehydrogenase (LDH) assay than in the "Ct<22" group. A negative PCR allowed us to exclude the P. jirovecii pneumonia. The real-time PCR assay seems to be an accurate diagnosis method and could replace the DFA. The semi-quantitative results should be helpful to distinguish colonized, subclinically infected and P. jirovecii pneumonia patients.     

Optimum contribution selection for conserved populations with historic migration

Background

In recent decades, local varieties of domesticated animal species have been frequently crossed with economically superior breeds which has resulted in considerable genetic contributions from migrants. Optimum contribution selection by maximizing gene diversity while constraining breeding values of the offspring or vice versa could eventually lead to the extinction of local breeds with historic migration because maximization of gene diversity or breeding values would be achieved by maximization of migrant contributions. Therefore, other objective functions are needed for these breeds.

Results

Different objective functions and side constraints were compared with respect to their ability to reduce migrant contributions, to increase the genome equivalents originating from native founders, and to conserve gene diversity. Additionally, a new method for monitoring the development of effective size for breeds with incomplete pedigree records was applied. Approaches were compared for Vorderwald cattle, Hinterwald cattle, and Limpurg cattle. Migrant contributions could be substantially decreased for these three breeds, but the potential to increase the native genome equivalents is limited.

Conclusions

The most promising approach was constraining migrant contributions while maximizing the conditional probability that two alleles randomly chosen from the offspring population are not identical by descent, given that both descend from native founders.     

Effects of artificial stabilizing selection on Drosophila populations subjected to directional selection for another trait

Stabilizing selection for a set of morphometric wing traits was combined with directional selection for the increased expression of radius incompletus (ri) mutation of Drosophila melanogaster. Three experimental regimes were used: directional and stabilizing selection (stabilized lines); directional selection (unstabilized lines); no selection (controls). Response to selection for ri expression was similar in all selected lines but variation of this character was higher in the unstabilized lines compared to the stabilized ones. The competitive indices measured after termination of selection did not significantly differ under different treatments while fluctuating asymmetry was significantly lower in stabilized than in unstabilized lines. The possible causes of these differences are discussed.     

Analysis of disruptive selection in subdivided populations

Background

Analytical methods have been proposed to determine whether there are evolutionarily stable strategies (ESS) for a trait of ecological significance, or whether there is disruptive selection in a population approaching a candidate ESS. These criteria do not take into account all consequences of small patch size in populations with limited dispersal.

Results

We derive local stability conditions which account for the consequences of small and constant patch size. All results are derived from considering R_m, the overall production of successful emigrants from a patch initially colonized by a single mutant immigrant. Further, the results are interpreted in term of concepts of inclusive fitness theory. The condition for convergence to an evolutionarily stable strategy is proportional to some previous expressions for inclusive fitness. The condition for evolutionary stability stricto sensu takes into account effects of selection on relatedness, which cannot be neglected. It is function of the relatedness between pairs of genes in a neutral model and also of a three-genes relationship. Based on these results, I analyze basic models of dispersal and of competition for resources. In the latter scenario there are cases of global instability despite local stability. The results are developed for haploid island models with constant patch size, but the techniques demonstrated here would apply to more general scenarios with an island mode of dispersal.

Conclusions

The results allow to identity and to analyze the relative importance of the different selective pressures involved. They bridge the gap between the modelling frameworks that have led to the R_m concept and to inclusive fitness.

     

Bayesian variable selection for detecting adaptive genomic differences among populations

We extend an F(st)-based Bayesian hierarchical model, implemented via Markov chain Monte Carlo, for the detection of loci that might be subject to positive selection. This model divides the F(st)-influencing factors into locus-specific effects, population-specific effects, and effects that are specific for the locus in combination with the population. We introduce a Bayesian auxiliary variable for each locus effect to automatically select nonneutral locus effects. As a by-product, the efficiency of the original approach is improved by using a reparameterization of the model. The statistical power of the extended algorithm is assessed with simulated data sets from a Wright-Fisher model with migration. We find that the inclusion of model selection suggests a clear improvement in discrimination as measured by the area under the receiver operating characteristic (ROC) curve. Additionally, we illustrate and discuss the quality of the newly developed method on the basis of an allozyme data set of the fruit fly Drosophila melanogaster and a sequence data set of the wild tomato Solanum chilense. For data sets with small sample sizes, high mutation rates, and/or long sequences, however, methods based on nucleotide statistics should be preferred.