Analytical and numerical comparisons of two methods of estimation of additive × additive × additive interaction of QTL effects

This paper presents the analytical and numerical comparison of two methods of estimation of additive × additive × additive (aaa) interaction of QTL effects. The first method takes into account only the plant phenotype, while in the second we also included genotypic information from molecular marker observation. Analysis was made on 150 doubled haploid (DH) lines of barley derived from cross Steptoe × Morex and 145 DH lines from Harrington × TR306 cross. In total, 153 sets of observation was analyzed. In most cases, aaa interactions were found with an exert effect on QTL. Results also show that with molecular marker observations, obtained estimators had smaller absolute values than phenotypic estimators.

37 Single-step Genomic BLUP Fitting Snps or Haplotypes in Genetically-diverse Populations: A Simulation Study

The successful implementation of genomic selection in more genetically diverse populations (e.g., sheep and goats) require larger training populations. Haplotype-based genomic predictions are hypothesized to perform better in comparison to single-SNP methods mainly due to the possibility of better capturing QTL effects in linkage disequilibrium (LD) with the markers. However, most genomic-prediction studies based on haplotypes were performed in populations with low effective population size (Ne < 150). We aimed to investigate alternative approaches for fitting haplotypes using the single-step GBLUP method (ssGBLUP) in a genetically diverse population (Ne = 400). We simulated a composite sheep population, mimicking real populations based on literature parameters, using the QMSim software, with five replicates. We simulated a HD panel (600K) and two traits with different heritabilites (0.10 and 0.30). Pseudo-SNPs from unique haplotype alleles derived from LD blocks with thresholds of 0.1, 0.3, and 0.6 (LD01, LD03, and LD06, respectively) were used in the analyses. The LD-blocks were constructed using a 50K panel designed from the simulated HD. The training population was composed of 60,000 individuals with phenotypes, 8,000 of them also had genotypes, and 2,000 young genotyped individuals were used as the validation set. The genomic relationship (G) in the ssGBLUP was constructed using both independent markers and pseudo-SNPs (haplotypes). A linear mixed effects model was used to test the effect of the G on the accuracies of prediction, followed by the Tukey test with 5% of significance. No blocks were created with LD06. The accuracies with the 50K panel, LD01, and LD03 for the moderate heritability were 0.41(0.00), 0.40(0.01) and 0.41(0.00), respectively, and 0.24(0.01) 0.23(0.01), and 0.24(0.01) for the low heritability scenario. No statistical differences were observed. Based on our findings, haplotype-based predictions did not improve the accuracy of genomic breeding values in genetically diverse populations.

Insights into the genetic basis of the pre-breeding potato clones developed at the Julius Kühn Institute for high and durable late blight resistance

Due to the high yield losses caused by late blight in potato cultivation, the development of resistant pre-breeding material is of great importance for cultivar breeding. The gene pool of the Julius Kühn Institute (JKI) includes a large collection of resistant clones whose resistance has not yet been analysed in detail with markers for relevant resistance genes. A panel of 52 pre-breeding potato clones developed at the JKI via interspecific crosses and highly resistant to late blight were tested for the presence of seven resistance genes (Rpi-blb1/Rpi-sto1, Rpi-blb2, Rpi-blb3/R2/Rpi-abpt, R1, R3a, R3b, Rpi-phu1) and one QTL allele (QTL_phu-stn) from Solanum species S. bulbocastanum, S. demissum, S. phureja and S. stoloniferum, respectively. Molecular marker assays based on sequence-specific primers revealed that 36 of the 52 pre-breeding clones carried either 1, 2, 3 or 4 resistance genes introgressed from these wild Solanum species. Results indicate that these resistance genes were retained over generations of breeding. Although highly resistant to late blight, 16 pre-breeding clones did not carry any of these resistance genes. Resistance in the gene pool may, thus, be based not only on individual resistance genes but also on QTL effects. Results help to better understand both inheritance and expression of late blight resistance of this unique gene pool and may be used for breeding programmes.

A variance component estimation approach to infer associations between Mendelian polledness and quantitative production and female fertility traits in German Simmental cattle

Background Managing beneficial Mendelian characteristics in dairy cattle breeding programs implies that the correlated genetic effects are considered to avoid possible adverse effects in selection processes. The Mendelian trait polledness in cattle is traditionally associated with the belief that the polled locus has unfavorable effects on breeding goal traits. This may be due to the inferior breeding values of former polled bulls and cows in cattle breeds, such as German Simmental, or to pleiotropic or linkage effects of the polled locus. Methods We focused on a variance component estimation approach that uses a marker-based numerator relationship matrix reflecting gametic relationships at the polled locus to test for direct pleiotropic or linked quantitative trait loci (QTL) effects of the polled locus on relevant traits. We applied the approach to performance, health, and female fertility traits in German Simmental cattle. Results Our results showed no evidence for any pleiotropic QTL effects of the polled locus on test-day production traits milk yield and fat percentage, on the mastitis indicator ‘somatic cell score’, and on several female fertility traits, i.e. 56 days non return rate, days open and days to first service. We detected a significant and unfavorable QTL effect accounting for 6.6% of the genetic variance for protein percentage only. Conclusions Pleiotropy does not explain the lower breeding values and phenotypic inferiority of polled German Simmental sires and cows relative to the horned population in the breed. Thus, intensified selection in the polled population will contribute to increased selection response in breeding goal traits and genetic merit and will narrow the deficit in breeding values for production traits.

Genomic prediction using a reference population of multiple pure breeds and admixed individuals

Background In dairy cattle populations in which crossbreeding has been used, animals show some level of diversity in their origins. In rotational crossbreeding, for instance, crossbred dams are mated with purebred sires from different pure breeds, and the genetic composition of crossbred animals is an admixture of the breeds included in the rotation. How to use the data of such individuals in genomic evaluations is still an open question. In this study, we aimed at providing methodologies for the use of data from crossbred individuals with an admixed genetic background together with data from multiple pure breeds, for the purpose of genomic evaluations for both purebred and crossbred animals. A three-breed rotational crossbreeding system was mimicked using simulations based on animals genotyped with the 50 K single nucleotide polymorphism (SNP) chip. Results For purebred populations, within-breed genomic predictions generally led to higher accuracies than those from multi-breed predictions using combined data of pure breeds. Adding admixed population’s (MIX) data to the combined pure breed data considering MIX as a different breed led to higher accuracies. When prediction models were able to account for breed origin of alleles, accuracies were generally higher than those from combining all available data, depending on the correlation of quantitative trait loci (QTL) effects between the breeds. Accuracies varied when using SNP effects from any of the pure breeds to predict the breeding values of MIX. Using those breed-specific SNP effects that were estimated separately in each pure breed, while accounting for breed origin of alleles for the selection candidates of MIX, generally improved the accuracies. Models that are able to accommodate MIX data with the breed origin of alleles approach generally led to higher accuracies than models without breed origin of alleles, depending on the correlation of QTL effects between the breeds. Conclusions Combining all available data, pure breeds’ and admixed population’s data, in a multi-breed reference population is beneficial for the estimation of breeding values for pure breeds with a small reference population. For MIX, such an approach can lead to higher accuracies than considering breed origin of alleles for the selection candidates, and using breed-specific SNP effects estimated separately in each pure breed. Including MIX data in the reference population of multiple breeds by considering the breed origin of alleles, accuracies can be further improved. Our findings are relevant for breeding programs in which crossbreeding is systematically applied, and also for populations that involve different subpopulations and between which exchange of genetic material is routine practice.

Multiple Loci Control Variation in Plasticity to Foliar Shade Throughout Development in Arabidopsis thaliana

The shade avoidance response is a set of developmental changes exhibited by plants to avoid shading by competitors, and is an important model of adaptive plant plasticity. While the mechanisms of sensing shading by other plants are well-known and appear conserved across plants, less is known about the developmental mechanisms that result in the diverse array of morphological and phenological responses to shading. This is particularly true for traits that appear later in plant development. Here we use a nested association mapping (NAM) population of Arabidopsis thaliana to decipher the genetic architecture of the shade avoidance response in late-vegetative and reproductive plants. We focused on four traits: bolting time, rosette size, inflorescence growth rate, and inflorescence size, found plasticity in each trait in response to shade, and detected 17 total QTL; at least one of which is a novel locus not previously identified for shade responses in Arabidopsis. Using path analysis, we dissected each colocalizing QTL into direct effects on each trait and indirect effects transmitted through direct effects on earlier developmental traits. Doing this separately for each of the seven NAM populations in each environment, we discovered considerable heterogeneity among the QTL effects across populations, suggesting allelic series at multiple QTL or interactions between QTL and the genetic background or the environment. Our results provide insight into the development and variation in shade avoidance responses in Arabidopsis, and emphasize the value of directly modeling the relationships among traits when studying the genetics of complex developmental syndromes.

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

The main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

Purebred and Crossbred Genomic Evaluation and Mate Allocation Strategies To Exploit Dominance in Pig Crossbreeding Schemes

We investigated the effectiveness of mate allocation strategies accounting for non-additive genetic effects to improve crossbred performance in a two-way crossbreeding scheme. We did this by computer simulation of 10 generations of evaluation and selection. QTL effects were simulated as correlated across purebreds and crossbreds, and (positive) heterosis was simulated as directional dominance. The purebred-crossbred correlation was 0.30 or 0.68 depending on the genetic variance component used. Dominance and additive marker effects were estimated simultaneously for purebreds and crossbreds by multiple trait genomic BLUP. Four scenarios that differ in the sources of information (only purebred data, or purebred and crossbred data) and mate allocation strategies (mating at random, minimizing expected future inbreeding, or maximizing the expected total genetic value of crossbred animals) were evaluated under different cases of genetic variance components. Selecting purebred animals for purebred performance yielded a response of 0.2 genetic standard deviations of the trait “crossbred performance” per generation, whereas selecting purebred animals for crossbred performance doubled the genetic response. Mate allocation strategy to maximize the expected total genetic value of crossbred descendants resulted in a slight increase (0.8%, 4% and 0.5% depending on the genetic variance components) of the crossbred performance. Purebred populations increased homozygosity, but the heterozygosity of the crossbreds remained constant. When purebred-crossbred genetic correlation is low, selecting purebred animals for crossbred performance using crossbred information is a more efficient strategy to exploit heterosis and increase performance at the crossbred commercial level, whereas mate allocation did not improve crossbred performance.

The influence of QTL allelic diversity on QTL detection in multi-parent populations: a simulation study in sugar beet

Multi-parent populations (MPPs) are important resources for studying plant genetic architecture and detecting quantitative trait loci (QTLs). In MPPs, the QTL effects can show various levels of allelic diversity, which is an important factor influencing the detection of QTLs. In MPPs, the allelic effects can be more or less specific. They can depend on an ancestor, a parent or the combination of parents in a cross. In this paper, we evaluated the effect of QTL allelic diversity on the QTL detection power in MPPs.We simulated: a) cross-specific QTLs; b) parental and ancestral QTLs; and c) bi-allelic QTLs. Inspired by a real application, we tested different MPP designs (diallel, chessboard, factorial, and NAM) derived from five or nine parents to explore the ability to sample genetic diversity and detect QTLs. Using a fixed total population size, the QTL detection power was larger in MPPs with fewer but larger crosses derived from a reduced number of parents. The use of a larger set of parents was useful to detect rare alleles with a large phenotypic effect. The benefit of using a larger set of parents was however conditioned on an increase of the total population size. We also determined empirical confidence intervals for QTL location to compare the resolution of different designs. For QTLs representing 6% of the phenotypic variation, using 1600 offspring individuals, we found 95% empirical confidence intervals of 50 and 26 cM for cross-specific and bi-allelic QTLs, respectively.MPPs derived from less parents with few but large crosses generally increased the QTL detection power. Using a larger set of parents to cover a wider genetic diversity can be useful to detect QTLs with a reduced minor allele frequency when the QTL effect is large and when the total population size is increased.

Variation in phosphorus and sulfur content shapes the genetic architecture and phenotypic associations within wheat grain ionome

SummaryDissection of the genetic basis of ionome is crucial for the understanding of the physiological and biochemical processes underlying mineral accumulation in seeds, as well as for efficient crop breeding. Most of the elements essential for plants are metals stored in seeds as chelate complexes with phytic acid or sulfur-containing compounds. We assume that the involvement of phosphorus and sulfur in metal chelation is the reason for strong phenotypic associations within ionome. Thus, we adjusted element concentrations for the effect of variation in phosphorus and sulfur seed content. The genetic architecture of wheat grain ionome was characterize by QTL analysis using a cross between durum and wild emmer wheat. Adjustment for variation in P and S drastically changed phenotypic associations within ionome and considerably improved QTL detection power and accuracy, resulting in identification of 105 QTLs and 437 QTL effects for 11 elements. A search for candidate genes revealed some strong functional associations of genes involved in transport and metabolism of ions and elements. Thus, we have shown that accounting for variation in P and S is crucial for understanding of the physiological and genetic regulation of mineral composition of wheat grain ionome and can be implemented for other plants.