BLUP Research Articles

Impact and utility of shallow pedigree using single-step genomic BLUP for prediction of unbiased genomic breeding values.

In unstructured dairy programs, pedigree is usually shallow, which leads to biased prediction of breeding values using best linear unbiased prediction (BLUP). The objective of this study was to come out with a genomic prediction strategy that can utilize shallow pedigree information and predict unbiased and more accurate GEBV for sex-limited traits in a small population using single-step GBLUP (ssGBLUP). The data and models for a population under selection were simulated. Out of current 10 generations, 10th generation with 1000 candidates served as validation population. For the complete pedigree scenario, pedigree (P)BLUP estimated breeding values (EBV) were unbiased with accuracy (r) of 0.35 ± 0.02 and 0.26 ± 0.01 for 0.3 and 0.1 h2 scenario, respectively. For the shallow pedigree, biased prediction of breeding values and low accuracies were obtained with linear decline in the accuracy of EBV for removal of information on more distant pedigree. Accuracy and bias (ρ) for scenario with removing 4 distant generations from pedigree were 0.30 ± 0.02 and 0.55 ± 0.03, respectively, in moderate h2 scenario. Use of Genomic (G)BLUP, especially with "extreme phenotypic contrast selective genotyping," (TB) resulted in higher accuracy for a small reference of females; however, GEBV were highly biased. We observed that ssGBLUPF, where the numerator relationship matrix is corrected for inbreeding, resulted in more accurate and unbiased estimates of GEBV across shallow pedigree scenario, with TB all female reference (missing 4 distant generations: r = 0.50 ± 0.02; ρ = 0.96 ± 0.02). We recommend use of ssGBLUPF with two tailed selectively genotyped all female reference in shallow pedigree scenarios, to obtain unbiased and accurate GEBV for sex-limited traits, when resources are limited.

Genotype-by-Environment Interaction Analysis of Metabolites in Pearl Millet Genotypes with High Concentrations of Slowly Digestible and Resistant Starch in Their Grains.

Genotype × environment interactions (GEIs) should play an important role in the selection of suitable germplasm in breeding programmes. We here assessed GEI effects on pearl millet (Pennisetum glaucum L.) genotypes, selected to possess a high concentration of slowly digestible starch (SDS) and resistant starch (RS) in their grains. Entries were grown in a randomized complete block design with three replications at locations in Bawku-Ghana, Sadore-Niger, Bamako-Mali, Konni-Nigeria, and Gampella-Burkina Faso across West Africa. Harvested grains from these locations were metabolomically profiled using flow injection ionization-high-resolution mass spectrometry (FIE-HRMS). A total of 3144 mass features (m/z) (1560 negative ion mode and 1584 positive ion mode) were detected, of which, 475 m/z were linked to metabolites be involved in starch, antioxidant and lipid biosynthesis, and vitamin metabolism. Combined ANOVA revealed that the GEI was significantly evident for 54 health-benefiting metabolites, many associated with sugar, especially galactose, metabolism. Additive main effects and multiplicative interaction (AMMI) analysis examined genotype variation and GEI effects, which, when combined with principal component analysis (PCA), found that m/z 171.14864 (positive ionisation, propenyl heptanoate) accounted for 89% of the GEI variation along PC1. The AMMI-based stability parameter (ASTAB), modified AMMI stability value (MASV), and modified AMMI stability index (MASI) were then applied to identify stable and high-performing genotypes for all the health-benefiting metabolites. Similarly, the best-linear-unbiased-prediction (BLUP)-based stability estimation was also performed using the harmonic mean of genotypic values (HMGV), relative performance of genotypic values (RPGV), and harmonic mean of relative performance of genotypic values (HMRPGV), to identify genotype rankings across multiple environments. The multi-trait stability index (MTSI) was calculated and found that the genotypes G1 (ICMH-177111) and G24 (ICMX-207137) were the most stable and were the best mean performers across 52 health-benefiting metabolic traits. These findings demonstrate the potential of G × E assessments on the delivery of health-benefiting metabolite-rich grains in future varieties and hybrids of pearl millet.

On Predictors and Estimators under a Constrained Partitioned Linear Model and its Reduced Models

In this study, we consider a partitioned linear model with linear partial parameter constrains, known as a constrained partitioned linear model (CPLM), and its reduced models. A group of formulas on best linear unbiased predictors (BLUPs) and best linear unbiased estimators (BLUEs) in CPLM is derived via some quadratic matrix optimization methods, and further many basic properties of the predictors and estimators are established under some general assumptions. Our main purpose is to derive various inequalities and equalities for the comparison of covariance matrices of BLUPs and BLUEs under CPLM and its reduced models.

377 Derivation and Use of Genomic Enhanced Estimated Breeding Values for Selection in Sheep

Abstract The efficacy of selection programs in sheep depends on accurate evaluation of genetic merit. Globally, genetic evaluations are based on best linear unbiased prediction (BLUP) in which fixed effects and breeding values are estimated simultaneously. With genotyping becoming increasingly routine, pedigree, performance, and genomic data are being combined in single-step genomic BLUP (ssGBLUP) evaluations to generate genomic enhanced estimated breeding values (GEBV). Since GEBV are more accurate, younger animals can be selected more reliably; a corollary benefit is a shortened generation interval. Traits that are lowly heritable, expressed in one sex, measured late in life, or difficult and/or expensive to measure benefit most from genomic evaluation. Adoption of this technology has lagged in sheep most likely due to the higher cost of genotyping compared to the value of an animal. Nevertheless, genomics has been successfully incorporated in sheep breeding programs in several countries (e.g., Australia, France, New Zealand). Increases in accuracy of EBV of up to 60% have been reported. In 2021, the efficacy of genomic selection in a U.S. sheep breed (Katahdin) was tested by comparing predictive ability and bias of EBV using ssGBLUP and pedigree BLUP for weight and fecal egg counts at weaning. Approximately 5,000 animals, most born within the last 5 years, were genotyped with a 50k array. Nearly 70k body weights and 14k fecal egg counts were available. Genomic information improved predictive ability for both traits by as much as 10%, and reduced bias in the evaluation of fecal egg counts. Accuracies of EBV improved appreciably in younger genotyped animals (by as much as 1.81 times). An outcome was release of GEBV in the evaluation of U.S. Katahdin sheep. With integration of genomics into genetic evaluation, there is clear opportunity to augment the reliability of genetic evaluation and thereby rates of selection response in sheep.

Genome-wide association study of coleoptile length with Shanxi wheat.

In arid and semi-arid regions, coleoptile length is a vital agronomic trait for wheat breeding. The coleoptile length determines the maximum depth that seeds can be sown, and it is critical for establishment of the crop. Therefore, identifying loci associated with coleoptile length in wheat is essential. In the present study, 282 accessions from Shanxi Province representing wheat breeding for the Loess Plateau were grown under three experimental conditions to study coleoptile length. The results of phenotypic variation indicated that drought stress and light stress could lead to shortening of coleoptile length. Under drought stress the growth rate of environmentally sensitive cultivars decreased more than insensitive cultivars. The broad-sense heritability (H2) of BLUP (best linear unbiased prediction) under various conditions showed G × E interaction for coleoptile length but was mainly influenced by heredity. Correlation analysis showed that correlation between plant height-related traits and coleoptile length was significant in modern cultivars whereas it was not significant in landraces. A total of 45 significant marker-trait associations (MTAs) for coleoptile length in the three conditions were identified using the 3VmrMLM (3 Variance-component multi-locus random-SNP-effect Mixed Linear Model) and MLM (mixed linear model). In total, nine stable genetic loci were identified via 3VmrMLM under the three conditions, explaining 2.94–7.79% of phenotypic variation. Five loci on chromosome 2B, 3A, 3B, and 5B have not been reported previously. Six loci had additive effects toward increasing coleoptile length, three of which are novel. Molecular markers for the loci with additive effects on coleoptile length can be used to breed cultivars with long coleoptiles.

Incorporating kernelized multi-omics data improves the accuracy of genomic prediction

BackgroundGenomic selection (GS) has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes. Besides genome, transcriptome and metabolome information are increasingly considered new sources for GS. Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics.ResultsWe utilized the Cosine kernel to map genomic and transcriptomic data as {n}times {n} symmetric matrix (G matrix and T matrix), combined with the best linear unbiased prediction (BLUP) for GS. Here, we defined five kernel-based prediction models: genomic BLUP (GBLUP), transcriptome-BLUP (TBLUP), multi-omics BLUP (MBLUP, boldsymbol M=mathrm{ratio}timesboldsymbol G+(1-mathrm{ratio})timesboldsymbol T), multi-omics single-step BLUP (mssBLUP), and weighted multi-omics single-step BLUP (wmssBLUP) to integrate transcribed individuals and genotyped resource population. The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that (1) MBLUP was far preferred to GBLUP (ratio = 1.0), (2) the prediction accuracy of wmssBLUP and mssBLUP had 4.18% and 3.37% average improvement over GBLUP, (3) We also found the accuracy of wmssBLUP increased with the growing proportion of transcribed cattle in the whole resource population.ConclusionsWe concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy. Moreover, wmssBLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.

Genetic improvement of soybeans in Brazil: South and Midwest regions

AbstractSoybean [Glycine max (L.) Merril] is one of the main crops produced worldwide, and on‐farm yields have increased considerably in the last decades in Brazil. We evaluated the genetic gain for agronomic, phenological, and end‐use quality traits in 29 cultivars in the South Region, and in 38 cultivars in the Midwest Region in Brazil, released from 1966 to 2011. Field trials were conducted in Macroregions 1, 2, and 4, in 2016–2017, 2017–2018, and 2018–2019 crop seasons. The best linear unbiased predictors (BLUP) of the cultivars were obtained for each trait using a linear model. The BLUPs were regressed with the year of release using linear and quadratic regression models. The rates of genetic gain for seed yield ranged from 11.98 to 15.31 kg ha–1 yr–1 (0.33 to 0.42% yr–1) in the South Region, and from 13.58 to 21.84 kg ha–1 yr–1 (0.47 to 0.77% yr–1) in the Midwest Region. New cultivars presented taller plants and more seed oil content, oil and protein yield, and lower seed weight, days to flowering, days to maturity, and seed protein content than old cultivars in the South Region, although with differences between the Macroregions. In the Midwest Region, new cultivars showed higher seed oil content, oil and protein yield, and lower bottom pod height and seed protein content than old cultivars. Our results showed that breeding programs have been efficient to improve soybean yield and other traits across the years, without yield plateaus in sight.

Genotype × Environment Interaction Analysis for Yield Stability of Hybrid Cotton Across Production Environments Through Multiple Biometrical Tools

ABSTRACT The expected gains in the productivity and sustainability of cotton cultivars across environmental spectra may face many challenges. To address this issue, a set of 572 cotton accessions, including 284 F1 hybrids along with their parental lines and checks, were evaluated and compared across multiple test environments. Popular biometrical tools viz; best linear unbiased prediction (BLUP), additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype × environment interaction effects (GGE) were utilized for reliable estimation graphical depiction of GEI effects. The first two interaction coordinates as principal components in BLUP, AMMI, and GGE biplots explained the maximum proportion of GEI regarding seed cotton yield. The contrasting environments did not cluster together, thus revealing their varying influences on the genotypes. A set of 44 F1 hybrids featuring higher stability, 46 with higher adaptability, and 5 F1 hybrids exhibited higher stability, adaptability, and highest seed cotton yield across the investigated environments simultaneously. F1 hybrids of cotton were concluded as more stable and adaptable regarding seed cotton yield across environments. Accordingly, switching from line to hybrid breeding could enable a cotton breeding program to address the broader issue of growing this crop in a wide range of target environments.

Genotype X Environment Interactions of Fodder Barley Genotypes as Estimated by Ammi, Blup and Non Parametric Measures

Field experiments were carried out at six locations in Northern Hill Zone to evaluate twenty three promising fodder barley genotypes in a randomized complete block design (RCBD) during 2020-21 cropping seasons . Using analytic methods Additive Main Effects and Multiplicative Interactions (AMMI), Best Linear Unbiased Predictor (BLUP) along with Non Parametric compared to decipher the GxE interactions under multi environment trials. Highly significant about 67.5% variations accounted by environments, 14.1% of GxE interactions and marginally 3.2% by the genotypes in the total sum square of variations for yield the present study. AMMI1 explained 53.7%, 32.1% by AMMI2, 6.9% for AMMI3, AMMI4 accounted for 4.8% respectivelyof a total variation. ASV and ASV1 measures considered 85.9% of the total variation identified G4, G5, G9 genotypes. MASV1 exploited 97.7% of interactions favoured for G18, G15, G8 genotypes. BLUP-based settled for G6, G11, G5 genotypes. Non parametric measures found G9, G8, G1 as suitable genotypes. Further non parametric composites measures selected G9, G4, G8 as suitable genotypes. Measures Si1, Si2, Si3, Si4, Si5,Si6 ,Si7, HMPRVG, ASV1, ASV, accounted more in first principal component whereas NPi (1), NPi (2), NPi (3), NPi (4), PRVG, Si1, GM, Mean, Average were major contributors in second principal component. Very tight positive relationships observed for IPC3, IPC1with BLUP based measures GM, HM, PRVG, HMPRVG, Average in one quadrant. CV closely related to Stdev, IPC2, IPC4 in opposite quadrant. ASV, ASV1 expressed very tight association with Si6,Si7 whereas NPi(1) , exhibited close affinity with Si1 , Si4, Si2 ,Si5 values. Methods utilized in study showed high to moderate degree of association among themselves, however non parametric measures would be recommended for multi environment trials.

Extension of the reduced animal model to single-step methods.

For a few decades, animal models (AMs) in the form of best linear unbiased prediction (BLUP) have been used for the genetic evaluation of animals. An equation system is set in the order of all the effects in the model, including all the animals in the pedigree. Solving these large equation systems has been a challenge. Reduced AM (RAM) was introduced in 1980, which allowed setting up equations for parents instead of all animals. That greatly reduced the number of equations to be solved. The RAM is followed by a back-solving step, in which progenies' breeding values are obtained conditional on parental breeding values. Initially, pedigree information was utilized to model genetic relationships between animals. With the availability of genomic information, genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), and single-step marker models were developed. Single-step methods utilize pedigree and genomic information for simultaneous genetic evaluation of genotyped and nongenotyped animals. There has been a shortage of studies on RAM development for genetic evaluation models utilizing genomic information. This study extended the concept of RAM from BLUP to the single-step methods. Using example data, three RAMs were described for ssGBLUP. The order of animal equations was reduced from the total number of animals to (1) genotyped animals and nongenotyped parents, (2) genotyped animals and nongenotyped phenotyped animals, and (3) genotyped animals and nongenotyped parents of phenotyped nongenotyped nonparents. Solutions for the remaining animals are obtained following a back-solving step. All the RAMs produced identical results to the full ssGBLUP. Advances in computational hardware have alleviated many computational limitations, but, on the other hand, the size of data is growing rapidly by the number of animals, traits, phenotypes, genotypes, and genotype density. There is an opportunity for a RAM comeback for the single-step methods to reduce the computational demands by reducing the number of equations.

Progress in Genomic Mating in Domestic Animals.

Simple SummarySince animal domestication, breeders have been selecting candidates for breeding based on phenotypic performance. Estimating breeding values through the best linear unbiased prediction method represents a revolutionary shift in animal breeding. On this basis, selection and mating are utilized to improve the production level of animals. The application of genomic selection has once again revolutionized animal breeding methods. However, although this kind of truncated selection based on breeding values can significantly improve genetic gain, the genetic relationship between individuals with a high breeding value is usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the rate of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic gain and inbreeding is required. Genomic mating is the use of candidate individuals’ genomic information to implement optimized breeding and mating, which can effectively control the rate of inbreeding in the population and achieve long-term and sustainable genetic gain. It is more suitable for modern animal breeding, especially for conservation and genetic improvement of local domestic animal breeds.Selection is a continuous process that can influence the distribution of target traits in a population. From the perspective of breeding, elite individuals are selected for breeding, which is called truncated selection. With the introduction and application of the best linear unbiased prediction (BLUP) method, breeders began to use pedigree-based estimated breeding values (EBV) to select candidates for the genetic improvement of complex traits. Although truncated selection based on EBV can significantly improve the genetic progress, the genetic relationships between individuals with a high breeding value are usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the level of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic progress and inbreeding is required. As livestock and poultry breeding enters the genomic era, using genomic information to obtain optimal mating plans has formally been proposed by Akdemir et al., a method called genomic mating (GM). GM is more accurate and reliable than using pedigree information. Moreover, it can effectively control the inbreeding level of the population and achieve long-term and sustainable genetic gain. Hence, GM is more suitable for modern animal breeding, especially for local livestock and poultry breed conservation and genetic improvement. This review mainly summarized the principle of genomic mating, the methodology and usage of genomic mating, and the progress of its application in livestock and poultry.

Association of Root Hair Length and Density with Yield-Related Traits and Expression Patterns of TaRSL4 Underpinning Root Hair Length in Spring Wheat.

Root hairs play an important role in absorbing water and nutrients in crop plants. Here we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope. A collection of 24 century wide spring wheat cultivars released between 1911 and 2016 were phenotyped for RHL and RHD. The results revealed significant variations for both traits with five and six-fold variation for RHL and RHD, respectively. RHL ranged from 1.01 mm to 1.77 mm with an average of 1.39 mm, and RHD ranged from 17.08 mm−2 to 20.8 mm−2 with an average of 19.6 mm−2. Agronomic and physiological traits collected from five different environments and their best linear unbiased predictions (BLUPs) were correlated with RHL and RHD, and results revealed that relative-water contents (RWC), biomass and grain per spike (GpS) were positively correlated with RHL in both water-limited and well-watered conditions. While RHD was negatively correlated with grain yield (GY) in four environments and their BLUPs. Both RHL and RHD had positive correlation indicating the possibility of simultaneous selection of both phenotypes during wheat breeding. The expression pattern of TaRSL4 gene involved in regulation of root hair length was determined in all 24 wheat cultivars based on RNA-seq data, which indicated the differentially higher expression of the A- and D- homeologues of the gene in roots, while B-homeologue was consistently expressed in both leaf and roots. The results were validated by qRT-PCR and the expression of TaRSL4 was consistently high in rainfed cultivars such as Chakwal-50, Rawal-87, and Margallah-99. Overall, the new phenotyping method for RHL and RHD along with correlations with morphological and physiological traits in spring wheat cultivars improved our understanding for selection of these phenotypes in wheat breeding.

Identification of two new QTLs of maize (Zea mays L.) underlying kernel row number using the HNAU-NAM1 population

BackgroundMaize kernel row number (KRN) is one of the most important yield traits and has changed greatly during maize domestication and selection. Elucidating the genetic basis of KRN will be helpful to improve grain yield in maize.ResultsHere, we measured KRN in four environments using a nested association mapping (NAM) population named HNAU-NAM1 with 1,617 recombinant inbred lines (RILs) that were derived from 12 maize inbred lines with a common parent, GEMS41. Then, five consensus quantitative trait loci (QTLs) distributing on four chromosomes were identified in at least three environments along with the best linear unbiased prediction (BLUP) values by the joint linkage mapping (JLM) method. These QTLs were further validated by the separate linkage mapping (SLM) and genome-wide association study (GWAS) methods. Three KRN genes cloned through the QTL assay were found in three of the five consensus QTLs, including qKRN1.1, qKRN2.1 and qKRN4.1. Two new QTLs of KRN, qKRN4.2 and qKRN9.1, were also identified. On the basis of public RNA-seq and genome annotation data, five genes highly expressed in ear tissue were considered candidate genes contributing to KRN.ConclusionsThis study carried out a comprehensive analysis of the genetic architecture of KRN by using a new NAM population under multiple environments. The present results provide solid information for understanding the genetic components underlying KRN and candidate genes in qKRN4.2 and qKRN9.1. Single-nucleotide polymorphisms (SNPs) closely linked to qKRN4.2 and qKRN9.1 could be used to improve inbred yield during molecular breeding in maize.

First single-step genomic prediction and genome-wide association for body weight in Russian sturgeon (Acipenser gueldenstaedtii)

Russian sturgeon (Acipenser gueldenstaedtii)—a species of sturgeon—has a high economic value as a source of caviar and high-quality meat products. Genomic selection (GS) has the potential to shorten generation intervals and improve genetic gain for the key target traits of a breeding program. However, it has not been applied on sturgeon. We evaluated the performance of GS in breeding value estimation and carried out genome-wide association studies (GWAS) to dissect the genetic architecture of economically vital growth trait (body weight) in Russian sturgeon. Of a total of 3430 Russian sturgeons with phenotype records, 236 were sequenced. Through 10 replicates of fivefold cross-validation, we compared the accuracy of prediction obtained from a pedigree-based best linear unbiased prediction (BLUP), a single-step genomic BLUP (ssGBLUP), and a weighted ssGBLUP (WssGBLUP). We used single-step GWAS (ssGWAS) method to perform GWAS. The results showed that predictions made using ssGBLUP and WssGBLUP were 6.9% and 12.4% more accurate, respectively, compared with pedigree-based BLUP. Among the single-step methods, the performance of WssGBLUP was the best with the smallest mean squared (absolute) error. In addition, ssGWAS identified 3 potential SNPs with suggestive significance that were associated with body weight. Moreover, NOS1AP, CHMP4A, and RBMS1 genes were highlighted as functionally plausible candidate genes underlying the genetic architecture of body weight. These findings illustrate that the using GS in Russian sturgeon breeding could help in better selection of breeding candidates to facilitate complex economic growth trait.

Genetic Analysis of Agronomic and Quality Traits from Multi-Location white Yam Trials using Mixed Model with Genomic Relationship Matrix

Traits that define the suitability of a crop for production and consumption are often assessed and predicted to identify superior genotypes for commercial deployment. This study assessed genetic parameter estimates and prediction for 25 agronomic and quality traits in 49 white yam clones. It employed best linear unbiased prediction (BLUP) in a mixed model analysis using genomic relationship matrix derived from 6337 Diversity Array Technology (DArT) molecular markers, multivariate technique of the principal component and canonical discriminant analysis with BLUP predicted values to select key traits for yam breeding. Findings revealed that additive genetic, non-additive genetic and non-genetic factors contributed substantially to phenotypic variation of the studied yam traits. The non-genetic effects accounted for higher variation than the total genetic effects for majority of the traits except yam mosaic virus (YMV), tuber number per plant, ash content, flour yield, peel loss, and protein content. The narrow sense heritability was generally low (<0.30) for all traits except yam anthracnose (0.31), ash content (0.30) and peel loss (0.89). Trait selection with multivariate analysis identified 15 from the 25 traits with fresh tuber yield, tuber dry matter content (DMC), YMV, root-knot and Scutellonema bradys nematode susceptibility as the most important traits for white yam variety testing. This paper presents the importance of complementing BLUP prediction that accounts for the relationship among the genotypes with multivariate analysis for genetic parameter estimation, prediction and selection in yam breeding trials to accelerate the genetic gains.

Consequences of incorrect genetic parameter estimates for single-trait and multi-trait genetic evaluations in honeybees.

Genetic and residual variances of traits are important input parameters for best linear unbiased prediction (BLUP) breeding value estimation. In honeybees, estimates of these variances are often associated with large standard errors, entailing a risk to perform genetic evaluations under wrong premises. The consequences hereof have not been sufficiently studied. In particular, there are no adequate investigations on this topic accounting for multi-trait selection or genetic peculiarities of the honeybee. We performed simulation studies and explored the consequences of selection for honeybee populations with a broad range of true and assumed genetic parameters. We found that in single-trait evaluations, the response to selection was barely compromised by assuming erroneous parameters, so that reductions in genetic progress after 20 years never exceeded 21%. Phenotypic selection appeared inferior to BLUP selection, particularly under low heritabilities. Parameter choices for genetic evaluation had great effects on inbreeding development. By wrongly assuming high heritabilities, inbreeding rates were reduced by up to 74%. When parallel selection was performed for two traits, the right choice of genetic parameters appeared considerably more crucial as several incorrect premises yielded inadvertent negative selection for one of the traits. This phenomenon occurred in multiple constellations in which the selection traits expressed a negative genetic correlation. It was not reflected in the estimated breeding values. Our results indicate that breeding efforts heavily rely on detailed knowledge on genetic parameters, particularly when multi-trait selection is performed. Thus, considerable effort should be invested into precise parameter estimations.

Mixed Modeling in Genetic Divergence Study of Elite Popcorn Hybrids (Zea mays var. everta)

Popcorn is a food highly appreciated throughout the world, generating billions of dollars annually just in the North American market alone. Even in the face of the historical superiority of American hybrids, which occupy almost 100% of the grain production fields in Brazil, our researchers have been working to develop cultivars that combine important traits for the crop, such as tolerance to leaf diseases and high rates of grain yield and popping expansion. This work investigated the degree of genetic divergence among 40 diallel hybrids of popcorn, 16 parents and 6 elite controls using mixed models to discriminate superior materials to be adopted in the Brazilian agribusiness. Based on the individual Best Linear Unbiased Prediction (BLUP) of each of the 15 variables analyzed, the Unweighted Pair-Group Method using Arithmetic Averages (UPGMA) and Tocher clusters were performed, and the study of Pearson correlation was carried out. The results pointed out that there is genetic variability among the genotypes evaluated and that the best candidates for composing superior genotypes are in the combination between the P10×L77 parents and/or between the P7 and L88 lines. Linear correlations showed that earlier flowering and taller genotypes exhibited an association with materials more tolerant to Exserohilum turcicum intensity.

Genetic parameters and selection in full-sib families of tall fescue using best linear unbiased prediction (BLUP) analysis

BackgroundBetter understanding of genetic structure of economic traits is crucial for identification and selection of superior genotypes in specific breeding programs. Best linear unbiased prediction (BLUP) is the most efficient method in this regard, which is poorly used in forage plant breeding. The present study aimed to assess genetic variation, estimate genetic parameters, and predict breeding values of five essential traits in full sib families (recognized by EST-SSR markers) of tall fescue using REML/BLUP procedure.MethodForty-two full-sib families of tall fescue (included of 120 individual genotypes), recognized by EST-SSR markers along with twenty-one their corresponding parental genotypes were assessed for biomass production and agro-morphological traits at three harvests (spring, summer, and autumn) in the field during 4 years (2017–2020).ResultsConsiderable genotypic variability was observed for all traits. Low narrow-sense heritability (h2n) for dry forage yield (DFY) at three harvest indicates that non-additive gene actions may play an important role in the inheritance of this trait. Higher h2n of yield related traits and flowering time and also significant genetic correlation of these traits with forage yield, suggests that selection based on these traits may lead to indirect genetic improvement of DFY.ConclusionOur results showed the adequacy of REML/BLUP procedure for identification and selection of preferable parental genotypes and progenies with higher breeding values for future breeding programs such as variety development in tall fescue. Parental genotypes 21 M, 1 M, and 20 L were identified as superior and stable genotypes and could also produce the best hybrid combinations when they were mostly used as maternal parent.

Dynamic Quantitative Trait Loci Mapping for Plant Height in Recombinant Inbred Line Population of Upland Cotton

Plant height (PH) is a key plant architecture trait for improving the biological productivity of cotton. Ideal PH of cotton is conducive to lodging resistance and mechanized harvesting. To detect quantitative trait loci (QTL) and candidate genes of PH in cotton, a genetic map was constructed with a recombinant inbred line (RIL) population of upland cotton. PH phenotype data under nine environments and three best linear unbiased predictions (BLUPs) were used for QTL analyses. Based on restriction-site-associated DNA sequence (RAD-seq), the genetic map contained 5,850 single-nucleotide polymorphism (SNP) markers, covering 2,747.12 cM with an average genetic distance of 0.47 cM. Thirty-seven unconditional QTL explaining 1.03–12.50% of phenotypic variance, including four major QTL and seven stable QTL, were identified. Twenty-eight conditional QTL explaining 3.27–28.87% of phenotypic variance, including 1 major QTL, were identified. Importantly, five QTL, including 4 stable QTL, were both unconditional and conditional QTL. Among the 60 PH QTL (including 39 newly identified), none of them were involved in the whole period of PH growth, indicating that QTL related to cotton PH development have dynamic expression characteristics. Based on the functional annotation of Arabidopsis homologous genes and transcriptome data of upland cotton TM-1, 14 candidate genes were predicted within 10 QTL. Our research provides valuable information for understanding the genetic mechanism of PH development, which also increases the economic production of cotton.

Genetic basis of heterosis in a common wheat cross withstrong-heterosis

<p indent="0mm">Heterosis refers to the phenomenon that a heterozygous hybrid exhibits superior performances over its parents, which has been suggested as a solution to overcome yield stagnation of wheat. However, the molecular basis of wheat heterosis is still poorly understood. We have previously found that a cross derived from the winter common wheat Nongda3338 (ND3338) and Jingdong 6 (JD6) showed strong mid-parent heteroses on plant height (PH) and thousand grain weight (TGW). In this study, we further aimed to investigate the genetic underpinnings of PH and TGW using an “immortalized F₂” (IF₂) derived from the ND3338/JD6 doubled haploid (DH) population. The PH and TGW of DH and IF₂ populations were evaluated across two different environments, and the mid-parent heterosis value (HV) was used to measure heterosis. The direction and magnitude of HV greatly varied between PH and TGW across the environments. A two-way analysis of variance (ANOVA) was performed for PH and TGW in the IF₂ population, revealing that genetic variation was the main source, despite the significant variations found from the environments and genotype by environment interaction. The broad sense heritability of PH and TGW in IF₂ population was 94.05% and 88.14%, respectively. Pearson’s correlation coefficients of PH, TGW, HV_PH and HV_TGW in the IF₂ population were estimated based on the best linear unbiased prediction (BLUP) values of two environments, showing that PH was significantly positively correlated with TGW and HV_TGW, TGW was significantly positively correlated with HV_TGW and negatively correlated with HV_PH, while HV_PH had a strong positive correlation with HV_TGW. The genotypes of the IF₂ population were deduced from corresponding DH parents. Based on the inclusive composite interval mapping method, quantitative trait locus (QTL) mapping was separately conducted with PH, TGW, HV_PH and HV_TGW using the QTL IciMapping software. We identified a total of 32 and 25 QTL that exhibited additive, dominant and overdominant pattern associated with PH and TGW, which accounted for 0.54%–36.05% and 0.87%–25.78% of the variance, respectively. Consistent with the results found in the ND3338/JD6 DH population, major and stable QTL on chromosomes 2D (<italic>QPh</italic>.<italic>2D</italic>.<italic>1</italic> and <italic>QTgw</italic>.<italic>2D</italic>), 4B (<italic>QPh</italic>.<italic>4B</italic>.<italic>1</italic> and <italic>QTgw</italic>.<italic>4B</italic>.<italic>1</italic>) and 4D (<italic>QPh</italic>.<italic>4D </italic>and <italic>QTgw</italic>.<italic>4D</italic>) that were coincident with the dwarfing genes <italic>Rht1</italic>, <italic>Rht2</italic> and <italic>Rht8</italic> were detected, suggesting that the IF₂ population design is feasible and reliable for identifying the genetic components of heterosis. Furthermore, 13 QTL for HV_PH and 15 QTL for HV_TGW were identified, of which most of these loci exhibited overdominant pattern. However, only few of the QTL controlling PH and TGW were overlapped with those controlling HV_PH and HV_TGW, suggesting potentially different genetic mechanisms between phenotypic traits and heterosis. In addition, epistasis analysis for PH and TGW revealed that four types of digenic interactions, including additive by additive (AA), additive by dominance (AD), dominance by additive (DA) and dominance by dominance (DD), were involved in the genetic basis of hybrid performance and heterosis in the IF₂ population. Notably, the DD-oriented QTL with the greatest interactive effects possessed the highest frequency. Overall, this study provides strong evidence for supporting the classical genetic hypotheses of heterosis (dominance, overdominance, and epistasis) with an IF₂ population, establishing the foundation to develop and select crosses with strong heterosis.