Field pea (Pisum sativum L.) needs improvement to increase productivity due to its high price and demand. However, the incidence of powdery mildew (PM) disease limits its production. This study aimed to analyze the diversity of qualitative and quantitative traits against powdery mildew resistance by utilizing cluster and principal component analysis to explore PM resistance high-yield potential field peas. Shannon-Weaver\'s diversity index (H\') displayed high intra-genotype diversity for quantitative and qualitative aspects. Heterogeneity was identified for resistance against powdery mildew infections. Eighty-five genotypes were divided into five groups using Mohalanobis generalized distance (D2) statistics. The highest inter-cluster D2 value was observed between clusters 2 and 3 (11.89) while the lowest value was found between clusters 3 and 4 (2.06). Most of the genotypes had noticeable differences, so these could be employed in a crossing scheme. Twelve genotypes were extremely resistant, 29 genotypes were resistant, 25 genotypes were moderately resistant, 18 genotypes were fairly susceptible, and 1 genotype was susceptible to powdery mildew disease. Among 29 resistant genotypes, BFP77, BFP74, BFP63, BFP62, BFP43, and BFP80 were high yielders and, could be used directly and/or transferred through hybridization to high-yielding disease-susceptible genotypes. Among the 25 moderately resistant genotypes, BFP78, BFP45, BFP79, and BFP48 were found to be high yielders. In principal component analysis (PCA), the first four PCs with Eigen values > 1 accounted for 88.4% variability for quantitative traits. Clustering sorted genotypes into five groups, where groups 1 to 5 assembled 37, 28, 1, 8, and 11 genotypes, respectively. Genotypes of cluster 4 were identified as high yielders with its attributes. Pearson correlation significantly and positively correlated across all traits except for PM. This variation suggested that there is a mechanism to select promising genotypes for field pea breeding. Considering all features, BFP78, BFP77, BFP74, BFP63, BFP62, BFP45, BFP79, and BFP80 could be preferred as high yielders and PM resistance owing to longer pod lengths, seeds per pod and pods per plant.

BACKGROUND: The selection of individuals based on their predicted breeding values and mating of related individuals can increase the proportion of identical-by-descent alleles. In this context, the objectives of this study were to estimate inbreeding coefficients based on alternative metrics and data sources such as pedigree (FPED), hybrid genomic relationship matrix H (FH), and ROH of different length (FROH); and calculate Pearson correlations between the different metrics in a closed Nellore cattle population selected for body weight adjusted to 378 days of age (W378). In addition to total FROH (all classes) coefficients were also estimated based on the size class of the ROH segments: FROH1 (1-2 Mb), FROH2 (2-4 Mb), FROH3 (4-8 Mb), FROH4 (8-16 Mb), and FROH5 (> 16 Mb), and for each chromosome (FROH_CHR). Furthermore, we assessed the effect of each inbreeding metric on birth weight (BW), body weights adjusted to 210 (W210) and W378, scrotal circumference (SC), and residual feed intake (RFI). We also evaluated the chromosome-specific effects of inbreeding on growth traits.
RESULTS: The correlation between FPED and FROH was 0.60 while between FH and FROH and FH and FPED were 0.69 and 0.61, respectively. The annual rate of inbreeding was 0.16% for FPED, 0.02% for FH, and 0.16% for FROH. A 1% increase in FROH5 resulted in a reduction of up to -1.327 ± 0.495 kg in W210 and W378. Four inbreeding coefficients (FPED, FH, FROH2, and FROH5) had a significant effect on W378, with reductions of up to -3.810 ± 1.753 kg per 1% increase in FROH2. There was an unfavorable effect of FPED on RFI (0.01 ± 0.0002 kg dry matter/day) and of FROH on SC (-0.056 ± 0.022 cm). The FROH_CHR coefficients calculated for BTA3, BTA5, and BTA8 significantly affected the growth traits.
CONCLUSIONS: Inbreeding depression was observed for all traits evaluated. However, these effects were greater for the criterion used for selection of the animals (i.e., W378). The increase in the genomic inbreeding was associated with a higher inbreeding depression on the traits evaluated when compared to pedigree-based inbreeding. Genomic information should be used as a tool during mating to optimize control of inbreeding and, consequently, minimize inbreeding depression in Nellore cattle.

The sunflower (Helianthus annuus L.) is one of the most essential oil crops in the world. Several component traits, including flowering time, plant height, stem diameter, seed weight, and kernel weight, determine sunflower seed and oil yield. Although the genetic mechanisms governing the variation of these yield-related traits have been studied using various approaches, genome-wide association studies (GWAS) have not been widely applied to sunflowers. In this study, a set of 342 sunflower accessions was evaluated in 2019 and 2020 using an incomplete randomized block design, and GWAS was conducted utilizing two complementary approaches: the mixed linear model (MLM) and the fixed and random model circulating probability unification (farmCPU) model by fitting 226,779 high-quality SNPs. As a result, GWAS identified a number of trait-associated SNPs. Those SNPs were located close to several genes that may serve as a basis for further molecular characterization and provide promising targets for sunflower yield improvement.

Proper variance partitioning and estimation of genetic parameters at appropriate time interval is crucial for understanding the dynamics of trait variance and genetic correlations and for deciding the future breeding strategy of the population. This study was conducted on the same premise to estimate genetic parameters of major economic traits in a White Leghorn strain IWH using Bayesian approach and to identify the role of maternal effects in the regulation of trait variance. Three different models incorporating the direct additive effect (Model 1), direct additive and maternal genetic effect (Model 2) and direct additive, maternal genetic and maternal permanent environmental effects (Model 3) were tried to estimate the genetic parameters for body weight traits (birth weight, body weight at 16, 20, 40 and 52 weeks), Age at sexual maturity (ASM), egg production traits (egg production up to 24, 28, 40, 52, 64 and 72 weeks) and egg weight traits (egg weight at 28, 40 and 52 weeks). Model 2 and Model 3 with maternal effects were found to be the best having the highest accuracy for almost all the traits. The direct additive genetic heritability was moderate for ASM, moderate to high for body weight traits and egg weight traits and low to moderate for egg production traits. Though the maternal heritability (h2mat) and permanent environmental effect (c2mpe) was low (<0.1) for most of the traits, they formed an important component of trait variance. Traits like egg weight at 28 weeks (0.14±0.06) and egg production at 72 weeks (0.13±0.07) reported comparatively higher values for c2mpe and h2mat respectively. Additive genetic correlation was high and positive between body weight traits, between egg weight traits, between consecutive egg production traits and between body weight and egg weight traits. However, a negative genetic correlation existed between egg production and egg weight traits, egg production and body weight traits, ASM and early egg production traits. Overall, a moderate positive genetic correlation was estimated between ASM and body weight traits and ASM and egg weight traits. Based on our findings, we can deduce that maternal effects constitute an important source of variation for all the major economic traits in White Leghorn and should be necessarily considered in genetic evaluation programs.

This study seeks a comprehensive exploration of genome-wide selective processes impacting morphometric traits across diverse cattle breeds, utilizing an array of statistical methods. Morphometric traits, encompassing both qualitative and quantitative variables, play a pivotal role in characterizing and selecting livestock breeds based on their external appearance, size, and physical attributes. While qualitative traits, such as color, horn structure, and coat type, contribute to adaptive features and breed identification, quantitative traits like body weight and conformation measurements bear a closer correlation with production characteristics. This study employs advanced genotyping technologies, including the Illumina BovineSNP50 Bead Chip and next-generation sequencing methods like Reduced Representation sequencing, to identify genomic signatures associated with these traits. We applied four intra-population methods to find evidence of selection, such as Tajima\'s D, CLR, iHS, and ROH. We found a total of 40 genes under the selection signature, that were associated with morphometric traits in five cattle breeds (Kankrej, Tharparkar, Nelore, Sahiwal, and Gir). Crucial genes such as ADIPDQ, DPP6, INSIG1, SLC35D2 in Kankrej, LPL, ATP6V1B2, CDC14B in Tharparkar, HPSE2, PLAG1 in Nelore, PCSK1, PRKD1 in Sahiwal, and GNAQ, HPCAL1 in Gir were identified in our study. This approach provides valuable insights into the genetic basis of variations in body weight and conformation traits, facilitating informed selection processes and offering a deeper understanding of the evolutionary and domestication processes in diverse cattle breeds.

Plants express diverse nutrient use and acquisition traits, but it is unclear how trait combinations at the species level are constrained by phylogeny, trait coordination, or trade-offs in resource investment. One trait - nitrogen (N) fixation - is assumed to correlate with other traits and used to define plant functional groups, despite potential confounding effects of phylogeny. We quantified growth, carbon metabolism, fixation rate, root phosphatase activity (RPA), mycorrhizal colonization, and leaf and root morphology/chemistry across 22 species of fixing and nonfixing tropical Fabaceae trees under common conditions. Belowground trait variation was high even among closely related species, and most traits displayed a phylogenetic signal, including N-fixation rate and nodule biomass. Across species, we observed strong positive correlations between physiological traits such as RPA and root respiration. RPA increased ~ fourfold per unit increase in fixation, supporting the debated hypothesis that N-fixers \'trade\' N for phosphatases to enhance phosphorus acquisition. Specific root length and root N differed between functional groups, though for other traits, apparent differences became nonsignificant after accounting for phylogenetic nonindependence. We conclude that evolutionary history, trait coordination, and fixation ability contribute to nutrient trait expression at the species level, and recommend explicitly considering phylogeny in analyses of functional groupings.

BACKGROUND: Heritability partitioning approaches estimate the contribution of different functional classes, such as coding or regulatory variants, to the genetic variance. This information allows a better understanding of the genetic architecture of complex traits, including complex diseases, but can also help improve the accuracy of genomic selection in livestock species. However, methods have mainly been tested on human genomic data, whereas livestock populations have specific characteristics, such as high levels of relatedness, small effective population size or long-range levels of linkage disequilibrium.
RESULTS: Here, we used data from 14,762 cows, imputed at the whole-genome sequence level for 11,537,240 variants, to simulate traits in a typical livestock population and evaluate the accuracy of two state-of-the-art heritability partitioning methods, GREML and a Bayesian mixture model. In simulations where a single functional class had increased contribution to heritability, we observed that the estimators were unbiased but had low precision. When causal variants were enriched in variants with low (< 0.05) or high (> 0.20) minor allele frequency or low (below 1st quartile) or high (above 3rd quartile) linkage disequilibrium scores, it was necessary to partition the genetic variance into multiple classes defined on the basis of allele frequencies or LD scores to obtain unbiased results. When multiple functional classes had variable contributions to heritability, estimators showed higher levels of variation and confounding between certain categories was observed. In addition, estimators from small categories were particularly imprecise. However, the estimates and their ranking were still informative about the contribution of the classes. We also demonstrated that using methods that estimate the contribution of a single category at a time, a commonly used approach, results in an overestimation. Finally, we applied the methods to phenotypes for muscular development and height and estimated that, on average, variants in open chromatin regions had a higher contribution to the genetic variance (> 45%), while variants in coding regions had the strongest individual effects (> 25-fold enrichment on average). Conversely, variants in intergenic or intronic regions showed lower levels of enrichment (0.2 and 0.6-fold on average, respectively).
CONCLUSIONS: Heritability partitioning approaches should be used cautiously in livestock populations, in particular for small categories. Two-component approaches that fit only one functional category at a time lead to biased estimators and should not be used.

Drought, especially terminal drought, severely limits wheat growth and yield. Understanding the complex mechanisms behind the drought response in wheat is essential for developing drought-resistant varieties. This study aimed to dissect the genetic architecture and high-yielding wheat ideotypes under terminal drought. An automated high-throughput phenotyping platform was used to examine 28 392 image-based digital traits (i-traits) under different drought conditions during the flowering stage of a natural wheat population. Of the i-traits examined, 17 073 were identified as drought-related. A genome-wide association study (GWAS) identified 5320 drought-related significant single-nucleotide polymorphisms (SNPs) and 27 SNP clusters. A notable hotspot region controlling wheat drought tolerance was discovered, in which TaPP2C6 was shown to be an important negative regulator of the drought response. The tapp2c6 knockout lines exhibited enhanced drought resistance without a yield penalty. A haplotype analysis revealed a favored allele of TaPP2C6 that was significantly correlated with drought resistance, affirming its potential value in wheat breeding programs. We developed an advanced prediction model for wheat yield and drought resistance using 24 i-traits analyzed by machine learning. In summary, this study provides comprehensive insights into the high-yielding ideotype and an approach for the rapid breeding of drought-resistant wheat.

This study aimed to identify important non-genetic factors and estimate genetic parameters for efficiency-related traits in Boer x Central Highland goats. The genetic parameters were estimated using the Average Information Restricted Maximum Likelihood algorithm using the WOMBAT program fitting animal model. The least-squares means for growth efficiency from birth to 3 months (GE1), 3-6 months (GE2), 6-12 months (GE3), relative growth rate from birth to 3 months (RGR1), 3-6 months (RGR2) and 6-12 month (RGR3) were 294.0 ± 5.06, 36.6 ± 1.20, 44.9 ± 1.81, 1.46 ± 0.01, 0.32 ± 0.01 and 0.19 ± 0.01, respectively. Birth type, blood level, sex of the kid, and year of kidding had a sizable effect on efficiency-related traits. About 18, 3.0, 23, 20, and 12% of the phenotypic variation in GE2, GE3, RGR1, RGR2, and RGR3 was explained by the direct additive genetic effect. Except for RGR3, all investigated traits were under the influence of maternal genetic effect, and maternal heritability ranged from 0.09 to 0.17. The total heritability estimate depicts that slow genetic progress would be expected from selection. Nevertheless, even with this level of heritability, selection for efficiency-related traits would improve the efficiency of chevon production as these traits are economically important traits. Nearly six-months of age was when farmers sold Boer crossbred goats. Therefore, improving the growth efficiency till the marketing age (GE2) in such a scenario could increase the production efficiency.

Genomic prediction was conducted using 2494 Japanese Black cattle from Hiroshima Prefecture and both single-nucleotide polymorphism information and phenotype data on monounsaturated fatty acid (MUFA) and oleic acid (C18:1) analyzed with gas chromatography. We compared the prediction accuracy for four models (A, additive genetic effects; AD, as for A with dominance genetic effects; ADR, as for AD with the runs of homozygosity (ROH) effects calculated by ROH-based relationship matrix; and ADF, as for AD with the ROH-based inbreeding coefficient of the linear regression). Bayesian methods were used to estimate variance components. The narrow-sense heritability estimates for MUFA and C18:1 were 0.52-0.53 and 0.57, respectively; the corresponding proportions of dominance genetic variance were 0.04-0.07 and 0.04-0.05, and the proportion of ROH variance was 0.02. The deviance information criterion values showed slight differences among the models, and the models provided similar prediction accuracy.