Proportion Of Variance Research Articles

Genome-wide association study and genomic prediction of root system architecture traits in Sorghum (Sorghum bicolor (L.) Moench) at the seedling stage

Root system architecture (RSA) plays an important role in plant adaptation to drought stress. However, the genetic basis of RSA in sorghum has not been adequately elucidated. This study aimed to investigate the genetic bases of RSA traits through genome-wide association studies (GWAS) and determine genomic prediction (GP) accuracy in sorghum landraces at the seedling stage. Phenotypic data for nodal root angle (NRA), number of nodal roots (NNR), nodal root length (NRL), fresh shoot weight (FSW), dry shoot weight (DSW), and leaf area (LA) were collected from 160 sorghum accessions grown in soil-based rhizotrons. The sorghum panel was genotyped with 5,000 single nucleotide polymorphism (SNP) markers for use in the current GWAS and GP studies. A multi-locus model, Fixed and random model Circulating Probability Unification (FarmCPU), was applied for GWAS analysis. For GP, ridge-regression best linear unbiased prediction (RR-BLUP) and five different Bayesian models were applied. A total of 17 SNP loci significantly associated with the studied traits were identified, of which nine are novel loci. Among the traits, the highest number of significant marker-trait associations (MTAs) was identified for nodal root angle on chromosomes 1, 3, 6, and 7. The SNP loci that explain the highest proportion of phenotypic variance (PVE) include sbi32853830 (PVE = 18.2%), sbi29954292 (PVE = 18.1%), sbi24668980 (PVE = 10.8%), sbi3022983 (PVE = 7%), sbi29897704 (PVE = 6.4%) and sbi29897694 (PVE = 5.3%) for the traits NNR, LA, SDW, NRA, NRL and SFW, respectively. The genomic prediction accuracy estimated for the studied traits using five Bayesian models ranged from 0.30 to 0.63 while it ranged from 0.35 to 0.60 when the RR-BLUP model was used. The observed moderate to high prediction accuracy for each trait suggests that genomic selection could be a feasible approach to sorghum RSA-targeted selection and breeding. Overall, the present study provides insights into the genetic bases of RSA and offers an opportunity to speed up breeding for drought-tolerant sorghum varieties.

Longitudinal and temporal variation in fish assemblage structure along lowland prairie Neotropical streams and relationships with environmental variables

The structure of fish assemblages in lotic systems usually varies from headwaters to the mouth, related to geomorphological and physicochemical changes. We evaluated the main processes that structure the fish assemblages on spatial, seasonal, and decadal (2009-2010; 2019-2020) scales and relationships with environmental variables in two low-impacted Neotropical Pampean streams tributaries to the Río de la Plata estuary (RdlP). Environmental variables related to the longitudinal variation in the fish assemblage were depth, macrophyte cover, and altitude, showing higher values in the headwater streams compared to downstreams. Species richness increased towards lower stream sectors due to the migration of riverine species from the RdlP. Beta diversity partitioning analysis showed that the greatest proportion of variability was explained by the turnover component in both the longitudinal and seasonal analyses. The headwater streams showed a more stable composition, consisting of species of the order Characiformes associated with aquatic macrophytes, while the downstream streams recorded greater variability due to the entry of migrant Siluriformes from the RdlP, highlighting the essential ecological function of Pampean streams for RldP fish assemblages. The decadal comparison showed that the longitudinal structure is persistent in the long term regardless of the study period. Therefore, the consistent pattern of longitudinal variation in fish assemblages and its explanatory variables have applicability in the conservation and biomonitoring of lowland streams, serving as reference for recovery efforts.

An Individual Participant Data Meta-Analysis to Support Power Analyses for Randomized Intervention Studies in Preschool: Cognitive and Socio-Emotional Learning Outcomes

There is a need for robust evidence about which educational interventions work in preschool to foster children’s cognitive and socio-emotional learning (SEL) outcomes. Lab-based individually randomized experiments can develop and refine such interventions, and field-based randomized experiments (e.g., cluster randomized trials) evaluate their effectiveness in real-world daycare center settings. Applying reliable estimates of design parameters in the context of a priori power analyses is essential to ensure that the sample size of these studies is adequate to support strong statistical conclusions regarding the strength of the intervention effect. However, there is little knowledge on relevant design parameters with preschool children. We therefore utilized a systematic collection of individual participant data from four German probability samples (554 ≤ N ≤ 2928) with preschool children (aged two to six years) to estimate and meta-analyze design parameters. These parameters are relevant for planning single-level (e.g., in non-clustered lab-based settings), two-level (children nested in daycare centers), and three-level (children nested in groups, with groups nested in daycare centers) randomized intervention studies targeting cognitive and SEL outcomes assessed with three methods (standardized tests, parent ratings, and educator ratings). The design parameters depict between-group and -center differences as well as the proportion of variance in the outcomes explained by different covariate sets (socio-demographic characteristics, baseline measures, and their combination) at the child, group, and center level. In conclusion, this paper provides a rich source of design parameters, recommendations, and illustrations to support a priori power analyses for randomized intervention studies in early childhood education research.

Spinach Seed Microbiome Characteristics Linked to Suppressiveness Against Globisporangium ultimum Damping-Off.

Recently we demonstrated that the seed microbiome of certain spinach (Spinacia oleracea) seed lots can confer disease suppression against Globisporangium ultimum damping-off (previously known as Pythium ultimum). We hypothesised that differences in the microbial community composition of spinach seed lots correlate with the levels of damping-off suppressiveness of each seed lot. Here, we show that a large proportion of variance in seed-associated bacterial (16S) and fungal (ITS1) amplicon sequences was explained by seed lot identity, while 9.8% of bacterial and 7.1% of fungal community variance correlated with disease suppression. More specifically, a higher relative abundance of basidiomycetous dimorphic yeasts such as Vishniacozyma, Filobasidium and Papiliotrema and of the bacterial genus Massilia was a key feature of suppressive seed microbiomes. We suggest that the abundance of these genera is indicative of seed lot suppressive potential. Seed processing and treatment can become more targeted with indicator taxa being used to evaluate the presence of beneficial seed-associated microbial functions. This process in turn could contribute to the sustainable management of seedling diseases. Finally, this study highlights the ubiquity of yeasts in spinach seed microbiota and their potential beneficial roles for seed health.

Biochemical Parameters of Laboratory Animals in Iraq: A Meta-analysis Study

Our objective in this work was to estimate normal values of biochemical parameters in laboratory animals in Iraq. Values were pooled from studies conducted in Iraqi universities using a meta-analysis approach. Pertinent peer-reviewed published studies were retrieved from the Iraqi Academic Scientific Journals (IASJ) database. Random-effects inverse-variance model was used to obtain pooled means of the parameters from the set of studies qualified for the analysis. Among a total of 460 records identified from IASJ, 264 records were eligible for the statistical analysis, which reported 102 values for different biochemical parameters. The mean of the sample size for normal animals that was used in the studies including in the analysis were 8, 8, and 6 for rats, mice, and rabbits respectively. The mean ± standard deviation of ages (months) was 2.8 ± 1.0, 3.14 ± 1.15, and 9.2 ± 3.3 for rats, mice, and rabbits respectively. In this analysis, we estimated 31, 14, and 15 parameters from 121, 41, and 26 studies of rats, mice, and rabbits correspondingly. The proportion of variance in the parameters for the analyzed studies due to heterogeneity was significant. Evidence of bias in the estimated values due to small studies was found in some parameters. In conclusion, we encourage researchers to consider the values we provided in this analysis and report details of the sample size, number of animals in each group, sex, exact age, and the accurate units for the estimated values in their studies to facilitate further analyses for more reference values in further analyses‎‎‎.

Biochemical Parameters of Laboratory Animals in Iraq: A Meta-analysis Study

Our objective in this work was to estimate normal values of biochemical parameters in laboratory animals in Iraq. Values were pooled from studies conducted in Iraqi universities using a meta-analysis approach. Pertinent peer-reviewed published studies were retrieved from the Iraqi Academic Scientific Journals (IASJ) database. Random-effects inverse-variance model was used to obtain pooled means of the parameters from the set of studies qualified for the analysis. Among a total of 460 records identified from IASJ, 264 records were eligible for the statistical analysis, which reported 102 values for different biochemical parameters. The mean of the sample size for normal animals that was used in the studies including in the analysis were 8, 8, and 6 for rats, mice, and rabbits respectively. The mean ± standard deviation of ages (months) was 2.8 ± 1.0, 3.14 ± 1.15, and 9.2 ± 3.3 for rats, mice, and rabbits respectively. In this analysis, we estimated 31, 14, and 15 parameters from 121, 41, and 26 studies of rats, mice, and rabbits correspondingly. The proportion of variance in the parameters for the analyzed studies due to heterogeneity was significant. Evidence of bias in the estimated values due to small studies was found in some parameters. In conclusion, we encourage researchers to consider the values we provided in this analysis and report details of the sample size, number of animals in each group, sex, exact age, and the accurate units for the estimated values in their studies to facilitate further analyses for more reference values in further analyses‎‎‎.

A cross-cultural examination of passion and risk of exercise addiction in Hungarian and Indian exercisers

ABSTRACT While the risk of exercise addiction (REA) sharing a large proportion of variance with passion is rarely researched in collectivist nations, recent research evidence suggests that exercisers in these societies might exhibit greater REA than those in individualist countries. Using an online cross-sectional survey, this study examined the differences in REA, obsessive passion (OP), and harmonious passion (HP) between Hungary, a small central European individualist country, and India, the world’s largest collectivist society. Criteria for participation, which included age 18 or over and exercising at least four times per week, were met by 987 participants (50.9% from Hungary), with a female majority (59.8%). The prevalence of REA was greater among Indian (21.86%) than Hungarian (5.38%) exercisers. A nation-by-gender interaction revealed that Indian men exercised more frequently and reported more REA, OP, and HP than Indian women. Hungarians scored higher on HP and lower on REA than their Indian counterparts. Obsessive passion was a significant predictor of REA in both nations, but HP contributed 11.2% of variance to REA only in Indian exercisers. These results have implications for the field, suggesting that generalising research results on REA, OP, and HP may be unreliable because gender and cultural factors affect these measures. Thus, passion and exercise addiction should be examined in a cultural context.

Secure Base Leadership in military training: enhancing organizational identification and resilience through work engagement.

This study examines the relationships between secure base leadership, organizational identification, and resilience among military cadets, utilizing the Job Demands-Resources (JD-R) model as a theoretical framework. Specifically, it explores the mediating role of work engagement in these associations within the context of military training. A cross-sectional study was conducted with 363 cadets from the General Military Academy of the Army in Zaragoza, Spain. The sample comprised second-year cadets (n = 170; 46.8%) and third-year cadets (n = 193; 53.2%), with a gender distribution of 84% male and 16% female. Participants evaluated their section chief captains using the Leader as Security Provider Scale and completed validated questionnaires measuring work engagement, organizational identification, and resilience. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to test the hypothesized relationships and mediation effects. The findings revealed that secure base leadership is positively associated with work engagement among cadets. Work engagement significantly mediated the relationships between secure base leadership and both organizational identification and resilience. The structural model explained a substantial proportion of variance in the outcome variables, supporting the applicability of the JD-R model in this context. These results underscore the importance of secure base leadership in promoting work engagement, which in turn enhances organizational identification and resilience among military cadets. The study highlights the role of leaders as secure bases in fostering personal and organizational well-being. Implications suggest that incorporating secure base leadership principles in military training programs could contribute to the professional development and overall well-being of military personnel.

Teacher characteristics as predictors of self-regulated learning encouragement

The aim of the research was to examine the predictors of teachers’ encouragement of Self-Regulated Learning (SRL), specifically focusing on the independent contribution of various teacher characteristics. These characteristics include: a) sociodemographic factors (gender, age), b) teacher beliefs, teaching approaches, and teaching efficacy, c) personality traits, d) type of school where the teacher works, school subject’s field of study and subject status, and e) the quality of teacher interactions in explaining teachers’ perceptions of SRL encouragement. The research was conducted on a sample of 251 teachers from primary and secondary schools using an online questionnaire. The results indicated that teacher characteristics (gender, age, teacher beliefs, teaching approaches, teaching efficacy, personality traits, subject’s field and subject status, and teacher behaviours) were related to SRL encouragement. This set of variables explained a moderate to relatively high proportion of the criterion variance (28% - 52%), with the most significant contributors being teacher beliefs, gender and age, personality traits, subject’s field of study and subject status, and teacher interactions, respectively. A mastery-oriented approach to teaching emerged as the most significant predictor of encouraging SRL. The encouragement of self-regulated learning is influenced by several factors, including the perception of teaching and learning as a process of empowerment, a mastery-oriented approach, higher teacher self-efficacy, female gender, older age, agreeableness, conscientiousness, intellect, subject’s positioning within the humanistic and social fields, optional subjects, and greater teacher helpfulness and friendliness. This research holds significant theoretical and practical implications for teacher encouragement of self-regulated learning.

Delineating the genetic variability, correlation, path and principal component analysis in brinjal genotypes

An experiment was conducted to evaluate 25 different brinjal genotypes for growth and yield-contributing traits. Variability analysis, association studies and Principal Component Analysis (PCA) were performed to explore the relationships among traits and identify significant factors influencing performance. In the present study, high genotypic and phenotypic coefficient of variation values were observed for fruit breadth, fruit weight gram per fruit, number of fruits per plant and fruit weight gram per plant. In contrast, traits such as days to fifty per cent flowering, plant height, number of branches per plant, branch length, fruit length and fruit borer infestation exhibited medium GCV and PCV values. High heritability coupled with high GAM was noticed for all the traits. Correlation analysis on the number of fruits per plant recorded a positive and significant association with fruit weight per gram. The higher magnitude of a positive direct effect on fruit yield was exerted by the number of fruits per plant followed by fruit weight gram per fruit, plant height and fruit breadth. Principal component analysis showed three principal components with unity eigenvalues, accounting for 78.647 % of total variance. Principal Component 1 (PC1) contributed a high proportion of total variance (42.282) with an eigenvalue of 4.228. PC2 and PC3 showed total variance percentages of 24.045 and 12.32 with the eigenvalues 2.404 and 1.232, respectively.

Genome-wide association studies and functional annotation of pre-weaning calf mortality and reproductive traits in Nellore cattle from experimental selection lines

BackgroundReproductive efficiency is crucial for the long-term economic sustainability of beef cattle production. Pregnancy loss and stillbirth are complex reproductive traits that do not yet have their genomic background fully understood, especially in zebu breeds (Bos taurus indicus). Hence, this study aimed to perform a genome-wide association study (GWAS) and functional annotation for conception success (CS), pregnancy loss (PL), stillbirth (SB), and pre-weaning calf mortality (PWM) in Nellore cattle. In this study, 3,728 cows with 17,094 reproductive records and 11,785 calves were evaluated. A total of 3,351 genotyped animals and 383,739 SNP markers were considered for GWAS analyses. SNP effects were estimated using the weighted single-step GWAS (WssGWAS), which considered two iterations. The top ten genomic windows with the highest contribution to the additive genetic variance of the traits were selected for gene annotation. Candidate genes were then analyzed for Gene Ontology terms (GO) and metabolic pathways.ResultsThe top ten genomic windows that explained the largest proportion of the direct additive genetic variance () for CS, PL, SB, and PWM accounted for 17.03% (overlapping with 79 genes), 16.76% (57 genes), 11.71% (73 genes), and 12.03% (65 genes) of the total , respectively. For CS, significant GO terms included Somitogenesis (GO:0001756), Somite Development (GO:0061053), and Chromosome Segregation (GO:0007059). Considering PL, the processes annotated were the Regulation of Hormone Secretion (GO:0046883), and Hormone Transport (GO:0009914), along with the Glucagon Signaling Pathway (bta04922). Embryonic Development (GO:0045995), and Cerebellum Development (GO:0021549) were the main biological processes found in the gene enrichment analysis for SB. For PWM, the Regulation of Glucose metabolic processes (GO:0010906), Zinc Ion Homeostasis (GO:0055069), Lactation (GO:0007595), and Regulation of Insulin Secretion (GO:0050796) were the most significant GO terms observed.ConclusionsThese findings provide valuable information on genomic regions, candidate genes, biological processes, and metabolic pathways that may significantly influence the expression of complex reproductive traits in Nellore cattle, offering potential contributions to breeding strategies and future genomic selection strategies.

Speech Perception and Language Abilities Among Children Using Cochlear Implants: Findings From a Primary School Age Cohort in South India.

This study aimed to profile the speech perception and language abilities of a cohort of pediatric cochlear implant (CI) users in primary school years. It also aimed to understand the intercorrelations among audiological, child, and environmental characteristics, speech perception, and language skills and to explore the predictors of speech perception and language skills. A cross-sectional design was used for the study. The participants were 222 pediatric CI users (106 boys; 116 girls) with a mean chronological age of 10.51 (SD ± 1.28) years. Participants had received CIs at a mean age of 2.93 (SD ± 0.95) years, with the mean duration of CI use being 7.43 (SD ± 1.15) years at the time of assessment. Participants completed an assessment battery comprising speech perception (phoneme discrimination, open-set speech perception in quiet) and language (semantics, syntax) tasks. Selected audiological, child, and environmental characteristics were documented. The mean and SDs of the measures across age categories (8 to 12 years) and the proportion of children attaining scores better than 80%, between 50 and 80%, and poorer than 50% of the total possible score in each task were computed to generate a profile of speech perception and language abilities. Correlational and regression analyses assessed the intercorrelations among the variables and predictors of speech perception and language abilities. A large proportion (79.0%) of children in the study group obtained scores better than 80% for phoneme discrimination, whereas only 17.8% scored better than 80% for open-set speech perception in quiet. Additionally, 42.8 and 20.8% of children scored better than 80% for semantics and syntax, respectively. Speech perception and language abilities demonstrated moderate-strong intercorrelations, contributing to a significant proportion of the total variance explained in phoneme discrimination (42.9%), open-set speech perception (61.8%), semantics (63.0%), and syntax (60.8%). Phoneme discrimination and open-set speech perception emerged as large contributors to variance in overall language abilities. Among the audiological factors, only hearing age contributed to a small proportion of variance (3 to 6%) across children's speech perception and language performance. Children using CI demonstrated highly variable performance in speech perception and expressive language skills during primary school. Although children demonstrated improved performance in phoneme discrimination and semantics, they continued to face challenges in the (quiet) speech perception and syntax abilities. The effect of audiological, child, and environmental factors was minimal in explaining the variance in speech perception and language abilities, which shared a bidirectional relationship. The findings relating to mid-term outcomes, ranging from 4 to 9 years after cochlear implantation, suggest the need for continued support and targeted interventions for developing speech perception and language abilities in the primary school years to optimize outcomes among pediatric CI users.

Optimization of extraction in supercritical fluids in obtaining Pouteria lucuma seed oil by response surface methodology and artificial neuronal network coupled with a genetic algorithm

When processing lucuma (Pouteria lucuma), waste such as shells and seeds is generated, which is a source of bioactive compounds. Recently, lucuma seed (LS), especially its oily fraction, has been studied for containing phytosterols and tocopherols, powerful antioxidants with health benefits. This study proposes lucuma seed oil (LSO) extraction using supercritical fluid (SCF) to improve the quality of the extract and minimize the environmental impact. LS was previously characterized, and the extraction parameters were optimized using a Box-Behnken design, considering temperature (40–60°C), pressure (100–300 bar), and CO2 flow rate (3–7 mL/min), applying the response surface methodology (RSM) and neural networks with genetic algorithm (ANN+GA). The optimal parameters were 45°C, 300 bar, and 6 mL/min, obtaining 97.35% of the total oil content. The RSM and ANN+GA models showed R2 values of 0.9891 and 0.9999 respectively, indicating that both models exhibited a good fit to the experimental data. However, ANN+GA provided a greater proportion of the total variability, which facilitates the identification of the optimal parameters for the extraction of oil from lucuma seeds. Compared to the Soxhlet method, the LSO obtained by SCF presented better acidity (4.127 mg KOH/g), iodine (100.294 g I2/100 g), and refraction indices (1.4710), as well as to a higher content of mono- and polyunsaturated fatty acids. Supercritical CO2 extraction is presented as a sustainable green alternative to Soxhlet extraction for extracting oil from lucuma seed due to its high extraction efficiency and similar fatty acid profile.

The mouth of America: the oral microbiome profile of the US population.

The oral microbiome is increasingly recognized to play key roles in human health and disease; yet, population-representative characterizations are lacking. Characterize the composition, diversity, and correlates of the oral microbiome among US adults. Cross-sectional population-representative survey. The National Health and Nutrition Examination Survey (NHANES, 2009-2012), a stratified multistage probability sample of the US population. NHANES participants aged 18-69 years (n=8,237, representing 202,314,000 individuals). Demographic, socioeconomic, behavioral, anthropometric, metabolic, and clinical characteristics. Oral microbiome, characterized through 16S rRNA sequencing. Microbiome metrics were alpha diversity (number of observed Amplicon Sequence Variants [ASV], Faith's Phylogenetic diversity, Shannon-Weiner Index, and Simpson Index); beta diversity (unweighted UniFrac, weighted UniFrac, and Bray-Curtis dissimilarity); and prevalence and relative abundance at taxonomic levels (phylum through genus). Analyses accounted for the NHANES complex sample design. Among US adults aged 18-69 years, the oral microbiome encompassed 37 bacterial phyla, 99 classes, 212 orders, 446 families, and 1,219 genera. Five phyla-Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Fusobacteria and six genera-Veillonella, Streptococcus, Prevotella7, Rothia, Actinomyces, and Gemella, were present in nearly all US adults (weighted-prevalence >99%). These genera also were the most abundant, accounting for 65.7% of abundance. Observed ASVs showed a quadratic pattern with age (peak at 30 years), was similar by sex, significantly lower among non-Hispanic White individuals, and increased with higher body mass index (BMI) categories, alcohol use, and periodontal disease severity. All covariates together accounted for a modest proportion of oral microbiome variability, as measured by beta diversity (unweighted UniFrac=8.7%, weighted UniFrac=7.2%, and Bray-Curtis=6.3%). By contrast, relative abundance of a few genera explained a high percentage of variability in beta diversity (weighted UniFrac: Aggregatibacter=22.4%, Lactococcus=21.6%, Haemophilus=18.4%). Prevalence and relative abundance of numerous genera were significantly associated (Bonferroni-corrected Wald-p<0.0002) with age, race and ethnicity, smoking, BMI categories, alcohol use, and periodontal disease severity. We provide a contemporary reference standard for the oral microbiome of the US adult population. Our results indicate that a few genera were universally present in US adults and a different set of genera explained a high percentage of oral microbiome diversity across the population.

Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes

BackgroundIt is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS.MethodsOur analysis included 8635 participants (52.5% females) aged 40–70 years at baseline from the UK Biobank. Metabolomic profiles were assessed using nuclear magnetic resonance at baseline (between 2006 and 2010). Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. Machine learning was used to generate metabolomic state and GRS for each of 21 brain phenotypes.ResultsIndividuals in the top 20% of metabolomic state had 2.4–35.7% larger volumes of 21 individual brain phenotypes compared to those in the bottom 20% while the corresponding number for GRS ranged from 1.5 to 32.8%. The proportion of variance of brain volumes (R [2]) explained by the corresponding metabolomic state ranged from 2.2 to 19.4%, and the corresponding number for GRS ranged from 0.8 to 8.7%. Metabolomic state provided no or minimal additional prediction values of brain volumes to age and sex while GRS provided moderate additional prediction values (ranging from 0.8 to 8.8%). No significant interplay between metabolomic state and GRS was observed, but the association between metabolomic state and some regional brain volumes was stronger in men or younger individuals. Individual metabolomic profiles including lipids and fatty acids were strong predictors of brain volumes.ConclusionsIn conclusion, metabolomic state is strongly associated with multiple brain volumes but provides minimal additional prediction value of brain volumes to age + sex. Although GRS is a weaker contributor to brain volumes than metabolomic state, it provides moderate additional prediction value of brain volumes to age + sex. Our findings suggest metabolomic state and GRS are important predictors for multiple brain phenotypes.

Multi-population GWAS detects robust marker associations in a newly established six-rowed winter barley breeding program

Genome-wide association study (GWAS) is a powerful tool for identifying marker-trait associations that can accelerate breeding progress. Yet, its power is typically constrained in newly established breeding programs where large phenotypic and genotypic datasets have not yet accumulated. Expanding the dataset by inclusion of data from well-established breeding programs with many years of phenotyping and genotyping can potentially address this problem. In this study we performed single- and multi-population GWAS on heading date and lodging in four barley breeding populations with varying combinations of row-type and growth habit. Focusing on a recently established 6-rowed winter (6RW) barley population, single-population GWAS hardly resulted in any significant associations. Nevertheless, the combination of the 6RW target population with other populations in multi-population GWAS detected four and five robust candidate quantitative trait loci for heading date and lodging, respectively. Of these, three remained undetected when analysing the combined populations individually. Further, multi-population GWAS detected markers capturing a larger proportion of genetic variance in 6RW. For multi-population GWAS, we compared the findings of a univariate model (MP1) with a multivariate model (MP2). While both models surpassed single-population GWAS in power, MP2 offered a significant advantage by having more realistic assumptions while pointing towards robust marker-trait associations across populations. Additionally, comparisons of GWAS findings for MP2 and single-population GWAS allowed identification of population-specific loci. In conclusion, our study presents a promising approach to kick-start genomics-based breeding in newly established breeding populations.

The Contribution of Perceptual-Cognitive Skills to Reactive Agility in Early and Middle Adolescent Soccer Players.

Horníková, H, Hadža, R, and Zemková, E. The contribution of perceptual-cognitive skills to reactive agility in early and middle adolescent soccer players. J Strength Cond Res XX(X): 000-000, 2024-Agility performance increased from childhood to mid-adolescence, followed by a plateau until early adulthood. Because adolescence is crucial for the development of perceptual-cognitive skills, the question is whether and to what extent its contribution to reactive agility increases during developmental periods of adolescence in invasive sports athletes. This study investigated the association of reactive agility with the speed of decision making, sprint speed, and explosive strength in early and middle adolescent soccer players. They performed reactive and preplanned Y-shaped agility tests, choice reaction time test, modified half T-test, 30-m sprint (10 m split), and countermovement jump (CMJ). The reactive agility time in both age groups significantly correlated with 30-m sprint time (r = 0.839, p < 0.001, and r = 0.745, p = 0.001, respectively), pre-planned Y-shaped agility time (r = 0.828, p < 0.001, and r = 0.644, p = 0.007, respectively), 10-m sprint time (r = 0.823, p < 0.001, and r = 0.775, p < 0.001, respectively), time in modified half T-test (r = 0.723, p < 0.001, and r = 0.630, p = 0.009, respectively), and CMJ height (r = -0.744, p < 0.001, and r = -0.615, p = 0.013, respectively), although with the index of reactivity in middle adolescent soccer players only (r = 0.615, p = 0.011). Based on R2, the explained proportion of variance was 53.6% for linear sprint speed and 30.1% for a change of direction speed in early adolescence, whereas it was 54.6% for acceleration speed and 31.8% for decision-making in middle adolescence. This indicates the significant contribution of speed factors to reactive agility in both adolescent groups, whereas perceptual-cognitive factors in middle adolescent soccer players only. It seems that even during the relatively short period of adolescence, the changes occur in the contribution of perceptual-cognitive skills to reactive agility in athletes practising invasive sports.

Elucidation of the genetic diversity of desi-type chickpeas in relation to their adaptability to various autumn weather conditions

Chickpeas are one of the most important sources of protein, vitamins, and minerals. It has been proven that cultivating them in autumn leads to an increase in seed yield. However, the cultivation of this crop is limited in many parts of the world during autumn due to its low tolerance for cold temperatures. The objective of this study was to determine the genetic diversity of 27 desi-type chickpea genotypes in three regions with different climates during autumn cultivation using multivariate analysis. Agro-morphological attributes were used to differentiate and group the selected genotypes. The seed yield in Mashhad ranged from 0.10 to 1.71 t ha−1, in Jolgeh rokh from 0.12 to 1.66 t ha−1, and in Shirvan from 0.63 to 1.48 t ha−1. In all three regions, there was a strong and positive correlation between seed yield and biological yield. Three principal components were identified in all three regions, explaining a significant proportion of the total variability. The first principal component had a high negative coefficient in all three regions for seed yield and biological yield. According to the cluster analysis, the genotypes of the fifth group in Mashhad exhibited superiority in terms of five traits. In Jolgeh rokh and Shirvan, the genotypes of the first group, which had seven traits (all of which were significant based on ANOVA), were also superior to the genotypes of the other groups. The results of the stepwise regression analysis showed that in three regions, the two traits, biological yield and harvest index, were found to be significant factors in explaining the seed yield of chickpeas. The findings of this study indicate that the use of multivariate statistical methods is appropriate for identifying the agro-morphological traits that affect the yield of chickpeas in different climates.

Validation of KASP Markers Associated with Hydrogen Cyanide in Fresh Cassava Roots in Uganda Cassava Germplasm

Low hydrogen cyanide (HCN) concentration is a high-priority trait for cassava varieties targeting their fresh and dry product profiles. To be acceptable, varieties bred and developed for these market segments must meet international safety standards for maximum acceptable residual levels of cyanide in cassava food and food products. The discovery of molecular markers that co-segregate with low HCN has not yet resulted in widespread usage in marker-assisted selection (MAS) in breeding programs. To deploy these HCN markers in regular MAS, assessing their reliability in various genetic backgrounds is crucial. The aim of this study was to assess the predictive accuracy of trait specific markers for HCN. The study used six HCN kompetitive allele-specific polymerase chain reaction (KASP) markers that had previously been developed in a Brazilian population and verified in segregating West African cassava populations. For most markers used in the study, the average call rate was more than 91.7%. Three markers—snpME00404, snpME00405 and snpME00406—showed a significant co-segregation of genotypes with the HCN phenotype. On average, genotypes that carried at least one copy of the favourable allele had lower HCN scores. The proportion of phenotypic variance accounted for by the three most important markers was 14% (snpME00406), 17% (snpME00405) and 27% (snpME00404). The validation of identified HCN SNP-markers marks a significant step in their deployment to support selection and advancement decisions in cassava breeding programs.

Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition.

Existing studies examining the predictive ability of biomarkers for cognitive outcomes do not account for variance due to measurement error, which could lead to under-estimates oftheproportion ofvariance explained. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 1084) to estimate the proportion of variance explained by Alzheimer's disease (AD) imaging biomarkers in four cognitive outcomes: memory, executive functioning, language, and visuospatial functioning. We compared estimates from standard models that do not account for measurement error, and multilevel models that do account for measurement error. We also examined estimates across diagnostic subgroups (normal, MCI, AD). Estimates of the proportion of variance explained from multilevel models accounting for measurement error were larger (e.g., for language, 9-47% vs. 7-34% under standard modeling), with relatively greater differences between standard and multilevel measurement models for cognitive outcomes that have larger measurement error variance. Heterogeneity across subgroups also emphasized the importance of sample composition. Future studies should evaluate measurement error adjustments when considerable measurement error in cognitive outcomes is suspected.