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Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for their genome-wide association studies. Meanwhile, methods designed for binary or continuous phenotypes are commonly used to inappropriately analyze ordinal traits, which leads to the loss of original phenotype information and the detection power of quantitative trait nucleotides (QTN). To address these issues, fast multi-locus ridge regression (FastRR), which was originally designed for continuous traits, is used to directly analyze binary or ordinal traits in this study. FastRR includes three stages of continuous transformation, variable reduction, and parameter estimation, and it can computationally handle categorical phenotype data instead of link functions introduced or methods inappropriately used. A series of simulation studies demonstrate that, compared with four other continuous or binary or ordinal approaches, including logistic regression, FarmCPU, FaST-LMM, and POLMM, the FastRR method outperforms in the detection of small-effect QTN, accuracy of estimated effect, and computation speed. We applied FastRR to 14 binary or ordinal phenotypes in the Arabidopsis real dataset and identified 479 significant loci and 76 known genes, at least seven times as many as detected by other algorithms. These findings underscore the potential of FastRR as a very useful tool for genome-wide association studies and novel gene mining of binary and ordinal traits. [ABSTRACT FROM AUTHOR]

Yaks are grazed extensively on the Qinghai–Tibet Plateau, which has a long history of semi-domestication. The predicted weight of yaks over consecutive years helps make strategic decisions when selecting yak calves for breeding. To achieve more accurate predictions of genomic estimated breeding values, we used a dataset comprising the genotype and weight records of 396 Maiwa yaks collected from 2015 to 2020. We compared the predictive accuracy of the genome best linear unbiased prediction (GBLUP) model with that of six other models. Based on the GBLUP model, we applied two prediction strategies. In the first strategy, the year was treated as a fixed effect in the GBLUP model, and the kinship from all individuals and the markers were treated as random effects. In the second strategy, all individuals were divided into six age groups, with GBLUP performed for each group, and the phenotypes of the closest age groups were treated as fixed effects. Although the GBLUP model provided better prediction accuracy than other single-trait models, most of the predictive capacity was derived from the best linear unbiased estimation. Additionally, incorporating the phenotype of the closest age group as a factor in multitrait prediction enhanced the accuracy of the model. Our findings provide a robust and credible strategy for predicting continuous economic traits in the presence of strong correlations.

Genome-wide association studies (GWASs) are flexible and comprehensive tools for identifying single nucleotide polymorphisms (SNPs) associated with complex traits or diseases. The whole-genome Bayesian models are an effective way of incorporating important prior information into modeling. Bayesian methods have been widely used in association analysis. However, Bayesian analysis is often not feasible due to the high-throughput genotype and large sample sizes involved. In this study, we propose a new Bayesian algorithm under the mixed linear model framework: the expectation and maximization BayesB Improved algorithm (emBBI). The emBBI algorithm corrects polygenic and environmental noise and reduces dimensions; then, it estimates and tests marker effects using emBayesB and the LOD test, respectively. We conducted two simulation experiments and analyzed a real dataset related to flowering time in Arabidopsis to demonstrate the validation of the new algorithm. The results show that the emBBI algorithm is more flexible and accurate in simulation studies compared to established methods, and it performs well under complex genetic backgrounds. The analysis of the Arabidopsis real dataset further illustrates the advantages of the emBBI algorithm for GWAS by detecting known genes. Furthermore, 12 candidate genes are identified in the neighborhood of the significant quantitative trait nucleotides (QTNs) of flowering-related QTNs in Arabidopsis. In addition, we also performed enrichment analysis and tissue expression analysis of candidate genes, which will help us better understand the genetic basis of flowering-related traits in Arabidopsis. [ABSTRACT FROM AUTHOR]

The extractivism of Dimorphandra mollis Benth., which is a native tree from the Brazilian Cerrado biome, popularly known as fava d'anta, combined with the reduction in native vegetation area in the country over the years may result in a decrease in the specie's natural populations. The objective of this study was to estimate the quantitative genetic parameters in nursery, hardening, and field phases, based on a progeny test. The experimental design adopted was randomized blocks (six blocks for the nursery and hardening phases, and four blocks for the field phase with 5 plants/plot and 72 mother trees), with evaluations of the collar diameter and seedling height at 30, 90, 150, 480, and 570 days after sowing, between the production and planting phases. Among the coefficients of variance, the phenotypic and additive ones showed the highest values. Heritabilities for height ranged from moderate to high (0.15 to 0.43), indicating good genetic control of the traits, high potential for selection, and possibility of genetic gains. The genetic divergence of the progenies resulted in division into five groups, confirming the existence of genetic variability among the evaluated progenies and the potential for conservation and breeding programs. [ABSTRACT FROM AUTHOR]

Tobacco (Nicotiana tabacum L.) use is the leading cause of preventable death, due to deleterious chemical components and smoke from tobacco products, and therefore reducing harmful chemical components in tobacco is one of the crucial tobacco breeding targets. However, due to complexity of tobacco smoke and unavailability of high-density genetic maps, the genetic architecture of representative hazardous smoke has not been fully dissected. The present study aimed to explore the genetic architecture of nine hazardous component traits of mainstream smoke through QTL mapping using 271 recombinant inbred lines (RILs) derived from K326 and Y3 in multiple environments. The analysis of genotype and genotype by environment interaction (GE) revealed substantially greater heritability over 95% contributed mostly by GE interaction effects. We also observed strong genetic correlations among most studied hazardous smoke traits, with the highest correlation coefficient of 0.84 between carbon monoxide and crotonaldehyde. Based on a published high-density genetic map, a total of 19 novel QTLs were detected for eight traits using a full QTL model, of which 17 QTLs showed significant additive effects, six showed significant additive-by-environment interaction effects, and one pair showed significant epistasis-by-environment interaction effect. Bioinformatics analysis of sequence in QTL region predicted six genes as candidates for four traits, of which Nt21g04598.1, Nt21g04600.1, and Nt21g04601.1 had pleiotropic effects on PHE and TAR. [ABSTRACT FROM AUTHOR]

This paper proves that in mixed linear model, the analysis of variance estimation (ANOVAE), the minimum norm quadratic unbiased estimation (MINQUE), the spectral decomposition estimation (SDE) and the restricted maximum likelihood estimation (RMLE) of variance components are the same under some conditions. Based on this result, we construct a linear Bayes estimation (LBE) for the parameter vector consisting of variance components and establish its superiorities. Numerical computations and an illustration show that the LBE is comparable to Lindley's approximation, Tierney and Kadane's approximation and the usual Bayes estimation (UBE) obtained by the MCMC method and easy to use as well. [ABSTRACT FROM AUTHOR]

This paper uses descriptive statistics and a mixed linear model to explore the social -ecological factors affecting the hospitalization expenses of cross - provincial cancer patients to understand the influencing factors of their hospitalization expenses based on the framework of social ecology theory. The results showed that patients with malignant tumors of digestive organs in cross - province medical treatment were the highest among sampled population (18.66 %), with the highest average hospitalization cost of tumors found in patients with uncertain or unknown dynamics (45 535.24 yuan). The hospitalization expenses were influenced by tumor types, personal behaviors like the length of hospitalization days in personal behavior, the distance of medical treatment at the environmental level, and the actual reimbursement ratio at the policy level (P < 0.05). The hospitalization cost of cancer patients in different provinces was relatively high, which was affected by many social and ecological factors. It is necessary to analyze the influencing factors of hospitalization expenses from both the individual perspective of cross -province cancer patients and the ecological environment in which they live, to rationally allocate health resources and guide patients to seek medical treatment in order. [ABSTRACT FROM AUTHOR]

Categorical (either binary or ordinal) quantitative traits are widely observed to measure count and resistance in plants. Unlike continuous traits, categorical traits often provide less detailed insights into genetic variation and possess a more complex underlying genetic architecture, which presents additional challenges for their genome-wide association studies. Meanwhile, methods designed for binary or continuous phenotypes are commonly used to inappropriately analyze ordinal traits, which leads to the loss of original phenotype information and the detection power of quantitative trait nucleotides (QTN). To address these issues, fast multi-locus ridge regression (FastRR), which was originally designed for continuous traits, is used to directly analyze binary or ordinal traits in this study. FastRR includes three stages of continuous transformation, variable reduction, and parameter estimation, and it can computationally handle categorical phenotype data instead of link functions introduced or methods inappropriately used. A series of simulation studies demonstrate that, compared with four other continuous or binary or ordinal approaches, including logistic regression, FarmCPU, FaST-LMM, and POLMM, the FastRR method outperforms in the detection of small-effect QTN, accuracy of estimated effect, and computation speed. We applied FastRR to 14 binary or ordinal phenotypes in the Arabidopsis real dataset and identified 479 significant loci and 76 known genes, at least seven times as many as detected by other algorithms. These findings underscore the potential of FastRR as a very useful tool for genome-wide association studies and novel gene mining of binary and ordinal traits.

Background: The widespread adoption of information and communication technologies (ICTs) further deepens disparities in resource access, particularly among the aging population. However, the relationship between these factors and their resulting impact on cognitive abilities remains uncertain. Objective: This study aims to investigate the potential impact of the digital divide on individuals' cognitive function and its association with the development and reversion of mild cognitive impairment (MCI). Methods: This cohort study used data from Beijing Aging Brain Rejuvenation (BABRI) study applying a multistage cluster sampling design between 2008 and 2020. The digital divide was quantified by the frequency of using ICTs. Analysis of covariance (ANCOVA), mixed linear models, and Cox proportional hazards models were used to model the association of digital divide and multidomain cognition. Results: Among the 10098 participants, nearly half (n=4941, 48.9%) faced the digital divide, which was associated with a worse performance in processing speed (F10096=10.67; P <.001; effect size r =0.42), rather than memory, executive function, and language. The model indicated that individuals' physical and mental health, combined with their educational and occupational prestige, influenced the resources they attained, which ultimately caused the digital divide. Moreover, longitudinal data revealed that older adults who successfully crossed the digital divide during the tracking process and those who had already done so prior to tracking showed significantly slower rates of decline in processing speed (B=–1.98, P <.05; B=–2.62, P <.01) and general cognitive function (B=3.50, P <.001; B=3.13, P <.01). Additionally, overcoming the digital divide was also associated with a lower risk of developing MCI (hazard ratio [HR] 0.5, 95% CI 0.34-0.74; HR 0.43, 95% CI 0.29-0.62) and a greater probability of reversion from MCI to normal cognition (HR 6, 95% CI 3.77-9.56; HR 9.22, 95% CI 5.63-15.11). Conclusions: Overcoming the digital divide was significantly associated with improved cognitive function, a slower aging rate in cognitive performance, a reduced risk of developing MCI, and a higher likelihood of reverting from MCI to normal cognition. [ABSTRACT FROM AUTHOR]

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Wheat is a staple food crop that acts as a primary source of dietary calories, protein and most of the bioavailable micronutrients such as iron (Fe) and zinc (Zn) for the world's population. Understanding genetic control of micronutrients uptake is necessary for the development of bio-fortified wheat genotypes. Salinity as an abiotic stress could affect the nature of this process. To study the inheritance of Zn and Fe efficiency under non-saline and saline conditions, two Iranian facultative wheat varieties, Navid (salt-sensitive, Fe and Zn-deficient) and Roshan (salt-tolerant, Fe and Zn-efficient) were crossed to generate six basic generations. The six generations were evaluated for grain Zn and Fe content, agronomic and biochemical traits under non-saline and saline conditions and generation mean analysis was performed using mixed linear models. The results revealed a greater contribution of non-additive gene actions in controlling of the traits compared to additive gene actions. Additive effects were negative and significant for all the traits under non-saline and saline conditions, except for 100 seed weight (100-SW) at non-saline condition, malondialdehyde (MDA) in 100 mM NaCl, electric leakage (EL) and leaf Na+ concentration (LeaNa+C) under all salinity treatments. Additive gene actions had high contribution in grain Zn and Fe content, while for the rest of the traits both fixable and non-fixable genetic effects were important. A duplicate dominant type of epistasis was involved in the inheritance of all the traits. Broad-sense heritability for most traits under non-saline and saline conditions were high (> 0.8), whereas the narrow-sense heritability values for most of the studied traits were low to moderate. Therefore, the Zn and Fe efficiency indices could be used to select Zn/Fe-efficient wheat genotypes among segregating populations. [ABSTRACT FROM AUTHOR]

Abstract Background Yam (Dioscorea spp.) is multiple species with various ploidy level and considered as cash crop in many producing areas. Selection based phenotyping for yield and its related traits such as mosaic virus and anthracnose diseases resistance and plant vigor in multiple species of yam is lengthy however, marker information has proven to enhance selection efficiency. Methodology In this study, a panel of 182 yam accessions distributed across six yam species were assessed for diversity and marker-traits association study using SNP markers generated from Diversity Array Technology platform. For the traits association analysis, the relation matrix alongside the population structure were used as co-factor to avoid false discovery using Multiple random Mixed Linear Model (MrMLM) followed by gene annotation. Results Accessions performance were significantly different (p

Genome-wide association studies (GWASs) are flexible and comprehensive tools for identifying single nucleotide polymorphisms (SNPs) associated with complex traits or diseases. The whole-genome Bayesian models are an effective way of incorporating important prior information into modeling. Bayesian methods have been widely used in association analysis. However, Bayesian analysis is often not feasible due to the high-throughput genotype and large sample sizes involved. In this study, we propose a new Bayesian algorithm under the mixed linear model framework: the expectation and maximization BayesB Improved algorithm (emBBI). The emBBI algorithm corrects polygenic and environmental noise and reduces dimensions; then, it estimates and tests marker effects using emBayesB and the LOD test, respectively. We conducted two simulation experiments and analyzed a real dataset related to flowering time in Arabidopsis to demonstrate the validation of the new algorithm. The results show that the emBBI algorithm is more flexible and accurate in simulation studies compared to established methods, and it performs well under complex genetic backgrounds. The analysis of the Arabidopsis real dataset further illustrates the advantages of the emBBI algorithm for GWAS by detecting known genes. Furthermore, 12 candidate genes are identified in the neighborhood of the significant quantitative trait nucleotides (QTNs) of flowering-related QTNs in Arabidopsis. In addition, we also performed enrichment analysis and tissue expression analysis of candidate genes, which will help us better understand the genetic basis of flowering-related traits in Arabidopsis.

Soybean is a short-day crop, and its sensitivity to photoperiod is an important trait for its adaptability. Rapid changes in the climate on a global scale could be a threat to future food security. There is a need to increase the heat tolerance of soybean to sustain its production. The availability of global germplasm provides plant breeders with the opportunity to explore the genetic potential of the existing soybean germplasm. For this purpose, a panel of 203 soybean accessions from different global climatic zones was evaluated for adaptability under long-day conditions. Field screening of the soybean germplasm was conducted over two consecutive years, 2016 and 2017, in Faisalabad, Pakistan. This study screened 15 diverse soybean accessions that exhibited economically viable yields in both years, i.e., 1500–2200 kg/ha. Data related to plant height, node number, pods per plant, seeds per plant, seed weight per plant, hundred-grain weight, and total yield were recorded. Two soybean accessions, PI548271 (MG-IV) and PI553039 (MG-IV,) produced the highest yield in both years. The genotypic and phenotypic coefficients of variance, broad-sense heritability, and genetic advances observed in their yields were also high. These results highlight the potential pre-existing genetic variation required to improve the adaptation of soybean to long-day conditions. Some of the accessions identified in the current study could be recommended for general cultivation under long-day and high-temperature conditions. [ABSTRACT FROM AUTHOR]

The development of nutrient-use efficient rice lines is a priority amidst the changing climate and depleting resources viz., water, land, and labor for achieving sustainability in rice cultivation. Along with the traditional transplanted irrigated system of cultivation, the dry direct-seeded aerobic system is gaining ground nationwide. The root-related traits play a crucial role in nutrient acquisition, adaptation and need to be concentrated along with the yield-attributing traits. We phenotyped an association panel of 118 rice lines for seedling vigour index (SVI) traits at 14 and 21 days after sowing (DAS), root-related traits at panicle initiation (PI) stage in polythene bags under controlled aerobic condition, yield and yield-related traits under the irrigated condition at ICARIIRR, Hyderabad, Telangana; irrigated and aerobic conditions at ARS, Dhadesugur, Raichur, Karnataka. The panel was genotyped using simple sequence repeats (SSR) markers and genome-wide association studies were conducted for identifying marker-trait associations (MTAs). Significant correlations were recorded for root length, root dry weight with SVI, root volume at the PI stage, number of productive tillers per plant, spikelet fertility, the total number of grains per panicle with grain yield per plant under irrigated conditions, and the total number of grains per panicle with grain yield per plant under aerobic condition. The panel was divided into three sub-groups (K = 3) and correlated with the principal component analysis. The maximum number of MTAs were found on chromosomes 2, 3, and 12 with considerable phenotypic variability. Consistent MTAs were recorded for SVI traits at 14 and 21 DAS (RM25310, RM80, RM22961, RM1385), yield traits under irrigated conditions (RM2584, RM5179, RM410, RM20698, RM14753) across years at ICAR-IIRR, grain yield per plant (RM22961, RM1146) under the aerobic condition, grain yield per plant at irrigated ICAR-IIRR and SVI (RM5501), root traits at PI stage (RM2584, RM80, RM410, RM1146, RM18472). Functionally relevant genes near the MTAs through in-silico expression analysis in root and panicle tissues viz., HBF2 bZIP transcription factor, WD40 repeat-like domain, OsPILS6a auxin efflux carrier, WRKY108, OsSCP42, OsMADS80, nodulin-like domain-containing protein, amino acid transporter using various rice expression databases were identified. The identified MTAs and rice lines having high SVI traits (Langphou, TI-128, Mouli, TI-124, JBB-631-1), high yield under aerobic (Phouren, NPK-43, JBB-684, Ratnamudi, TI-112), irrigated conditions (KR-209, KR-262, Phouren, Keibi-Phou, TI-17), robust root traits like root length (MoirangPhou-Angouba, Wangoo-Phou, JBB-661, Dissi, NPK-45), root volume (Ratnachudi, KJ-221, Mow, Heimang-Phou, PUP-229) can be further employed in breeding programs for the targeted environments aimed at improving seedling vigour, yield-related traits under irrigated condition, aerobic condition as adaptability to water-saving technology. [ABSTRACT FROM AUTHOR]

Background: Yam (Dioscorea spp.) is multiple species with various ploidy level and considered as cash crop in many producing areas. Selection based phenotyping for yield and its related traits such as mosaic virus and anthracnose diseases resistance and plant vigor in multiple species of yam is lengthy however, marker information has proven to enhance selection efficiency. Methodology: In this study, a panel of 182 yam accessions distributed across six yam species were assessed for diversity and marker-traits association study using SNP markers generated from Diversity Array Technology platform. For the traits association analysis, the relation matrix alongside the population structure were used as co-factor to avoid false discovery using Multiple random Mixed Linear Model (MrMLM) followed by gene annotation. Results: Accessions performance were significantly different (p < 0.001) across all the traits with high broad-sense heritability (H2). Phenotypic and genotypic correlations showed positive relationships between yield and vigor but negative for yield and yam mosaic disease severity. Population structure revealed k = 6 as optimal clusters-based species. A total of 22 SNP markers were identified to be associated with yield, vigor, mosaic and anthracnose diseases resistance. Gene annotation for the significant SNP loci identified some putative genes associated with primary metabolism, pest and resistance to anthracnose disease, maintenance of NADPH in biosynthetic reaction especially those involving nitro-oxidative stress for resistance to mosaic virus, and seed development, photosynthesis, nutrition use efficiency, stress tolerance, vegetative and reproductive development for tuber yield. Conclusion: This study provides valuable insights into the genetic control of plant vigor, anthracnose, mosaic virus resistance, and tuber yield in yam and thus, opens an avenue for developing additional genomic resources for markers-assisted selection focusing on multiple yam species. [ABSTRACT FROM AUTHOR]

Objective Explore the implementation of generalized estimation equations (GEE) and mixed linear models (MLM) in longitudinal data analysis using Python software, and expand its application in statistical analysis.Methods GEE and MLM were constructed by Python software to explore the impact of PM2.5 on lung function (forced expiratory volume in1second, FEV1) with an example of environmental epidemiology, and compared with the results of R software.Results With PM2.5 increases of 1 μg/m3, the FEV1 of the subjects decreased by 8 mL after 2 days. Python software can use a statsmodels library to analyze MLM and GEE, and the program language is concise, the program logic has a certain similarity when compared with R, the calculation results of parameter estimation and confidence interval are almost the same, and the Python result is reliable.Conclusion Python software can flexibly construct MLM and GEE, which has a certain reference value in practical research.

Abstract Background In football matches, passing is an important means of organizing attacks, creating shots, changing tactics, and achieving tactical objectives and is a frequently used technique. The purpose of the study was to explore the relevant factors that affect passes in the Chinese Super League (CSL) in different game contexts. Methods A total of 1,440 matches (2,880 samples) of 24 teams participating in the CSL in the 2014–2019 seasons were selected as the research object, and a mixed linear model was constructed to analyse the influencing factors of passes. Results (a) The passing success rate and the total number of forward passes were greatly affected by the stage of the season; (b) except for the passing success rate in the 30 m attack area, all other pass indicators in the home matches were significantly higher than those in the away matches; (c) the number of passes in the 30 m attack area was mainly affected by the team level (table position quartile); the higher the team level, the greater the number of passes in the 30 m attack area; (d) all passing indices for the matches between the first eight and the last eight were significantly greater than the matches between the first eight; (e) the passing success rate in the 30 m attack area in winning matches was higher than that in drawn matches; (f) the number of forward passes was significantly greater in matches won by two or more goals, and the number of defender passes was significantly greater in losing matches. Conclusion The various passing indices of the CSL were affected by different game contexts (including season stage, venue, team level, match type and match outcome), and understanding these influencing factors of passes will help coaches and players better understand football matches.

The development of nutrient-use efficient rice lines is a priority amidst the changing climate and depleting resources viz., water, land, and labor for achieving sustainability in rice cultivation. Along with the traditional transplanted irrigated system of cultivation, the dry direct-seeded aerobic system is gaining ground nationwide. The root-related traits play a crucial role in nutrient acquisition, adaptation and need to be concentrated along with the yield-attributing traits. We phenotyped an association panel of 118 rice lines for seedling vigour index (SVI) traits at 14 and 21 days after sowing (DAS), root-related traits at panicle initiation (PI) stage in polythene bags under controlled aerobic condition, yield and yield-related traits under the irrigated condition at ICAR-IIRR, Hyderabad, Telangana; irrigated and aerobic conditions at ARS, Dhadesugur, Raichur, Karnataka. The panel was genotyped using simple sequence repeats (SSR) markers and genome-wide association studies were conducted for identifying marker–trait associations (MTAs). Significant correlations were recorded for root length, root dry weight with SVI, root volume at the PI stage, number of productive tillers per plant, spikelet fertility, the total number of grains per panicle with grain yield per plant under irrigated conditions, and the total number of grains per panicle with grain yield per plant under aerobic condition. The panel was divided into three sub-groups (K = 3) and correlated with the principal component analysis. The maximum number of MTAs were found on chromosomes 2, 3, and 12 with considerable phenotypic variability. Consistent MTAs were recorded for SVI traits at 14 and 21 DAS (RM25310, RM80, RM22961, RM1385), yield traits under irrigated conditions (RM2584, RM5179, RM410, RM20698, RM14753) across years at ICAR-IIRR, grain yield per plant (RM22961, RM1146) under the aerobic condition, grain yield per plant at irrigated ICAR-IIRR and SVI (RM5501), root traits at PI stage (RM2584, RM80, RM410, RM1146, RM18472). Functionally relevant genes near the MTAs through in-silico expression analysis in root and panicle tissues viz., HBF2 bZIP transcription factor, WD40 repeat-like domain, OsPILS6a auxin efflux carrier, WRKY108, OsSCP42, OsMADS80, nodulin-like domain-containing protein, amino acid transporter using various rice expression databases were identified. The identified MTAs and rice lines having high SVI traits (Langphou, TI-128, Mouli, TI-124, JBB-631-1), high yield under aerobic (Phouren, NPK-43, JBB-684, Ratnamudi, TI-112), irrigated conditions (KR-209, KR-262, Phouren, Keibi-Phou, TI-17), robust root traits like root length (MoirangPhou-Angouba, Wangoo-Phou, JBB-661, Dissi, NPK-45), root volume (Ratnachudi, KJ-221, Mow, Heimang-Phou, PUP-229) can be further employed in breeding programs for the targeted environments aimed at improving seedling vigour, yield-related traits under irrigated condition, aerobic condition as adaptability to water-saving technology.

Tobacco (Nicotiana tabacum L.) is an economic crop and a model organism for studies on plant biology and genetics. A population of 271 recombinant inbred lines (RIL) derived from K326 and Y3, two elite flue-cured tobacco parents, has been constructed to investigate the genetic basis of agronomic traits in tobacco. Six agronomic traits including natural plant height (nPH), natural leaf number (nLN), stem girth (SG), inter-node length (IL), length of the largest leaf (LL) and width of the largest leaf (LW) were measured in seven environments, spanning the period between 2018 and 2021. We firstly developed an integrated SNP-indel-SSR linkage map with 43,301 SNPs, 2,086 indels and 937 SSRs, which contained 7,107 bin markers mapped on 24 LGs and covered 3334.88 cM with an average genetic distance of 0.469cM. Based on this high-density genetic map, a total of 70 novel QTLs were detected for six agronomic traits by a full QTL model using the software QTLNetwork, of which 32 QTLs showed significant additive effects, 18 QTLs showed significant additive-by-environment interaction effects, 17 pairs showed significant additive-by-additive epistatic effects and 13 pairs showed significant epistasis-by-environment interaction effects. In addition to additive effect as a major contributor to genetic variation, both epistasis effects and genotype-byenvironment interaction effects played an important role in explaining phenotypic variation for each trait. In particular, qnLN6-1 was detected with considerably large main effect and high heritability (h2a =34.80%). Finally, four genes including Nt16g00284.1, Nt16g00767.1, Nt16g00853.1, Nt16g00877.1 were predicted as pleiotropic candidate genes for five traits. [ABSTRACT FROM AUTHOR]

Maize seedlings contain high amounts of 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one (DIMBOA), and the effect of DIMBOA is directly associated with multiple insect-resistance against insect pests such as Asian corn borer and corn leaf aphids. Although numerous genetic loci for multiple insect-resistant traits have been identified, little is known about genetic controls regarding DIMBOA content. In this study, the best linear unbiased prediction (BLUP) values of DIMBOA content in two ecological environments across 310 maize inbred lines were calculated; and their phenotypic data and BLUP values were used for marker-trait association analysis. We identified nine SSRs that were significantly associated with DIMBOA content, which explained 4.30–20.04% of the phenotypic variation. Combined with 47 original genetic loci from previous studies, we detected 19 hot loci and approximately 11 hot loci (in Bin 1.04, Bin 2.00–2.01, Bin 2.03–2.04, Bin 4.00–4.03, Bin 5.03, Bin 5.05–5.07, Bin 8.01–8.03, Bin 8.04–8.05, Bin 8.06, Bin 9.01, and Bin 10.04 regions) supported pleiotropy for their association with two or more insect-resistant traits. Within the 19 hot loci, we identified 49 candidate genes, including 12 controlling DIMBOA biosynthesis, 6 involved in sugar metabolism/homeostasis, 2 regulating peroxidases activity, 21 associated with growth and development [(auxin-upregulated RNAs (SAUR) family member and v-myb avian myeloblastosis viral oncogene homolog (MYB)], and 7 involved in several key enzyme activities (lipoxygenase, cysteine protease, restriction endonuclease, and ubiquitin-conjugating enzyme). The synergy and antagonism interactions among these genes formed the complex defense mechanisms induced by multiple insect pests. Moreover, sufficient genetic variation was reported for DIMBOA performance and SSR markers in the 310 tested maize inbred lines, and 3 highly (DIMBOA content was 402.74–528.88 μg g−1 FW) and 15 moderate (DIMBOA content was 312.92–426.56 μg g−1 FW) insect-resistant genotypes were major enriched in the Reid group. These insect-resistant inbred lines can be used as parents in maize breeding programs to develop new varieties. [ABSTRACT FROM AUTHOR]

A close relationship between math performance and math anxiety has been demonstrated, but how different presentations of mathematics are associated with math anxiety has not been investigated. This study recruited 826 elementary school students in grades 5 and 6. All students were given math trait and state anxiety questionnaires; a nonverbal matrix reasoning task; and verbalized, symbolic, and situational fraction problem‐solving tasks. After data cleaning, 475 boys and 323 girls (798 in total) were included in the analysis (mean age = 11.79, SD = 0.82). Partial correlation analysis showed that students' math traits and state anxiety were more closely related to symbolic fraction problem‐solving than to verbalized and situational fraction problem‐solving. Mixed linear model analysis showed that math state anxiety for symbolic problem‐solving was significantly greater than that for verbalized and situational problem‐solving. Based on these findings, we concluded that the presentation of symbolic math is more likely to induce math anxiety than verbalized or situational math. This finding has potential practical applications in math anxiety interventions and education. [ABSTRACT FROM AUTHOR]

Background: Rice crop is damaged extremely by abiotic stress world-wide. The best approach to enhance drought tolerance in rice varieties is to identify and introgress yield QTLs with major effects. The Association mapping approach helps in the identification of genomic regions governing physiological, yield and yield attributes under moisture and heat stress conditions in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Methods and Results: The association mapping panel of 110 rice germplasm lines exhibited significant variation for all the traits in both irrigated and moisture stress conditions. The extent of yield reduction ranged to 83% during rabi, 2018–19, 53% in rabi, 2019–20 and 68% in pooled analysis. The genotypes Badami, Badshabhog, Pankaj, Varalu, Vasundhara, Vivekdhan, Krishna and Minghui63 exhibited drought tolerance with least yield penalty under moisture stress conditions. The genotypes Konark, MTU3626, NLR33671, PR118 and Triguna exhibited minimal reduction in heat stress tolerance traits. Association mapping of germplasm using 37808 SNP markers detected a total of 10 major MTA (Marker-trait association) clusters distributed on chromosomes 1, 3, 4 and 11 through mixed linear model (MLM) governing multiple traits from individual data analysis which are consistent across the years and situations. The pooled data generated a total of five MTA clusters located on chromosome 6. In addition, several novel unique MTAs were also identified. Heat stress analysis generated a total of 23 MTAs distributed on chromosomes 1, 5, 6 and 11. Candidate gene analysis detected a total of 53 and 38 genes under individual and pooled data analysis for various yield and yield attributes under control and moisture stress conditions, respectively and a total of 11 candidate genes in heat stress Conditions. Conclusion: The major and novel MTAs identified in the present investigation for various drought and heat tolerant traits can be utilized for breeding climate-resilient rice varieties. The candidate genes predicted for key MTAs are of great value to deploy into the rice breeding after functional characterization. [ABSTRACT FROM AUTHOR]

• Multitrait genomic models improve prediction accuracy for yak BW. • The multitrait model outperforms single-trait models in predicting BW. • Genetic correlations enhance prediction accuracy for complex traits in yaks. • Five years of BW data support genomic selection in yak breeding. • The multitrait model accelerates early-stage genetic potential assessment in yaks. Yaks are grazed extensively on the Qinghai–Tibet Plateau, which has a long history of semi-domestication. The predicted weight of yaks over consecutive years helps make strategic decisions when selecting yak calves for breeding. To achieve more accurate predictions of genomic estimated breeding values, we used a dataset comprising the genotype and weight records of 396 Maiwa yaks collected from 2015 to 2020. We compared the predictive accuracy of the genome best linear unbiased prediction (GBLUP) model with that of six other models. Based on the GBLUP model, we applied two prediction strategies. In the first strategy, the year was treated as a fixed effect in the GBLUP model, and the kinship from all individuals and the markers were treated as random effects. In the second strategy, all individuals were divided into six age groups, with GBLUP performed for each group, and the phenotypes of the closest age groups were treated as fixed effects. Although the GBLUP model provided better prediction accuracy than other single-trait models, most of the predictive capacity was derived from the best linear unbiased estimation. Additionally, incorporating the phenotype of the closest age group as a factor in multitrait prediction enhanced the accuracy of the model. Our findings provide a robust and credible strategy for predicting continuous economic traits in the presence of strong correlations. [ABSTRACT FROM AUTHOR]

Building on the health belief model (HBM), this research tests, over six months, how the exposure to COVID-related information in the media affects fear, which in turn conditions beliefs about the severity of the virus, susceptibility of getting the virus, and benefits of safety measures. These health beliefs ultimately lead to social distancing and panic buying. As a first contribution, we find that fear is not directly triggered by the objective severity of a crisis, but rather formed over time by the way individuals are exposed to media. Second, we show that fear affects behaviors through the components of the HBM which relate to the risks/benefits of a situation. Last, we find that critical thinking about media content amplifies the "adaptive" responses of our model (e.g., health beliefs, social distancing) and reduces its "maladaptive" responses (e.g., panic buying). Interestingly, we note that the beneficial effect of critical thinking about media content disappears as the level of fear increases over time. The implications of these findings for policymakers, media companies, and theory are further discussed. [ABSTRACT FROM AUTHOR]

Currently a hot topic, genomic selection (GS) has consistently provided powerful support for breeding studies and achieved more comprehensive and reliable selection in animal and plant breeding. GS estimates the effects of all single nucleotide polymorphisms (SNPs) and thereby predicts the genomic estimation of breeding value (GEBV), accelerating breeding progress and overcoming the limitations of conventional breeding. The successful application of GS primarily depends on the accuracy of the GEBV. Adopting appropriate advanced algorithms to improve the accuracy of the GEBV is time-saving and efficient for breeders, and the available algorithms can be further improved in the big data era. In this study, we develop a new algorithm under the Bayesian Shrinkage Regression (BSR, which is called BayesA) framework, an improved expectation-maximization algorithm for BayesA (emBAI). The emBAI algorithm first corrects the polygenic and environmental noise and then calculates the GEBV by emBayesA. We conduct two simulation experiments and a real dataset analysis for flowering time-related Arabidopsis phenotypes to validate the new algorithm. Compared to established methods, emBAI is more powerful in terms of prediction accuracy, mean square error (MSE), mean absolute error (MAE), the area under the receiver operating characteristic curve (AUC) and correlation of prediction in simulation studies. In addition, emBAI performs well under the increasing genetic background. The analysis of the Arabidopsis real dataset further illustrates the benefits of emBAI for genomic prediction according to prediction accuracy, MSE, MAE and correlation of prediction. Furthermore, the new method shows the advantages of significant loci detection and effect coefficient estimation, which are confirmed by The Arabidopsis Information Resource (TAIR) gene bank. In conclusion, the emBAI algorithm provides powerful support for GS in high-dimensional genomic datasets. [ABSTRACT FROM AUTHOR]

Complex engineering and technological processes typically generate data with a non‐trivial hierarchical structure. To this end, in this article we propose a full procedure for optimizing such processes through optimal experimental designs and modeling. In order to study a hierarchical structure, several types of experimental factors may arise, making the building of the experimental design challenging. Starting from the analysis of a preliminary dataset and a pilot design including nested, branching, and shared experimental factors, as well as a new type of experimental factor called composite‐form‐factor, we build a hierarchical D‐optimal experimental design using genetic algorithms. We apply our proposal to a real case‐study in the rail sector aimed at optimizing the payload distribution of freight trains. In this case‐study we also achieve the best train configuration by minimizing the in‐train forces. The results are very satisfactory and confirm that our full procedure represents a valid method to be successfully applied for solving similar technological problems. [ABSTRACT FROM AUTHOR]

Soybean is a short-day crop, and its sensitivity to photoperiod is an important trait for its adaptability. Rapid changes in the climate on a global scale could be a threat to future food security. There is a need to increase the heat tolerance of soybean to sustain its production. The availability of global germplasm provides plant breeders with the opportunity to explore the genetic potential of the existing soybean germplasm. For this purpose, a panel of 203 soybean accessions from different global climatic zones was evaluated for adaptability under long-day conditions. Field screening of the soybean germplasm was conducted over two consecutive years, 2016 and 2017, in Faisalabad, Pakistan. This study screened 15 diverse soybean accessions that exhibited economically viable yields in both years, i.e., 1500–2200 kg/ha. Data related to plant height, node number, pods per plant, seeds per plant, seed weight per plant, hundred-grain weight, and total yield were recorded. Two soybean accessions, PI548271 (MG-IV) and PI553039 (MG-IV,) produced the highest yield in both years. The genotypic and phenotypic coefficients of variance, broad-sense heritability, and genetic advances observed in their yields were also high. These results highlight the potential pre-existing genetic variation required to improve the adaptation of soybean to long-day conditions. Some of the accessions identified in the current study could be recommended for general cultivation under long-day and high-temperature conditions.

Background and aim: Controversy remains as to pegylated interferon-α (PEG-IFNα) antiviral therapy to renal function in chronic hepatitis B (CHB) patients. The aim of this study was to evaluate the influence of PEG-IFNα2b (Y shape, 40 kD) add-on treatment for renal function in CHB patients who received entecavir therapy. Methods: This was a retrospective observational study to investigate factors related to renal function in 114 CHB patients who received PEG-IFNα2b add-on therapy to entecavir for 48 weeks. Changes of blood urea nitrogen (BUN), serum creatinine (sCr), and estimated glomerular filtration rate (eGFR), which was calculated with both Chronic Kidney Disease Epidemiology Collaboration and Modification of Diet in Renal Disease (MDRD) formulas, were analyzed by one-way analysis of variance. A linear mixed effects model for repeated measures was used to assess the correlation between baseline information and eGFR changes at 24 and 48 weeks of therapy. The model considered the baseline age, gender, body weight, viral load, hepatitis B surface antigen, BUN, sCr, and treatment strategy as fixed effects and incorporated random effects for individual subjects. Results: BUN and sCr was decreased, while eGFR was increased at 12 weeks of therapy. Only eGFR maintained at 24 and 48 weeks of therapy. Patients with female gender, age ≥ 40 years, and baseline HBsAg level < 250 IU/mL showed significant improvement of renal function with PEG-IFNα2b add-on therapy. The linear mixed effects model revealed that female gender, baseline sCr, and PEG-IFNα2b add-on were significant positive predictors for eGFR elevation at 24 and 48 weeks of therapy. Conclusion: In real-world experience, PEG-IFNα2b add-on therapy might be associated with increased eGFR in CHB patients. [ABSTRACT FROM AUTHOR]

Background: In football matches, passing is an important means of organizing attacks, creating shots, changing tactics, and achieving tactical objectives and is a frequently used technique. The purpose of the study was to explore the relevant factors that affect passes in the Chinese Super League (CSL) in different game contexts. Methods: A total of 1,440 matches (2,880 samples) of 24 teams participating in the CSL in the 2014–2019 seasons were selected as the research object, and a mixed linear model was constructed to analyse the influencing factors of passes. Results: (a) The passing success rate and the total number of forward passes were greatly affected by the stage of the season; (b) except for the passing success rate in the 30 m attack area, all other pass indicators in the home matches were significantly higher than those in the away matches; (c) the number of passes in the 30 m attack area was mainly affected by the team level (table position quartile); the higher the team level, the greater the number of passes in the 30 m attack area; (d) all passing indices for the matches between the first eight and the last eight were significantly greater than the matches between the first eight; (e) the passing success rate in the 30 m attack area in winning matches was higher than that in drawn matches; (f) the number of forward passes was significantly greater in matches won by two or more goals, and the number of defender passes was significantly greater in losing matches. Conclusion: The various passing indices of the CSL were affected by different game contexts (including season stage, venue, team level, match type and match outcome), and understanding these influencing factors of passes will help coaches and players better understand football matches. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the efficacy of existing performance models to assess the progression of male and female adolescent swimmers through a quantitative and qualitative mixed-methods approach. Methods: Fourteen published models were tested using retrospective data from an independent sample of Dutch junior national-level swimmers from when they were 12-18 y of age (n = 13). The degree of association by Pearson correlations was compared between the calculated differences from the models and quadratic functions derived from the Dutch junior national qualifying times. Swimmers were grouped based on their differences from the models and compared with their swimming histories that were extracted from questionnaires and followup interviews. Results: Correlations of the deviations from both the models and quadratic functions derived from the Dutch qualifying times were all significant except for the 100-m breaststroke and butterfly and the 200-m freestyle for females (P < .05). In addition, the 100-m freestyle and backstroke for males and 200-m freestyle for males and females were almost directly proportional. In general, deviations from the models were accounted for by the swimmers' training histories. Higher levels of retrospective motivation appeared to be synonymous with higher-level career performance. Conclusion: This mixed-methods approach helped confirm the validity of the models that were found to be applicable to adolescent swimmers at all levels, allowing coaches to track performance and set goals. The value of the models in being able to account for the expected performance gains during adolescence enables quantification of peripheral factors that could affect performance. [ABSTRACT FROM AUTHOR]

Recognizing the growing importance of optimizing work environments for better cognitive performance, this study examined the relationships between general office lighting, psychophysiological responses and cognitive performance. Sixteen healthy adults in their 20s participated in experiments under nine different lighting conditions combining three levels of correlated color temperature (i.e., 4,000K, 5,000K, and 6,500K) and three levels of illuminance (i.e., 200 lx, 500 lx, and 800 lx). In the experiments, subjects' primary and complex cognitive performances were measured and their psychophysiological responses such as mental workload, drowsiness, mental fatigue and stress, and visual fatigue were evaluated. Statistical analyses revealed that higher correlated color temperature and illuminance significantly enhanced primary cognitive performance but did not significantly impact complex cognitive performance or most psychophysiological responses. Notably, psychophysiological responses and cognitive performances were found to have mutual relationships rather than one being an absolute independent variable for another. The study suggests that tailored smart lighting systems could enhance cognitive performance in office environments by dynamically adjusting lighting conditions based on real-time psychophysiological feedback. • Cooler and brighter lighting improve primary cognitive performance. • Visual fatigue and mental workload significantly affect creativity. • Psychophysiological responses and cognitive performance impact each other. • Mixed linear models and multiple regression analysis were used. • Psychophysiological responses should be considered for future lighting systems. [ABSTRACT FROM AUTHOR]

Drought is one of the important abiotic stresses that affects the phenological stages and crop productivity. Phenological stages can seriously influence the crop yield and quality. In this study, phenological traits including days to germination, days to tillering, days to stem elongation, days to head emergence, days to dough development in kernel, days to physiological maturity, number of grains per spike, thousand kernels weight, grain yield were evaluated in 148 commercial barley cultivars in both years under normal and drought stress conditions. In molecular experiment, the molecular profile of barley cultivars was assessed by 14 AFLP primer combinations and 32 SSRs loci. The phenotypic results showed the high level of diversity between studied cultivars. The studied barley cultivars were divided into two subgroups. Linkage disequilibrium analysis revealed that r2 values among all possible markers pairs have an average value of 0.0178. Mixed linear model procedure showed that totally, 137 loci had significant association with nine investigated traits under normal and drought stress conditions. The identified markers potentially can be used in marker-assisted selection in barley drought resistant breeding programs. [ABSTRACT FROM AUTHOR]

Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments. [ABSTRACT FROM AUTHOR]

Forest fragmentation is a widespread phenomenon that directly or indirectly affects the processes that take place both in forest ecosystems and in their immediate surroundings. So far, many studies confirm its negative effects, especially on biodiversity. On the other hand, there are few studies that address the effects of forest fragmentation on the amount of accumulated biomass or carbon, as well as on the characteristics of wood resources in managed forests. Therefore, issues related to timber production, which are important from the point of view of multifunctional forest management, are omitted. The aim of our research was to add to the knowledge in this area. In particular, we focused on assessing the impact of forest fragmentation on wood resources based on an analysis of edge effects in forest patches (units formed by combining forest fragments characterized by structural connectivity). Vector data describing the topography of forest fragments in Poland and the results of the National Forest Inventory (NFI) from 2015–2019 were used as material for solving this problem. The results of our research showed that the effects of fragmentation on managed pine stands depend on the age of the stand and the fertility of the habitat. In young stands growing on barren or strongly barren habitats, growing stock volume turned out to be significantly higher in the edge zone. In older stands, especially on moderately fertile habitats, significantly higher resources were found in the interior zone of forest patches. Habitat quality also had a significant effect on the amount of carbon accumulated. In strongly barren habitats, higher carbon mass was found in edge zones, while in moderately fertile habitats, stands had higher carbon volume in the interior zone. Our results illustrate that forest fragmentation is a very complex process that can increase or reduce wood resources, depending on the age of the stand and the quality of the habitat. From the standpoint of measurable benefits, it was concluded that protection from the negative effects of fragmentation should focus primarily on older stands and more fertile habitats. [ABSTRACT FROM AUTHOR]

Genome-wide association study (GWAS) has been a standard approach to discover the genetic determinants underlying complex traits. It is a major challenge in GWAS how to improve analysis power, uncover complex genetic correlation, and reveal gene-gene and gene-environment interactions through integrated analysis of multiple genetically related traits. To combat these challenges, we proposed a mixed linear model-based joint association analysis method for multiple traits, which include epistasis and gene-environment interaction in the mapping model and utilize within-trait variance and between-trait covariance simultaneously; A F-statistics based on Wilks statistics is used to test the significance of each SNP and paired interacted SNPs, each genetic effects of QTS are estimated and tested by the MCMC method based on a QTS full model. Simulations showed that the multi-trait GWAS method could provide increased power in detecting pleiotropic loci affecting more than one trait, and can unbiasedly estimate effects of QTS. To demonstrate the performance of the proposed method, we analyzed four blood lipid traits in Multi-Ethnic Study of Atherosclerosis (MESA) Cohort and two yield-related traits in a rice immortalized F2 dataset. A software package was developed for the proposed method.

Objective This experiment was conducted in order to assess genetic variation, determine of the best genetic structure and association analysis of safflower to identify markers associated with different agronomic traits. Materials and methods In this study, 17 genotypes of safflower were planted as the randomised complete block design at research farm of Shahid Bahonar University of Kerman in 2016. Traits including seed yield, plant height, number of capitulum per plant, number of seed per capitulum, 1000- seed weight, capitulum diameter, day to 50% flowering and day to maturity were measured. The AFLP Technique was performed by the eight EcoR1/Mse1 primer combinations. Results In total, 147 polymorphic bands were generated with average 81.58 polymorphic percentage. Cluster analysis using UPGMA and Jacard as similarity index discriminated safflower genotypes into two groups. Based on GLM and MLM model, 45 and 39 markers had significant association with the studied traits, respectively. M14/E6-10, M14/E11-16, M14/E11-13, M3/E10-14 and M4/E36-12 markers with seed yield, M3/E10-12, M3/E36-29, M59/E36-21 and M14/E11-10 with capitulum number per plant, M4/E36-8, M59/E36-21, M3/E10-9, M14/E11-14 and M14/E11-13 with number of seed per capitulum, M4/E36-19, M4/E36-12, M14/E11-1 and M4/E10-1 with 1000- seed weight, M4/E10-2, M59/E36-21, M3/E36-30, M4/E10-11 and M4/E36-24 with plant height, M3/E36-24, M3/E36-6, M3/E10-20 and M4/E36-18 with capitulum diameter, M14/E11-10, M3/E36-30 and M59/E36-21 with day to 50% flowering, M14/E11-10 and M59/E36-22 markers with day to maturity in both models. Conclusions Detected AFLP markers with strong effects in this study could be desirable candidates for the marker-assisted selection in breeding programs and conversion into other specific markers.

Introduction One of the most important goals of grapes breeding is to obtain seedless grapes with large berries and good taste. In order to achieve table and raisin grape varieties with good quality, desirable taste and larger berries with various colors, it is necessary to estimate the breeding value of the desired traits. In this study, breeding values of fourteen characters in 45 grape cultivars were predicted using the best linear prediction (BLUP) pediction. The purpose of the study was to estimate the breeding value of the grape cultivars and to identify the superior cultivars. The results of this study can be of great help to breeders in selecting cultivars with favorable market traits and cultivars with better yield. Materials and Methods In this study, 45 grape cultivars in the collection of Agricultural and Natural Resources Research Center of West Azerbaijan Province were used. From each of the cultivars in the first year of research, ten 14-year-old plants were selected and TSS, pH, TA, berry weight, flesh weight, single seed weight, seed number, juice volume, pollen germination, cluster length, cluster width, cluster weight, fruit set in open and under controlled pollination were investigated. The characteristics were measured for three consecutive years. Results and Discussion Based on the results, considering the sum of the breeding values of all the studied traits, Agh Shani, Taifl, Qzl Ouzum, Lal Qermez, Tabarze Qermez, Garmian, Rishbaba Qermez and Qara Shani cultivars had the highest rank. Among the studied seedless cultivars, Rejin cultivar had the highest breeding value in terms of single seed weight, and Keshmeshi Sefid cultivar in terms of seed number. Among the seeded cultivars, Shirazi cultivar showed the highest breeding value in terms of single seed weight and Qara Shani cultivar in terms of seed number. Garmian, Rishbaba Qermez, Keshmeshi Qermez, Tabarze Qermez, Agh Shani and shirazi had positive breeding value for pollen germination, fruit set in open pollination and fruit set under controlled pollination. Sarghola, Gazandaii and Qzl Ouzum cultivars had the high and positive breeding value for berry weight and fresh weight. Lal Qermez, Rishbaba Qermez and Tabarze Qermez cultivars had high and positive breeding value for cluster weight, cluster width and cluster weight. Genotypes with the highest breeding value had the highest ability to transfer their traits to offspring. Therefore, they can be used as a desirable parent to modify these traits in crop breeding programs. Conclusion In this study, breeding values of fourteen characters including total soluble solids, titratable acidity, berry weight, flesh weight, single seed weight, seed number, juice volume, fruit set in open pollination, pollen germination, cluster length, cluster width, cluster weight, fruit set under controlled pollination in 45 grape cultivars were predicted using the best linear prediction (BLUP) prediction. Studied cultivars showed different breeding values for the studied traits. Genotypes with the highest breeding value can be used as a desirable parent to modify these traits in grape breeding programs as these genotypes have the highest ability to transfer their traits to offspring. [ABSTRACT FROM AUTHOR]

Multi-locus association studies have been widely used in human, animal and plant genetics. In this study we summarized the advances of main-stream methodologies and important software platforms for multi-locus association studies based on the mixed linear model(MLM)framework. It included the establishment and development for genome-wide association study(GWAS)mixed linear model methodology, the development of multi-locus model method, the development of multi-locus mixed linear model method, and the influencing factors of GWAS methodology. Finally, we presented the prospect of GWAS developments in the future. [ABSTRACT FROM AUTHOR]

Objective The aim of this research was to identify marker loci associated with some agronomic traits under drought stress conditions in Common millet. Materials and methods In this study, 30 genotypes of Common millet were planted as augmented design at research farm of Shahid Bahonar University of Kerman in April 2016. In order to induce stress irrigation was stopped at 50% panicling stage. Traits including grain yield, biological yield, 1000- grain weight, number of grain in tiller, number of tiller in plant, panicle length, number of panicle branches, plant height, number of leaf in plant, leaf length, leaf width and harvest index were measured. The AFLP Technique was performed by the six EcoR1/Mse1 primer combinations. Results In total, 246 polymorphic bands were generated and the average percentage polymorphic was 89.13%. Association analysis based on GLM and MLM model indicated that 52 and 64 markers associated to traits under drought stress condition, respectively. Among them, four markers (M4/E10-65, M4/E10-74, M4/E10-67 and M59/E36-105) with grain yield, three markers (M59/E36-84, M10/E1-244 and M10/E1-245) with 1000- grain weight, two markers (M59/E36-106 and M10/E1-224) with number of tiller in plant and five markers (M3/E2-31, M3/E2-35, M3/E2-38, M59/E36-114 and M4/E8-128) with number of grain in tiller showed highly significant and strong relationship under drought stress condition in both models. Conclusions In this study, several common genetic locations for the studied traits were identified. The existence of common markers between different traits may be due to pleiotropic effects or to linked genomic regions affecting several traits.

The mixed linear model (MLM) has been widely used in genome-wide association study (GWAS) to dissect quantitative traits in human, animal, and plant genetics. Most methodologies consider all single nucleotide polymorphism (SNP) effects as random effects under the MLM framework, which fail to detect the joint minor effect of multiple genetic markers on a trait. Therefore, polygenes with minor effects remain largely unexplored in today’s big data era. In this study, we developed a new algorithm under the MLM framework, which is called the fast multi-locus ridge regression (FastRR) algorithm. The FastRR algorithm first whitens the covariance matrix of the polygenic matrix K and environmental noise, then selects potentially related SNPs among large scale markers, which have a high correlation with the target trait, and finally analyzes the subset variables using a multi-locus deshrinking ridge regression for true quantitative trait nucleotide (QTN) detection. Results from the analyses of both simulated and real data show that the FastRR algorithm is more powerful for both large and small QTN detection, more accurate in QTN effect estimation, and has more stable results under various polygenic backgrounds. Moreover, compared with existing methods, the FastRR algorithm has the advantage of high computing speed. In conclusion, the FastRR algorithm provides an alternative algorithm for multi-locus GWAS in high dimensional genomic datasets.

Labor figures for Mexico's municipalities were estimated during 2018's first quarter by using Small Area Estimation (SAE) techniques with the incorporation of a spatial component – given there is no recent information source with such a level of geographic disaggregation. To achieve this, combined information from different sources was used to build statistical models in which the Economically Active Population, the Employed Population and the Informal Employed Population were taken as variables object of estimation – this information was taken from the National Survey of Occupation and Employment (ENOE for its acronym in Spanish). Auxiliary variables were selected from population censuses, administrative records, and population projections. The results were contrasted with those calculated by applying the percentage structures of 2010 Population and Housing Census to the figures provided by ENOE at a federal entity level, and with the data in this survey (obtained by direct estimation for those municipalities which had a sufficient sample with acceptable coefficients of variation). It is observed that the results obtained by Small Area Estimation are plausible and register coefficients of variation below 10 percent. [ABSTRACT FROM AUTHOR]

The mixed linear model (MLM) has been widely used in genome-wide association study (GWAS) to dissect quantitative traits in human, animal, and plant genetics. Most methodologies consider all single nucleotide polymorphism (SNP) effects as random effects under the MLM framework, which fail to detect the joint minor effect of multiple genetic markers on a trait. Therefore, polygenes with minor effects remain largely unexplored in today's big data era. In this study, we developed a new algorithm under the MLM framework, which is called the fast multi-locus ridge regression (FastRR) algorithm. The FastRR algorithm first whitens the covariance matrix of the polygenic matrix K and environmental noise, then selects potentially related SNPs among large scale markers, which have a high correlation with the target trait, and finally analyzes the subset variables using a multi-locus deshrinking ridge regression for true quantitative trait nucleotide (QTN) detection. Results from the analyses of both simulated and real data show that the FastRR algorithm is more powerful for both large and small QTN detection, more accurate in QTN effect estimation, and has more stable results under various polygenic backgrounds. Moreover, compared with existing methods, the FastRR algorithm has the advantage of high computing speed. In conclusion, the FastRR algorithm provides an alternative algorithm for multi-locus GWAS in high dimensional genomic datasets. [ABSTRACT FROM AUTHOR]

In the present study, 148 commercial barley cultivars were assessed by 14 AFLP primer combinations and 32 SSRs primer pairs. Population structure, linkage disequilibrium, and genomic regions associated with physiological traits under drought stress were investigated. The phenotypic results showed a high level of diversity between studied cultivars. The studied barley cultivars were divided into two subgroups. Linkage disequilibrium analysis revealed that r2 values among all possible marker pairs have an average value of 0.0178. The mixed linear model procedure showed that totally, 207 loci had a significant association with investigated traits. 120 QTLs out of 207 were detected for traits under normal conditions, and 90 QTLs were detected for traits under drought stress conditions. Identified QTLs after validation and transferring to SCAR markers in the case of AFLPs can be used to develop MAS strategies for barley breeding programs. Some common markers were identified for a particular trait or some traits across normal and drought stress conditions. These markers show low interaction with environmental conditions (stable markers); therefore, selection by them for a trait under normal conditions will improve the trait value under stress conditions, too. [ABSTRACT FROM AUTHOR]