Association mapping in multiple yam species (Dioscorea spp.) of quantitative trait loci for yield-related traits

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.

Genome-Wide Association Studies (GWAS)

Most of the breeding targets are quantitative traits. In exploring the quantitative trait locus (QTL) system of a trait, linkage mapping was established using sparse polymerase chain reaction (PCR) markers. With the genome-wide sequencing technology advanced, genome-wide association study (GWAS) was developed for natural (germplasm) populations using dense genomic markers, which facilitates the identification of the complete QTL system with their multiple alleles on genomic locations. GWAS makes use of the linkage disequilibrium (LD) due to historical saturate recombination and high-density genomic markers to detect QTLs through statistical test for the association between molecular markers and phenotypes. However, due to inbreeding and mixture of source populations, the germplasm population often has complex and unknown structure, which leads to false positives/negatives in GWAS. Various GWAS methods have been proposed to reduce false positives/negatives, including those of the general linear model and the mixed linear model, which focused mainly on finding a handful of major QTLs under single-locus model for major gene cloning and could not detect directly the multiple alleles using bi-allelic single-nucleotide polymorphism (SNP) marker. As a relatively thorough detection of QTLs with their multiple alleles is required for germplasm population, the restricted two-stage multi-locus multi-allele GWAS (RTM-GWAS) procedure was proposed for identifying the QTL system with varying multiple alleles. From the RTM-GWAS results, a QTL-allele matrix is constructed as a compact form of the population genetic constitution, which can be further used for crop genetic and breeding studies, including major gene mining, population evolution, and breeding by genetic design.

Identification of genomic regions governing moisture and heat stress tolerance employing association mapping in rice (Oryza sativa L.)

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.

Analysis of influencing factors of passes in the chinese super league

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.

An extended health belief model for COVID-19: understanding the media-based processes leading to social distancing and panic buying

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.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11747-022-00865-8.

Genome-wide association studies dissect the G × E interaction for agronomic traits in a worldwide collection of safflowers (Carthamus tinctorius L.)

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01295-8.

Interpretation of Manhattan Plots and Other Outputs of Genome-Wide Association Studies

With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile-quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.

Performing Genome-Wide Association Studies with Multiple Models Using GAPIT

Genome-wide association study (GWAS) is based on the linkage disequilibrium (LD) between phenotypes and genetic markers covering the whole genome. Besides the genetic linkage between the genetic markers and the causal mutations, many other factors contribute to the LD, including selection and nonrandom mating formatting population structure. Many methods have been developed with accompany of corresponding software such as multiple loci mixed model (MLMM). There are software packages that implement multiple methods to reduce the learning curve. One of them is the Genomic Association and Prediction Integrated Tool (GAPIT), which implemented eight models including GLM (General Linear Model), Mixed Linear Model (MLM), Compressed MLM, MLMM, SUPER (Settlement of mixed linear models Under Progressively Exclusive Relationship), FarmCPU (Fixed and random model Circulating Probability Unification), and BLINK (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway). Besides the availability of multiple models, GAPIT provides comprehensive functions for data quality control, data visualization, and publication-ready quality graphic outputs, such as Manhattan plots in rectangle and circle formats, quantile-quantile (QQ) plots, principal component plots, scatter plot of minor allele frequency against GWAS signals, plots of LD between associated markers and the adjacent markers. GAPIT developers and users established a community through the GAPIT forum ( https://groups.google.com/g/gapit-forum ) with over 600 members for asking questions, making comments, and sharing experiences. In this chapter, we detail the GAPIT functions, input data frame, output files, and example codes for each GWAS model. We also interpret parameters, functional algorithms, and modules of GAPIT implementation.

Association analysis and molecular tagging of phytochemicals in the endangered medicinal plant licorice (Glycyrrhiza glabra L.)

Licorice (Glycyrrhiza glabra L.) is a medicinal plant species valued in many countries in Asia and Europe for its phytochemical characteristics. Licorice biodiversity is becoming threatened nowadays in Iran due to increasing demand and a drastic decline of its natural habitats. Therefore, licorice domestication would be necessary in the near future, and molecular breeding would help to introduce genotypes suitable for cultivation. The present study was carried out with 170 individual licorice plants sampled in the wild in 59 localizations in 21 provinces of Iran. The association of 436 polymorphic AFLP markers, produced by 15 primer combinations (EcoRI/MseI), with six phenotypic phytochemical traits was studied. The AMOVA analysis show gene diversity among and within localizations. The population structure analysis identified two main sub-populations with significant genetic variation. Significant associations were identified between three markers (E3/M40-4, E34/M4-12 and E12/M31-15) and glycyrrhizin concentration, and between four markers (E11/M34-12, E11/M34-15, E9/M7-29, and E9/M7-30) and phenolic compounds contents. Markers detected can be useful in the domestication of licorice as well as in breeding programs. Licorice sampled in four localizations (KBA1, KBA2, SKh2 and Fa1) were found to be superior in terms of glycyrrhizin and antioxidants content, and therefore they can be considered as elite genotypes which could be included in the domestication process.

Semen production and semen quality of indigenous buffalo breeds under hot semiarid climatic conditions in India

Semen data comprising of 97,023 ejaculates of 197 bulls from 6 buffalo breeds were analyzed. The traits considered were as follows: ejaculate volume, sperm concentration, mass activity, initial and post-thaw motility, total sperm, and total motile sperm before and after thawing as well as a composite trait equal to the theoretical number of doses which can be produced from each given ejaculate. The objective was to measure the semen production potential of indigenous buffalo bulls and identify factors affecting these traits. A linear mixed model was used, including a random bull effect along with other fixed factors: the order of the ejaculate on a particular day, the interval between collections, the time of collection, the breed, the age at collection, the semen collector, and the year and month of collection. The study showed breed wise variation for all traits. The first ejaculate of a bull on a particular day was superior to the second for nearly all the traits. Longer collection intervals are better than shorter intervals for all the parameters, although short collection interval of 2 to 4 days produced higher yield in terms of total semen doses without hampering semen quality. The study also showed a slight decrease of semen quality with time of collection within a day. The Murrah breed showed comparatively consistent performance during their whole life compared with the other breeds. Repeatability estimates for semen traits were found to be low (0.09 for mass activity) to relatively large (> 0.4 for volume and concentration). A negative correlation was found between bull effects for semen volume and concentration while a high positive correlation was found between mass activity, initial motility, and post-thaw motility. Results of the study will help in suggesting suitable management and breeding plans for semen production traits.

Dissection of complicate genetic architecture and breeding perspective of cottonseed traits by genome-wide association study

BACKGROUND

Cottonseed is one of the most important raw materials for plant protein, oil and alternative biofuel for diesel engines. Understanding the complex genetic basis of cottonseed traits is requisite for achieving efficient genetic improvement of the traits. However, it is not yet clear about their genetic architecture in genomic level. GWAS has been an effective way to explore genetic basis of quantitative traits in human and many crops. This study aims to dissect genetic mechanism seven cottonseed traits by a GWAS for genetic improvement.

RESULTS

A genome-wide association study (GWAS) based on a full gene model with gene effects as fixed and gene-environment interaction as random, was conducted for protein, oil and 5 fatty acids using 316 accessions and ~ 390 K SNPs. Totally, 124 significant quantitative trait SNPs (QTSs), consisting of 16, 21, 87 for protein, oil and fatty acids (palmitic, linoleic, oleic, myristic, stearic), respectively, were identified and the broad-sense heritability was estimated from 71.62 to 93.43%; no QTS-environment interaction was detected for the protein, the palmitic and the oleic contents; the protein content was predominantly controlled by epistatic effects accounting for 65.18% of the total variation, but the oil content and the fatty acids except the palmitic were mainly determined by gene main effects and no epistasis was detected for the myristic and the stearic. Prediction of superior pure line and hybrid revealed the potential of the QTSs in the improvement of cottonseed traits, and the hybrid could achieve higher or lower genetic values compared with pure lines.

CONCLUSIONS

This study revealed complex genetic architecture of seven cottonseed traits at whole genome-wide by mixed linear model approach; the identified genetic variants and estimated genetic component effects of gene, gene-gene and gene-environment interaction provide cotton geneticist or breeders new knowledge on the genetic mechanism of the traits and the potential molecular breeding design strategy.

[CD(4+) T lymphocyte responses to anti-retroviral therapy, among HIV/AIDS patients aged 18 and over]

Objective: To compare the differences of CD(4) (+) T lymphocyte (CD(4)) counts between patients aged 18 and over, to explore the effect of age on treatment, 36 months after having received the China National Free AIDS Antiretroviral Treatment on HIV/AIDS. Methods: Through the National ART Information Ssystem, we selected those HIV/AIDS patients who initiated the ART 36 months after the ART, between January 1, 2010 and December 31, 2012 in Guangzhou, Liuzhou and Kunming. Patients were divided into age groups as 18-49, 50-59 and 60 or over year olds, at the baseline of treatment. Under different levels of baseline CD(4) counts, we chose the baseline and different time-point of CD(4) counts as dependent variables, applied mixed linear model to analyze the effects of age, viral suppression, gender, baseline CD(4)/CD(8) ratio and initial treatment regimen. Results: A total of 5 331 HIV/AIDS patients were recruited. No differences were found on age group ratios between different levels of baseline CD(4) counts. At the level of baseline CD(4)<200 cells/μl, both the 50-59 and 60 or above years old groups had lower CD(4) counts than the 18-49 year-old group, within 36 months after the initiation of ART. However, at the baseline CD(4) level of 200-350 cells/μl, no significant differences on CD(4) counts between the 50-59 year-old and 18-49 year-old groups were noticed. CD(4) counts seemed lower in the 60 and above year-old group than in the 18-49 year-old group. Conclusion: Age might serve as an influencing factor on CD(4) counts within 36 months after the initiation of ART, suggesting that earlier initiation of ART might be of help to the recovery of immune function in the 50-59 year-old group.

Preliminary evidence for associations between molecular markers and quantitative traits in a set of bread wheat (Triticum aestivum L.) cultivars and breeding lines

The identification of polymorphic markers associated with various quantitative traits allows us to test their performance for the exploitation of the extensive quantitative variation maintained in gene banks. In the current study, a set of 97 wheat germplasm accessions including 48 cultivars and 49 breeding lines were evaluated for 18 agronomic traits. The accessions were also genotyped with 23 ISSR, nine IRAP and 20 REMAP markers, generating a total of 658 clear and scorable bands, 86% of which were polymorphic. Both neighbor-joining dendrogram and Bayesian analysis of clustering of individuals revealed that the accessions could be divided into four genetically distinct groups, indicating the presence of a population structure in current wheat germplasm. Associations between molecular markers and 18 agronomic traits were analyzed using the mixed linear model (MLM) approach. A total of 94 loci were found to be significantly associated with agronomic traits (P≤0.01). The highest number of bands significantly associated with the 18 traits varied from 11 for number of spikelets spike^-1 (NSS) to two for grain yield in row (GRY). Loci ISSR16-9 and REMAP13-10 were associated with three different traits. The results of the current study provide useful information about the performance of retrotransposon-based and ISSR molecular markers that could be helpful in selecting potentially elite gene bank samples for wheat-breeding programs.

Hg concentrations in fish from coastal waters of California and Western North America

The State of California conducted an extensive and systematic survey of mercury (Hg) in fish from the California coast in 2009 and 2010. The California survey sampled 3483 fish representing 46 species at 68 locations, and demonstrated that methylHg in fish presents a widespread exposure risk to fish consumers. Most of the locations sampled (37 of 68) had a species with an average concentration above 0.3μg/gwet weight (ww), and 10 locations an average above 1.0μg/gww. The recent and robust dataset from California provided a basis for a broader examination of spatial and temporal patterns in fish Hg in coastal waters of Western North America. There is a striking lack of data in publicly accessible databases on Hg and other contaminants in coastal fish. An assessment of the raw data from these databases suggested the presence of relatively high concentrations along the California coast and in Puget Sound, and relatively low concentrations along the coasts of Alaska and Oregon, and the outer coast of Washington. The dataset suggests that Hg concentrations of public health concern can be observed at any location on the coast of Western North America where long-lived predator species are sampled. Output from a linear mixed-effects model resembled the spatial pattern observed for the raw data and suggested, based on the limited dataset, a lack of trend in fish Hg over the nearly 30-year period covered by the dataset. Expanded and continued monitoring, accompanied by rigorous data management procedures, would be of great value in characterizing methylHg exposure, and tracking changes in contamination of coastal fish in response to possible increases in atmospheric Hg emissions in Asia, climate change, and terrestrial Hg control efforts in coastal watersheds.

Modeling the effects of temperature and pH on the resistance of Alicyclobacillus acidoterrestris in conventional heat-treated fruit beverages through a meta-analysis approach

In this work, all publicly-accessible published findings on Alicyclobacillus acidoterrestris heat resistance in fruit beverages as affected by temperature and pH were compiled. Then, study characteristics (protocols, fruit and variety, °Brix, pH, temperature, heating medium, culture medium, inactivation method, strains, etc.) were extracted from the primary studies, and some of them incorporated to a meta-analysis mixed-effects linear model based on the basic Bigelow equation describing the heat resistance parameters of this bacterium. The model estimated mean D* values (time needed for one log reduction at a temperature of 95 °C and a pH of 3.5) of Alicyclobacillus in beverages of different fruits, two different concentration types, with and without bacteriocins, and with and without clarification. The zT (temperature change needed to cause one log reduction in D-values) estimated by the meta-analysis model were compared to those ('observed' zT values) reported in the primary studies, and in all cases they were within the confidence intervals of the model. The model was capable of predicting the heat resistance parameters of Alicyclobacillus in fruit beverages beyond the types available in the meta-analytical data. It is expected that the compilation of the thermal resistance of Alicyclobacillus in fruit beverages, carried out in this study, will be of utility to food quality managers in the determination or validation of the lethality of their current heat treatment processes.

Bayesian reconstruction of projection reconstruction NMR (PR-NMR)

Projection reconstruction nuclear magnetic resonance (PR-NMR) is a technique for generating multidimensional NMR spectra. A small number of projections from lower-dimensional NMR spectra are used to reconstruct the multidimensional NMR spectra. In our previous work, it was shown that multidimensional NMR spectra are efficiently reconstructed using peak-by-peak based reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. We propose an extended and generalized RJMCMC algorithm replacing a simple linear model with a linear mixed model to reconstruct close NMR spectra into true spectra. This statistical method generates samples in a Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of a protein HasA.

Association mapping in crop plants: opportunities and challenges

The research area of association mapping (AM) is currently receiving major attention for genetic studies of quantitative traits in all major crops. However, the level of success and utility of AM achieved for crop improvement is not comparable to that in the area of human health care for diagnosis of complex human diseases. These AM studies in plants, as in humans, became possible due to the availability of DNA-based molecular markers and a variety of sophisticated statistical tools that are evolving on a regular basis. In this chapter, we first briefly review the significance of a variety of populations that are used in AM studies, then briefly describe the molecular markers and high-throughput genotyping strategies, and finally describe the approaches used for AM studies. The major part of the chapter is, however, devoted to analysis of reasons why the results of AM have been underutilized in plant breeding. We also examine the opportunities available and challenges faced while using AM for crop improvement programs. This includes a detailed discussion of the issues that have plagued AM studies, and the solutions that have become available to deal with these issues, so that in future, the results of AM studies may prove increasingly fruitful for crop improvement programs.

Covariate-Adjusted Constrained Bayes Predictions of Random Intercepts and Slopes

Constrained Bayes methodology represents an alternative to the posterior mean (empirical Bayes) method commonly used to produce random effect predictions under mixed linear models. The general constrained Bayes methodology of Ghosh (1992) is compared to a direct implementation of constraints, and it is suggested that the former approach could feasibly be incorporated into commercial mixed model software. Simulation studies and a real-data example illustrate the main points and support the conclusions.