Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand. According to projections, the worldwide population is anticipated to increase by 2 billion individuals within the next three decades, resulting in a total of 9.7 billion people by the year 2050, up from 7.7 billion as recorded in 2019 . There iApart from the rising population and increased food demand, climate change is also escalating food insecurity, threatening crop productivity and agricultural sustainability. The combination of an El Niño event and heat-trapping greenhouse gases will undoubtedly result in record-breaking global temperatures during the next five years. It is projected that there is a 66% risk of the annual mean near-surface global temperature surpassing pre-industrial levels by at least 1.5 °C for a minimum of one year during the period spanning from 2023 to 2027. This entire five-year span has a 98% risk of becoming the warmest [2) emissions by 5.2% in 2020. However, as COVID-related limits were lifted throughout 2021, energy-related CO2 emissions increased by 6%, reaching a record high [Reduced energy demand owing to the societal and economic disruptions brought on by COVID-19 had decreased global carbon dioxide , rice (Oryza sativa), and maize (Zea mays), is inadequate to satisfy future demand [Traditional breeding methods have yielded crops that are rich in nutrients and have high yields, which can be mechanically harvested to gratify the growing requirements for food among the population. Nonetheless, the at-present rate of yield enhancement for major crops, including wheat  is a comprehensive approach to landscape management that encompasses cropland, livestock, forests, and fisheries and seeks to rationalize the effects of climatic alteration while ensuring food security. CSA has set its sights on concurrently accomplishing three goals: increasing productivity, increasing the adaptability to abiotic and biotic stressors, and reducing emission patterns, thereby protecting the environment . ClimateOne of the versatile crop-breeding selection methods based on gene-sequencing technology is genomic selection. Genomic selection analyses the variation within a crop population using gene-sequencing techniques . The GS Moreover, the breeding cycle is accelerated through GS, making it easier to identify superior genotypes rapidly . GenomicThis review intends to systematically and comprehensively integrate all the relevant, scientific, and policy information that are available in relation to climate change, population census, food demand, integrated genomic selection, climate-smart crops (CSCs), and incidental material with special reference to abiotic stresses. The review has been split into parts such as CSCs, integrated genomic selection strategy, the importance of genetic gain, and the benefit of GS that forms the basis of preferring IGS over other breeding approaches, The review also highlights the opportunities and challenges associated with IGS vis-à-vis climate-smart crops.The review incorporates the information in an integrative mode and the available information was pooled by retrieving papers from web-based resource depositories such as PubMed, Google Scholar, Web of Science, Scopus, and individual journal platforms. The information was collected by using keywords like genomic selection, integrated genomic selection, omics approaches for genomic selections, genomic selection + climate smart cereals, integrated genomic selection + cereal, climate-smart crops, climate-smart crops + cereals, speed breeding, approaches of plant breeding, climate change + impacts on cereals, population growth + food demand, sequencing technologies, etc. Recent papers were selected on priority except for the pioneering work, which required due mention in the manuscript.The rising population and rapidly changing climate pose many challenges and risks to our ecosystem. Climate change has affected ecosystem productivity and is expected to reduce staple crop yield by 30% ,28,29. PO. sativa L.), GS demonstrated improved prediction accuracy when used in conjunction with a phenological model to forecast the emergence date of untested genotypes in untested conditions [Lolium perenne L.) breeding [The issue of climate change has gained widespread recognition as a significant challenge. The decline in yields of key food crops has resulted in an expansion of the demand–supply gap for the worldwide population . Thus, dnditions . Sensor-breeding  Genotyping and phenotyping of entities in a reference population and the building of a statistical format to study the effects of SNPs on morphological makeup, creating relational forecasting equations.(2)Newer candidates might not be phenotyped but are genotyped. Additionally, breeding values are calculated using phenotypic data and prediction models . Owing t(a) To overcome the limitations of conventional phenotypic selectionPhenotypic behaviors of crop plants are the pivotal characteristics on which the entire plant breeding and management approach is based. Classical plant breeding techniques rely on morphological or phenotype-based (marker) selection methods ,57. The Traits that are difficult to be improved by conventional phenotypic selection such as low penetrance or complex inheritance can be improved by molecular markers as the former selection method is time-consuming.(b) Freedom of choice of selection at a specific stageSelection at specific environmental and developmental stages is helped by MAS, as applied in excluding two biotic problems of Cassava genetic improvement, namely the assortment for resistance to CMD in Colombia and whitefly disease.(c) Helpful for backcross breedingMAS is used for speeding up backcrossing for the maintenance of recessive alleles where molecular markers help the linkage drag, a challenging phenomenon to remove all unwanted genes.(d) Pyramiding multiple monogenic traitsPyramiding indicates the process where multiple genes/QTLs are combined into a single genotype. It is helpful for those traits that involve several QTLs for a single target trait with multipart inheritance, namely pest and disease resistance or quality traits. Although traditional or classical breeding can also be used to create pyramids, this is exceedingly difficult or impossible during the early generations. The molecular markers clear all these obstacles of conventional breeding and facilitate the pyramiding process.Apart from the above advantages, MAS can be performed by obtaining material directly from plant sources, ranging from seed tissue to any stage or part of the plant, making it useful to work with at any stage. It also gives additional opportunities to work at certain crucial stages when specific traits are expressed, such as pollen development to study male sterility, study of grain sensitivity to photoperiod, or fruit quality . MoreoveA variation of the MAS technique that utilizes SNP markers is known as GS. These markers span an organism’s whole genome. The number of impacts per QTL that must be estimated is minimal because the markers are expected to be in LD related to the QTL. According to simulation studies, the breeding value may be anticipated with a precision of 0.79 to 0.85 ,77. A stGenomic selection possesses the ability to considerably boost the genetic improvement rates in plant breeding programs. It offers the advantage of estimating all marker effects simultaneously, resulting in greater gains from selection . Unlike The problem of association mapping and QTL discovery is finding and measuring uncommon genetic markers with delicate impacts on economically relevant traits that are heavily impacted by environmental influences. To address this difficulty, statistical models for predicting the link between markers and traits are being created, taking into consideration the genetic makeup of the particular feature of concern . The intAdvances in GS and GP have increased data volume and complexity. This has inspired multidisciplinary research endeavors that combine various fields such as computer science, quantitative genetics, genetics, mathematics, physics, statistics, machine learning, and bioinformatics . This frGenomic prediction models tackle various prediction challenges by simulating real-world forecasting scenarios. To address the prediction issues encountered in GS research, several random cross-validation procedures have been developed. These procedures involve combinations of untested lines (LU), tested lines (LT), tested environments (ET), and untested environments (EU), resulting in four fundamental scenarios. For example, LU-ET  represents the prediction of newly generated lines or cultivars that have not previously been tested . A signiIn the context of forecasting features related to Fall Armyworm (FAW) and Maize Weevil (MW) resistance, several GP models have been employed, utilizing statistical and machine-learning algorithms from semi-parametric, parametric, and nonparametric approaches. As observed in previous model benchmarking reports, these GP algorithms perform differently across various features, but the differences in predictive variances are generally minor, even when large training sets are involved .To accurately predict GEBVs in a variety of crops, for GS, various methods have been chosen. These include non-linear semi-parametric methods like reproducing kernel Hilbert space (RKHS) , linear In GS, which makes use of genomic data to predict an individual’s genetic potential for particular traits, the construction of a TP is an essential stage. Breeders try to replicate the genetic variation found in the target population when creating the TP . This enThe data provide the foundation for determining the relationship between genotype and phenotype and allows for the precise prediction of breeding values. Breeders also need to prepare the genotyping method to provide enough genetic coverage while balancing economic considerations and marker density . BreederDespite the assumptions of the GBLUP model, quantitative traits are frequently impacted by only a small subset of markers, contrary to predictions. When predicting closely related individuals, both BLUP and Bayesian models exhibit similar accuracy. However, for populations distantly related to the training and test populations, Bayesian models have demonstrated superior performance over GBLUP .(i)The size, genetic diversity, and relationship of the training population (TRN) to the test population (TST) are all critical factors determining genomic prediction precision. Specifically, the relationship between the cultivars in the TRN and those in the TST set, whether they are closely or distantly related, has been shown to impact the effectiveness of genomic prediction ,109.(ii)The heritability of the traits under selection is another crucial factor affecting the accuracy of genomic forecasting. Characters with increased heritability, which are less complex and influenced by fewer genetic factors, can be effectively predicted using a smaller number of markers with comparatively greater effects ,109.(iii)The accuracy or truth of genomic prediction is poorer for complicated traits that are influenced by an abundance of markers that do not exist in LD associated with QTL. Where there is a lack of correlation between markers and actual genetic factors influencing the trait, the accuracy of genomic prediction decreases ,109.The accuracy of GP is influenced by genetic factors when a large number of loci affect a trait. Applying genomic prediction (GP) in breeding poses challenges on multiple levels and depends on various other factors such as:Researchers and analysts rely heavily on the statistical models provided as an essential instrument when investigating the connection between independent and dependent variables ,111. TheThe core concepts of validity and reliability serve as the conceptual foundations for the theoretical underpinnings of statistical design ,113. The(a)Model StructureDetermining the model’s structure is the first stage in the statistical model creation process. When discussing a model, the term “structure” refers to the mathematical form used to represent the association among the dependent and independent variable(s) . Linear (b)Variable SelectionThe next stage after the assessment of the structure of the model is to choose the independent variables that are pertinent to the study. In this process stage, you may have to identify potential predictor variables based on previous research or your theoretical understanding of the system being investigated ,122. Res(c)Parameter EstimationAfter the model’s structure and variables have been determined, the following stage is to estimate the model’s parameters by utilizing the information gained. Calculating the coefficients that explain the association among the dependent and independent variables is a necessary step in estimating the model parameters ,125. Cal(d)Model EvaluationThe final phase in the process of building statistical models is to analyze the performance of the model. Evaluation of a model involves determining how well the model matches the available data and how well it can predict outcomes based on newly acquired data. When evaluating a model, several different metrics are taken into consideration . Some ofIn the GS process, the initial step involves a basic linear model, often referred to as ordinary least-squares regression (OLS). This model is represented by the equation Y = 1 nμ + Xβ + ε, where Y is the response variable, μ is the overall mean, X represents the marker genotype, β is the marker effect vector, and ε is the residual error.p >> n), especially when thousands of genome-wide markers are used [However, a challenge arises when the number of markers (p) surpasses the number of observations (n), which means that there are more markers than genotype/individual/line data points. This is known as the overparameterization problem or the “big p and small n” problem  is a valuable method. RR is a type of penalized regression that introduces a penalty term to the least-squares regression equation. This penalty helps to stabilize the estimates and can effectively handle situations with a large number of markers relative to the number of observations .In the ridge regression (RR) model, each marker is assumed to contribute equally to the variance. However, this assumption does not hold for all traits, as different markers may have varying impacts on the trait variance. Therefore, it becomes essential to predict marker variations based on the specific genetic architecture of the trait under consideration .To address this, several Bayesian models have been proposed that incorporate a prior distribution of marker effects. These models utilize the posterior distributions of marker effects and other derived quantities, such as Bayesian LASSO and Bayesian ridge regression (BRR), to conclude the model’s parameters. Another approach is the best linear unbiased prediction (BLUP), which offers different variations for marker-based models. Examples of these variations include single-step GBLUP (ssGBLUP), genomic BLUP (GBLUP), GBLUP with linear ridge kernel regression (rrGBLUP), and ridge regression BLUP (RRBLUP) . AlthougPlant genomics and molecular breeding offer a wide range of technologically advanced solutions that can handle well the issues around sustainable living and help to increase the genetic diversity of different plant species . GenomicCoffea arabica [Triticum sp. [Cajanus cajan [Cicer arietinum, Arachis hypogaea [Avena Sativa [O. sativa [Genomics-assisted breeding, at present, is employed to genetically improve plant species like  arabica , sorghum arabica , Virgini arabica , Triticuicum sp. ,157,158,icum sp. , Cajanusus cajan ,160, Cica Sativa , O. sati. sativa , and manPrograms for GAB rely heavily on advanced BPs like Nested Association Mapping (NAM) and Multiparent Advanced Generation Inter-Cross (MAGIC). These populations offer priceless genetic resources and make it possible to quickly identify and apply favorable features for crop improvement ,164. TheThese advanced BPs help us dissect complex traits  and nailCereals play an important role in our daily diet, accounting for around half of our entire dietary energy supply. With global food security concerns and difficulties created by changing climates, there is an urgency to produce better-yielding bread wheat types that are more resistant to unfavorable environmental circumstances .Genomic selection is now recognized as a viable breeding technique to solve these issues, especially with regard to complex stress tolerance traits. In the instance of maize breeding, where tolerance to both abiotic and biotic stress is critical, GS has proven to be quite efficient. GS is the process of incorporating genome-wide marker data into a model to assess the genetic abilities of prospective plants for selection. For example, maize, which is the world’s fastest-growing crop and a significant contributor to the coarse grains trade, not only provides essential nutrition but also has diverse industrial applications. However, maize is very vulnerable to biotic and abiotic stressors, resulting in lower worldwide yields. As a result, increasing maize productivity has been a primary emphasis in maize breeding efforts, particularly given the problems posed by climate change .To overcome the daily challenges faced due to climate change and an increasing population, an efficient and sustainable production system is required that minimizes the pressure on the ecosystem. Crop varieties with high yields and low resource requirements are essential for such production systems to address these difficulties. In the wake of uncertain worldwide food security and changing climates, breeding bread wheat with high production potential and enhanced resistance to adverse conditions, for example, is critical. However, developing such varieties is a complex task due to the genetic system governing most crop productivity traits, where the majority of genes have minimal effects. This complexity is further compounded by low heritability and high levels of epistasis. While conventional breeding methods have generated various varieties, the genetic gain per unit of time is not as substantial as with GS, although they offer the opportunity to accelerate the selection cycle .Fusarium head blight [Septoria tritici blotch [Stagonospora nodorum blotch [Grain yield is an important factor that is impacted directly or indirectly by other qualities such as the thousand-grain weight, the number of tillers bearing panicles, the number of grains per panicle, and the number of filled grains per panicle. The effectiveness of genomic prediction for these traits has been evaluated using different TP and model types, with the accuracy of genomic prediction varying based on the trait’s heritability, TP, and models employed. While GS has been applied to only a limited number of cereals, it has demonstrated its value in enhancing tolerance to quantitatively controlled biotic stressors in cereals. Among the investigated biotic stressors, wheat has been the focus of the majority of studies on the use of GS for disease confrontation, including various rusts, d blight , Septorii blotch , tan spoi blotch , and Stam blotch , along wm blotch . TraditiIn plant breeding, a revolutionary method known as speed breeding has evolved. This method enables researchers to accelerate the growth of crops beyond what is possible with traditional methods ,181. TheThe procedure of GS entails the investigation of plant DNA to detect particular markers that are connected to desirable qualities, such as greater productivity or resistance to diseases. Plant breeders now have access to a powerful tool that enables them to accelerate the generation of novel and improved agricultural cultivars in a substantially shorter amount of time. This tool is a combination of speed breeding and GS . BreederThe acceleration of breeding cycles has gained significance in creating novel and enhanced crop cultivars that can cater to the demands of the expanding populace . The conThe objective of expediting breeding cycles is to diminish the duration between consecutive plant generations, thereby enabling breeders to expedite the process of cultivating favorable characteristics such as enhanced resistance to drought or diseases ,190. ThrMarker-assisted selection is a technique that has been increasingly utilized in recent times. Marker-assisted selection utilizes molecular markers, which are distinctive DNA sequences that serve as indicators for the existence of a particular gene or trait ,192. ThrAccelerating breeding cycles is a promising innovation in plant breeding, with the potential to increase agricultural yields ,202, bolIn modern genetic research and analysis, high-throughput genotyping (HTG) and genotype imputing are two critical procedures. They are crucial in unraveling the intricacies of genetic variants and their effects on many traits and diseases . High-thGenotype imputing, on the other hand, is a computational technique that allows researchers to predict or “impute” the genotypes of individuals for markers that have not been directly genotyped but that are correlated with the genotyped markers . ImputatHTG and genotype imputing are approaches that work well together. While HTG gives direct genotype data for a selection of markers, imputation broadens the accessible genotyping information by estimating the genotypes for additional markers. These technologies, when combined, provide a comprehensive and profitable alternative for investigating genetic variants on a large scale . The appHigh-throughput phenotyping is an innovative approach that aims to accelerate the analysis and characterization of plant traits on a large scale. It involves the use of advanced technologies, automated systems, and data analytics to efficiently capture and analyze phenotypic data from plants . PhenotyHigh-throughput phenotyping platforms utilize a range of technologies and imaging techniques, including high-resolution cameras, sensors, and robotics, to capture detailed phenotypic data at various scales, from individual plants to entire fields or greenhouse setups . These aOne of the primary benefits of HTP in plants is its potential to speed up breeding programs. By rapidly and accurately assessing numerous plant traits, breeders can select individuals with desirable characteristics for further breeding, resulting in the production of better crop variants in a shorter time frame . High-thIn addition to breeding, HTP has broad applications in plant research and agricultural studies. It allows scientists to investigate the impacts of temperature, humidity, and nutrient availability on plant growth and development . High-thFurthermore, HTP plays a crucial role in phenomics research, where large-scale datasets of phenotypic information are integrated with genotypic data such as genetic markers or sequencing data. This integration enables the identification of genotype–phenotype associations and the discovery of genes or genomic regions underlying specific traits . High-thThe adoption of HTP in plant sciences has transformed the field, opening up new opportunities for research, breeding, and crop improvement. It allows researchers to study complex plant traits, capture phenotypic variation in diverse environments, and generate large datasets for comprehensive analyses . MoreoveHigh-throughput phenotyping in plants is a commanding instrument that revolutionizes the scrutiny and understanding of plant traits. By automating data collection, leveraging advanced imaging technologies, and employing data analytics, HTP enables the rapid and comprehensive assessment of plant phenotypes on a large scale. This approach has significant implications for crop breeding, plant research, and agricultural sustainability, contributing to the generation of improved crop variants and the advancement of global food security.In addition to the HTG assay, NGS has unusually accelerated the development pace of genetic techniques for staple crops. Other than technological upliftment, conceptualization is also being used to design population experiments. The concept of traditional QTL mapping is gradually changing and being replaced by second-generation sequencing of multiple alleles, traits, and recombination. A plethora of methods has been introduced, like restriction-site-associated DNA (RAD) sequencing, genotyping by sequencing (GBS), and whole genome resequencing (WGRS) for genotyping, which is a major paradigm shift in discovering and mapping DNA markers ,227. TheSecond-generation sequencing advancements allow the identification of innumerable SNPs in the plant genome, which are explained as follows:This is a large-scale genotyping technique that can analyze 1536 polymorphic sites in 384 individuals. It utilizes allele-specific oligo (ASO) hybridization along with fluorescently labeled universal primers for distinguishing genotypes . SeveralThis is a reduced form of representation of sequencing data that uses restriction-digested genomic DNA samples and the multiplexing of samples within the same lane by “skim GBS”. This was first demonstrated in barley and maize . This meKompetitive Allele-Specific PCR is a competitive allele-specific PCR-based genotyping technology. It employs allele-specific primers and a competitive allele-specific PCR technique to differentiate between genotypes. This method is inexpensive, scalable, and enables high-throughput SNP genotyping. It is widely used in plant breeding programs and genetic research .The TaqMan test is a probe-based genotyping technology that detects SNP alleles using allele-specific fluorogenic probes. For SNP genotyping, it employs the fluorescence resonance energy transfer (FRET) concept. In SNP identification, this approach provides great specificity, sensitivity, and accuracy. It has seen widespread application in plant genetic research and breeding programs .High-resolution melting analysis is a post-PCR technique for detecting SNP differences based on the melting behavior of DNA samples. It entails progressively increasing the temperature of PCR-amplified DNA fragments, observing the melting curve, and determining the SNP genotypes based on the unique melting profiles. HRM analysis is a quick, low-cost, and sensitive method for genotyping SNPs in plant genomes .MassARRAY is a genotyping platform that measures the mass of allele-specific PCR products using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) . To idenRAD-seq is a method for sequencing genomic areas flanked by particular restriction-enzyme recognition sites. It entails digesting genomic DNA with restriction enzymes, ligating adapters to the fragments, and analyzing the fragments using next-generation sequencing systems. RAD-seq enables the identification and genotyping of thousands of SNPs across the genome and has been extensively utilized in population genetics, phylogenetics, and association studies .Amplicon sequencing entails utilizing PCR to amplify specific target sections of the genome containing the SNPs of interest and then sequencing the amplicons. It is a targeted method for efficiently identifying and genotyping SNPs in specified genomic areas. Amplicon sequencing is frequently utilized in targeted resequencing investigations because it is both inexpensive and accurate .To increase the genetic variety of our crops, we must make use of the natural gene polymorphisms that exist in the populations. Globally, scientists are working to create pangenomes and super-pangenomes of different plant species, which will eventually pave the way for groundbreaking work and speed up the study of molecular breeding and plant genomics ,244. WhiO. sativa [O. sativa [Pangenomes of many crops have been developed to date, including . sativa ,255,256,. sativa , wheat [. sativa , pearl m. sativa , soybean. sativa , chickpe. sativa  and othe. sativa , legumes. sativa , etc. Ef. sativa  and O. s. sativa .Recently, scientists all around the globe have started working on graph pan-genomes, which provide better visualization of the information related to the positioning of the novel sequences; they even help preserve the contiguity of the sequences as well as structural variations ,265,266.The main bottlenecks in GS usage are the requirement for a large number of markers and the costs associated with generating them . HoweverRidge regression (RR) was among the first methods of GS. The RR method, in general, performs better than subset selection when prediction is of primary interest in regression problems . It is c(1)Bwgs. cv, which performs replicated random model cross-validation on a training set of lines having genotypic and phenotypic data;(2)Bwgs.predict, which predicts the GEBV for those lines for which the genotype is known .Breed Wheat Genomic selection (BWGS) is an integrated R library developed in a French cooperative private–public partnership project called Breedwheat for the easy computation of GEBVs . Its two(a)imputation of missing data;(b)dimension reduction;(c)GEBV estimation.The workflow involves:The program offers 15 choices among non-parametric and parametric methods. The most influential factors for the prediction ability are the extent of the TP as well as the minimum number of markers required for encompassing every piece of QTL information.p-values to construct an optimal marker set with an increased phenotypic prediction accuracy. The best prediction model is subsequently built with the optimal marker set. This tool has three steps.This is also an R-based marker-set selection tool  for the (1)Preparation:This needs four types of input files: a genotype information file, a phenotype information file, results from the GWAS, and a test sample list.(2)Marker selection:Here, the forward selection technique of regression scrutiny is applied and the sequential selection of SNP markers is performed.(3)Final modeling.Here, model prediction is executed using various methods such as RRB, random forest (RF), deep neural network (DNN), and convolution neural network (CNN).SolGS is open-source and runs on a Linux server. It utilizes RRBLUP and GBLUP methods for estimating GEBVs . It addrThe BGLR R-package is an extension of BLR, which simplifies the genomic data analyses in those regressions where the number of parameters is larger than the sample size. It relies on Bayesian regression methods that permit the integration of various parametric and non-parametric shrinkage and variable selection procedures. The software is useful for genomic as well as non-genomic applications. The algorithm is based on a Gibbs sampler and has scalar updates .The GenSel program was developed at Iowa State University, USA, as a part of the project entitled “Bioinformatics to Implement Genomic Selection (BIGS)”. It has been widely used to estimate molecular breeding values during animal selection trials, and it is based on the SNP for the desired phenotype. Three different input files are required for this program: a file containing genotype or marker data information, a phenotype file, and a map file. The software can be used to estimate the marker effect of a training data set using different Bayesian approaches . In the STGS, another program in R, stands for single-trait-based GS as it performs GS only for one trait. This package is a single-step solution for genomic predictions by estimating marker effects in terms of GEBVs. The program uses common methods such as RR, BLUP, ANN, LASSO, SVM, and RF.MTGS, as opposed to STGS, performs genomic predictions and GEBV computations by predicting marker effects using multi-trait data . It alsoIme4GS is the successor package to the Ime4R package for fitting linear mixed models. Ime4Rs do not allow for the correlation of individuals or groups of persons, which poses a constraint in genetic studies. Ime4GS focuses on fitting LMMs with user-defined covariance structures, bandwidth selection, and genomic prediction. Hence, it can fit LMMs using different variance–covariance matrices. The program introduces random and fixed impacts, as well as accompanying variance–covariance matrices that yield fixed and random effects .The three most prevalent cereals with respect to their consumption and production are rice, maize, and wheat . The susThe significant genome-assisted selection approaches are GAB, NAM, and MAGIC , as discSpeed breeding can be achieved within just 12–18 months to hasten the editing, crossing, and selfing of generations in cereals . As it rAlthough innovations in genomics and phenomics are providing a better understanding of the complex biological mechanisms behind plants’ response to environmental stresses, associating genotype with phenotype is a major problem that delays the optimized use of high-throughput genomics and phenomics . There iAs per the FAO estimate, global cereal production (2022) has shown a decline in maize, followed by rice and sorghum. The Sixth Assessment Report (AS6) of IPCC (2023) has revealed that a global temperature rise of 1.1 °C has taken place and that additional warming is expected in the coming future that will lead to an increased loss of crop biodiversity and yield. Thus, apart from conventional agricultural practices, modern rapid, efficient molecular marker-based technologies are urgently required to be scaled up for crop improvement in such a way that the emerging crop varieties show high yield as well as tolerance to multidimensional stress. Genomic selection, an advanced MAS tool in plant breeding methods, is one among several approaches to meet these requirements. It uses genome-wide markers (SNP markers) and phenotype information that make it rapid and accurate in identifying superior lines. Genomic selection achieves further higher genetic gain by integrating machine-learning-based predictive modeling in MAS that reduces the life cycle of crops. But as a limitation, the improved accuracy of GS requires an extensive period and a high developmental cost of SNP markers. Nevertheless, technological advancements such as high-throughput genotyping, phenotyping, genotype imputing, and sequencing technologies provide data at a low cost. To improve GS further, phenotypic data integration from diverse sources such as light, cameras, sensors, computers, and environmental data is being performed. The statistical tools for integrated genomic selection  are also being continuously improved to be more accurate and user-friendly. Currently, CRISPR /Cas9, RNA-directed nucleases (RGENs), TAL effector proteins (TALENs), and zinc finger nucleases (ZFNs) are bringing a revolution in genetics and impacting plant breeding by editing the genomes of economically important plants.Apart from molecular technologies, ecosystem-based adaptation is being suggested by IPCC, which can help in mitigating climate change. Climate-smart crops  can be developed using GS and incorporated into sustainable agricultural practices. This will not only contribute to increased productivity and enhanced resilience but also create crop diversity and improved carbon sequestration by C4 cereal crops. There is a pressing need to convert the climate change-induced food insecurity threat into an opportunity by pushing future research priorities supported by prompt government policies that give impetus to diversifying the food basket.


Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in social interaction and communication, whose etiology is heterogeneous, including genetic, epigenetic, and environmental factors. It is associated with restricted interests and stereotyped behaviors with high prevalence rates in general population, neurobiological bases and high heritability.The aim of our study is to identify the possible phenotype-genotype relationships regarding neurodevelopmental disorders and to evaluate a correlation between genomic alterations and the manifestations of the overt and subthreshold ASD in a family administered psychiatric clinical evaluations at our hospital.The family M underwent a psychiatric evaluation through the MINI interview according to the SCID-5 criteria, the AQ, ADAS, PAS-SR, SHI-SHY,SHY-OBS to assess respectively the subthreshold traits of ASD in adulthood, the panic-agoraphobic, social-phobic and obsessive-compulsive spectrum and CAT-Q Italian version, to evaluate social camouflage behaviors typical of ASD individuals. Array Comparative Genomic Hybridization was used for studying DNA imbalances in this family.We found that Mrs.A, her father, her brothers and her older sister had a microduplication, very likely pathogenic, since it has been never reported in healthy subjects and harbors several genes. It could be related with overt and subthreshold traits of ASD. From the questionnaires administered and from the clinical interview, it emerged that Mrs.A is affected by ASD and Bipolar Disorder. Her twin brothers have been evaluated at early ages by child neuropsychiatric clinic and they were diagnosed with ASD and mental and psychomotor impairment. Her father was reported a significant trend in ADAS, AQ, PAS-SR, SHI-SHY and SHY-OBS scores. Finally, about her older sister, even if our results were not significant for an ASD diagnosis, we speculated that she performed a high score in some ADAS items and in the CAT-Q but not in the AQ. Females generally tend to attract fewer attention than males thanks to their better coping and camouflaging mechanisms as well as their ability to “disappear” in large groups.Genetic knowledge can have a relevant clinical impact; a genetic etiology can be identified in individuals with ASD, leading to the identification of treatable psychiatric comorbidities. Furthermore, knowing the causative genetic variants of ASD could provide crucial information for genetic counseling as well as to understand the neurobiology of these disorders and to contribute to an early diagnosis.None Declared


Zea mays L.). Studying the genetic basis of ED in maize is of great significance in enhancing maize GY. Against this backdrop, this study was framed to (1) map the ED-related quantitative trait locus (QTL) and SNPs associated with ED; and (2) identify putative functional genes that may affect ED in maize. To accomplish this, an elite maize inbred line, Ye107, which belongs to the Reid heterotic group, was used as a common parent and crossed with seven elite inbred lines from three different heterotic groups  that exhibited abundant genetic variation in ED. This led to the construction of a multi-parent population consisting of 1215 F7 recombinant inbred lines (F7RILs). A genome-wide association study (GWAS) and linkage analysis were then conducted for the multi-parent population using 264,694 high-quality SNPs generated via the genotyping-by-sequencing method. Our study identified a total of 11 SNPs that were significantly associated with ED through the GWAS, and three QTLs were revealed by the linkage analysis for ED. The major QTL on chromosome 1 was co-identified in the region by the GWAS at SNP_143985532. SNP_143985532, located upstream of the Zm00001d030559 gene, encodes a callose synthase that is expressed in various tissues, with the highest expression level in the maize ear primordium. Haplotype analysis indicated that the haplotype B  of Zm00001d030559 was positively correlated with ED. The candidate genes and SNPs identified in this study provide crucial insights for future studies on the genetic mechanism of maize ED formation, cloning of ED-related genes, and genetic improvement of ED. These results may help develop important genetic resources for enhancing maize yield through marker-assisted breeding.Ear diameter (ED) is a critical component of grain yield (GY) in maize ( Ear diameter (ED) is one of the most important components of grain yield (GY) in maize, and understanding its genetic base will facilitate increasing GY, which is a major breeding goal for maize. Maize is an important source of cooking oil, biofuel, and animal feed. By 2050, the predicted 9 billion people in the world will require 70% more food than today’s population .Qednr9-1, Qednr9-2, Qedns4-1, Qedns4-2, and Qedns9-1) on chromosomes 4 and 9 in high and low nitrogen environments, respectively, using a segregating population consisting of 239 recombinant inbred lines (RILs) at the ninth generation or F9RILs [2 segregating population from a sweet maize inbred line and its space-induced mutant [q10sED8-1 and q10sED9-1) on chromosomes 8 and 9, respectively, using a segregating population of 233 families derived from maize inbred lines B73 and SICAU1212 [2 population derived from maize inbred lines PB260 and PB266 [qEAD-1, qEAD-2, qEAD-3, qEAD-4, and qEAD-5) on chromosomes 1, 4, and 7, using a segregating population of 199 F2 individuals obtained by crossing maize inbred lines SG-5 and SG-7 [As a typical quantitative genetic trait, ED is regulated by multiple genes and greatly impacted by environmental factors ,3. QTL md mutant . Yang etICAU1212 . Mendes-A genome-wide association study (GWAS) is a powerful tool for analyzing the genetic structure of complex traits and overcomes the limitations of QTL mapping by narrowing down candidate regions. In recent years, GWAS has been successfully used to identify QTLs and candidate genes for maize kernel-related traits, providing valuable insights into maize genetic architecture ,11,12,13To overcome spurious associations and increase the detection power of rare alleles in crop species, multi-parent populations using different cross designs have been developed . Among t7RILs was constructed from the above crosses [To gain a better understanding of the genetic mechanism behind maize ED, and to identify significant QTLs, SNPs, and important candidate genes, seven elite inbred lines , with abundant genetic variations in ED from the Suwan1, Reid, and nonReid heterotic groups, were used as donor parents and crossed with an elite maize inbred line, Ye107, from the Reid heterotic group. A multi-parent population consisting of 1215 F crosses . This mu7RIL families was constructed by utilizing an elite maize inbred line, Ye107 , from the Reid heterotic group as a common parent crossed with seven elite female inbred lines: D39 (Suwan1), Y32 (Suwan1), Q11 (Reid), TRL02 (nonReid), CML373 (nonReid), CML312 (nonReid), and CML395 (nonReid) from the three heterotic groups, respectively.Our team proposed a “three heterotic group” pattern consisting of Suwan1xReid, Suwan1×nonReid, and ReidxnonReid, based on long-term breeding practice ,32,33. Ip < 0.001), and combining abilities, as well as heterosis in actual breeding practice [These inbred lines were purposely selected based on their heterotic group classification, significant differences in ED  and Baoshan  in Yunnan Province, China in 2019 using a randomized complete block design (RCBD) with three replications at each location. Each F7RIL was planted in a two-row plot with row spacing of 70 cm and 14 plants per row, resulting in a plant density of approximately 62,112 plants per hectare. The detailed methods of measuring ED are as follows:The multi-parent population, consisting of 1215 maize F7RIL. The width of the mid-section of each drying ear was measured using a vernier caliper, and the mean ED of the ten ears was recorded as the final ED (cm) of each F7RIL.Ten ears were consecutively harvested from 10 plants of each FThe collected data was analyzed using the best linear unbiased prediction (BLUP) method with the lme4 package in R (v3.2.2). The analysis was performed using the following formula:Multi-locations:ijlkY, μ, iLine, and lLoc represent the ED phenotype values of each ear, intercept, ith line effects, and lth location effects, respectively. Repj represents the jth replication effect, and ijlkε represents the random effects. (Line × Loc)ij is used to display the interaction of the ith line at the jth location, and Rep (Loc)jl shows the nested effect of the jth replication within the lth location. The One-location:Mid-parent heterosis (MPH) was determined as follows:7RIL using the cetyl trimethyl ammonium bromide (CTAB) method [7RIL was digested with the restriction endonucleases PstI and MspI  and ligated with barcode adapters using T4 ligase (New England BioLabs). GBS DNA libraries were constructed and sequenced following the GBS protocol [The genomic DNA was extracted from the seedling leaves of each F) method . Then, tprotocol .https://github.com/Euphrasiologist/GBS_V2_Tassel5, accessed on 6 March 2022) [ftp://ftp.ensemblgenomes.org/pub/plants/release-37/fasta/zea_mays/DNA, accessed on 6 March 2022) [All the ligated samples were pooled and purified using the QIAquick PCR Purification Kit . Primers complementary to both adaptors were used for polymerase chain reaction (PCR) amplication. The PCR products were then purified and quantified with the Qubit dsDNA HS Assay Kit . After selecting PCR products of 200–300 bp size in an Egel system (Life Technologies), the library concentration was estimated using a Qubit 2.0 fluorometer and the Qubit dsDNA HS Assay Kit (Life Technologies). The sequencing was carried out using an Ion Proton sequencer  with P1v3 chips. The final reads were generated using TASSEL v5.0 (ch 2022) . Prior tch 2022)  with thech 2022) . In totach 2022) .p-value thresholds (p < 1 × 10−6) were set to control the genome-wide type 1 error rate.The GWAS was conducted using the efficient mixed-model association (EMME) analysis method in the GEMMA (genome-wide efficient mixed-model association) software package . The folWe used PLINK  to calcu7RILs families were selected from the multi-parent population for genetic linkage analysis. We have analyzed all the other populations and phenotypes. Finally, we carefully selected the population and phenotypes with the best effect to display the results on the map. We defined the best results based on whether the SNPs identified by GWAS and QTL by linkage analysis were present at the same location. The Ye107/Y32 was the only population where the SNPs and QTL overlapped. Allelic SNPs were then utilized to construct genetic linkage maps through the JoinMap 4 software [p ≤ 0.05 [2).The subpopulation Y32×Ye107 and its 162 Fsoftware . The linsoftware . The LODp ≤ 0.05 . QTLs wiWith the dataset obtained from GWAS, and with SNPs that were significantly associated (−log10(P) >= 4.5), we utilized the PLINK software v1.9  to calcup < 0.01) than Ye107. Subsequently, the F7RILs families of the seven subpopulations showed significant differences in ED (p < 0.0001) for the ED phenotype. Overall, the multi-parent population exhibited wide variations for ED; however, the variation of ED was consistent between the two environments, indicating high reliability of the phenotypic data for further analysis.The eight parents of the multi-parent population exhibited large variations in ED, ranging from 3.4 to 4.6 cm. The common parent, Ye107, had the smallest ED, while the seven donor parents had statistically significantly larger ED  in Dehong and Baoshan and tended to follow a normal distribution (7RILs families of the multi-parent population. A threshold of −log10(P) > 4.5 was set, and 11 SNPs were identified to be significantly associated with ED, 6 in Dehong and 5 in Baoshan  demonstrated similar relationships between the haplotype and ED . One significant QTL was detected on chromosome 1, explaining 7.1% of the ED phenotypic variation. Additionally, two significant QTLs were detected on chromosome 9, explaining 9.2% and 10.5% of the phenotypic variation, respectively, for ED . Notablynthase 1 C. The Zm tissues , with thpulation E. The pr of HapB F. The pre and ED F,G. ThesZm00001d015649, Zm00001d015650, Zm00001d015651, and Zm00001d015653) in this chromosomal region. Among these genes, Zm00001d015651 encodes lycopene β-cyclase and chloroplast-specific lycopene β-cyclase, which are involved in carotenoid biosynthesis. Unfortunately, the functions of the other three genes have not been reported yet. These genes represent important candidate genes that may affect ED and warrant further investigation in future studies.Epistasis analysis of significant SNPs in the gene revealed that the SNP (SNP_143985532) identified through both linkage and GWAS analysis exhibited a significant epistasis effect on an SNP (SNP_103192371) from chromosome 5. Further gene mining analysis conducted within 50 kb upstream and downstream of SNP_103192371 identified four genes . Additionally, a significant positive correlation was found between ED_MPH and GY_F1  and between ED_MPH and GY_MPH . These findings indicate that the SNPs sites, QTLs, and candidate genes of ED carried by these donor parents have positive effects on increasing GY and GY_MPH in maize.The ED, GY, and mid-parent heterosis (MPH) are listed in Although many QTLs for ED have been detected in recent years ,5,6,7,8,7RILs from seven subpopulations to map ED QTLs and identify functional genes. A total of 11 SNPs significantly associated with ED were detected by GWAS analysis, including 2 SNPs (SNP_143985532 and SNP_157559780) on chromosomes 1 and 6, which were commonly detected in both the Dehong and Baoshan environments  was located upstream of the gene synthase . CalloseCalS), also known as GSL for glucan synthase-like [GSL7 mutants display semi-dwarf phenotypes and have small and short flowers, peduncles, and siliques [The major composite of callose is β-1,3-glucan, which is synthesized by callose synthases  shows a delayed ovary expansion, small grain, and defective vascular cell pattern [Zm00001d030559 gene was expressed at a low level in various tissues, with the highest expression level in maize ear primordium  produces leaves and associated axillary meristems (AMs), which have the potential to develop into ear primordium. As maize transitions from the vegetative to the reproductive phase, the SAM and AM transform into inflorescence meristem (IM), which ultimately develops into tassels and ears. In normal ear development, the IM initially generates indeterminate spikelet pair meristems (SPMs) in the peripheral region. Each SPM produces two spikelet meristems (SMs), and each SM develops into an upper floral meristem (FM) and a lower floral meristem. The lower floral meristem usually aborts, and only the upper floret develops into a kernel . TherefoZm00001d015649, Zm00001d015650, Zm00001d015651, and Zm00001d015653) in this chromosomal region. Further analysis showed that the gene Zm00001d015651 encodes lycopene β-cyclase (gene: crtY) and chloroplast-specific lycopene β-cyclase (gene: LCY1), which are involved in carotenoid biosynthesis. In Arabidopsis, crtY catalyzes the double cyclization reaction that converts lycopene to β-carotene and neurosporene to β-zeacarotene, and then converts neurosporene to 7,8-dihydro-β-carotene via monocyclic β-zeacarotene. LCY1 is involved in improving salt tolerance by increasing the synthesis of carotenoids, which helps mitigate reactive oxygen species (ROS) and protects the photosynthetic system under salt stress [Epistatic analysis of the significant SNP_143985532 revealed a significant epistasis effect on the SNP_103192371 on chromosome 5. Further gene mining analysis within 50 kb upstream and downstream of SNP_103192371 identified four genes (t stress . HoweverZm00001d034629 (ts6) from the candidate region of locus chr1-298535881 on chromosome 1, which encodes an AP2-EREBP transcription factor. The gene belongs to the AP2/EREBP family and exhibits higher expression in pre-pollination cob. In our study, we also identified the gene Zm00001d030559, located downstream of SNP_143985532 on chromosome 1. This gene appears to be involved in ear primordium formation and is located in a different region on chromosome 1 compared to ts6, discovered by Yang et al., (2020). Hence, we strongly believe that this is a novel candidate gene associated with ED. Another intriguing finding from Yang’s study is the sharing of significant loci by two or more traits. For instance, a significant locus (chr4-190080689) located on chromosome 4 was associated with both ED and ERN. Comparisons with previous studies revealed that the candidate region of the locus (chr1-296366056) is related to both ERN and grain yield, while the candidate region of the locus (chr4-163086906) is associated with ED, KNR, and grain number. These results strongly suggest that candidate genes for one maize yield component trait could also be candidate genes for other yield component traits. Thus, conducting GWAS and QTL linkage analysis for all maize yield components in a single experiment could provide valuable information.A comprehensive study was conducted by Yang et al. (2020) , where tOur research group has proposed a “three heterotic group” pattern for the Suwan1×Reid, Suwan1×nonReid, and Reid×nonReid crosses through long-term breeding practices ,32,33. T1 and GY_MPH, indicating the importance of ED in guiding breeding application research using the “three heterotic group” pattern. These findings have been successfully applied in our breeding practices. For instance, the excellent hybrid variety Yunrui88 (TRL02×Ye107) was approved by Yunnan Province in 2009 and was selected as a leading variety by the Ministry of Agriculture of China in 2014 and 2015. From 2012 to 2014, it was promoted and cultivated in a total area of 350,000 hectares. Additionally, another elite hybrid variety, Yunrui 62, was approved by Yunnan Province in 2017 and passed the national examination and approval by the relevant Chinese authorities in 2020. This hybrid has been promoted and cultivated on approximately 20,000 hectares annually. These results indicate that the “three heterotic group” pattern theory is a feasible guide to deciphering the genetic basis of ED, which could lead to increased GY in future maize breeding programs.Furthermore, our results demonstrated that ED heterosis was significantly positively correlated with GY_FZm00001d030559, located downstream of SNP_143985532, which appears to be involved in ear primordium formation. To validate this finding, the expression level of Zm00001d030559 should be tested in mature ears in future projects. The results obtained from this study not only provide new genetic markers and genomic resources for exploring the genetic structure and molecular mechanisms but also establish a crucial foundation for validating and cloning functional genes related to maize ear diameter.In our study, we conducted whole-genome association mapping and linkage analysis in a multi-parent population, successfully identifying QTLs, SNPs, and candidate genes significantly associated with maize ear diameter. Notably, we discovered a novel gene,


It has been frequently suggested that overall genomic heterozygosity and, particularly, heterozygosity of loci on the so-called major histocompatibility complex (MHC), which is responsible for the recognition of foreign substances/ pathogens and the recognition of self and non-self, is associated with better health and better resistance to infections and parasites. It has further been speculated that such a potentially beneficial heterozygosity can be detected through body odor and facial attractiveness.On the basis of genome wide SNP data  of participants from the Wisconsin Longitudinal Study, we therefore investigated whether homozygosity either on the MHC (measured as inbreeding coefficient) or genome-wide (measured as runs of homozygosity and inbreeding coefficient) is associated with rated facial attractiveness.Although we found that the genome-wide average length of homozygous segments and the genome-wide inbreeding coefficient are significantly negatively associated with some measures of facial attractiveness, if corrected for multiple testing, any significant association was no longer formally significant after correction. In addition, the variance in facial attractiveness explained by the genome wide homozygosity is very low (<0.15%). We did not find any significant association between the inbreeding coefficient on the MHC and facial attractiveness.We only find a weak association of genome- wide heterozygosity and facial attractiveness. It has been frequently argued that fitness benefits may be gained by a preference for mates having traits that indicate “genetic makeup.” However, it remains unclear, which traits do indicate a preferable genetic makeup . An impoIn particular, the so-called major histocompatibility complex (MHC) is assumed to be a genomic area where heterozygosity may confer fitness benefits as it has been shown that heterozygous MHC alleles cause higher immune competence . AccordiThe detection of heterozygous as well as genetically more dissimilar mating partners may be a mechanism to ensure heterozygosity in the progeny , which hIt has also been speculated, how humans may recognize MHC-heterozygosity as well as genetic dissimilarity in potential mates. Two possible characteristics have been proposed: body odor  and was conducted by the University of Wisconsin). Detailed information on individual recruitment, genotyping, and quality control can be found at https://www.ssc.wisc.edu/wlsresearch/documentation/GWAS/Herd_QC_report.pdf. We used the survey question on ethnicity (white versus non-white), but also accounted for population stratification by inclusion of the first 10 principal components of the population structure provided by Wisconsin Longitudinal. Kinship was estimated by Kinggenome wide inbreeding coefficient (IBD), the observed and expected autosomal homozygous genotype counts on the basis of a minimal allele frequency of 5% (MAF 0.05), as well as (b) the inbreeding coefficient for SNPs on the MHC complex  without restrictions to a minimal allele frequency as the number of SNPs would have been too low. As genes on the MHC complex have different functions, we divided the analysis additionally in MHC complex I  and MHC complex II . As only 152 SNPs on the MHC complex had been genotyped, we were not able to calculate any measure of runs of homozygosity (ROH) for the MHC complex.As a measure for homozygosity, using Plink 1.9, we calculated, (a) the ROH is a good indicator not only for describing overall genomic homozygosity but also for the size of genomic local homozygous segments, the number of homozygous segments, and the average size of those segments. Additionally, the structure and distribution of ROH renders it possible to describe population history. ROH correlates with overall estimates of homozygosity such as the genome-wide inbreeding coefficient as well as with the inbreeding coefficient for the MHC complex. In its simplest form, ROH is calculated by moving a window of fixed size in search of stretches of consecutive homozygous SNPs over each chromosome. The algorithms estimate the proportion of completely homozygous windows that surrounds a SNP. If this proportion is higher than a threshold, the SNP is marked as being in an ROH area. A review of the significance and advantage in the realm of genomic and phenotypic associations as well as a profound description of the various methods of ROH calculation can be found in We calculated ROH on the basis of the recommendations of Runs of homozygosity calculated in Plink produces 3 different measures of genome wide ROH: total number of genome wide homozygous segments , the genome-wide summary of the length of all homozygous segments in kilo base pairs (KB), and the average length of homozygous segments (AVGKB = KB/NSEG). Distribution of the different ROH/IBD estimates is displayed in M = 18.2, SD = 0.26)—were rated by 33 judges recruited from roughly the same cohort as the WLS participants and, thus, at the time of rating aged between 63 and 91 years (M = 78.5). Yearbook pictures were not standardized in terms of facial expressions and head posture and showed mostly—but not exclusively—faces , father’s years of schooling (surveyed 1957) to control for SES and, as recommended by WLS, the first 10 principals components of the population structure .” Data use agreement from the 30. May 2019 between the boards of regents of the University of Wisconsin-Madison, Wisconsin Longitudinal Study (WLS) and the University of Vienna (receiving agency), wherein Martin Fieder is the researcher responsible for using the data files in the project entitled “Genome Wide Association Study and Runs of Homozygosity.” All participants provided informed written consent under a protocol approved by the Institutional Review Board of the University of Wisconsin-Madison. The status of the informed consent has been documented in detail by Wisconsin Longitudinal,p = 0.29), IBD for the MHC complex explaining only a very small proportion of the variance (amount variance explained between 0.0005 and 0.084%). The same holds true for other measures of attractiveness , no estimate is significant anymore. Education of the father is significantly positively associated with the different indicators of the attractiveness rating, and being male is significantly negatively associated with female rating only , neither for the whole MHC complex, nor separately for MHCI or MHCII complex. The reasons for this null result could be various, possibly the number of cases and/or the number of SNPs on the MHC complex were too low. Whereas genome wide, with two exceptions, a significant  negative association between the average length of homozygous segments (AVGKB) and various indicators of attractiveness ratings was found. Also, IBD was significantly negatively associated with some indicators of attractiveness, indicating that higher genome wide homozygosity is associated with lower attractiveness. Though, if corrected for multiple testing, none of these associations remained significant. In addition, the total variance explained by the different indicators of ROH and IDB was very low, ranging from 0.01 to 0.13%. Furthermore, cultures with a higher pathogen prevalence may exhibit a stronger link between MHC heterozygosity and attractiveness, as signaling heterozygosity could be more important. However, as we have no data this must remain purely speculative for the moment.As the genome wide analysis did come up at least with some (weak) results, the following discussion focuses mainly on genome-wide measures of homozygosity/heterozygosity. In addition, according to From our findings we conclude that, although genome-wide we find an association between higher homozygosity and lower attractiveness, effect sizes are rather small and not robust if corrected for multiple testing see also . Accordip < 0.0001). Hence, facial attractiveness may not be influenced directly by homozygosity but indirectly via the effect of parent’s homozygosity on father’s education (SES), and parents’ SES may in turn affect facial attractiveness of the children. As we do not have any data on homozygosity of parents, we cannot directly test this assumption. As for AVGKB, only a marginally significant negative association with father’s education did appear, we cannot exclude a link via parent’s SES although we have no further confirmation. We are not able to draw any final conclusions on causalities regarding the association between parent’s SES and facial attractiveness as it is, for instance, possible that genes which are associated with lower SES ”. Ethical approval for the study was received by the IRB Board of the University of Vienna, Project Number 00409: Phenotypic Associations of Homozygosity. The patients/participants provided their written informed consent to participate in this study.MF and SH: study design and manuscript preparation. MF: data analysis. All authors contributed to the article and approved the submitted version.This research uses data from the Wisconsin Longitudinal Study, funded by the National Institute on Aging .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Mirounga leonina) breeding at Marion Island in the Southern Ocean (1989–2019), and to identify the factors that correlate with phenological shifts within and between individuals. We found strong support for covariation in the timing of breeding arrival dates and the timing of the preceding moult. Breeding arrival dates were more repeatable at the individual level, as compared with the population level, even after accounting for individual traits  associated with phenological variability. Mother–daughter similarities in breeding phenology were also evident, indicating that additive genetic effects may contribute to between-individual variation in breeding phenology. Over 30 years, elephant seal phenology did not change towards earlier or later dates, and we found no correlation between annual fluctuations in phenology and indices of environmental variation. Our results show how maternal genetic (or non-genetic) effects, individual traits and linkages between cyclical life-history events can drive within- and between-individual variation in reproductive phenology.Population-level shifts in reproductive phenology in response to environmental change are common, but whether individual-level responses are modified by demographic and genetic factors remains less well understood. We used mixed models to quantify how reproductive timing varied across 1772 female southern elephant seals ( For example, long-term warming trends have advanced spring events such as flowering, migration and breeding in many plants and animals . AlteredPopulation-level phenological change in life-history events (e.g. birth or egg-laying dates) arises from individual responses to changing environmental conditions, and/or via changes in population structure that may then shape trait distributions . We can Although animals might exhibit phenotypic plasticity (responsiveness to changing environments) at the within-individual level, there often remain consistent between-individual differences in average phenology, even after environmental and demographic factors are accounted for. One explanation for consistent between-individual differences is that phenotypic flexibility within individuals is constrained by linkages between cyclical life-history events . Carry-over effects between different phenological events may therefore limit phenotypic flexibility within individuals ,14. AnotMirounga leonina; hereafter elephant seals) are long-lived Southern Ocean marine mammals that breed and moult on land. Female elephant seals maintain highly synchronized seasonal reproduction, probabaly through photoperiodic cueing but also through variables in addition to daylength  )  . We calcp = 0.001, h2 = 0.40) (R2 = 0.05). Permutation tests showed that the predicted slope between arrival dates of random pairs of animals was mostly non-significant and centred around zero , and that the observed parent–offspring regression slope coefficient was higher than expected by chance  .Parent–offspring regression analysis indicated that the arrival dates of daughters were significantly correlated to their mothers’ arrival dates  . Daughte. 4Our 30-year study of female elephant seal breeding phenology demonstrated five important points. It (1) highlighted a strong relationship between the timing of different parts of the annual cycle (moult and breeding phenology), (2) showed associations between breeding arrival dates and individual traits , (3) revealed mother–daughter similarities in breeding phenology, and (4) indicated high repeatabilities  of arrival date within females. We did not find evidence for (5) any strong relationship between breeding season arrival dates and measures of environmental variation .Leptonychotes weddellii) [Halichoerus grypus) [Mirounga angustirostris) [Arrival dates were consistently early among young first-time breeding females, aligning with the findings of previous studies on elephant seals . In contddellii) , grey se grypus)  and nortrostris) . In thesElephant seal females time their pre-breeding migrations so that they return to the breeding colony a few days before birth ,29. BreePhotoperiod is a major determinant of the reproductive cycles of seals, but other environmental cues and physiological condition would also determine when implantation occurs and embryonic growth commences, leading to annual variability in the timing of births . The reaParent–offspring regression showed that a significant proportion of the total variance in arrival dates was explained by additive genetic effects. Our permutation analysis, which drew dyads at random from the population, showed that the probability of obtaining a heritability estimate as high or higher than ours by chance was negligible. Establishing parent–offspring relationships in the field is not always easy, and our heritability analysis was therefore conducted on a subset of mother–pup pairs (paternity could not be established). Nevertheless, the significantly positive linear slope of the weaning date covariate in our main analysis supports the mother–daughter regression results. Pup weaning dates are related to the arrival date of a mother: mothers that arrive early in the breeding season have pups with earlier weaning dates, and these pups tend towards earlier breeding arrival dates as adults, plausibly because of a heritable component. Parent–offspring regression sometimes overestimates heritability of traits compared with analyses based on more complex pedigrees . This maPhoca vitulina [Our results describe, for the first time to our knowledge, how maternal genetic (or non-genetic) effects, individual traits and linkages between cyclical life-history events can drive within- and between-individual variation in reproductive phenology in elephant seals. By contrast, breeding arrival dates did not show a relationship with measures of environmental variation. Irrespective of the level of environmental variability, secondary consumers like elephant seals often show lower levels of phenological variability compared with primary producers and consumers . Limitedvitulina  and greyvitulina ). These vitulina . Marked vitulina ) or longvitulina ,66). Elevitulina ,29. Unlivitulina ), but fe (a)We found no correlation between annual fluctuations in phenology and indices of environmental variation, but this does not mean that environmental controls of breeding phenology are absent. For example, the timing of breeding may be affected by unmeasured environmental conditions, or at a finer scale than we could detect. Indeed, for wide-ranging marine predators with extensive distributions, such as elephant seals, it is often challenging to obtain environmental covariates that accurately reflect changes in marine food webs and thus the availability of essentail resources .Our inferences were based on AIC model selection, which selects models based on their ability to reproduce the in-sample data (the data used to estimate the parameters of the model). The identified relationships therefore do not necessarily extend to causality . Cause-aWe acknowledge that improved data or analysis methods can potentially increase confidence in our results. Alternative observation methods (e.g. passive integrated transponders or geolocation tags) or increased survey frequencies would allow a more accurate estimation of breeding arrival times in our population, but these methods are difficult to implement under current field conditions. Analytical approaches such as state–space models make it possible to model the true state of the system  as a latent process, with the observed data conditional on this process and an observation error. Bayesian state–space models could potentially be used to incorporate an observation error distribution in one direction. Furthermore, site occupancy models ,71 and n. 5Our analysis of long-term individual-based data revealed that additive genetic effects, early-life traits, age and breeding experience, and the timing of other parts of the annual cycle influence the reproductive phenology of elephant seals. While many studies consider phenological shifts in animal life-history events in relation to changes in current environmental conditions, fewer consider how transgenerational effects, early-life events, demography and carry-over effects between cyclical life-history events may contribute to shape individual and population phenology distributions. Yet, population-level studies that do not account for these effects may reach misleading conclusions about environmental drivers of phenological change . Much of


Glycine max (L.) Merr.] for animals is influenced by soluble carbohydrates, such as sucrose and stachyose. Although sucrose is nutritionally desirable, stachyose is an antinutrient causing diarrhea and flatulence in non-ruminant animals. We conducted a genome-wide association study of 220 soybean accessions using 21,317 single nucleotide polymorphisms (SNPs) from the SoySNP50K iSelect Beadchip data to identify significant SNPs associated with sucrose and stachyose content. Seven significant SNPs were identified for sucrose content across chromosomes (Chrs.) 2, 8, 12, 17, and 20, while thirteen significant SNPs were identified for stachyose content across Chrs. 2, 5, 8, 9, 10, 13, 14, and 15. Among those significant SNPs, three sucrose-related SNPs on Chrs. 8 and 17 were novel, while twelve stachyose-related SNPs on Chrs. 2, 5, 8, 9, 10, 13, 14, and 15 were novel. Based on Phytozome, STRING, and GO annotation, 17 and 24 candidate genes for sucrose and stachyose content, respectively, were highly associated with the carbohydrate metabolic pathway. Among these, the publicly available RNA-seq Atlas database highlighted four candidate genes associated with sucrose (Glyma.08g361200 and Glyma.17g258100) and stachyose (Glyma.05g025300 and Glyma.13g077900) content, which had higher gene expression levels in developing seed and multiple parts of the soybean plant. The results of this study will extend knowledge of the molecular mechanism and genetic basis underlying sucrose and stachyose content in soybean seed. Furthermore, the novel candidate genes and SNPs can be valuable genetic resources that soybean breeders may utilize to modify carbohydrate profiles for animal and human usage.The nutritional value of soybean [ Glycine max (L.) Merr.] is one of the most economically valuable crops, providing high protein meal and vegetable oil for human and animal diets worldwide. Annually, almost 76% of global soybean production is used to feed livestock for the meat and dairy industries  for soluble carbohydrates have been identified and reported through genetic linkage analysis. Historically, pulation . A year Genome-wide association study (GWAS) is a valid alternative to genetic linkage analysis to understand the genetic basis of quantitative traits by examining a significant association between genetic markers and a trait of interest. To date, GWAS has been successfully applied in soybean research to discover and characterize key traits, such as seed protein and oil , amino aIn this study, a diverse panel of 220 soybean accessions and 21,317 polymorphic single nucleotide polymorphisms (SNPs) were used to conduct GWAS to identify significant marker-trait associations for sucrose and stachyose through a mixed linear model (MLM). Among the three main soluble carbohydrates, raffinose was excluded from this study due to the little phenotypic variation and non-Gaussian distribution in the accession panel. The gene function, protein interaction, biochemical pathway, and gene expression of potential candidate genes associated with sucrose and stachyose content were further studied.https://www.ars-grin.gov/) (A diverse panel of 220 soybean plant introductions (PIs) was selected based on the 100-seed weight (> 23 g) and relevant maturity groups (MGs) from the USDA-ARS Soybean Germplasm Collection  120 series , equipped with the Prevail Carbohydrate ES columns (5 μm 250 × 4.6 mm) and guard columns (7.5 × 4.6 mm) . Standard mixtures were prepared in HPLC-grade water with 50, 100, 300, 500, and 1000 μg/mL concentrations to create calibration curves.Soybean seeds of each accession in each replication were harvested at maturity. Ten seeds were sampled per plot to quantify soluble carbohydrates in the Soybean Genetics & Genomics Laboratory under the supervision of Dr. Henry Nguyen at the University of Missouri, Columbia. The content of soluble carbohydrates was measured using the established High-Performance Liquid Chromatography (HPLC) protocol described by Genotype’ within ‘Maturity group’ as fixed effects and ‘Environment’, ‘Maturity group’, ‘Genotype × Environment’, and ‘Replication’ as random effects. The best linear unbiased prediction (BLUP) was computed using the lmer function in R software to minimize the effects of environmental variation and used as an additional environment in GWAS analysis. Pearson’s correlation coefficients between sucrose and stachyose were calculated using the chart.Correlation function in R software across environments and within each environment. The significant difference in mean values across seven environments between favorable and unfavorable alleles was determined using the PROC ANOVA function in the SAS software.Analysis of variance (ANOVA) was conducted using the GLM procedure of SAS software version 9.4 with ‘https://www.soybase.org/). The SNPs with a minor allele frequency (MAF) less than 0.05 were removed using GAPIT  for all pairwise marker comparisons and visualized using Haploview software to identify potential candidate genes  value was selected.Principal components (PCs) and a kinship matrix were incorporated in MLM as covariates to correct for population structure and cryptic relatedness. The MG was additionally used as a categorical covariate since the MG effects were significant on sucrose and stachyose based on ANOVA. The significant threshold of SNP-trait association at a Glycine max cv. Williams 82 reference-genome gene models version 2.0 in Soybase. Also, the predicted amino acid sequences of the potential candidate genes were obtained from Soybase and used to study the protein interaction network using the STRING database (https://string-db.org/) (http://david.ncifcrf.gov/)  . Relevanrf.gov/) .http://soybase.org/soyseq/), were used to compare the gene expression levels between the relevant candidate genes in 14 different soybean plant tissues, including young leaf, flower, 1-cm pod, pod shell 10 days after flowering (DAF), pod shell 14 DAF, seed 10 DAF, seed 14 DAF, seed 21 DAF, seed 25 DAF, seed 28 DAF, seed 35 DAF, seed 42 DAF, root, and nodule (https://www.ebi.ac.uk/). Another large set of RNA-Seq databases of gene-level transcript abundances (https://soyatlas.venanciogroup.uenf.br/) was used for additional exploration in the differential gene expression across 19 parts of the soybean plant, including cotyledon, embryo, endosperm, epicotyl, flower, hypocotyl, leaf, nodule, petiole, pod, radicle, root, seed, seed coat, seedling, shoot, suspensor, unknown, and whole plant (https://www.ebi.ac.uk/).The RNA-Seq Atlas data, publicly available on Soybase  (Manhattan and quantile-quantile (Q-Q) plots for each environment for sucrose and stachyose content are shown in 10 (P) value (5.2) was found on the SNP ss715627853 on Chr. 17. The MAF of the significant SNPs ranged from 0.05 (ss715627820) to 0.39 (ss715636857) , 8 (ss715602502), 12 (ss715613179), 17 (ss715627820 and ss715627853), and 20 (ss715636857 and ss715637428) (10 (P) value (5.0) was found on the SNP ss715592442 on Chr. 5. The MAF of the significant SNPs ranged from 0.08 (ss715606330) to 0.47 (ss715639178) , 5 , 8 (ss715601133), 9 (ss715603880 and ss715639178), 10 (ss715606330), 13 (ss715614101 and ss715615716), 14 (ss715617675), and 15 (ss715622806)    were found in the LD block of ss715602502 on Chr. 8 related to three sugar transporter-related genes, Glyma.08g360400, Glyma.08g360500, and Glyma.08g361200, and four other genes, Glyma.08g356800, Glyma.08g357200, Glyma.08g358700, and Glyma.08g358800, that interact with carbohydrate metabolism-related proteins . Also, four genes, Glyma.12g072800, Glyma.20g017400, Glyma.20g018000, and Glyma.20g018200, are involved in carbohydrate metabolism, while five genes, Glyma.02g129200, Glyma.17g257800, Glyma.17g258100, Glyma.17g260300, and Glyma.17g260400, have functional interactions with carbohydrate metabolism-related proteins.Out of 107 genes, 17 relevant candidate genes for sucrose content were closely associated with carbohydrate metabolism , GO:0006012 , GO:0008643 (carbohydrate transport), GO:0006096 (glycolytic process), GO:0006004 (fucose metabolic process), and GO:0005975 (carbohydrate metabolic process)  were found in the LD block of ss715614101 on Chr. 13, where carbohydrate metabolism-related genes, Glyma.13g128300 (sugar kinase), Glyma.13g128400 (sugar kinase), Glyma.13g132700 -trisphosphate degradation), and Glyma.13g133800 (UDP-glucuronate decarboxylase), are located. Also, nine genes, Glyma.02g255100, Glyma.05g024800, Glyma.05g025300, Glyma.05g025400, Glyma.05g026800, Glyma.08g028600, Glyma.10g040700, Glyma.14g113100, and Glyma.15g275400, are involved in carbohydrate metabolism, while eight genes, Glyma.02g255400, Glyma.02g258000, Glyma.02g258200, Glyma.05g027100, Glyma.05g166800, Glyma.10g040000, Glyma.15g275300, and Glyma.15g276700, have functional interactions with carbohydrate metabolism-related proteins.Out of 155 genes, 24 relevant candidate genes for stachyose content were closely associated with carbohydrate metabolism , GO:0009969 (xyloglucan biosynthetic process), GO:0019323 (pentose catabolic process), GO:0006012 , GO:0019252 (starch biosynthetic process), GO:0042732 (D-xylose metabolic process), and GO:0005975 (carbohydrate metabolic process)  than other candidate genes in the developing seeds from Seed 10 DAF to Seed 42 DAF , followed by Glyma.13g077900  across 19 plant parts in soybean . The gene expression data of Glyma.13g128400 and Glyma.14g113100  = 5.6); however, no significant SNP was detected. Thus, a general consensus value of 0.001 (-log10(P) = 3.0) was used as a significant cut-off value to detect significant SNPs  = 3.3) associated with fructose, which was located in the same region as ss715581183 at 13,523,639 bp on Chr. 2. Fructose is one of the monosaccharides that comprise the sucrose molecule. 10(P) = 8.8) associated with total soluble carbohydrate at 5,036,567 bp on Chr. 12, which was mapped approximately 449 Kbp downstream of ss715613179 . Total soluble carbohydrates were positively correlated with sucrose in soybeans (10(P) = 2.9) at 34,981,501 bp on Chr. 20, which was mapped approximately 690 Kbp upstream of ss715637428 . Another marker, Satt270 , on Chr. 20 was reported as an associated marker to a sucrose-related QTL (LOD = 1.9). It was mapped approximately 1 Mbp upstream of ss715637428  .Thirteen significant SNPs for stachyose content were identified across eight chromosomes   gene family , which play essential roles in transporting glucose molecules across a membrane (Glyma.05g026800), sucrose synthase (Glyma.05g027100), glycosyltransferases (Glyma.02g258000), and clathrin propeller repeat (Glyma.02g258200) , widely used in soybean breeding programs to reduce RFOs, also has the highest expression level in shoots, followed by petioles and leaves in soybean , while Glyma.13g077900 is responsible for the proton-transporting V-type ATPase (GO:0033179)  . Therefo0033179) .Glyma.05g003900 , Glyma.06g179200 , and Glyma.11g238800 , primarily responsible for increased sucrose and reduced RFOs, have been identified. Even though molecular markers related to these genes have been utilized for marker-assisted selection (MAS) breeding, a limited number of soybean cultivars for human and animal consumption have been developed and commercialized. Recently, Benson Hill, Inc. (https://bensonhill.com/) announced the release of a new soybean cultivar with ultra-high protein and low RFOs content, coupled with other improved agronomic traits. In this study, we reported new genomic regions harboring candidate genes associated with increased sucrose and reduced stachyose, which did not overlap with the known genes, RS2, RS3, and MIPS1. Therefore, the promising soybean germplasms and novel genetic sources newly identified in this study would be critical to broadening germplasm resources and enriching our understanding of the genetic architecture for the development of new soybean cultivars for animal feed efficiency and natural sweetness in human consumption. Future studies, including genome-editing technologies, a fine-mapping strategy, and molecular marker development, will be required for the functional validation of the novel findings in this study.To date, three major genes, Glyma.08g361200 and Glyma.17g258100) and stachyose (Glyma.05g025300 and Glyma.13g077900) content were further identified. We also reported three promising lines  as germplasm resources that can be valuable for developing new soybean cultivars with desirable soluble carbohydrate profiles. The novel discoveries in this study will extend the current knowledge of the genetic basis underlying sucrose and stachyose content in soybean seed. Overall, new genetic resources will benefit soybean breeders in modifying carbohydrate profiles to meet the high demand for animal and human consumption.This study identified three and 12 novel SNPs associated with sucrose and stachyose content, respectively, through a GWAS using 220 soybean accessions. Four novel candidate genes for sucrose (The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/DL: Data curation, Formal Analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. LL: Data curation, Formal Analysis, Investigation, Methodology, Writing – review & editing. DM: Formal Analysis, Methodology, Data curation, Investigation, Writing – review & editing. TV: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing. GS: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing. DX: Methodology, Writing – review & editing. HN: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing.