Advances in disease-related gene discovery have led to tremendous innovations in the field of epilepsy genetics. Identification of genetic mutations that cause epileptic encephalopathies has opened new avenues for the development of targeted therapies. Clinical testing using extensive gene panels, exomes, and genomes is currently accessible and has resulted in higher rates of diagnosis and better comprehension of the disease mechanisms underlying the condition. Children with developmental disabilities have a higher risk of developing epilepsy. As our understanding of the mechanisms underlying encephalopathies and epilepsies improves, there may be greater potential to develop innovative therapies tailored to an individual’s genotype. This article provides an overview of the significant progress in epilepsy genomics in recent years, with a focus on developmental and epileptic encephalopathies in children. The aim of this review is to enhance comprehension of the clinical utilization of genetic testing in this particular patient population. The development of effective and precise therapeutic strategies for epileptic encephalopathies may be facilitated by a comprehensive understanding of their molecular pathogenesis. Developmental and epileptic encephalopathies (DEEs) are a group of enervating neurological conditions that profoundly affect brain development and function. Diagnostic accuracy is crucial for the effective management of DEEs and is generally achieved through a combination of clinical investigations. These investigations may include a thorough review of the patient’s medical history as well as physical examination, neurodevelopmental assessment, and neuroimaging findings. Recently, genetic testing has become increasingly important in the diagnosis and management of DEEs as genetic mutations reportedly play a significant role in numerous cases. A flowchart outlining the DEE investigation process is shown in Epilepsy, a group of neurological disorders affecting over 50 million people worldwide, has multiple causes, including structural, infectious, metabolic, and immune causes . GeneticIntellectual disability affects 1–3% of the population in Western countries. Children with developmental disabilities are at an increased risk of epilepsy, with a higher prevalence rate than in the general population . The fieSTXBP1, ARX, SLC25A22, KCNQ2, CDKL5, SCN1A, and PCDH19, have been associated with early-onset EEs. Identifying the genetic basis of EEs with developmental delay or intellectual disability is valuable for diagnosing and optimizing anticonvulsant treatment and disease prognosis [To date, approximately 265 genes have been identified in epilepsy; of these, several genes, including Advances in disease-related gene discovery have led to the identification of new genes or mutations that contribute to the development of specific diseases, such as epilepsy. This area of research has made tremendous strides in recent years owing to advances in genomic technologies, such as WES, WGS, and gene panels. Using these tools, researchers have identified a growing number of disease-related genes, particularly for complex diseases such as EEs. The discovery of disease-related genes has also opened new avenues for personalized medicine, in which treatments can be tailored to an individual’s genetic profile, leading to more effective and precise therapies. This is particularly important for children with DEEs, who are often resistant to existing treatments and have a poor prognosis. Overall, advancements in disease-related gene discovery have profoundly impacted our understanding of epilepsy and other diseases, leading to a new era of personalized medicine with the potential to improve the lives of millions of people worldwide.Clinical testing using gene panels, exomes, and genomes has revolutionized the diagnosis of genetic epilepsy. Exome sequencing (ES) covers nearly all coding portions of known genes and is a powerful tool for identifying pathogenic variants in the coding regions of known genes, including those independent of the initial indication for testing . Over the last decade, the arrival of high-throughput sequencing technologies, collectively referred to as NGS or massive parallel sequencing, has greatly reduced sequencing costs and increased speed, leading to a surge in gene discovery for human disorders, and up to 50% monogenic epilepsy cases are now diagnosed with precision . In 2016Instead of testing one or a few genes, laboratories can now offer the sequencing of comprehensive gene panels, exomes, or genomes. For ES, analysis of parental samples is recommended, ideally through trio analysis or duo analysis if only one parent is available. This can effectively reduce the number of candidate variants that require review and help with the interpretation of the identified variants ,13. MultPrevious studies indicate that NGS techniques, with their capability for massive parallel sequencing of numerous genes, are efficient and cost-effective diagnostic tools for identifying the genetic causes of EEs  Table 1Table 1.Despite state-of-the-art genetic testing, a significant number of patients with DEEs lack genetic diagnosis. WES is becoming increasingly popular for uncovering the role of noncoding genetic elements in the human genome . PreviouIn conclusion, the adoption of comprehensive gene panels, exomes, and genomes has significantly increased diagnostic rates and deepened our understanding of the underlying disease processes of DEEs . Given tGenetic testing helps patients and their families understand the underlying causes of their medical conditions, which can assist with the planning of appropriate treatment and management strategies. Chromosomal microarray analysis (CMA) is a powerful tool for detecting clinically significant genomic variants, such as microdeletions and duplications. It can detect changes as small as 5–10 Kb in size, with a resolution up to 1000 times higher than that of conventional karyotyping. This technology is commonly used to identify CNVs that contribute to neurodevelopmental disorders and congenital anomalies. In contrast, WGS and WES can provide more comprehensive and timely diagnoses of genetic diseases. The detection of genomic variants has been greatly enhanced by ES and CMA; however, interpreting their impact on health and development can be challenging.As a result, some variants may be classified as “variants of uncertain significance” (VUS) based on the available evidence. Studies have shown that patients undergoing CMA for developmental delay, intellectual disability, autism spectrum disorder, or multiple congenital anomalies carry at least one VUS an estimated 7.9–19% of the time ,30,31,32Individualized genetic testing is crucial to optimize the diagnostic yield and cost-effectiveness of genetic testing. This is particularly important, considering the high cost of many new tests and the possibility that they may not provide informative results for some families. An individualized approach should consider factors such as a specific clinical course, physical findings, and family history to ensure that the most relevant tests are conducted.To minimize misunderstandings and manage variable emotional reactions to genetic test results, healthcare providers should discuss inconclusive results and, if necessary, refer patients to providers with genetic expertise. This is especially important in cases where VUS is identified as these results can be difficult to interpret and may require additional evaluation to determine their impact on patient health and development.Providers who present VUS results are responsible for providing families with appropriate information, support, and resources to facilitate their understanding of the results. Additionally, providers should consider whether further evaluations, such as parental or familial testing, imaging, or specialist referrals, could contribute to determining the pathogenicity of the variant and provide a more comprehensive understanding of the patient’s health status. By adopting an individualized approach to genetic testing and providing effective support to families throughout the process, healthcare providers can help ensure that patients receive the most accurate and informative results. The clinical practice is still ongoing.SCN2A and KCNQ2, where loss-of-function versus gain-of-function variants lead to differences in the clinical presentation [DEEs are a group of severe and early-onset epilepsies characterized by persistent seizures, developmental delay or regression, and poor prognosis. In 2017, the International League Against Epilepsy renamed the term “epileptic encephalopathy” as “developmental and epileptic encephalopathy” in consideration of the new insights into the genetic causes of epilepsy . This chentation . Even whentation ,40. Therentation .CACNA1E have been recognized in individuals with DEEs [NBEA, FBXO11, and SMARCC2 [NBEA, in particular, is a long-standing candidate gene for NDDs and idiopathic autism [DEEs display genetic and phenotypic heterogeneity, and there are several options for genetic testing available. These range from gene panels that cover a few or hundreds of genes to ES, which investigates all ~20,000 genes . Geneticith DEEs ,49. Stud SMARCC2 ,51,52. Nc autism .PIGB, reportedly cause autosomal recessive epilepsy [Recessive genes may be uncommon but can be significant contributors to the underlying cause of DEEs, with many autosomal recessive epilepsies arising from inborn metabolic errors and cortical malformations . In partepilepsy . The detection of pathogenic CNVs using CMA has demonstrated that CNVs contribute to 5–10% of childhood epilepsies, including DEEs ,57,58. TIncreasing evidence suggests that DNA methylation and histone modifications play causal or contributory roles in several medical conditions ,62. A reSCN1A variant [SCN1A is a commonly detected epilepsy gene in DS patients. The current focus of treatment for EEs is primarily on controlling seizures, but newer therapies, such as genetic treatments and antisense oligonucleotides, target specific causes, such as SCN1A channelopathy. A summary of drugs that have demonstrated recent progress in clinical studies, including gene therapy, is presented in Precision medicine involved an individualized approach to medical care, emphasizing targeted treatment that is based on genetic tests, identification of biomarkers, and development of specific drugs . Identif variant ,69,70. AKCNQ2  [GLUT1 deficiency, SLC2A1 or CDKL5 (ketogenic diet) [PCDH19 (clobazam) [KCNQ2, SCN2A, and SCN8A (ion sodium channel blockers), and mTORopathies (mTOR inhibitors) [GABRB3 can lead to both gain-of-function and loss-of-function mutations, leading to unique clinical phenotypes [CACNA1A result in various clinical manifestations, with both gain-of-function and loss-of-function mutations associated with equally severe DEEs [For example, treatment options for DS currently include stiripentol, fenfluramine, and cannabidiol, while research is exploring the potential of antisense oligonucleotides as a therapeutic approach ,86. Patienytoin) , GLUT1 dic diet) ,92, PCDHlobazam) , mutatioibitors) ,94. In aenotypes . Similarere DEEs . SCN2A mutations can also result in different functions at different times, causing early-onset DEEs owing to gain-of-function mutations and later-onset DEEs arising from loss-of-function mutations [utations ,98. Thesutations . The useutations . The disMoreover, genetic testing may be important in guiding personalized treatment decisions for individuals with DEEs by providing insights into disease progression and treatment response. As our understanding of the genetics of epilepsy continues to increase, genetic testing is likely to be increasingly important in treatment decisions. Genetic counseling can also provide parents of children with DEEs with information on preimplantation or prenatal genetic diagnostics. These tests can offer choices and possibilities for prevention, allowing parents to make informed decisions about their options for family planning . TherefoLimited availability of testing: In many regions, access to genetic testing is limited, particularly comprehensive testing, which may require specialized facilities and expertise.Interpreting test results: Interpreting genetic test results can be challenging, particularly when multiple genes are involved, and not all genetic variations have clear implications for the diagnosis or treatment of DEEs.False positive results: False positive results can occur, leading to anxiety and further testing of patients and families, which may result in the misallocation of limited healthcare resources.Limitations of current genetic tests: Current genetic tests are limited by their inability to detect some mutations and structural variations, and their potential for missing disease-causing mutations.Cost: The cost of genetic testing can be a barrier for some families, especially if insurance excludes it.Ethical concerns: Genetic testing raises ethical concerns, including the potential for discrimination based on genetic information, privacy, and security of genetic information and the potential for emotional distress caused by the knowledge of genetic risk.Difficulty extrapolating test results: Finally, it is difficult to extrapolate test results to guide therapeutic strategies. For example, even if a genetic mutation is identified, it may be unclear how it contributes to the development of DEEs or how it should inform therapeutic strategies.Several limitations and challenges of genetic testing persist as follows:Therefore, genetic testing for DEEs should be approached with caution, considering the limitations and challenges discussed above and with the support of an expert healthcare professional.Neurodevelopmental disorders are frequently occurring conditions, and many of them are caused by uncommon copy number and exon sequence variations. These variations can be identified by genetic testing, which helps in the diagnosis and management of such disorders. Children with severe developmental delays and epilepsy mostly have poor prognoses. Identifying the genetic cause of a disease can enable clinicians to provide more precise prognostication and counseling on the risk of recurrence, as well as to prevent and treat medical comorbidities. In addition, It can also direct patients and families toward appropriate resources, both locally and internationally, and refine treatment options where possible. Among the multiple genetic tests available today, NGS is considered a valuable and dependable diagnostic tool for detecting gene mutations in children with epilepsy and developmental disabilities. In particular, this cost-effective method shortens the time from seizure onset to genetic diagnosis. By comprehending the gene families involved in epilepsy, we can gain insight into the intricate neuropathogenic pathways underlying the disease and develop more effective and precise therapeutic approaches. Therefore, we highlight the possibility of genetic testing as a first-line investigation in children with DEEs. Advances in epilepsy genomics have provided significant benefits in the field of DEEs. With the availability of complete gene panels, exomes, and genomes, genetic testing has become an important tool for improving diagnostic rates and understanding the underlying disease processes. Their use has also opened new avenues for personalized medicine, allowing for more precise therapeutic strategies based on a patient’s unique genetic profile. However, much work remains to be done in this regard. Although current testing methods have been successful in identifying causal mutations, many limitations and challenges remain to be overcome, particularly with regard to understanding the complex interactions between genetic and environmental factors. Therefore, continued research on the genetics of DEEs is critical. By exploring new technologies and refining existing methods, scientists can gain a deeper understanding of the underlying causes of these debilitating conditions, leading to better diagnostics, treatments, and, ultimately, improved outcomes for affected individuals and their families.Further research is suggested, including 1. new techniques and algorithms to enhance the accuracy of genetic testing for DEEs and identify more disease-causing mutations, 2. using genetic testing results to inform further studies aimed at understanding the biological basis of DEEs, and 3. developing new therapies based on genetic testing results to improve the outcomes of patients with DEEs.


Sinapis alba L.) accessions was characterized in a greenhouse to identify germplasm with polycotyledony and to assess the genetic segregation of tricot and tetracot seedlings in selfed S1 and S2 generations. The effort identified a set of 46 yellow mustard accessions with frequent occurrences of polycotyledony. The revealed genetic segregations seemed to suggest the development of tricot and tetracot seedlings in yellow mustard was largely controlled by a combination of genes at multiple diallelic loci. The identified tricot germplasm can be used to facilitate the genetic and/or genomic analysis of polycotyledonous germplasm for a better understanding of genetic and developmental mechanisms conditioning polycotyledony and the development of yellow mustard lines for explorable tricot breeding.A world collection of 132 yellow mustard ( Cupressus lusitanica Mill. had significantly greater one-year heights than dicotyledonous seedlings. Venkatesh and Sharma [Eucalyptus L’Hér. seedlings are more vigorous than dicotylous seedlings. Raiora and Zsuffa [Populus L. seedlings in poplar breeding. Rick et al. [Arabidopsis cotyledons influenced the amount and proportion of hybrid vigor in total plant growth [Agriophyllum squarrosum) was associated with increased seed yield [Helianthus annuus L. inbred lines and a wild H. annuus strain and developed sunflower germplasm called ‘Tricot’ with up to 50% tricot penetrance (USDA-GRIN PI642084).Polycotyledons, which are plants that have more than two cotyledons, have been long known as a sporadic occurrence in dicotyledonous plants  and are d Sharma  reportedd Zsuffa  exploredk et al.  developek et al.  identifit growth  and thated yield . Taylor ed yield  releaseded yield ,14 identArabidopsis Heynh. due to the release of the Arabidopsis thaliana (L.) Heynh. genome. This research helped to identify and characterize many loci conditioning the development of polycotyledony in Arabidopsis, allowing for a better understanding of polycotyledony origin and development [Early genetic analyses indicate that polycotyledony is a heritable trait ,15 and felopment . Howeverelopment .Sinapis alba L.) accessions. These accessions were collected over several decades from more than 23 countries representing mainly Europe, Asia, and North America , but li America . The colThree sets of greenhouse characterization revealed 89 tricot seedlings from 46  accessions . The freWe also examined the tricot seedling growth A–C and oThe effort to self the surviving tricot plants resulted in 31 plants bearing S1 seeds, but only 17 S1 lines from 11 accessions had 50 seeds or more . PlantinContinuing to self S1 plants for S2 progeny generated 49 S2 tricot lines and 2 S2 tetracot lines with 40 or more seeds . These aA Chi-square test for the 3:1 genetic segregation of dicot vs. tricot occurrences in S1 and S2 seedlings revealed the presence of a recessive diallelic genetic locus in one (out of 17) S1 line  and six Arabidopsis tricots can be caused by abnormal chromosome segregation at meiosis [Arabidopsis . Howeve.g., see ,18,19,20entified ,27). Thuentified ) and genentified . Furtherentified .The study materials included 132 yellow mustard accessions with seeds available for distribution that were obtained in April 2013 from the yellow mustard collection maintained at Plant Gene Resources of Canada. The acquired seeds of each accession were randomly separated into three sets: the first set with 54 seeds, the second set with 36 seeds, and the third set with the remaining seeds (ranging from 0 to 108 seeds). The first set was planted in May 2013 in the Saskatoon Research and Development Centre greenhouse. The layout was based on 9 × 4 planting trays, with 4 accessions per tray, and 6 seeds in each of the 36 cells. The soil mix consisted of a regular soilless mix  blended To assess the genetic segregation of tricot seedlings, efforts were made from January to August 2014 to evaluate S1 progeny in the greenhouse following the same evaluation procedures as described above. Those surviving S1 tricot and tetracot plants were also bagged to generate selfed S2 progeny. Seeds from each S1 line were collected and labeled separately for S2 tricot lines. Further efforts were made in August 2014 and April 2015 to evaluate the genetic segregation of tricots and tetracots in the S2 progeny. The collected data of tricot and tetracot occurrences in the original S0 generation, S1, and S2 progeny were summarized and analyzed concerning growth patterns and genetic segregation.p > 0.05) with one degree of freedom for the critical value of 3.84 means that the tricot (and tetracot) allelic segregation follows a recessive genetic locus.A Chi-square test was applied to infer a recessive diallelic genetic locus by assuming the 3:1 genetic segregation of dicot vs. tricot (and tetracot if existed) occurrences in S1 and S2 progeny. A non-significant test . It could be used to facilitate the genetic and/or genomic analysis of polycotyledonous germplasm for a better understanding of genetic and developmental mechanisms conditioning polycotyledony and the development of yellow mustard lines for explorable tricot breeding.The present characterization generated a set of yellow mustard germplasm with frequent occurrences of polycotyledony and novel results on the polygenic inheritances of yellow mustard tricots and tetracots. The germplasm accessions in which we observed the polycotyledons occurring in various frequencies (


Anthropometric measures show high heritability, and genetic correlations have been found between obesity-related traits. However, we lack a comprehensive analysis of the genetic background of human body morphology using detailed anthropometric measures.Height, weight, 7 skinfold thicknesses, 7 body circumferences and 4 body diameters  were measured in 214 pairs of twin children aged 3–18 years (87 monozygotic pairs) in the Autonomous Region of Madeira, Portugal. Factor analysis (Varimax rotation) was used to analyze the underlying structure of body physique. Genetic twin modeling was used to estimate genetic and environmental contributions to the variation and co-variation of the anthropometric traits.Together, two factors explained 80% of the variation of all 22 anthropometric traits in boys and 73% in girls. Obesity measures  and limb circumferences loaded most strongly on the first factor, whereas height and body diameters loaded especially on the second factor. These factors as well as all anthropometric measures showed high heritability (80% or more for most of the traits), whereas the rest of the variation was explained by environmental factors not shared by co-twins. Obesity measures showed high genetic correlations (0.75–0.98). Height showed the highest genetic correlations with body diameter measures (0.58–0.76). Correlations between environmental factors not shared by co-twins were weaker than the genetic correlations but still substantial. The correlation patterns were roughly similar in boys and girls.Our results show high genetic correlations underlying the human body physique, suggesting that there are sets of genes widely affecting anthropometric traits. Better knowledge of these genetic variants can help to understand the development of obesity and other features of the human physique. Anthropometric measures are the key method to assess a child’s nutrition and development . While bHowever, an area which is still poorly understood is how much these different anthropometric measures share common genetic variation. Previous twin studies have shown genetic correlations between BMI and waist circumference , as wellKnowledge on the genetic correlations between anthropometric traits can provide insight into the genetic regulation and development of body morphology. This knowledge may also have practical implications since it can guide which traits are most informative to assess obesity. However, a limitation in the previous studies is that they include only a few traits and thus can only partly capture the complexity of the human body physique. In this study, we use a twin data set of children that includes 22 anthropometric measures providing detailed information on human body size and morphology. Using genetic twin modeling, we analyze how these traits are mutually correlated and how much they share common genetic variation.The data were derived from the Madeira Twin Study conducted in the Autonomous Region of Madeira, Portugal . First, 2). Four body diameters  were measured with a spreading caliper with an accuracy of 1 mm . Seven body circumferences  were measured with a flexible steel tape accurate to 1 mm . Seven skinfold thicknesses  were assessed using a skinfold caliper and recorded to the nearest 2 mm . We did not have missing cases in our data. However, we removed a few outliers  since otherwise they may have disproportionally affected heritability estimates.A team of six experienced researchers from the Laboratory of Growth and Development of the University of Madeira conducted detailed anthropometric measures based on a standardized protocol . All meaWe found that the distributions of waist and hip circumferences, BMI, weight and all seven skinfold thicknesses were skewed and thus used logarithmic transformation to normalize them. After this transformation, the distributions of all traits were roughly normal (the skewness parameters varied between 0.06 and 0.98). In our previous study reporting the heritability estimates of 10 of these 22 traits, we did not find systematic differences when comparing children younger and older than 12 years of age . Thus, iWe started the statistical modeling with a factor analysis using the Varimax rotation, which creates uncorrelated  factors, separately in boys and girls. The Eigenvalue statistics suggested a two-factor solution in boys and a three-factor solution in girls. However, in girls, the Eigenvalue for the third factor was only slightly over 1 (1.027), and the factor explained only 5% of the total variance. Thus, we used the two-factor solution in both boys and girls to have comparable results. In this analysis, the first factor explained 67% of the variation in boys and 53% in girls whereas the second factor explained 13 and 20% of the variation in boys and girls, respectively. These factor scores were estimated using the maximum likelihood estimator and then stored as additional variables for the genetic modeling. The factor analysis was conducted using the SPSS statistical software version 28.0 for Windows .We continued the analyses using genetic twin modeling based on the principle that while MZ twins are virtually genetically identical at the gene sequence level, DZ twins share, on average, half of their segregating genes, as with ordinary siblings . Since tp < 0.002). We did not find any evidence for a sex-specific genetic effect, which would be seen as a lower genetic correlation of OSDZ pairs than the 0.5 expected for SSDZ pairs. Additionally, we were able to eliminate the shared environmental component from the model without a statistically significant decrease in the model fit. Thus, we used the additive genetic/ unique environment (AE) model without the sex-specific genetic effect in further analyses; this model showed good fit when compared to the saturated model. However, for some of the traits, we found a decrease in the model fit if using the same estimates for boys and girls. Nevertheless, since this was the case for only a few traits, we presented the genetic modeling results for boys and girls together using the AE model and then compared them to the sex-specific results (see supplementary files).We started the genetic modeling with univariate models to find the best fitting model and calculate heritability estimates. Based on co-twin correlations  heritability estimates – and unique environmental factors. Then, we utilized bivariate Cholesky decomposition, a model-free method to decompose all variation and covariation in the data into uncorrelated latent factors . This meTable p-value of sex-difference <0.05 , mesomorphy  and ectomorphy (relative linearity) . In our We found that the heritability estimates were higher for most of the anthropometric traits in boys than in girls. This sexual dimorphism parallels the findings of a large pooled twin study in that heritability estimates of BMI were systematically higher in boys than in girls over childhood ; for heiOur study has both strengths and limitations. Our main strength is the very detailed measures of the human body – 22 anthropometric traits together – in a twin data set allowing us to analyze the genetic regulation of human body morphology in detail. In addition, genetic studies in Southern European populations are rare compared to Northern European and North American populations of European ancestry. Our main limitation is that the sample size was not large enough to study potential differences over the age range studied. For example, in a very large twin study pooling data from several cohorts, environmental factors shared by co-twins affected BMI variation in early childhood but its effect disappeared in adolescence . In our In conclusion, the correlation structure of detailed anthropometric measures suggested that there are two factors – general body fatness and body height/robustness of the skeleton – underlying body morphology. In particular, body fatness measures showed high genetic correlations suggesting that there is a set of genes affecting overall body fatness. These genetic variants common for various anthropometric traits probably play an important role in the formation of human body size and morphology. Considering the role of obesity and other human physique features behind metabolic and many other chronic diseases, a better understanding on these pleiotropic effects can also shed more light on individual variation in health risk profiles.Supplemental material


Sparus aurata) is a species of relevance for the Mediterranean aquaculture industry. Despite the advancement of genetic tools for the species, breeding programs still do not often include genomics. In this study, we designed a genomic strategy to identify signatures of selection and genomic regions of high differentiation among populations of farmed fish stocks. A comparative DNA pooling sequencing approach was applied to identify signatures of selection in gilthead seabream from the same hatchery and from different nuclei that had not been subjected to genetic selection. Identified genomic regions were further investigated to detect SNPs with predicted high impact. The analyses underlined major genomic differences in the proportion of fixed alleles among the investigated nuclei. Some of these differences highlighted genomic regions, including genes involved in general metabolism and development already detected in QTL for growth, size, skeletal deformity, and adaptation to variation of oxygen levels in other teleosts. The obtained results pointed out the need to control the genetic effect of breeding programs in this species to avoid the reduction of genetic variability within populations and the increase in inbreeding level that, in turn, might lead to an increased frequency of alleles with deleterious effects.The gilthead seabream ( S. aurata) is one of the most relevant species of the Mediterranean aquaculture industry, with an annual production that exceeds 208,808 metric tons, mainly obtained in Turkey, Greece, Croatia, and Italy [Gilthead seabream  is a cost-effective approach to determine unbiased genome-wide allele frequencies in populations under investigation [Thus far, several population genetic studies have been carried out in gilthead seabream mainly investigating wild and, in few cases, farmed gilthead seabream stocks using mitochondrial DNA markers, microsatellites, or high-density SNP data ,23,24,25tigation .In this study, we applied a comparative DNA pooling sequencing approach in gilthead seabream from farmed genetic stocks to detect signatures of selection in the genome of this species. The experimental design was based on the genomic analysis of offspring of different broodstock nuclei out of the same hatchery. As gilthead seabream are mass spawners, reproduction in hatchery facilities is managed in broodstock nuclei that contain several males and females to generate offspring that can be tracked back to the nucleus from which they were generated. These nuclei are not subjected to genetic base selection and were compared to detect genomic regions of high divergence possibly originated by some genetic advantage or by genetic drift and bottleneck effects. These regions were further investigated to detect SNPs with high predicted impact. This study designed a genomic strategy to identify signatures of selection and genomic regions of high differentiation among populations of farmed fish stocks and provided a first picture of the impact of gilthead seabream breeding at the genome level.The analysed gilthead seabream juveniles were sampled from one commercial hatchery, located in Italy. The samples were offspring from three different broodstock nuclei . Each nucleus was composed of 60–87 males and 60–185 females of Atlantic origin. Individual DNA extraction  was carried out from the offspring of these families, which aged approximately 60–66 days post-hatching. Then, five DNA pools were constructed with equimolar contributions of 20 to 30 individual DNA samples: three pools for nucleus A , one for nucleus B, and one for nucleus C. Whole-genome re-sequencing was carried out from the five DNA pools using Illumina HiSeq 2500, 150 bp paired-end sequencing technology, following the manufacturer’s protocols . In addimem function and standard options. Reads were filtered removing PCR duplicates and retaining only those with mapping quality of at least 30 using Samtools v1.10 [For each group, pool-seq data (newly produced or retrieved from a previous study ) were trls v1.10 . VariantThe effect of each detected SNP was assessed with the Variant Effect Predictor  based onhttps://www.R-project.org/ (accessed on 20 May 2022)).Using the filtered SNPs from all DNA-pools, a Principal Component Analyses (PCA) was obtained with the “prcomp” function of R  were calculated by averaging the SNP-based STF in 50% overlapping window sizes of 500 kb, as already described in previous studies [FST values above the 99th percentile of the empirical distribution and with more than 200 SNPs were retained for further analyses.For comparisons among the farmed Italian juvenile DNA pools, mean  studies ,13. HereDanio rerio selected as the reference gene set. In this analysis, only Gene Ontology (GO) terms related to biological pathways with false discovery rate (FDR) <0.05 were considered as significantly enriched. Redundant GO terms were removed with REVIGO [D. rerio database and applying SimRel as semantic similarity measure.Genes within these outlier regions were retrieved with bedtools  with theh REVIGO  considerS. aurata reference genome produced an average depth of coverage of 60× ± 1.73×. Detailed information on the sequencing depth of all DNA pools, including those retrieved from Peñaloza et al. 2021 [The alignment of the sequencing data obtained from the five sequenced Italian farmed stocks to the STF values. As expected due to their common origin from the same nucleus, the three pairwise comparisons between DNA pools A1, A2, and A3 showed low STF values (<0.053). Also, they showed a low STF value when compared with farmed and wild populations retrieved from a previous study (STF = 0.010) suggesting that all these populations were not very differentiated from wild stocks. It is also worth mentioning that we analysed publicly available datasets by averaging information within two groups (farmed and wild) to reduce the genetic differentiation between the datasets. However, the closeness between nucleus A and these wild-related stocks may indicate that nucleus A experienced recent genetic introgression from wild populations or that neither selection pressure nor genetic drift drove substantial genetic differentiation from the original stocks. Nucleus A was quite different from both nuclei B and C. These two were also quite different from the other farmed and wild populations that we included in our study (STF = 0.405) suggesting very divergent selection trajectories and/or different origins.This picture is confirmed by the consideration of the global us study . This isus study . The latur study . NucleusThese differences can also be seen when investigating the percentage of fixed alleles within each group . DNA pooThe control of inbreeding rate is very important when a selection program is implemented since inbreeding depression might negatively counterbalance any genetic progress. Estimates of inbreeding depression in fish have consistently shown that consanguineous progeny presents lower viability, less growth, lower reproductive performance, and less resistance to infections ,38,39,40Considering the common origin of the three DNA pools derived from nucleus A, in the subsequent genome-wide comparison analyses we combined these three subpopulations into one.STF analyses between nuclei A, B, and C are graphically represented in the Manhattan plots of The results of the pairwise window-based Considering the different pairwise comparisons, some partially overlapping regions were observed : some weSome of the regions in the block 12–38 Mb of chromosome 6 were detected when comparing nuclei A and B and when comparing nuclei B and C, but they did not emerge when comparing nuclei A and C. These regions detected on chromosome 6 partially overlapped or were adjacent to regions that have been proposed as candidates for the lack of pigmentation defect in gilthead seabream . This agSTF values, this proportion is unchanged, both considering the overall number , which encodes for a subunit of the irisin protein, is known to regulate food intake, energy homeostasis, and glucose metabolism in teleosts [Hypophthalmichthys nobilis [SLC30A9) gene. Its encoded protein is involved in cellular zinc ion homeostasis and zinc ion transport. This gene has been proposed as a candidate for head length [FAM135B), which encodes for a factor that promotes the secretion of the granulin growth factor, which has high expression in epithelial, immune, chondrocytes, and neuronal cells [These variations with high impact were in 129 genes . A few oteleosts ,43. This nobilis ,45. Anotal cells . This geal cells  and in Cal cells . In thisal cells .The over-representation analyses revealed the enrichment of 27 GO terms in the regions that emerged in the comparison between nuclei A and B, 4 GO terms in the comparison between nuclei A and C, and 22 GO terms in the comparison between nuclei B and C. After filtering redundant GO terms, these numbers were reduced to a total of 24 GO terms in the contrast between nuclei A and B and 13 in the comparison between nuclei B and C . All non-redundant GO terms are reported in (ADAMTS9). This gene encodes for a cell-autonomous antiangiogenic metalloprotease that is involved in ovarian development [IL1RAPL2), included in a genomic region that emerged in the comparison between nuclei A and B and present in the enriched gene list for cellular process (GO: 0009987), was suggested to be involved in the main QTL explaining 0.8% of variation for the acute hypoxia tolerance in a rainbow trout population [SLC4A8), having a large impact variation in the comparison between nuclei A and B, encodes for an electroneutral Na+-driven Cl−/HCO3− exchanger whose expression in the gills seems to be reduced in salt water, as shown in Spotted seabass [Some of the genes containing high-impact variations were part of one or more GO terms derived from the previous enrichment analyses. For the genes detected in the comparison between nuclei A and B, it is worth mentioning the disintegrin and metalloprotease with thrombospondin type-1 motif member 9 elopment . In the elopment . Interlepulation . The sol seabass .STYKD) gene. This gene is a regulator of cell death, is expressed in multiple tissues, and was included in the GO terms linked with developmental processes. In zebrafish, a mutation on the stykd gene is linked with spinal deformity [A region that emerged in the comparison between nuclei B and C contains variations with predicted high impact in the dual serine/threonine and tyrosine protein kinase (eformity .MED12). This gene is involved in developmental processes and is part of the enriched GO terms linked to development and morphogenesis for the B vs. C comparison, for example, animal organ development (GO:0048513) and skeletal system morphogenesis (GO:0048705). It has also been proposed as a candidate gene explaining a QTL for body weight gain in rainbow trout [One of the genes reported to have variations with high impact, present in one or more GO terms and shared in two comparisons (A vs. B and B vs. C), is the mediator of RNA polymerase II transcription subunit 12 homolog (ow trout .STF analyses underlined major genomic differences among the investigated gilthead seabream broodstock nuclei. Some of these differences highlighted genomic regions that contain genes involved in general metabolism and development; genes that have been detected in QTL for growth, size, and skeletal deformity; and adaptation to variation of oxygen level in other teleosts. The genomic diversity that was evidenced might be the results of the genetic histories of these nuclei, including the combined effect of directional selection, genetic drift, and bottleneck, that they might have experienced. Our results pointed out the need to control the genetic effects of breeding programmes in this species to avoid the negative effects of the reduction of genetic variability within the population with a consequent increase in the inbreeding level that, in turn, could lead to an increased frequency of deleterious alleles. Whole genome sequencing analyses might provide genomic pictures of breeding stocks that are useful to better design breeding strategies based on a whole genome-based information on the levels of within nucleus genetic variability and across nuclei genetic diversity so to enhance the production efficiency of the hatcheries.


Portunus trituberculatus, is one of the main aquaculture species in Chinese coastal regions due to its palatability and high economic value. To obtain a better understanding of the genetic diversity of P. trituberculatus in the Bohai Sea, the present study used 40 SSR loci to investigate the genetic diversity and population structure of 420 P. trituberculatus individuals collected from seven populations in the Bohai Sea. Genetic parameters revealed a low level of genetic diversity in the cultured population  in comparison with wild populations . The genetic differentiation index (Fst) and gene flow (Nm) ranged from 0.001 to 0.060 (mean: 0.022) and 3.917 to 249.750 (mean: 31.289) respectively, showing a low differentiation among the seven populations of P. trituberculatus. Population structure analysis, phylogenetic tree, and principal component analysis (PCA) demonstrated that the seven groups of P. trituberculatus were divided into four subpopulations (K = 4), but the correlation between genetic structure and geographical distribution was not obvious. These results are expected to provide useful information for the fishery management of wild swimming crabs.The swimming crab,  Portunus trituberculatus, is one of the important economic crabs in the Chinese marine fisheries and mariculture industry. It has a wide distribution in the coastal areas of South-East Asia and has been farmed for more than 30 years3. Over the past few decades, the consumption of swimming crab has gradually increased due to the delicious taste and versatile nutrients4. Among the main producers, China ranked first with an annual production of 559,796 tons according to the China Fisheries Statistical Yearbook (2022) published by the Ministry of Agriculture, China. However, with the development of intensive farming and marine fishing industry in recent years, germplasm resources of P. trituberculatus have dramatically declined due to over-exploitation and environmental deterioration6. In addition, the heavy demand for wild parents from artificial propagation resulted in the decline of the genetic diversity of the natural populations7. Such episodes emphasize the vital nature of monitoring the genetic diversity of P. trituberculatus populations to protect germplasm resources and facilitate molecular marker-assisted breeding (MAS).The swimming crab, 8. A high level of genetic diversity indicates strong biological survivability and environmental adaptation, which is required for sustained genetic improvement and stable inheritance of desirable traits9. Conversely, low genetic diversity can lead to reduced adaptability and viability, and ultimately to the degradation of species10. In aquaculture, genetic diversity constitutes a fundamental resource to improve the quality of stock11. However, for breeding populations of P. trituberculatus, long-term artificial directional selection eventually leads to a decline in genetic diversity12. Moreover, it is difficult to recover the declining genetic diversity caused by overfishing13. To formulate an effective conservation strategy, it is necessary to evaluate the genetic diversity and population structure of P. trituberculatus. In our previous study, SNP markers determined by genotyping-by-sequencing (GBS) revealed a low level of genetic diversity in P. trituberculatus along the coastal waters of China14. To evaluate the impact of the massive releases on natural populations, the researchers monitored the temporal variations in genetic diversity and structure in Panjin and Yingkou using microsatellite markers, which suggested that the large-scale stock enhancement of P. trituberculatus presented potential genetic risks to wild populations16. However, hatchery stock enhancements resulted in no reduction in genetic diversity for wild populations of P. trituberculatus in the Yangtze Estuary17.Investigating the genetic diversity of species is a prerequisite for the effective exploration and utilization of germplasms20. At present, RNA-seq has become a popular high-throughput sequencing technology that enables the development of SSR markers due to its characteristics of wide dynamic range, high accuracy, and strong sensitivity21. In addition, compared with genomic-derived SSRs, transcriptome-derived SSRs are characterized by high efficiency, strong transferability, and correlation with potential genes22. Cao et al.23 first analyzed the transcriptome of Crassadoma gigantean using RNA-seq technology, identified 12 polymorphic SSRs, and found several genes related to the growth and immunity of C. gigantean. These results would facilitate future studies of population structure and conservation genetics in this species. In aquatic crustaceans, Zhang et al.24 conducted transcriptome sequencing on the male and female gonads of Portunus sanguinolentus and detected 93,196 SSR loci. In Pachygrapsus marmoratus, 43,915 SSRs were excavated by RNA-seq, providing a reliable resource for investigating biological responses to pollution in intertidal and marine populations25. Lv et al.6 identified 22,673 SSRs with transcriptome analysis of P. trituberculatus, which provided a material basis for genetic linkage and quantitative trait loci analyses. The objective of the current study is to evaluate the genetic diversity and population structure of P. trituberculatus in the Bohai Sea with transcriptomic SSRs. The findings will contribute to understanding the population genetic structure of P. trituberculatus in the Bohai Sea and be useful in improving management and conservation strategies for this species.The development of high-throughput sequencing technologies provides great convenience for the identification of DNA molecular markers in genetic research. Among known DNA molecular markers, simple sequence repeat (SSR) shows the advantages of co-dominant inheritance, highly polymorphic, and wide distribution throughout the genomeA total of seven populations were collected from the Bohai Sea Fig. , Table 126 , followed by 35 cycles of denaturation (30 s at 94 ℃), annealing (30 s), extension (30 s at 72 °C) and additional extension (10 min at 72 °C). After amplification, PCR products were diluted 10 times with sterile water. The pooled sample was composed of 20 μL Hi-Di formamide and 0.2 μL GeneScan 500 ROX size standard. An ABI 3730XL Genetic Analyzer  was used to conduct capillary electrophoresis (CE) following the manufacturer's instructions. Each CE sample contained 1μL diluted PCR product and 15 μL pooled sample. Allele sizes (in base pairs) were determined with GeneMarker®Fragment Analysis Software  on the comparison of the position of the internal size standard in each lane with the position of the peak value of each sample.Forty pairs of SSR primers were obtained from the transcriptome data in our previous studyP. trituberculatus populations was estimated by determining genetic parameters, including the number of alleles (Na), the effective number of alleles (Ne), Shannon’s diversity index (SI), observed heterozygosity (Ho) and expected heterozygosity (He) using POPGENE version 1.327. Based on allele frequency, polymorphism information content (PIC) was estimated by PIC-CALC software28. Null allele frequencies (Fna) for SSR loci were calculated using GenePOP29. P values were calculated for determining Hardy–Weinberg equilibrium (HWE) at each locus with POPGENE version 1.3. Genetic differentiation and variation were inferred using Nei's genetic distance (D)30 and genetic identity (I) calculated by POPGENE version 1.3 and F-statistics  calculated by analysis of molecular variance (AMOVA) with software GenAlEx 6.531 through 999 permutations. Gene flow (Nm) was inferred from the formula of Nm = (1 − Fst)/4Fst32.Genetic diversity within 33. Principal component analysis (PCA) was carried out using Canoco 4.5 to elucidate genetic relationships within and among P. trituberculatus populations. Based on the 40 polymorphic SSR loci, Bayesian model-based population genetic structure was inferred using STRUCTURE version 2.3.434. The putative number of populations (K) was set from 1 to 10 with 3 replicate simulations for each K value using 100,000 MCMC (Markov Chain Monte Carlo) iterations after an initial 100,000 burn-in period. With the log probability of data (LnP(D)) and an ad hoc statistic ΔK based on the rate of change in LnP(D) between successive K-values, the structure output was entered into Structure Harvester36 to determine the optimum K value. The best K value was analyzed by CLUMPP37 and visualized with Distruct 1.1 software38.The phylogenetic tree was constructed based on Nei’s genetic distance and used to test population grouping as implemented in MEGA7Ne) ranged from 1.785 to 10.271 with a mean of 4.264. Shannon’s diversity index (SI), observed heterozygosity (Ho) and expected heterozygosity (He) ranged from 0.885 to 2.404 (mean: 1.482), 0.405 to 0.950 (mean: 0.639) and 0.440 to 0.903 (mean: 0.725), respectively. PIC values ranged from 0.415 (TRAN1) to 0.895 (TRAN20) with an average of 0.685. Five SSRs  showed moderate polymorphism (0.25 < PIC < 0.5), and the remaining 35 SSRs showed high polymorphism (PIC > 0.5). Null allele frequencies (Fna) and fixation index (Fis) varied from 0.029 (DX19) to 0.564 (TRAN13) and -0.207 (DX19) to 0.478 (TRAN21) respectively, indicating the existence of null alleles and heterozygosity deficit. Additionally, nine SSR loci fitted with HWE (P > 0.05), and the remaining 7 and 24 loci deviated from HWE at P < 0.05 and P < 0.01 levels, respectively.In this study, all parameters of the 40 SSR loci were calculated and presented in Table Na, Ne, SI, Ho, He, and PIC of seven P. trituberculatus populations ranged from 5.225 to 5.375, 3.794 to 4.103, 1.374 to 1.449, 0.624 to 0.654, 0.687 to 0.714, and 0.643 to 0.673, respectively  in comparison with wild populations .The mean values of P. trituberculatus individuals was performed to infer the optimal K value with the ΔK method. When the highest ΔK value was observed, the optimal K value was 4  and 3.68% (PC2) of total variation and could distinguish cultivated individuals from wild populations  and high gene flow (Nm = 249.750) were observed between the PL and QHD populations, and the high differentiation (Fst = 0.060) and low gene flow (Nm = 3.917) was observed between the HC and DY populations (Table D) and genetic identity (I) showed similar results between HC and DY populations  and PL and QHD populations  , Shannon’s diversity index (SI), and heterozygosity (H). However, expected heterozygosity (He) could better reflect the genetic diversity of species than observed heterozygosity (Ho)41.Genetic diversity is a crucial criterion in estimating the adaptability of species to changing environments, hence a better understanding of the genetic diversity of species is vital for evaluating population structure and evolutionary dynamicsPIC values of 40 SSR loci of 0.415 ~ 0.895, indicating the polymorphic nature of the loci and their suitability for assessing genetic diversity in the seven P. trituberculatus populations. Genetic analysis revealed that the genetic diversity of the wild populations (He ≥ 0.692) was higher than that of the cultivated population (He = 0.687), which was consistent with our previous report42. A similar result was found in E. sinensis43. In general, genetic drift, selection, and inbreeding resulted in low genetic variability in farmed stocks44. In addition, many SSR loci significantly deviated from HWE (P < 0.05), which might be attributed to null allele and heterozygote deficiency (Fis > 0). Null alleles might be accounted for insufficient sampling45 and variation of microsatellite flanking sequence46. Loss of heterozygosity might be accounted for migration, artificial selection, and inbreeding48, which was common in marine species such as Scylla paramamosain51, Pinctada margaritifera52, and Hypophthalmichthys nobilis53. Chen et al.54 used ten SSRs to investigate the effect of artificial selection on the genetic structure of two abalone lines and found a loss of heterozygosity (Ho = 0.650 < He = 0.711). These studies indicated the negative impact of heterozygote deficiency on population genetic diversity. Therefore, it is necessary to maintain a high level of genetic diversity in aquatic animals to reduce heterozygous loss and prevent germplasm degradation.The current study reported P. trituberculatus in the Bohai Sea (He = 0.725) than that in the Yellow Sea47 (He = 0.814) and the East China Sea55 (He = 0.916), which was consistent with the results revealed by SNP markers14. It has been shown that when conducting genetic diversity analysis on aquatic animals, the number of SSR loci should be greater than 20 and the sample size should be greater than 4556. The number of loci and sample size in this study meet this standard, indicating the reliable result of low genetic diversity of swimming crabs in the Bohai Sea. Bohai Sea is a semi-enclosed and shallow body of water that limits the dispersal of P. trituberculatus, leading to a decline in genetic diversity47. In the SSR investigation of Exopalaemon carinicauda, Zhang et al.57 suggested that the Binzhou population in the Bohai Sea had the lowest level of genetic diversity, which illustrated that the Bohai Sea might hinder the gene flow. Moreover, marine pollution, aquaculture pollution, and reclamation also reduced genetic diversity58. Therefore, it is necessary to carry out long-term genetic monitoring of P. trituberculatus in the Bohai Sea for full protection and utilization of the germplasm resources of this species.In terms of expected heterozygosity, this study showed lower genetic diversity of P. trituberculatus, genetic monitoring of population structure is essential for the development of effective management strategies13. The results of the current study established that all P. trituberculatus individuals were divided into four subpopulations , an essential gauge of genetic differentiation among populations, is crucial to understand genetic relationships. 0 < Fst < 0.05, 0.05 < Fst < 0.15, 0.15 < Fst < 0.25, and Fst > 0.25 showed negligible, moderate, high, and strong genetic differentiation respectively61. In this study, HC and DY populations were medium differentiation (Fst = 0.060 > 0.05), which might be related to the geographical location of the two groups. Huanghua and Dongying were located at Bohai Bay and Laizhou Bay on both sides of the Yellow River estuary, respectively. The ecological environment, species distribution, and organic pollution in the Yellow River estuary led to the geographical differences between the two different sea areas63, which led to the differences in activity scope and habitat preference of P. trituberculatus, and ultimately resulted in high genetic differentiation between the HC and DY populations. In addition, geographic isolation also leads to low gene exchange between cultivated and wild populations compared to wild populations in the open sea, which can be proven by the genetic differentiation index. The average value of Fst between the HC and wild populations was 0.031, and between wild populations was 0.017 (Table Nm = 31.289), genetic distance (D = 0.08), and genetic identity (I = 0.924) also demonstrated low genetic differentiation and strong genetic admixture among the seven P. trituberculatus populations.The genetic differentiation index (P. trituberculatus throughout the coastal areas of the Bohai Sea. Forty microsatellite loci revealed a low level of genetic diversity in the seven P. trituberculatus populations in the Bohai Sea. A low level of genetic differentiation and frequent gene flow among these seven populations were revealed, suggesting high genetic connectivity. The structure analysis illustrated four subpopulations, but the clustering pattern was not related to geographical location. To increase the genetic diversity of P. trituberculatus, practical and effective protective measures are expected to be taken to prevent the degeneration of germplasm resources. This study also provides a theoretical basis for selecting parents from different geographical populations during the artificial breeding programs.In summary, this study provided useful insights into the population structure of


The effect of optimized mating methods for long-term selection has not been studied in cattle breeding. In this study, the linear programming and optimal contribution selection methods on the genetic gain and inbreeding level of beef cattle were explored and evaluated using a simulation strategy. Our results showed that the linear programming method can effectively improve the genetic gain in the population during long-term selection in the breeding process, and the optimal contribution selection method can maintain a balance between improving genetic gain and controlling inbreeding level. Our findings can provide theoretical guidance for the long-term and sustainable genetic gain in breeding populations in farm animals.The optimized selection method can maximize the genetic gain in offspring under the premise of controlling the inbreeding level of the population. At present, genetic gain has been largely improved by using genomic selection in multiple farm animals. However, the design of the optimal selection method and assessment of its effects during long-term selection in beef cattle breeding are yet to be fully explored. In this study, a simulated beef cattle population was constructed, and 15 generations of simulated breeding were carried out using the linear programming breeding strategy (LP) and optimal contribution selection strategy (OCS), respectively. The truncation selection strategy (TS−I and TS−II) was used as the control. During the breeding process, genetic parameters including genetic gain, average kinship coefficient, QTL effect variance, and average observed heterozygosity were calculated and compared across generations. Our results showed that the LP method can significantly improve the genetic gain in the population, especially the genetic performance of the traits with high heritability and the traits with high weight in the breeding process, but the inbreeding level of the population is higher under LP strategy. Although the genetic gain in the population under the OCS strategy is lower than the TS−II strategy, this method can effectively control the inbreeding level of the population. Our findings also suggest that the LP and OCS method can be used as an effective means to improve genetic gain, while the OCS method is a more ideal method to obtain sustainable genetic gain during long-term selection. Genomic selection (GS) has been widely applied in livestock breeding in the past few decades ,2,3,4,5.Many studies have pointed out that GS can lead to the increase in inbreeding level of livestock population during long-term selection, which will reduce the genetic diversity of the population, slow down the growth in genetic gain, and even cause inbreeding depression ,10,11,12Many optimized methods including the Linear Programming method (LP) and the Optimal Contribution Selection method (OCS) have been proposed in recent decades. The LP method is a branch of operational research . The maiThe OCS method was proposed by Meuwissen  based onTo achieve sustainable genetic gain under long-term selection, it is also necessary to consider the loss of rare favorable alleles. One recent study proposed a weighted genomic selection method by weighting the effects of rare favorable alleles, and achieved the goal of maintaining genetic diversity .Most studies on OCS methods are aimed at maximizing genetic gain by controlling the inbreeding coefficient. Few studies exist on the OCS method that minimize the population inbreeding coefficient as a breeding objective. Also, the LP methods or the OCS method have not been fully studied as a breeding program in cattle. Therefore, the purpose of this study is to: (1) explore the application effect of the LP method in controlling inbreeding level and improving genetic gain in cattle breeding; (2) evaluate the effects of OCS strategies with different breeding objectives during long-term selection; (3) verify the feasibility of OCS strategy in achieving sustainable genetic gain during long-term selection process. Our study may provide theoretical guidance for sustainable genetic gain achieved during long-term selection process in farm animal breeding.We generated a simulated historical population that consisted of 1000 individuals . The genome of each individual in the population contains 29 chromosomes with a length of 2.33 M. The number of SNPs per individual is 100 K, evenly arranged on chromosomes. A total of 750 QTLs were randomly arranged on chromosomes, including 125 dominant QTLs. Each individual has 5 simulated phenotypic traits with heritability of 0.44, 0.48, 0.45, 0.3, and 0.22 , and theAll males and females in the historical population randomly mated to produce a total of 1000 offspring  as the 2nd generation population. Then, all the males and females also mated randomly to generate 1000 offspring. Using the same breeding strategy, a total of 1000 generations were generated.−5.The population from the 1st generation to the 1001st generation were generated by random selection to generate initial linkage disequilibrium (LD) and mutation–drift equilibrium, while the population size was 1000 individuals and the sex ratio was 1:1. From the 1001st generation to 1006th generation, the random selection strategy was used, and the population size was gradually expanded in the breeding process. The population size was 1400 individuals in 1006th generation. During this process, the proportion of females gradually increases, and the proportion of males gradually decreases. Finally, 200 male individuals and 1200 female individuals remained in the 1006th generation population. The 1006th generation population were divided evenly into two populations as the Founder population (FP1 and FP2). In the above breeding process, the mutation rate of alleles was 10We designed two types of optimized breeding strategies: (1) linear programming strategy (LP), (2) optimal contribution selection strategy (OCS). Two different truncation selection strategies (TS−I and TS−II) were designed. The TS−I is the control strategy for LP, focusing on the genetic gain in Trait 1 to Trait 4, while TS−II is the control strategy for OCS, focusing on the genetic gain in Trait 5.k = 1, 2, 3, 4) are GEBVs of four traits of individual The main breeding goal of the LP strategy is to improve the genetic gain in Trait 1 to Trait 4. First, we calculated the weighted sum of GEBV (WSG) of four traits (from Trait 1 to Trait 4) per individual in FP1:Then, we constructed a selection index for potential offspring of each mating, and the calculation formula is :Iij=WSGig on WSG , fij is The aim of LP method is maximizing the sum of the selection index for the whole potential offspring:These two restrictions mean that each male candidate can be mated to 10 females, while each female candidate can be mated only once at most.Then, the next generation population was generated based on the calculated specific mating combinations. The same selection strategy was used to simulate the subsequent 14 generations. Population size and gender ratio are consistent with FP1 during breeding process.We calculated the GEBVs for Trait 1 to Trait 4 of all individuals in FP1 using GBLUP method. Next, we calculated the WSG of all individuals. Then, the top 10% male individuals and top 50% female individuals with highest WSG were selected as candidates. All candidate individuals were randomly mated to produce 700 offspring as the next generation. The following 14 generations were simulated using the same selection strategy. The population size and gender ratio remained unchanged during the breeding period.Under this breeding strategy, Trait 5 of individuals within the population is considered as the target trait. We first selected the top 10% male individuals in FP2 that had the highest genetic contributions to the offspring population as candidates, and then selected different female candidates according to two different breeding targets: (1) set an upper bound for the increase in the kinship of the offspring population and maximize the BVE of the offspring population under this upper bound. This breeding strategy is recorded as OCS_maxBVE; (2) set a certain lower bound for the BVE increase in the offspring population and minimize the kinship of the offspring population on the basis of meeting the lower bound. The lower bounds of BVE increase were set to four levels: 0.05, 0.07, 0.1, and 0.2, and the corresponding breeding strategies are recorded as OCS_minKin I, OCS_minKin II, OCS_minKin III and, OCS_minKin IV, respectively.Similarly, the mating of candidate individuals produces 700 offspring to maintain the population size, with a gender ratio of 1:6. Subsequently, the same breeding strategy was used to simulate the generation of the subsequent 14 generations.We calculated the GEBV of Trait 5 for all individuals in FP2 using GBLUP method. Then, we selected the individuals with the highest GEBV as a candidate with the same selection intensity as LP and OCS strategies. All candidate individuals are randomly mated to produce 700 offspring.The next 14 generations were simulated using the same selection strategy, and the population size and gender ratio of each generation remained unchanged.The generation of the founder populations and the simulated breeding process under various breeding strategies were shown in We calculated the following genetic parameters to compare different selection strategies in breeding simulation.The difference between the average GEBV of all individuals in each generation and the average GEBV of all individuals in the FP is the genetic gain in the corresponding traits in the current generation. The calculation formula is as follows :Genetic i and individual j is calculated as follows:M is the number of SNPs on the simulated genomes, kp and kq are the frequencies of the two alleles at locus k for the current generation, and The kinship coefficient between individual te (IIS) . Then, wThe QTL effect variances and averages observed heterozygosity of the FP and the 1021st generation populations were calculated to evaluate the degree of change in genetic diversity before and after the breeding process . The observed heterozygosity quantifies the amount of genetic variation caused by polymorphic loci . We usedThe QTL effect variances were calculated using the following formula:https://cran.r-project.org/package=MoBPS (accessed on 1 July 2023)). The GEBV of individual was calculated using R package emmreml (https://cran.r-project.org/package=EMMREML (accessed on 1 July 2023)). The solution of linear programming problem used R package lpSolve (https://cran.r-project.org/package=lpSolve (accessed on 1 July 2023)). The implementation of the OCS method called the R package optiSel (https://cran.r-project.org/package=optiSel (accessed on 1 July 2023)).The generation of the FP, simulation of phenotype and genotype, and creation of new generations were completed through R package MoBPS  in populations under LP and TS−I breeding strategies increase with generations, as shown in As for the WSG, this value increases with generations under LP and TS−I strategies. Under the LP strategy, the average WSG of the 1021st generation population is 3.411 higher than the FP1. Under the TS−I strategy, the average WSG of the 1021st generation population is 3.362 higher than the FP1.In this study, we found the genetic gain in the same trait varies under different breeding strategies . LP straSimilarly, the TS−II strategy and the five OCS breeding strategies can significantly improve the genetic progress of the population A. The TSDue to the existence of selection, the average kinship coefficient of the population gradually increases with generations. The average kinship coefficient of the 1021st generation under LP strategy is higher than that under TS−I strategy, which is 0.31 and 0.28, respectively .All OCS strategies can effectively control the inbreeding than TS−II strategies based on the estimation of average kinship coefficient across generations B. The avIn this study, we found the inbreeding level of populations has been improved to varying degrees due to the existence of selection in breeding strategies. The QTL effect variances and the average observed heterozygosity have also shown corresponding decline, as shown in The values of the QTL effect variances and the average observed heterozygosity of population at the last generation under different breeding strategies have a certain correlation with the population average kinship coefficient. In general, the higher the population average kinship coefficient, the lower the QTL effect variances and the average observed heterozygosity.Linear programming was previously used as an optimal strategy to select candidates under many constraints in livestock breeding . SeveralThe OCS method can maximize the weighted genetic value of parents while limiting the relationship among them , therebyThe genetic gain in populations under the LP strategy, OCS strategies and TS strategies gradually increased with generations. The reason is that the parents under these breeding strategies have been selected according to specific selection criteria, which is similar to previous analyses . In the LP strategy, breeding candidates are selected according to individual WSG. The goal of the linear programming problem is to maximize the selection index of the offspring population, thus the genetic gain in the offspring population is higher than that of the parent population, which is the same as the previous research conclusions . The aveUnder the LP and TS−I strategies, the weight of each trait in WSG has a higher effect on genetic gain than the heritability of the trait. Under the breeding strategy, the final genetic gain in the traits with high weight is higher than that of the traits with low weight. When the weights of the two traits are the same, the trait with higher heritability obtains higher genetic gain than the trait with lower heritability. A recent study  has poinThe genetic gain under all OCS strategies is lower than that under TS−II, which is similar to previous studies ,31. ImprThere are also differences in genetic gain among different OCS strategies. Under the OCS_minKin strategies, the increase in breeding value of offspring compared to that of parents is greater than the lower bound of breeding value growth, and we found that the higher the lower bound, the higher the final genetic gain. The genetic gain under OCS_maxBVE is maintained at a moderate level.During the breeding process, the candidate parents for breeding are individuals with high genetic performance; thus, the average kinship of the population increases from generation to generation.The average kinship of the population under LP strategy is higher than TS−I strategy. This may be due to the following points: (1) the objective function of linear programming is constructed based on maximizing the selection index of the offspring population, which leads to mating between individuals with high genetic performance, and the high kinship between these selected individuals may lead to the increase in the relationship among the offspring population; and (2) in the breeding process, the punishment for inbreeding is not sufficient.The average kinship coefficient of the population under OCS strategies was lower than the TS−II strategy, indicating that the OCS method can effectively control the inbreeding level during the breeding process. The population under the OCS strategy with the goal of maximizing BVE of offspring population has the lowest population kinship coefficient. The population kinship coefficient under the OCS_minKin strategies are lower than the TS−II strategy and higher than the OCS_maxBVE strategy. Among these breeding strategies, the higher the lower bound of breeding value growth, the higher the population kinship coefficient under the breeding strategy.The QTL effect variance and average observed heterozygosity are used to reflect changes in the genetic diversity of populations during the breeding process. Genetic diversity plays an important role in species’ ability to adapt environmental changes, breeding strategies for crops and farm animals, and protection of endangered species ,44. The Also, there is a correlation between the QTL effect variance and average observed heterozygosity and the population average kinship coefficient: the higher the final average kinship coefficient of the population, the lower the QTL effect variance and average observed heterozygosity. This also indicates that genetic diversity of population decreases with the inbreeding level increases. Under OCS strategy, population genetic diversity is higher than TS and LP strategies; thus, the OCS method is an optimal breeding method that can achieve sustainable genetic gain during the long-term selection.Further analysis about genetic correlation and multi-trait breeding may be considered for optimization and improvement in breeding strategy. In the current study, genetic correlation of the simulated population was not included in our analysis, and analysis of multiple traits using LP and OCS approaches for long-term selection should be useful in the actual situation. The objective function in the LP method may contain more parameters from economic aspect. Moreover, in the LP breeding strategy, further study may be considered to assess the impact of inbreeding on WSG when punishing the inbreeding level of the offspring population. In the OCS strategy, the feasibility of the OCS method for multi-trait breeding schemes can also be explored in the subsequent analyses. Many selection methods, such as the Weighted Genetic Selection method and the Genetic Algorithm method have been proposed, which also require further exploration in future research.Under the LP strategy, the population can achieve higher genetic gain, which means that it can create higher economic value in a shorter period of time. At the same time, shorter growth time also reduces feeding costs and disease risks. Therefore, LP strategy is a breeding strategy that can improve economic benefits in the short term. However, considering the inbreeding level of the population, the OCS strategy is a breeding strategy aimed at achieving long-term economic benefits.This experiment can provide theoretical guidance for breeders to choose appropriate breeding strategies for different breeding goals by integrating the genetic gain and average kinship changes of multiple generations under LP and OCS strategies. If breeders want to quickly improve the genetic gain in high heritability traits in the short term, they can choose the LP strategy. If breeders want to improve the genetic gain in low heritability traits, increasing the weight of this trait in LP breeding strategies is a considerable option. For populations that require long-term breeding, the OCS strategy is more suitable. The OCS strategy can also be used for the protection of endangered species.In this study, we performed breeding simulation to evaluate the effect of the LP method and OCS method during long-term selection in cattle breeding. The results showed that the LP method can significantly improve the genetic gain in the population compared with the TS method, especially for the traits with higher heritability and the traits highly weighted in the breeding process. However, the control effect of LP on population inbreeding level is not improved. The OCS method may balance the conflicting breeding goals of improving genetic gain and reducing population inbreeding levels during breeding process. Application of the OCS method is feasible strategy in breeding that can achieve sustainable genetic gain during long-term selection process.


IntroductionClinicians agree with the fact that the impact of genetics in the field of medicine is humongous. They have to cope with the rapid advances in the field of clinical genetics and offer the best treatment to the patients at the right time. Disease with an underlying genetic cause not only involves the patient but also the family and the community. In the process of genetic counseling, the patient and the family are educated about the genetic basis of the disorder. This helps the patient and the family to make a well-informed decision. It also helps to reduce the genetic burden of the disease in the community over a period of time. In this regard, knowledge, attitude, and practice about the process of genetic counseling among clinicians is imperative.MethodsA structured pre-validated questionnaire was distributed amongst 60 clinicians from different departments. Their responses were assessed based on the Likert scale. The data obtained were analyzed using descriptive statistics and expressed in percentages. ResultsIn the present study, nearly 90% of the clinicians felt that it was important to gather a multi-generational family history of the patient and advise them about inheritance patterns, recurrence risk, and genetic tests for a disorder with an underlying genetic cause. The need to educate the family members regarding the importance of genetic tests and referral to appropriate support groups if they test positive for a genetic disorder receive a positive response. Mostly the participants agreed that parents of children and couples at risk of having a child affected by a genetic disease should undergo genetic counseling.ConclusionClinicians may not always be aware of the underlying genetic cause and genetic tests available or may face a paucity of time to counsel the patient and the family. Genetic counseling needs to be done at length in multiple sessions, and it is essential to reduce the burden of genetic disorders in society. India is the most populous country in the world . The couStudy designThis cross-sectional study was conducted from July to December 2022 by the Genetics Unit, Department of Anatomy, All India Institute of Medical Sciences, Mangalagiri.Study setting All India Institute of Medical Sciences, Mangalagiri, is a multi-specialty tertiary care teaching hospital funded by the Central Government of India. Ethical clearanceBefore the commencement of the study, ethical clearance was obtained from the Institutional Ethical Committee (AIIMS/MG/IEC/2022-23/163 dated 08.07.2022).Data collection toolA structured pre-validated questionnaire was distributed amongst 60 clinicians through Google Forms. The questionnaire was devised from the existing studies and discussions with experts. It was written in the English language, and it consisted of 15 items pertaining to the knowledge, attitudes, and practices about the process of genetic counseling among clinicians. Data analysisThe questionnaire was assessed on a Likert scale of strongly agree, agree, strongly disagree, disagree, and neither agree nor disagree. Filling up the questionnaire was voluntary, and anonymity was maintained. All data from the questionnaire were entered into an Excel spreadsheet. To check for the accuracy of the entered data, 10% of the questionnaires were selected randomly and rechecked. The data, thus, collected were analyzed using descriptive statistics (i.e. percentages).The questionnaire was distributed to all the participants by email. It was answered by 42 out of 60 clinicians from various departments Figure . About 8The majority of the participants agreed 19%) or strongly agreed (73.8%) with the fact that gathering a multi-generational family history of the patient is essential to finding if a disease is inherited or not. Similarly, participants agreed (40%) or strongly agreed (47.6%) that it is necessary to refer the patient for appropriate genetic testing to determine the risk and avoid further transmission of the disease. Participants agreed (50%) or strongly agreed (40.5%) to the need to be aware of the genetic disease common in a particular region. When it comes to advising genetic tests, a difference of opinion was observed among the participants, with 23.8% strongly disagreeing, 38.1% disagreeing, 11.9 % neutral, and 23.8% agreeing (Table 9% or strParticipants agreed (38.1%) or strongly agreed 52.4%) that it is essential to communicate with patients about their genetic condition. Regarding the importance of educating the patient about the aftermath of detecting genetic disorders, participants agreed (47.6%) or strongly agreed (42.9%) with the need to educate the patients and their family members. Participants were of the opinion  that families should know about the prenatal genetic test and its importance in early detection of genetic conditions. Moreover, they agreed (31%) or strongly agreed (61.9%) that parents of children and couples at risk of having a child affected by a genetic disease should undergo genetic counseling. They disagreed (33%) or strongly disagreed (54.8%) with not providing genetic counseling to affected family members and couples at risk  or strongly agreed (26.2%) by the clinicians. They supported appropriate referral to the support groups by agreeing 38.1%) or strongly agreeing (47.6%). Participants backed carrier testing of family members: agree (31%) or strongly agree (61.9%). The majority of the participants agreed (59.5%) or strongly agreed (31%) with the use of audio-visual aids to perform genetic counseling, which may have more impact on the proband and the family members. There was a mixed response, namely, strongly disagree (7.1%), disagree (9.5%), neutral (11.9%), agree (52.4%), and strongly agree (19%), for informing the patient to do appropriate genetic tests for risk determination of disease transmission  or strongly agreed (52.4%) that it is essential to communicate with patients about their genetic condition and 47.6% participants agreed or 42.9% participants strongly agreed to the fact that it is important to educate the patients and the family members about the psychological stress the family may experience as a result of detection of a genetic disorder. This fact is not only imperative in the management of the disease condition but also to prevent and curtail the stigmatization of the proband and the family. Informing families about the genetic condition and associated risks may enable them to undergo early screening, prevention, and treatment or to utilize reproductive technologies to avoid having an affected child . ParticiWith our findings, the need to explain the basic concept of the probability of disease, susceptibility, and influence of genetic factors was agreed (57.1%) or strongly agreed (26.2%) by the clinicians. They supported appropriate referral to the support groups by agreeing (38.1%) or strongly agreeing (47.6%). In a study done by Truong et al.  on primaLimitations of the study Knowledge and attitude could not be completely assessed in this study since routine genetic counseling has to be done in multiple sittings and at length. Moreover, other components of counseling, such as advice for appropriate genetic tests, referral to support groups, and follow-up strategies, are not assessed in the present study. Therefore, the depth of the practice of genetic counseling by clinicians should be further evaluated in detail as a continuation of this study.Rapid advances in molecular genetics have influenced the clinical management of the disease with genetic etiology. In the present era, clinicians in India have in-depth knowledge about the genetic basis of diseases. They do agree to the fact that the patient and the family should be counseled at length for a particular genetic disorder. Very little research exists on the preaching and practice of genetic counseling by clinicians. Disorders having underlying genetic causes can present at any age and can involve any system of the body. Therefore, it is important for primary physicians and specialists to diagnose a genetic disorder, offer genetic counseling, and develop the necessary expertise in the field.


We are delighted to announce the publication of the Frontiers' Research Topic titled “Smart cities challenges, technologies and trends” organized in collaboration with Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology and National Institute of Technology Delhi, the organizers of the 2022 International Symposium on Smart Cities Challenges, Technologies, and Trends (SCCTT-2022). This peer-reviewed article Research Topic aims to raise awareness about the role of smart cities in the future, emphasizing their intelligent infrastructure, modern architecture, efficient communication, and citizen-friendly services. The Research Topic, accessible through Frontiers' open science platform and searchable across multiple journals, offers insights into the latest trends and challenges in Smart Cities technology, fostering collaboration to develop cost-effective solutions for urban issues such as traffic congestion, energy consumption, and inadequate infrastructure. However, the journey toward building smart cities is not without obstacles.This editorial preface provides an overview of the challenges, technologies, and trends that shape the landscape of smart cities.Implementing smart city initiatives involves overcoming several challenges including the integration of diverse technologies and legacy infrastructure, ensuring data privacy and cybersecurity, fostering citizen engagement and participation in decision-making, addressing the digital divide to provide equal access to services, managing sustainable and energy-efficient practices, navigating complex regulatory frameworks, securing sufficient funding and investments, and developing resilient strategies to handle potential disasters and emergencies. These challenges require collaborative efforts and innovative solutions to create efficient, inclusive, and sustainable urban environments for the future.Smart cities rely on various cutting-edge technologies to optimize urban operations and enhance quality of life. The Internet of Things forms the foundation, enabling real-time data collection and analysis through connected devices and sensors. Big data analytics extracts valuable insights from the vast amounts of data generated, facilitating informed decision-making, and optimized services. Artificial Intelligence automates processes and optimizes resource allocation, improving efficiency and personalizing citizen services. AI also reduces network costs, enhances performance, and enables a wide range of telecom services and applications in smart city networks. While technology undoubtedly plays a significant role in smart city developments, it is essential for all stakeholders to consider the broader societal impact. Alongside implementing state-of-the-art technology, having a clear mission, and measuring the effects on the environment, society, and governance are crucial for achieving long-term sustainability. A human-centric approach and design thinking are vital to enhance the quality of life for smart city residents.Smart cities are witnessing several emerging trends that shape their future. Sustainable energy management is a key focus, with a shift toward renewable sources, energy-efficient buildings, and smart grids. Among the prevailing trends, smart mobility encompasses intelligent traffic management, eco-friendly transportation, autonomous vehicles, and car-sharing initiatives. Additionally, tech solutions for digital citizens are gaining momentum, enabling remote healthcare, interactive education, and entertainment. Startups are also leveraging blockchain to enhance safety and security measures for residents. Furthermore, progress in robotics, extended reality, and drones is empowering urban planning, agriculture, and waste management practices. These trends contribute to reducing carbon emissions, improving transportation systems, and fostering inclusivity in smart city development.Total six articles are published on the given Research Topic. Summary of each article is as follows:Singh and Ujjwal on Topic “Hybridized bio-inspired intrusion detection system for Internet of things”: A hybridized bio-inspired intrusion detection system for Smart Cities utilizes advanced algorithms inspired by biological systems to detect and respond to security threats, enhancing the overall safety and resilience of urban environments. In this article, authors propose a hybrid bio-inspired-based intrusion detection system (IDS) for the IoT framework, using the sine-cosine algorithm (SCA) and salp swarm algorithm (SSA) to determine network traffic features. Selected features are passed to a machine learning (ML) classifier for the detection and classification of intrusive traffic.Nagvanshi et al. on Topic “Nonstationary time series forecasting using optimized-EVDHM-ARIMA for COVID-19”: The optimized-EVDHM-ARIMA approach leverages an evolutionary algorithm and the Ensemble Variational Mode Decomposition for nonstationary time series forecasting in both COVID-19 data and Smart Cities applications, enabling accurate predictions and proactive decision-making for public health and urban management. Here, Authors present a forecasting model for COVID-19 infections which combines multiple components forecasted by ARIMA and utilizes genetic algorithms to optimize parameter selection and decomposition results, providing valuable insights into virus transmission rate and future infection cases for effective decision-making and healthcare preparedness.Singh et al. on Topic “Blockchain-enabled access control to prevent cyber-attacks in IoT”: Blockchain-enabled access control provides a decentralized and tamper-proof mechanism for managing user permissions and data access in Smart Cities, reducing the risk of cyber-attacks by preventing unauthorized entry and ensuring data security and integrity across interconnected systems and devices.Duggal et al. on Topic “Dependable modulation classifier explainer with measurable explainability”: It employs interpretable machine learning techniques to elucidate the decision-making process behind modulation classification in wireless communication systems. By quantifying feature importance, model confidence, and providing human-understandable explanations, it ensures transparency and accountability, enabling stakeholders to assess and validate the system's performance and dependability in critical Smart City applications. The proposed dependable modulation classifier explainer (DMCE) focuses on the explainability of modulation classification, allowing for visualization and understanding of prediction results. The study presents a comparative analysis with existing methods and demonstrates the potential of deep learning in enhancing IoT network performance.Sharma et al. on Topic “An extended approach to appraise electricity distribution and carbon footprint of bitcoin in a smart city”: This study explores the energy consumption and carbon emissions associated with Bitcoin mining. It extends the Patch methodology by considering hardware efficiency limits and power generation sources, providing a more refined estimate of electricity consumption and carbon footprint. The study estimates that Bitcoin consumed between 38.495 and 120.72 terawatt hours of electricity in 2021 and released between 2.12 and 45.37 million metric tons of carbon dioxide. Efforts to address energy consumption include the relocation of miners to energy-intensive regions, such as aluminum mining sites powered by hydroelectricity.Shambharkar and Goel on Topic “From video summarization to real time video summarization in smart cities and beyond: A survey”: This article discusses the importance of video summarization in managing and retrieving large volumes of video data, particularly in the context of smart cities and surveillance systems. It presents an analysis of video summarization approaches, focusing on real-time video summarization techniques. The study aims to provide valuable insights for researchers and practitioners, highlighting the practical applications of video summarization in smart cities, such as aberrant detection in video surveillance systems.In summary, the construction of smart cities is a complex and ambitious endeavor. Despite the significant challenges associated with infrastructure upgrades, data privacy and security, and interoperability, the potential rewards are substantial. Smart cities have the potential to improve efficiency, sustainability, and the overall quality of life by harnessing technologies like the integration of the Internet of Things, artificial intelligence, cloud computing, and data analytics to support sustainable transformations across various city components. Notably, the prevailing trend in smart cities is smart mobility, covering intelligent traffic management, zero-emission transportation, autonomous vehicles, and car-sharing programs. Tech solutions such as remote healthcare, interactive education, and entertainment, are also gaining momentum. To ensure successful smart city developments, stakeholders must prioritize purposeful missions, measuring environmental and societal impacts, and embrace design thinking anchored in human-centric approaches. Encouraging an abundance mindset and empowering younger generations with creative thinking can lead to transformative advancements for the greater good and address long-standing societal challenges.NG: Writing—review and editing.