We assessed genetic diversity using mitochondrial cytochrome c oxidase subunit I gene sequences (COI-5P DNA barcode region), and also morphologically identified voucher specimens. We obtained 130 sequences representing four genera: Nanorchestes (n = 30 sequences), Stereotydeus (n = 46), Coccorhagidia (n = 18) and Eupodes (n = 36). Tree-based analyses (maximum likelihood) revealed 13 genetic clusters, representing as many as 23 putative species indicated by barcode index numbers (BINs) from the Barcode of Life Datasystems (BOLD) database. We found evidence for geographically-isolated cryptic species, e.g., within Stereotydeus belli and S. punctatus, as well as unique genetic groups occurring in sympatry . Collectively, these data confirm high genetic divergence as a consequence of geographic isolation over evolutionary timescales. From a conservation perspective, additional targeted sampling of understudied areas in the Ross Sea region should be prioritised, as further diversity is likely to be found in these short-range endemic mites.Free-living terrestrial mites (Acari) have persisted through numerous glacial cycles in Antarctica. Very little is known, however, of their genetic diversity and distribution, particularly within the Ross Sea region. To redress this gap, we sampled mites throughout the Ross Sea region, East Antarctica, including Victoria Land and the Queen Maud Mountains (QMM), covering a latitudinal range of 72–85 °S, as well as Lauft Island near Mt. Siple (73 °S) in West Antarctica and Macquarie Island (54 Antarctic terrestrial arthropods have persisted for millions of years in microhabitats that are ice-free yet have some liquid water available ,2,3. TheMany Antarctic taxa are short-range endemics , limitedFriesea grisea Schäffer with its type locality in sub-Antarctic South Georgia was widely reported from both maritime and continental Antarctica [Studies that incorporate both morphological and genetic data have shown that some Antarctic taxa that were originally thought to be widespread single species often consist of multiple, distinct species. For example, the collembolan tarctica ,26. Untitarctica ,22,27,28tarctica  and intec oxidase subunit I (COI-5P) gene [Stereotydeus have used the COI-3P region as an alternative [S. nunatakis and S. ineffabilis in Victoria Land [Genetic markers for species-level assessments include the 5’ region of the mitochondrial cytochrome 5P) gene , which i5P) gene ,31,32. U5P) gene , and preernative ,23,34,35ria Land . Howeverria Land , as wellNanorchestes (Endeostigmata), a widespread genus of small-bodied mites that are also found globally [Nanorchestes antarcticus Strandtmann, 1963  likely provides an analogous example. It is another species that was originally recorded from both the maritime and continental Antarctic [N. berryi and N. gressitti. Four new species were also described from Macquarie Island [N. antarcticus. Considering the widespread geographic distribution of this taxon and its acknowledged morphological variability, it is likely that further species-level divergences will be revealed by the application of modern molecular approaches.One Antarctic mite taxon that would particularly benefit from further attention is globally . Althougntarctic . Howeverntarctic  subsequee Island , and fouc oxidase subunit I gene sequences (COI-5P DNA barcode region) to assess levels of genetic diversity within and among locations, as well as assist with species-level identifications. Our data provide a framework for ongoing studies as well as initiate a reference library for the molecular identification (DNA barcoding) of Antarctic taxa.Here, using integrated morphological and molecular approaches, we assessed the species diversity of terrestrial mites collected from the Ross Sea region of Antarctica, including North Victoria Land (NVL), South Victoria Land (SVL) and the Queen Maud Mountains (QMM), covering a regional latitudinal range of 72–85 °S. We also obtained mites from Lauft Island near Mt. Siple (73 °S) in West Antarctica and Macquarie Island (54 °S) in the sub-Antarctic. We used mitochondrial DNA cytochrome sensu [Sampling for free-living mites was undertaken during austral summer field seasons between 2008 and 2018 at 16 locations throughout the Ross Sea region , spanning latitudes from 72 to 85 °S, and occupying the longitudinal wedge between 160 °E and 160 °W . This resensu ,12, Nortsensu , pitfallsensu  or flotasensu  and immec oxidase subunit I gene (COI-5P DNA barcode region) [oC for 1 min) of denaturation and polymerase amplification, with a final elongation at 72 °C for 5 min. At CCDB, thermal cycling conditions were: 94 °C for 1 min followed by 5 cycles  then 60 cycles  of denaturation and polymerase amplification, with a final elongation at 72 °C for 5 min. Successful amplification products were cleaned with 0.1 μL exonuclease (EXO) (10 U/μL) and 0.2 μL shrimp alkaline phosphate (SAP) (1 U/μL)  at 37 °C for 30 min then 80 °C for 15 min at the UoW, or Sephadex at CCDB. Sequencing was carried out in forward and reverse directions using an ABI 3130 at the UoW or an ABI 3730x1 sequencer at the CCDB. Sequences were uploaded to the Barcode of Life Datasystems (BOLD) database. Specimen collection details, photographs, primers used and full sequence data are available from the BOLD database under dataset DS-ANTMIT .For genetic analyses, we used DNA sequences from the mitochondrial cytochrome  region) . On retu region)  at the U region)  at the CFollowing DNA extraction, the exoskeletons for some individual specimens were carefully removed from the extract solution and permanently slide-mounted in polyvinyl alcohol (PVA) mounting medium  for morphological assessment. The slide-mounted specimens were then identified with the aid of a compound microscope using the available taxonomic literature e.g., ,9,40. AlNanorchestes, Eupodes and Rhagidia. No previous sequences were available for Stereotydeus or Coccorhagidia. None of the existing public records were from Antarctica or the sub-Antarctic. To simplify the analyses, we reduced these public records to one sequence per unique barcode identification number (BIN) or named species (n = 26). The final sequence alignment of 156 sequences, each ranging from 404 to 658 bp in length, included our new sequences (n = 130) as well as the 26 representative publicly available sequences . All sequences were aligned in Geneious Prime v2021.2.2 using MUSCLE 3.8.425 and then exported as a PHYLIP file for downstream analyses. A maximum likelihood tree was built using IQ-TREE 2 [Stereotydeus and Nanorchestes, using pegas v1.1 [In addition to our newly generated sequences, 491 publicly available COI-5P sequences of at least 400 bp in length were downloaded from GenBank in July 2022 for the genera Q-TREE 2   or otherwise based on location, as there was a clear genetic distinction between regions. A barcode gap analysis  was thenCoccorhagidia, Eupodes, Stereotydeus; Endeostigmata: Nanorchestes), and five taxa (12 specimens) were identified to species level . A maximum likelihood tree delineated the Antarctic sequences into 13 major monophyletic groups (genetic clusters), 5 of which represented the taxonomically identified species, and none of which contained the publicly available  COI-5P sequences  for C. gressitti, S. belli and S. punctatus (C. gressitti (>6% divergence) were all present at Christie Peak, while the BINs for S. belli and S. punctatus were geographically isolated. Stereotydeus belli and S. punctatus each had a unique population found only at Cape Hallett, which was genetically distinct from their respective populations found either at Redcastle Ridge  or Tombstone Hill .Our genetic data support the taxonomic species designations of unctatus . The thrNanorchestes sp. 1 and sp. 6, Stereotydeus sp. 4), which were identified morphologically to genus level, also included more than one unique BIN. These BINs were distributed differently across the landscape, depending on the taxon. For instance, some taxa had all BINs present at a single location, while for other taxa unique BINs were each found at a different location. All three BINs of Nanorchestes sp. 1 (>6% divergence) were found at Ebony Ridge, and the two BINs for Nanorchestes sp. 6 (>3% divergence) were found at the same site on Macquarie Island. In contrast, BINs were unique to single locations for three of the four Stereotydeus clusters, the exception being Cape Hallett, where two BINs for S. punctatus were present in sympatry , two genetic clusters each of Stereotydeus and Nanorchestes were present , representing 4–5 putative species.Based on the morphological and genetic data, we identified two locations at which four or more putative mite species were present . At Capesensu [Nanorchestes, Stereotydeus, Coccorhagidia and Eupodes. The sequences were grouped into 13 distinct genetic clusters and a total of 23 unique BINs, some of which were found at only a single location. We also identified locations where multiple clusters or BINs occurred in sympatry, highlighting the potential conservation priorities for protecting sites with high levels of diversity, in keeping with the Antarctic Specially Protected Area priorities outlined by the Protocol on Environmental Protection to the Antarctic Treaty [We assessed the genetic diversity of Antarctic mites from across a wide geographic and taxonomic range in the Ross Sea region. We obtained 130 standard COI-5P DNA barcode sequences c Treaty .Nanorchestes was the most genetically diverse and widespread genus in our dataset, with six major genetic clusters distributed across ten locations, covering all three ACBRs in the Ross Sea region [Nanorchestes antarcticus [Nanorchestes sp. 3 in NVL is either N. antarcticus or N. lalae, Nanorchestes sp. 5 in SVL could represent either N. bellus, N. lalae or N. antarcticus, and Nanorchestes sp. 1 and 4 in the QMM could represent N. lalae and N. brekkeristae. We also found a genetically distinct cluster (Nanorchestes sp. 2) at Lauft Island near Mt. Siple, a location from where microarthropods including Nanorchestes had not previously been sampled or described. The individuals collected from Lauft Island were genetically most similar to the Nanorchestes found in the QMM region (8% sequence divergence), and we speculate that refugial locations in the QMM may have potentially acted as a source habitat for Lauft Island. Based on available physical modelling and biological evidence [Nanorchestes populations at these two locations.a region ,12 as wearcticus , this taarcticus ,39,40. Bevidence ,59 areasevidence . Future Stereotydeus belli and S. punctatus are currently known only from the North Victoria Land region, with S. punctatus reported from only a single location, Crater Cirque [S. punctatus (three BINs) from two additional locations in NVL, suggesting that even further genetic diversity may exist within this species. Stereotydeus belli and S. punctatus were each represented by multiple BINs and some of these BINs were geographically isolated. Both species had a unique population found only at Cape Hallett and a genetically divergent population (7–11% sequence divergence) at either Tombstone Hill or Redcastle Ridge. Both Redcastle Ridge and Tombstone Hill are located towards the inner end of the Edisto Inlet, presumably isolated from Cape Hallett by local marine currents. Indeed, the isolation of Cape Hallett from other NVL sites has already been recognised for S. belli [r Cirque . HoweverS. belli .Stereotydeus in the QMM and it is plausible that at least one of these is S. shoupi, the only Stereotydeus species currently described and recorded from this region [S. shoupi from the QMM using the alternative COI-3P gene region and found 8% genetic divergence between the Darwin Glacier (79 °S) and Beardmore Glacier (83 °S). We also found high levels of genetic divergence in this region, of up to 14% between populations at Beardmore Glacier and the more southerly Shackleton Glacier (84 °S), for Stereotydeus sp. 4 . Such high levels of genetic variation between the Beardmore and Shackleton Glaciers have also been found for Collembola, with over 13% COI-5P sequence divergence in Antarctophorus subpolaris between these two locations [Nanorchestes had shared BINs between the Beardmore and Shackleton Glacier sites. One possible explanation is that a smaller body size and greater tolerance of lower environmental temperatures for Nanorchestes relative to Stereotydeus [sensu [Patterns of genetic variability among sites in the QMM differed depending on the taxon considered. We found two genetic clusters of s region . Demetras region  previousocations . In conteotydeus  facilitas [sensu ,62,63 woStereotydeus sp. 3 was found only in the vicinity of the Shackleton Glacier, and could represent a more southern population for a northern species of Stereotydeus. This has previously been found for the collembolan Gomphiocephalus hodgsoni, a common SVL species that was only recently reported from the QMM region [Stereotydeus sp. 3 may represent a previously undescribed species unique to the area. Similar to Stereotydeus sp. 3, Tullbergia mediantarctica (Collembola) is also only known from a small area near the mouth of the Shackleton Glacier in the QMM and has previously been identified as potentially vulnerable to extinction due to this narrow distributional range [M region . Alternaal range . We suggal range .Stereotydeus and two of Nanorchestes. While reductions in biodiversity are often observed with increasing latitude, we found no evidence of this occurring in the Ross Sea region, as a similar level of diversity was found at both QMM and NVL (four genetic clusters each). Caruso et al. [Nanorchestes species clusters, each with different distributional ranges, which could be due to different habitat preferences, a feature already hypothesised among other species of this genus from the Antarctic Peninsula region [Four genetic clusters were found at Mount Franke, near the Shackleton glacier in the QMM region, probably representing two species of o et al.  and Coleo et al.  also cona region . Notablya region ,69,70. Wa region , it is pa region ,73.Stereotydeus mollis was known from this area. With the recent description of an additional species in the same area, S. ineffabilis [S. mollis by McGaughran et al. [S. mollis and S. ineffabilis. Furthermore, all existing mite studies that include DNA-based methods [For the five species that were taxonomically verified, we found 0.46% to 11.04% sequence divergence among populations. Previous genetic studies of Antarctic mites have suggested that they may have differing levels of genetic divergence relative to sympatric Collembola. Possible explanations include ecological factors, such as mites being able to colonize and tolerate newly available habitats before Collembola  or that they have differing rates of evolution ,23,35. Tffabilis , we sugg methods ,23,34,35 methods . UnfortuStereotydeus villosus from the maritime Antarctic) or for targeting specific taxa that may not yet have been collected, such as the oribatid mite Maudheimia petronia from NVL or Protereunetes maudae (Eupodidae) from QMM [In conclusion, based on sequencing of the COI-5P gene region, we found between 13 and as many as 23 putative species representing four mite genera, far exceeding previously recognized diversity from this geographic region. Further mite diversity is likely to remain undetected and ongoing and targeted sampling initiatives, e.g.,  will advfrom QMM .Overall, our data suggest that the combination of highly isolated locations and long evolutionary timescales have resulted in high levels of genetic differentiation and local speciation for mites throughout the Ross Sea region. A fuller understanding of the locally unique and endemic mite taxa of Antarctica, which occupy the most southern terrestrial habitats on Earth, will provide valuable insights into the evolutionary history of the Antarctic landscape.


Smart technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are being adopted in cities and transforming them into smart cities. In smart cities, various network technologies, such as the Internet and IoT, are combined to exchange real-time information, making the everyday lives of their residents more convenient. However, there is a lack of systematic research on cybersecurity and cyber forensics in smart cities. This paper presents a comprehensive review and survey of cybersecurity and cyber forensics for smart cities. We analysed 154 papers that were published from 2015 to 2022 and proposed a new framework based on a decade of related research papers. We identified four major areas and eleven sub-areas for smart cities. We found that smart homes and the IoT were the most active research areas within the cybersecurity field. Additionally, we found that research on cyber forensics for smart cities was relatively limited compared to that on cybersecurity. Since 2020, there have been many studies on the IoT  that have utilized machine learning and deep learning. Due to the transmission of large-scale data through IoT devices in smart cities, ML and DL are expected to continue playing critical roles in smart city research. Many countries are exploring the implementation of smart cities to provide better lives for their citizens. The creation of smart and sustainable cities is enabled by current and emerging technologies, such as the Internet of Things (IoT), sensors, artificial intelligence (AI), robotics, unmanned systems, digital twins, Web 3.0 technologies, and smart grids. The rapid penetration of smartphones and information and communication technology (ICT) has been a fundamental enabler of smart cities, providing ubiquitous connectivity for smart city technologies and helping to improve connectivity, share information with the public, and provide better quality government services.In addition, many countries are making efforts to transform certain cities into smart cities, including Dubai, Tokyo, Singapore, Hong Kong, Seoul, Helsinki, London, and Riyadh ,2. In paSingapore is also striving to become a smart city in order to enhance the living environments of its residents . VirtualThe intelligent city platform of Valencia (VLCi) is a cloud-based internal city management system created in Spain. It enables the city to gather crucial information on its urban services, analyse that information using cutting-edge tools, and create dashboards to assist with decision-making . The citCities are constantly striving to make the lives of their citizens more convenient. Not only are traditional cities being transformed into smart cities but new cities are being constructed as smart cities. Smart cities have the potential to improve urban life through efficient transportation, energy efficiency, and improved public services. However, concerns around data privacy, social equity, dependence on technology, and environmental impacts must be addressed to ensure that smart cities can truly benefit all citizens and the environment.Many new technologies are being applied to facilitate the transition to smart cities, but these can also pose potential risks. Smart homes offer convenient features through the use of new technologies, such as the IoT, but there are also vulnerabilities that hackers can exploit to remotely control smart homes. One experiment highlighted the vulnerability of the Z-Wave technology that is used in smart homes, in which a smart door was opened remotely and the smart valve alert was disabled, leaving the user unaware of a fire in the house ,8. SmartState-sponsored hackers who employ advanced technologies, such as ransomware, have emerged as significant threats to smart cities ,12. AttaGiven the significance of smart cities, many researchers have been investigating their security and forensics. Research on smart cities has focused on various aspects. For example, Losavio conducted a study on the legal challenges of smart cities and IoT technologies, focusing on digital forensics, privacy, and security perspectives . Baig etThis study focused on reviewing the literature from 2015 to 2022 and analysing cybersecurity and cyber forensics research related to smart cities. To identify the key elements of smart cities, we conducted an initial search for related papers using relevant keywords. Then, for each key element, we systematically reviewed the related literature on cybersecurity and cyber forensics. The following keywords were used to search in Google Scholar: “cybersecurity smart cities”, “cyber forensics smart cities”, “digital forensics smart cities”, “smart home”, “smart mobility”, “smart vehicle”, “smart things”, and “smart people”. Additional recently published papers focusing on cybersecurity and cyber forensics were also selected.•A comprehensive review of 154 papers on cybersecurity and cyber forensics for smart cities that were published from 2015 to 2022, which revealed the key areas of research and identified major challenges and opportunities for improving the cybersecurity and cyber forensics readiness of smart cities;•A chronological organization of the research on cybersecurity and cyber forensics for smart cities, based on the key technologies that have been studied over time, such as the IoT, cloud computing, and AI, in order to provide a concise overview of the evolution of research on cybersecurity and cyber forensics for smart cities;•A comprehensive investigation of cybersecurity in smart cities, which found that the most common targets of cyberattacks are smart homes and that attacks related to the IoT are being studied very actively;•The observations that compared studies on cybersecurity in smart cities. There have been relatively few studies related to cyber forensics and that among the key city elements of smart cities, research on cyber forensics for IoT devices has been the most common.This review is intended for readers who are conducting research in the areas of cybersecurity and cyber forensics for smart cities. The contributions of this paper include the following:The remainder of this paper is organized as follows. To investigate and study smart cities, it was essential to identify their key elements. For this purpose, we used keywords, such as “smart city elements”, “smart city layers”, “smart city architecture”, and “smart city ecosystem”, to identify the key elements of smart cities. As shown in As depicted in Within the service and application layer, four key sub-areas were identified: smart transportation, smart communities, smart living, and smart environments. The smart transportation area comprises smart parking and smart mobility, while smart communities include smart economy and smart governance. Smart living can also be further divided into sub-areas, which include smart healthcare, smart people, smart homes, and smart education. Lastly, smart environments can be classified into smart factories, smart buildings, and smart energy.Smart city services and applications aim to enhance the quality of life of residents in smart cities. These services enable citizens to use urban services efficiently and conveniently by utilizing advanced technologies.Smart transportation systems offer a range of services, from guiding users to better routes or sharing traffic conditions to car and bike sharing opportunities, making public transportation systems and citizen mobility more efficient and convenient. Additionally, smart services can also cater to the parking needs of citizens, such as the identification of available parking spaces .From the governmental perspective, ICT can be used to promote transparency and community participation and effectively manage crowds and solve emergency problems based on public data . Smart eSmart living services allow citizens in smart cities to share information in real time, thereby enabling them to efficiently solve everyday challenges and stay connected . Remote Smart home systems allow users to remotely control IoT home appliances, such as smart TVs, by using smartphone applications to perform specific tasks . AdditioOne of the key goals of smart cities is to promote environmental sustainability through digitally integrated and improved energy management systems. Smart services can carry out the real-time monitoring and management of smart buildings, smart factories, and smart energy systems, which can enhance energy efficiency and waste management .Cyberattacks are primarily directed towards smart city services and applications, making cybersecurity countermeasures essential for ensuring the safety of smart cities. The risk of cybercrime also increases with the implementation of these technologies. To address cybercrime, policing must also evolve in the future, with data analysis playing a central role in this evolution . ResearcInnovative and popular technologies play crucial roles in more efficiently and safely solving various problems with smart cities by utilizing the vast amounts of data that are generated. The IoT is a fundamental technology that connects various ICT devices and lays the foundations for advanced smart cities . ResearcAI technology is rapidly evolving to enable the fast and efficient delivery of complex services for small-scale smart city elements, such as smart homes and smart buildings, as well as large-scale elements, such as smart infrastructure and smart transportation . Big datHowever, privacy and security issues arise when data are exchanged across a wide range of areas in smart cities. As a potential solution, blockchain is increasingly being used for some data exchanges. By enabling smart transactions through smart contracts and decentralized applications, blockchain provides a high degree of autonomy for the operation of smart cities . AdditioUser perceptions of smart city techniques depend on various factors, such as personal experiences, data privacy, and data security. Users are more likely to perceive smart city techniques positively when they see clear benefits, such as enhanced public services. A study conducted by Silver Spring Networks found that 75% of US consumers had a positive view of smart cities after being educated about their benefits, while only 3% had a negative view. The top two benefits perceived by respondents were reducing pollution and enhancing public safety, while the top two concerns were costs and privacy. Additionally, the study found that positive sentiments towards smart cities were expressed as an interest in living with smart city technologies.Network communication is a crucial element in smart cities. Applications that require communication over short distances, such as smart grids, smart water services, and smart buildings with very limited energy usage, typically utilize the IEEE 802.15.4 (Zigbee). These applications can last for years using the same battery. IEEE 802.15.1 (Bluetooth) can also be used for these applications. The wireless local area network (WLAN) protocol uses the 2.4 GHz band, with a data rate of 1 Mbps and a master/slave time division duplex (TDD) ranging from 10 to 100 m.The IEEE 802.11a/b/g/n protocols are used in almost all smart city systems. The latest version, the IEEE 802.11n protocol, operates in the 2.4 and 5.1 GHz ranges and uses orthogonal frequency division multiplexing (OFDM) and a direct sequence spread spectrum (DSSS). This protocol also enables reservation-based operations using point coordination functions (PCFs) and best effort operations using distributed coordination functions (DCFs). PCF services are useful for video, real-time, and multimedia audio data traffic that requires QoS guarantees for specific parameters, such as bandwidth, delay, and delay jitter .Smart water services, UAVs, smart grids, and pipeline monitoring can use cellular 3G and 4G protocols that employ packet switching for data communication and circuit switching or selective packets for voice communication . Long-teUAVs, pipeline monitoring, and smart transportation can also utilize satellite communication, which typically uses frequencies in the 1.53 and 31 GHz ranges and employs time division multiple access (TDMA) and frequency division multiple access (FDMA) at the data link layer . It alsoThe efficient spectra of narrow-band technologies are suitable for smart cities  as they It is essential to protect smart city wireless sensor networks (WSNs) and a comparative study on anomaly detection technology was conducted for this purpose . ResearcThe device and sensor layer is an essential component of smart cities. Smart devices equipped with sensors and actuators can measure, collect, and control various types of data . Data frCitizens can visually receive and control data through physical devices, such as wearable devices and smartphones. Facilities equipped with IoT sensors can monitor data in real time and analyse and control these data according to their own specific purposes.Smart city sensing is a new paradigm that promotes the transition to smart city services. A number of studies have been conducted on current and historic smart city sensing and its influence on related issues .While the mass proliferation of IoT devices has provided advanced services based on hyperconnectivity, end-node devices focus on confidentiality and have weak security , which cCybersecurity for smart cities includes a combination of technologies that have emerged to address the highly complex challenges of insecure devices and networks, which can lead to unbounded attacks. In this section, we highlight the issues discovered by research and their proposed solutions.Several studies have been conducted on the cybersecurity challenges in smart cities. For example, Kalinin et al.  classifiAlibasic et al.  explaineMa  providedButt and Afzaal  discusseIn this section, we summarized the cybersecurity-related research on each service element within smart cities, as shown in Javed et al.  conducteSharmila et al.  analysedITSs and autonomous vehicles are essential components of smart cities. Researchers have analysed the security of these systems, including their architecture, vulnerabilities, and potential attacks. They have proposed various strategies to safeguard connected vehicles and mitigate security threats, including digital signatures and encryption procedures, as well as the use of blockchain frameworks. Studies have also shown that existing security solutions can affect the quality of service and safety perception of vehicle applications.Several research papers have addressed the cybersecurity and privacy concerns regarding smart homes. Ryu and Kwak  investigGamundani et al.  and GhirElmisery and Sertovic  suggesteIn 2021, Rauti et al.  demonstrIn 2022, Mahor et al.  proposedThe security and privacy of smart homes are of concern within the field of cybersecurity. Research on IoT security in smart homes has focused on identifying and addressing issues, such as unauthorized device access, vulnerabilities in PII, cybersecurity vulnerabilities in smart home infrastructure, and potential attacks on authentication and home automation systems. Proposed solutions to these security concerns include using blockchain technology to ensure the integrity of smart home devices and implementing policy-based management to secure personalization data. Additionally, protocols using symmetric cryptography have been suggested to protect confidentiality, integrity, and mutual authentication in smart homes.In this section, we summarized the IoT cybersecurity research papers related to smart cities, as shown in Saadeh et al.  presenteNeshenko  generateIn 2019, James  attempteIn 2020, Sharma et al.  discusseIn 2021, research on IoT was very active, especially in relation to smart cities. Cvitić et al.  presenteBhargava et al.  suggesteKhan  suggesteCybersecurity is a critical concern for the widespread adoption of IoT devices and services. To address this challenge, robust cybersecurity solutions have been proposed, including vulnerability assessments for IoT devices, cyberthreat intelligence generation, AI/ML-based threat detection, and models for mitigating malicious activities targeting IoT devices. The combination of cloud computing and IoT devices is also being explored as a means of enhancing the security of smart city applications.Next, we summarized the findings of studies on smart city components from a cyber forensics perspective. Although research on cyber forensics related to IoT devices has been active for some time, progress in research on cyber forensics for smart cities is still slow compared to that on smart city cybersecurity. Most studies related to cyber forensics have focused on smart homes and autonomous vehicles.In this section, we summarized the Cyber forensics-related research on each service element within smart cities, as shown in Iqbal et al.  exploredSeveral researchers have also conducted forensics studies on autonomous vehicles to identify artefacts and evidence for investigations. Feng et al.  discusseIn 2022, Zhang et al.  implemenIn this section, we summarized the IoT Cyber forensics-related research on smart cities, as shown in In 2019, Hou et al.  presenteNumerous studies related to IoT forensics were published in 2020. In the same year, Stoyanova et al.  identifiAtlam et al.  reviewedIn 2021, Janarthanan et al.  reviewedSome studies have also been conducted across multiple domains. Sharma and Singh  discusseBased on our literature review, the IoT was identified as the most critical technological component of smart cities. Consequently, numerous researchers have been conducting research on IoT cybersecurity. Previous studies have focused on DoS attacks, authentication, and the control of access to IoT devices. Furthermore, various frameworks for defending IoT devices against attacks have been proposed and research on intrusion detection and prevention systems is also ongoing. With the recent technological advancements, research on the use of ML/DL for IoT devices is also progressing.Smart homes consist of smart hubs and other devices that are connected either via Zigbee, Z-Wave, or IoT communication protocols. As previous studies have suggested, digital forensics in smart homes is vital to reconstruct past events during forensic investigations. Relevant artefacts collected from smart home devices can help to identify the location of devices connected to the smart homes. Traces from these devices can be found in different data types, such as XML, protobuf, JSON, DB, and archive logs, which can coexist on devices or smartphone applications. Forensic investigators must familiarize themselves with device-level knowledge, communication protocols, and data types before attempting to investigate such events.The emergence of autonomous and semi-autonomous vehicles in smart cities is gaining popularity due to their relation to the highly connected infrastructure that is a perfect enabler of such vehicles. The control systems at the hearts of these vehicles collect data from sensors for collision warning systems (CWSs) and collision avoidance systems (CASs) to prevent accidents, often involving communication with smart city ITS. However, the susceptibility of AVs to cyberattacks cannot be ignored due to their connectivity with other IoT devices. Previous studies have suggested a few preventive measures to defend against such cyberattacks, as well as the use of Ethereum blockchain as a platform for AV digital forensics. Recent research on this new technology has not only helped to identify potential threats but has also guided investigators when examining AV incidents, such as crash investigations and security and privacy breaches.The potential of the IoT to meet the needs of smart cities is immense; however, the heterogeneity of IoT devices, embedded software, and hardware, coupled with their limited local data processing capabilities, presents new challenges for forensic investigators. According to some studies, digital forensics for the IoT should be tailored to specific applications within smart cities, while traditional digital forensics should be employed for smart homes. These studies have also suggested identifying potential data of interest in the IoT applications within smart cities. The integration of edge and fog computing in IoT ecosystems is on the rise due to its interoperability and increased efficiency. Meanwhile, although theoretical and conceptual IoT forensics frameworks for the device, cloud, fog, and edge levels exist, practical solutions are still lacking.In addition to the research areas mentioned, there are other emerging research areas in the smart city field that are attracting attention. For instance, the integration of blockchain technology into smart cities is a growing area of interest due to its potential for secure and transparent transactions. Blockchain technology can be used to secure IoT devices and protect the privacy of citizens living in smart cities. Furthermore, the use of drones for various purposes, such as monitoring and delivering goods, is another emerging research area.A thorough review of research on cybersecurity and cyber forensics in smart cities, which included papers that were published from 2015 to 2022, was conducted. Smart transportation, smart communities, smart living, and smart environments were identified as the major components of smart cities. Additionally, the service and application layer, technical layer, network communication layer, and device layer were recognized as the different layers of smart cities.Through our review of cybersecurity and cyber forensics studies, we discovered that research related to smart homes and smart mobility was the most active. Moreover, many studies have been conducted on IoT devices from a technical point of view. Compared to cybersecurity, research on cyber forensics in smart cities is relatively scarce, despite the fact that smart cities promote urban regeneration by creating more inclusive, safe, and sustainable cities. Research on IoT devices is still being carried out but not many studies have been conducted specifically on smart cities, highlighting the lack of research on cyber forensics.Since 2020, numerous studies based on ML and DL have been conducted on the IoT, which is an important technological element of smart cities. Because large-scale data are transmitted through IoT devices in smart cities, ML and DL remain essential factors in smart city research. Moreover, because cybercrime that affects everyday life in smart cities has become more complicated, research on cyber forensics in related devices is of great importance.Another important aspect to consider is the ethical implications of cybersecurity and cyber forensics in smart cities. Smart cities collect vast amounts of data from citizens, raising concerns about privacy and security. Researchers need to address the ethical concerns related to data collection, storage, and analysis in smart cities.Overall, the fields of cybersecurity and cyber forensics in smart cities are rapidly evolving and present numerous research opportunities. This review article aimed to assist scholars who are interested in cybersecurity and cyber forensics for smart cities in gaining a better understanding of their current status and possible future directions. In this way, other researchers can continue to contribute toward building more secure, inclusive, and sustainable smart cities.


The Xiongnu established the first nomadic imperial power, controlling the Eastern Eurasian steppe from ca. 200 BCE to 100 CE. Recent archaeogenetic studies identified extreme levels of genetic diversity across the empire, corroborating historical records of the Xiongnu Empire being multiethnic. However, it has remained unknown how this diversity was structured at the local community level or by sociopolitical status. To address this, we investigated aristocratic and local elite cemeteries at the western frontier of the empire. Analyzing genome-wide data from 18 individuals, we show that genetic diversity within these communities was comparable to the empire as a whole, and that high diversity was also observed within extended families. Genetic heterogeneity was highest among the lowest-status individuals, implying diverse origins, while higher-status individuals harbored less genetic diversity, suggesting that elite status and power was concentrated within specific subsets of the broader Xiongnu population. Members of the Xiongnu Empire were genetically diverse, but elite status was concentrated within specific genetic subsets. The Xiongnu Empire was the first of many historically documented steppe empires to arise in Eurasia, and its formation foreshadowed the rise of subsequent nomadic imperial powers, including the Mongol Empire, whose reach a millennium later stretched from the East Sea to the Carpathian Mountains  and the local elite cemetery of Shombuuzyn Belchir (SBB), located at the far western frontier of the empire in Mongolia’s present-day Khovd province. Analyzing the genome-wide data of 18 individuals from high and low-status burials, we show that both communities harbored an extremely high level of genetic diversity that is comparable to that of the Xiongnu Empire as a whole. High genetic diversity is reflected within individual tomb complexes and burial clusters and even extended family groups. Thus, we find that the same sociopolitical processes that produced a genetically diverse empire on a vast scale also operated at the smallest scale, creating highly diverse local communities over the span of only a few generations. There are also discernable genetic patterns with respect to social and political status at TAK and SBB, where individuals of the lowest status (based on grave form and mortuary remains) have the highest degree of genetic heterogeneity. In contrast, higher-status individuals are less genetically diverse and have high levels of eastern Eurasian ancestry. This further suggests the existence of an aristocracy in the Xiongnu empire, that elite status and power was concentrated within specific subsets of the broader population.Before this study, two archaeogenetic studies had intensively investigated Xiongnu-era cemeteries in the political core of the Xiongnu empire at Egyin Gol  The aristocratic cemetery of TAK, dating to ca. 40 BCE to 50 CE . The tomLocated approximately 50 km to the southwest of TAK, the local elite cemetery of SBB is situated along a strategic high mountain pass and spans a period from ca. 50 BCE to 210 CE  and concatenated our new genotype data with previously published genotype data for TAK001 , and we further enriched these DNA libraries for a panel (“1240K”) of 1,233,013 ancestry-informative single-nucleotide polymorphisms (SNPs) using an in-solution DNA capture method  and Early Iron Age (EIA) periods in central and eastern Mongolia  and to rn = 13) , and at an individual level most also fit the same model with 13.9 to 33.4% contributions from Khovsgol_LBA (data file S2A), with the exception of one individual with an unusually high Khovsgol_LBA contribution . On the basis of the admixture modeling results, we grouped 12 of 13 Ulaanzuukh individuals as an analysis unit (“Ulaanzuukh1”), excluding ULN005 for its much higher Khovsgol_LBA affinity , and used it as a representative of the Ulaanzuukh gene pool.First, we detected a subtle genetic shift in eastern Mongolia between the preceding pre–Bronze Age period and LBA Ulaanzuukh individuals (n = 16), we compared their genetic profiles with the preceding Ulaanzuukh individuals. Incorporating data from 11 newly published individuals  (P = 1.98 × 10−7 for Chandman_IA and qpWave P = 2.42 × 10−3 for earlyXiongnu_west). We then compared their genetic profile with earlyXiongnu_west by calculating f4 for a set of world-wide ancient and present-day populations. While there is no population showing significant extra affinity with earlyXiongnu_west, several populations show extra affinity with TAK008/TAK009. For TAK009, the top signals are mostly ancient individuals/populations from East Asia (fig. S4). In line with this, we adequately model TAK009 as a mixture of earlyXiongnu_west and various East Asian populations, including Khovsgol_LBA, SlabGrave1, and Han_2000BP (data file S2C). For TAK008, we observe an overall similar trend but find populations with high Ancient North Eurasian (ANE) affinity, such as the Upper Paleolithic individual from the Ust-Kyakhta-3 site (UKY)   followinP = 0.622), while SBB shows less diversity than all Xiongnu (P = 0.030). We also compared the genetic diversity of the previously published Xiongnu sites with at least five sampled individuals (n = 11) in northern Mongolia exhibits a high degree of heterogeneity similar to all Xiongnu (P = 0.411). The late Xiongnu site of Uguumur Uul  located further to the east shows a more intermediate level of diversity with higher contributions of western Eurasian ancestry, but is still statistically comparable to the all Xiongnu (P = 0.185); however, the small sample size for this site provides limited statistical resolution. Still, UGU shows much higher diversity than the preceding EIA groups . In contrast, another late Xiongnu site that is located further to the northeast, Il’movaya Pad , has predominantly eastern Eurasian ancestry. Its genetic diversity is much lower than all Xiongnu (P = 0.002), and is comparable to those of the preceding EIA groups  , we applied the Brown-Forsythe test for the null hypothesis that two groups have equal PC1 variances (data file S1D). TAK is highly heterogeneous to the level indistinguishable from the variance across all Xiongnu individuals (ctively) . We noteThe comparable level of genetic diversity of all Xiongnu individuals broadly and local communities suggests that the dynamic demographic processes that constituted the highly heterogeneous Xiongnu empire also occurred at local scales. High genetic diversity within the cemeteries of TAK, SBB, and UGU confirm the coresidence of individuals with diverse genetic backgrounds within a single local community and that this continued even into the late Xiongnu period, centuries after the political formation of the Xiongnu and the associated demographic processes of genetic admixture that were already in progress during the initial early Xiongnu period. Overall, the high genetic diversity found among the Xiongnu during all periods prevents any meaningful attempt to define a “representative” Xiongnu genetic profile, as it is instead population-level genetic heterogeneity spanning nearly the entire breadth of Eurasian genetic diversity that most characterizes the Xiongnu Empire.To better understand how Xiongnu genetic diversity might be structured by social status or social group affiliation, we examined archaeogenetic data from the aristocratic elite cemetery of TAK  and the For the other square tomb complex, THL-64, which contained an adult female buried in a square tomb (TAK002) along with two satellite graves of adolescents, we determined that both adolescents were males. Unlike THL-82, however, the two low-status young males were genetically dissimilar  derivingLast, we analyzed the three satellite graves associated with THL-25, for which the square tomb has not been excavated. Two of the three individuals yielded sufficient DNA for analysis: a child (TAK005) and an adult male (TAK006). We determined that both were unrelated males and that they were highly genetically dissimilar . The chiAt the SBB site, we found lower overall genetic diversity, and specifically, no individuals with very high levels of western Eurasian ancestry . HoweverP value of 0.146 provided by the Mantel test. Nor could we support a hypothesis that the individuals in the five-grave cluster in SBB  were more similar in their genetic profiles than the others. To determine this, we replaced the geographic distance of each individual pair with 1 for within-cluster pairs and 0 for the others, respectively . However, we did find that the relatives were placed significantly closer to each other , based on the two pairs of related individuals buried next to each other: the pair SBB001 and SBB005 and the pair SBB005 and SBB007. These were the only genetically related individuals identified at either SBB or TAK. Although the adult male SBB001 and the female child SBB005 were second-degree relatives, they were genetically dissimilar to one another, with the male having a much higher level of western Eurasian ancestry (Chandman_IA). SBB005 was also a second-degree relative of SBB007, the high-status woman buried with the gilded belt clasp and Han lacquer cup. Both females shared a minor ancestry component modeled as Han_2000BP. Previous investigations of genome-wide genetic relatedness among the Xiongnu have identified 10 other cases of kinship pairs  with higher levels of eastern Eurasian ancestry. Eastern Eurasian ancestry is represented by the higher PC1 values and the mean of PC1 of the high-status individuals is significantly greater than that of the low-status individuals . This suggests that elite status and power was disproportionately concentrated among individuals who traced their ancestry back to the preceding EIA Slab Grave groups. Three of the six elite females, one low-status female child, and one low-status male had minor ancestry contributions consistent with Han_2000BP . Two large square tombs were excavated in the 1990s  is located at the far western edge of the Xiongnu realms in the high mountain passes of the Southern Altai . Fifteen graves were excavated at the site, sampling across the burial ground but also giving special attention to the main cluster, which included males and females as well as a full spectrum of older and younger adults, and children and infant subadults  and B. K. Miller . The burial sites from which all human remains were excavated are (i) Takhiltyn-Khotgor  and (i) Shombuuzyn-Belchir . The time period of the burial sites was validated by radiocarbon dating (https://protocols.io/view/a-z-of-ancient-dna-protocols-for-shotgun-illumina-36wgq529xgk5/v2). We then performed shallow shotgun sequencing to screen 19 individuals and found that 17 individuals are sufficiently preserved with ≥0.1% reads mapped on hs37d5, the human reference genome GRCh37 with decoy sequences. To enrich these libraries for 1,233,013 nuclear SNPs (1240K), we used oligonucleotide probes targeting 1240K sites and performed in-solution DNA capture . Because our libraries were single-stranded, we used the “singleStrandMode” option to use only negative strand reads to genotype C/T SNPs and only positive strand reads to genotype G/A SNPs. We then merged genotyped data of 17 new individuals with genotype data of previously published worldwide present-day  from one high-quality read per each position and considered the chosen base as a homozygous genotype for that position (“pseudo-haploid genotype”), using the pileupCaller v1.4.0.5 program with the “randomHaploid” option  (data file S1A).To determine the genetic sex of each individual, we estimated the ratio of the sequence coverage of sex chromosomes to that of autosomes. It is expected that the ratio of X to autosomal coverage is ~0.5 for males and ~1 for females and the ratio of Y to autosomal coverage is ~0.5 for males and ~0 for females. The individuals with the ratio of Y to autosomal coverage >0.3 are reported as a male and individuals with the ratio <0.1 are reported as a female (data file S1A). We also retrieved uniparental haplogroups of each individual. First, we generated mitochondrial consensus sequences of quality ≧ 10, using the log2fasta program in the Schmutzi package  of pseudo-haploid genotypes between every pair of individuals by dividing the number of sites genotyped differently from one another by the number of sites covered by both individuals , Andamanese islander , Taiwanese Aborigine , Central American , Epipaleolithic Levantine  (n = 8) (n = 1) (n = 23) (n = 3) (n = 3) (n = 14)  and for the Brown-Forsythe test, we used the function leveneTest of R package car (v3.1.0). We also tested the association between the similarity of the genetic profiles and the spatial proximity of the burials. We performed the Mantel test on matrices of physical distances and the Euclidean distances between two points on the PC plane defined by the top two PCs, using the function mantel.rtest of R package ade4 (v1.7.19). To statistically test the association between sex and the social status and explain the sex bias in social status, we performed the Fisher’s exact test using the fisher.test function of R package stats .We performed several tests using the program R v4.1.2 (


Approximately 3% of all pregnancies are associated with conditions linked with disability, either mild or severe congenital diseases. This is a consequence of environmental and genetic exposures. Complications and poor management of these diseases arise due to limited knowledge, awareness about the disease, and limited resource settings. The current study assessed awareness, attitudes towards genetic diseases, and acceptability of genetic interventions among pregnant women. This was a cross-sectional study that was conducted among 664 pregnant women in six selected health centers in Burera district using a detailed questionnaire. The data were analysed using STATA Version 15 and entailed univariate, bivariate, and multivariable analyses. The level of significance was set at p < 0.05. The mean age of the study participants was 28, and most of them were in the age range of 21 to 30 (50%). Most of the participants were married (91.1%), Christians (98.4%), farmers (92.7%), used public health coverage (96.6%), and attained primary studies (66.1%). The findings from this study showed that among participants, adequate awareness was at 29.5%, inadequate awareness at 70.5%, positive attitudes at 87.1%, negative attitudes at 12.9%, high acceptability at 97.1%, and low acceptability at 2.9%. While there was no significant difference between awareness and acceptability, there was a statistical significance between attitudes towards genetic diseases and acceptability towards the use of genetic services (p < 0.01). There was no statistical significance between sociodemographic or obstetric characteristics and the acceptability of genetic interventions. Participants with positive attitudes towards genetic diseases were more likely to develop a high level of acceptability and willingness towards the use of genetic interventions (OR: 5.3 [2.1–13.5]). Improving awareness about genetic diseases and establishing genetic interventions in healthcare facilities are needed.The online version contains supplementary material available at 10.1186/s12889-023-16866-3. Genetic diseases are distributed throughout the world, and there are 20.40 birth defects for every 1,000 babies born in sub-Saharan Africa . GeneticCongenital genetic diseases are diseases that are born due to inheritance from parents or environmental exposures during conception, embryogenesis, or fetal development. The most well-known defect is a neural tube defect that is due to a deficiency of vitamin B9, known as folic acid . SmokingIn the last more than 100 years, Down syndrome has been the most common genetic disorder that has led to mental retardation, specific birth defects, decreased life expectancy, and other disease conditions . In TuniThe most common and routine reason to refer a pregnant woman for prenatal diagnosis is when an anomaly is identified during an ultrasound scan of the fetus. Another rare reason is a high-risk result from screening tests on maternal blood during pregnancy. It is worth noting that almost all mothers older than 39 are in danger or at risk of transmitting or transmitting a genetic disorder to their offspring, so women of this age should seek genetic testing during their pregnancy. Other common referrals for genetic testing include a family history of genetic disease. Discrimination against people with chronic disabilities (primarily caused by genetics) might take the form of job rejection, health insurance denial, or just social acceptability. This leads to the marginalization of these individuals and significantly compromises the principles and goals of public health. It is worth noting that integrated clinical and social support systems, which incorporate genetic disease and predisposition counselling for patients and their families, can lead to better patient care and treatment as well as an improved quality of life .Genetic information or results usually have an impact not only on the person being tested but also on their relatives across generations. One of the numerous applications of genetic testing is to assist couples in becoming aware of the hereditary traits of their born and unborn children. Another benefit of genetic tests is that they allow people to learn about their hereditary propensity for disease . In manyAwareness and attitudes vary internationally across continents, countries, and regions. In addition, these variables vary again according to different types of people, communities, and particular social determinants of health. In a study performed worldwide by Henneman et al., 2013, the results from study participants showed that the expectation of genetic and genome improvement achieves its goals with time . The resWhile there are studies done on genetics and genetic diseases, there is no single research done in Rwanda about awareness, attitudes, and acceptability towards genetic diseases or genetic intervention. The lack of genetic awareness and positive attitudes towards genetic testing pose consequences for newborns, including congenital genetic diseases that would have been prevented, taken care of, or prepared psychologically using genetic counselling for parents, children, the entire family, and throughout generations at large. Moreover, the acceptability of genetic intervention is not yet known, and this could inform policy on the way forward in establishing and improving genetic services. Thus, the aim of this study was to assess awareness, attitudes towards genetic diseases, and acceptability of genetic interventions among pregnant women in Burera District, Rwanda.This study was a cross-sectional study design that was conducted on 664 pregnant women in six (6) selected health centers in Burera district, located in the northern province of Rwanda. The sample size was calculated using the Fisher et al. formula . The stuData were entered in a database created using MS Excel and then exported to STATA Version 15. Quantitative data analysis entailed descriptive/univariate, bivariate, and multivariate analyses. Descriptive analyses were computed using frequency, percentages, the mean, and the standard deviation to summarize the basic characteristics of the respondents. The awareness assessment focused on types of genetic disorders, causes, transmission, and prevention of genetic diseases. For the awareness assessment score, after aggregating, those who scored 77.77% and above were categorized as having adequate awareness, and those below 77.77% were categorized as having inadequate awareness. For attitudes, the respondents who scored 71.42% and above were classified as having positive attitudes, whereas those who scored below 71.42% were considered to have negative attitudes. For acceptability level towards use of genetic interventions, respondents who scored 71.42% and above (5 and above out of 7) were classified as having a high acceptability level towards use of genetic interventions, whereas those who scored below 71.42% were considered to have a low acceptability level towards use of genetic interventions. In bivariate analysis, a chi-square test was applied to assess the association between dependent and independent variables. Multivariable analysis was performed to control confounding variables and determine the strength of the association. The significance level was set at 5%. The data are presented using tables.The demographic and obstetric characteristics of the study participants are described in Table Table Rwanda’s socioeconomic state is measured using the poverty social category developed by the Minister of Local Government and Social Affairs in 2015. There are four income groups, with the first being extremely poor, the second poor, the third self-sustaining, and the fourth prosperous .The awareness of the study participants is described in Table Table The attitudes of the study participants are described in Table Table The acceptability or willingness of genetic interventions is described in Table Table Table Table Table As illustrated in Table Again, the odds of developing positive attitudes in Muslims were 0.2 times less likely than in Christians  compared to their fellow Christians. From the same table, the odds of developing positive attitudes in pregnant women who never experienced stillbirth were 0.2 times less likely than those who experienced it . The odds of developing a high acceptability level in pregnant women who supported marriage between close relatives were 1.9 times more likely than those who discouraged marriage between close relatives.Table Table The findings from Table In the current study, most of the participants were in the age group of 21–30 Table . There wIn a study conducted in Nigeria in 2018, it was found that 69.4% of the participants enrolled in the study had knowledge of genetics and genetic diseases. In the same enrolled participants, 97.6% had very little knowledge about prenatal genetic disease testing or screening, 23.9% had positive attitudes towards genetic testing, and only 10.1% supported termination (abortion) of the affected pregnancies [In the cross-sectional study conducted on the Visegrad countries’ understanding, awareness, and attitudes towards genetic testing, these countries showed slightly different attitudes and awareness towards the testing of genetic illnesses. The results of this study revealed that Hungarian citizens had a more positive view of the personal gains or benefits (with a mean of 3.64) of going to genetic testing, whereas Czech, Slovak, and Polish citizens followed with a mean of 3.36. It was found that Slovak citizens had quite a significant belief (mean of 2.78) compared to Hungarians (mean of 2.68). The Hungarian public demonstrated a high level of acceptance in regard to the availability and use of genetic testing (mean of 3.87), and the Czech people came in second among the four countries. Slovaks were the least supportive, with a mean of 3.36 . Some ofThe study conducted in Pakistan by Tariq et al., 2018, on the assessment of knowledge, attitudes, and practices among pregnant women in Pakistan highlighted the need for education about screening for congenital conditions . This stAttitudes and knowledge on genetic testing among Jordanians, conducted by Khdair et al., 2021, found that 65.4% of the participants had good knowledge about genetics . The majIn the study conducted by Ogamba et al., 2018, on genetic diseases and prenatal genetic testing, focusing on knowledge and attitudes in Nigeria, it was found that 69.4% of the participants did not have good knowledge about genetic diseases, whereas 61.8% were comfortable undergoing genetic testing. According to the findings, 23.9% of the participants had positive, better, or favorable attitudes towards genetic testing. Moreover, only 10.1% of the respondents supported the termination of affected pregnancies [The InterGEN study looked at the attitudes of African American moms toward genetic testing. A meta-data study showed that there is a major negative attitude toward genome studies, especially genetic testing of African origin. The results highlighted that from 2005 to 2015, all 80% of the participants were of European ancestry, whereas approximately 2.4% of the participants were of African origin. The same negative belief or attitude towards genetic testing was also observed in Americans of African ancestry. The study results also revealed that positive attitudes towards genetic testing were correlated with demographic factors such as higher levels of income. On the other hand, negative predictors of negative attitudes towards genetic testing were maternal age, country of origin, education, and religion . AlthougIn the study done by Kvaratskhelia et al., 2021, on public attitudes toward genetic testing in Georgia, the majority of respondents showed that they are more curious about predictive genetic testing 75.3%); 40.6% of participants said they would rather get tested only for illnesses that can be treated or prevented . In addi%; 40.6% In the study done in the Netherlands by Morren et al., 2007, on the perception of patients with chronic diseases’ genetic knowledge, attitudes toward genetic testing, and the link between these variables, they found that the genetic information was much lower in older people and less educated people . These fIn the study conducted by Maftei and Dănilă at Romania University in 2022 on attitudes towards genetic testing and implications for disability, there was variation among participants towards attitudes towards genetic testing . In thisIn a study performed by Adane et al., 2020, it was found that in sub-Saharan Africa, the prevalence of neural tube defects is 2.98 per 1000 newborns . It was In the study conducted in Saudi Arabia by Arafah et al., 2021, 56.2% of the participants supported government running facilities for national citizens to be tested or screened for genetic diseases . In the The study results revealed that the awareness level of genetic disease is 29.5%. Thus, education is needed for the public about the predisposition, inheritance, and management of genetic diseases. The level of acceptability and willingness towards the utilization of genetic testing and genetic counselling was 97.1%. This highlights that although participants have little awareness, they are willing to use genetic services. In addition, 98.2% of the participants wish to have genetic services integrated into general healthcare systems. Therefore, there is a need to improve genetic interventions in terms of screening, diagnosis, and management of genetic diseases to reduce and prevent associated morbidities and mortalities.Below is the link to the electronic supplementary material.Supplementary Material 1


This new measure places an emphasis on rare and phylogenetically distant alleles which have relatively small effect on nucleotide diversity. Therefore, it can complementarily serve for conservation studies although it cannot be used to estimate population mutation rate. We compared ς with the other diversity measures in the experiments. Nucleotide diversity, expected heterozygosity and ς showed within 3% relative biases on average while Watterson’s theta was 31% overestimation on average. Thus, genetic diversities other than Watterson’s theta held good robustness under the spatial sampling.The genetic diversity of a taxon has often been estimated by genetic diversity measures. However, they assume random sampling of individuals which is often inapplicable. Except when the distribution of the taxon is limited, researchers conventionally choose several sampling locations from the known distribution and then collect individuals from each location. Spatial sampling is a formalized version of the conventional sampling, which objectively provides geographically even sampling locations to cover genetic variation in a taxon assuming isolation by distance. To evaluate the validity of the spatial sampling in estimating genetic diversity, we conducted coalescent simulation experiments. The sampling locations were selected by spatial sampling and one sample was collected from each location for the sake of theoretical simplicity. We also devised a new measure of genetic diversity,  Because estimating genetic diversity is costly, rougher and more inexpensive evaluation indices without using genetic data have recently been suggested  etymologically comes from “s” of “substitution rate”, avoiding σ which is often used for standard deviations. The ς ranges from 0 to 1; when a population contains only one allele, ς = 0 by its definition, and when all alleles in a population are completely different from each other (πij = 1), ς = 1. The statistical parameter ς is interpreted as the average substitution rate among all allele types within a taxon. The nucleotide diversity π includes the information of the allele frequency, and provides the highest value when all alleles with the same πij have an equal frequency. On the other hand, the average substitution rate ς does not count allele frequencies or the rarity of each allele. Because the new measure ς is not affected by allele frequency which is practically difficult to estimate and rare alleles are expected to be averagely sampled via spatial sampling, the new measure has theoretically its advantage in unbiased estimation under spatial sampling. Although the new measure cannot be used to estimate the population mutation rate like the nucleotide diversity, the new measure can serve as a complementary genetic measure of population which emphasizes rare and phylogenetically distant alleles. When all alleles are different from each other , ς is always larger than π and converges to π under N →∞. Therefore, when the observed sequence is long enough to distinguish all the samples and the sample size is large, ς and π provide an almost identical value. Concerning ς, we can suppose a statistic The new statistical parameter m indicates the number of allele types in the sample. Note that when m = n, m >2 is Here, ς. Thus, ς by missing rare distant alleles.However, this estimator assumes that all allele types are randomly sampled, and this assumption is usually not fulfilled by random sampling of individuals of a taxon. Generally speaking, closely related alleles are relatively common in a taxon, and distantly related alleles are rare. Therefore, the estimator ς. Then, how about spatial sampling of locations and random sampling of one individual in each location? We use another statistic as the estimator of ς. We define the following auxiliary measure Sn is a set of n. For example, ς2 is ςN = ςM = ς. In some cases, ςn can be equal to ςn+1. For example, when ς2 is calculated from the two sequences ‘AAA’ and ‘GGG’ and third sequence is ‘TTT’, then ς2 = ς3 = 1.0. Likewise, ς4 = 1.0 when the fourth sequence is ‘GGG’. Thus, ςn decreases or does not change as the sample size n increases. We now prove that ςn in the supporting information  every generation, and the portion is defined by the migration rate. Population means a group composed of all subpopulations. Meanwhile, statistical population and statistical sample mean data to be estimated and data extracted from the statistical population, respectively. There are two kinds of migration pattern, the stepping stone model and island model. In the stepping stone model, migration occurs only between adjacent subpopulations. The adjacency is judged in a grid manner, and the maximum number of adjacent subpopulations is four. On the other hand, migration in the island model occurs between all the pairs of subpopulations. Variables used in the simulation experiments are described as simulation parameters, whereas the true values of the genetic diversity measures to be estimated are described as statistical parameters.https://github.com/heavywatal/msutils. The resultant coalescent trees were processed with Seq-Gen  model , e.g., the means of e.g., the means of We start from the explanation of the common part of experiments. The experiments were conducted using coalescent simulations on 10,000 haploid individuals. The coalescent simulation was conducted using ms . However Seq-Gen  to obtaiR) model  was used using R . The acc using R , e.g., tmeasures .x × y in the following explanations means the numbers of longitudinal (x) and latitudinal (y) subpopulations. A: 100 ×1 linear structure. B: 100 × 1 circular structure. C: 10 × 10 plane structure. D: 50 × 2 cylindrical structure. These subpopulations were positioned on the artificial coordinates. Since all pairs of the subpopulations had an equal migration rate, coordinates were positioned to have an equal distance between adjacent locations or subpopulations. The distances were calculated in the web site of Geospatial Information Authority of Japan , and was based on Geodetic Reference System 1980 (GRS80). Gst . Gst  and Nst Gst . On the other hand, the subpopulation size of the experiment #4 was first approximately given by the next formula: dnorm(x) is the probability density function of the normal distribution N , lati is the latitude of the subpopulation and dnorm (0.027 ⋅ lati). The coefficient 0.027 was decided to make the minimum subpopulation size 10. The obtained relationship between the latitude and the subpopulation size is shown in the right bottom panel in The experiments #3 and #4 were based on the island model. A total of 100 subpopulations were set in random (uniform) coordinates . The biases and accuracies of θw were much worse than the other measures. This tendency was clearer in low migration rates in the structure A (linear).All the averaged biases for He were always negative and small. The biases and accuracies were always within 3% at 20 or more sample sizes. The biases of θw was always positive and ca. 10%.In most cases, the biases of He were also similar to those of He was about half of those of He had the almost same biases as θw greatly varied depending on the migration rates; they were ca. −15% for the migration rates 0.001 and 0.01 while they were ca. 15% and 80% for the rate 0.1 and 0.5, respectively.Just like the result of the experiment #3, the biases of He were similar. The biases of He ranged ca. 20–90%. The biases of θw were ca. 50–90%.The biases of Nst decreased as the migration rate increased. Since Nst reflects differentiation among subpopulations, this behavior of Nst is what is expected as more migration usually triggers lower differentiation among subpopulations. On the other hand, the Gst showed the opposite tendency. This may be because Gst does not consider the substitution rates and does not weight phylogenetically far alleles within a subpopulation which increase at high migration rates or in island model.The ς held within 3% unbiasedness and within 10% accuracy in our simulation experiments using spatial sampling in spite of the violation of the assumption of random sampling. This is good news for researches and monitoring of genetic diversity, in which almost always the assumption of random sampling is not satisfied. On the other hand, Watterson’s theta showed 31% overestimation and 42% accuracy on average. Therefore, its estimation should be avoided in this sampling scheme .The nucleotide diversity, heterozygosity and our new genetic diversity measure −4 while those in the experiments #1–4 were ca. 10−1–10−2 except for those at the migration rate 0.5 in the experiment #4 (theirs were ca. 10−4). The low statistical parameter exaggerates the positive relative biases. In addition, the minimum negative relative bias is −100% when the statistics is 0 while the positive relative bias can be larger than 100%. This large biases in the experiment #5 were also observed for He and θw. The bias overestimation of He in highly differentiated populations  reflects that spatial samples were more diverse than the statistical population. This tendency should be regarded but should be rather a rare situation. This is because the subpopulation structure of a species is usually plane (structure C in this study) rather than linear, circular or cylindrical  and very low migration rates triggers speciation.The smaller values of n = m. The bias and accuracy of ς not by its statistical parameter ςn in this study. The standard deviation of the biases were also similar, but the standard deviation of ςn ≥ς, ς. On average, this was not true for the designed subpopulation in all the experiments. The reason for the underestimation in the experiment #1 and 2 may be imperfection of spatial sampling. Imperfection of spatial sampling is caused by geometric plurality of the best combinations of the locations. For example, the best combinations of the ten locations sampled from the circular subpopulation structure B have ten patterns where every sampled location is ten locations away from the two nearest sampled locations. Even when there are multiple best combinations of the locations for spatial sampling, the best combinations that provides ςn are not always as many as ones that are best for spatial sampling. Therefore, the geometric plurality decreases the effectivity of spatial samples to estimate ςn. In practical, however, the geometric plurality is rarely problematic. This is because the distance among candidates of sampling locations usually varies, and the candidates are rarely arranged in a geometrically symmetrical manner. The reason of the underestimation in the experiments #3 and 4 are unknown, but the estimator probably should be adjusted up to obtain the unbiased estimator just as n/(2n − 1) from the statistical parameter even in non-random sampling.The bias of He as its biases were within 4%, and this result is consistent with that of He consistently tended to be underestimate. This means that the number of the allele types in the sample are small and that the allele frequencies in the sample are unbalanced compared to those in the statistical population. Because spatial sampling tries to obtain more types of alleles, this underestimated He is an unexpected result. Considering the result that Gst increased as the migration rate increased, the spatial sampling may do not provide diverse samples when not considering the substitution rate. The accuracy of He was smaller than that of He in highly differentiated populations were underestimation. The reason for this is unidentified, but maybe the same reason as the general underestimation of He discussed above. The reason why the biases of He and He is ca. 1/1,000 of The experiments also showed the robustness of θw was 34% on average. This tendency of overestimation is as expected because spatial sampling tries to collect more mutations than random sampling. The biases in highly differentiated population were 80–113% even when using 50 samples. Therefore, one probably had better avoid applying θw to species which are expected to be highly differentiated. Otherwise, one should use as many samples as possible to obtain accurate estimates. The biases of θw in highly differentiated population vary more largely depending on the sample sizes than those of the other genetic measures. This is consistent with the known result that the sample size has more effect on θw than The bias of ment}π ~ .The samples by spatial sampling can provide more accurate genetic diversity measures than random sampling of a single population. Although the unbiasedness of ς is interpreted as the average substitution rate among all the allele types in a population. As discussed above, its estimator ς cannot be used to estimate the population mutation rate θ, it can be used to complementarily quantify genetic diversity, regarding rare and phylogenetically distant alleles which do not have much effect on the value of π. Therefore, ς is appropriate for conservation studies rather than evolutionary studies.The new diversity ς under various assumption, but we failed to show its theoretical accuracy, such as mean square errors, relative to π. Therefore, we could not discuss the resultant accuracy from the theoretical point of view.The problem in the theory of relation . Howeverςn, one must ideally obtain the best sequence that maximizes ςn for every location. This means that the ideal method is to collect multiple samples per location, to select the best sample per location and to calculate ςn, where n is not the total number of collected samples but the number of sampling locations. However, conducting this method is difficult due to combination explosion. Even when two samples per location are collected for 50 sampling locations, that requires searching 250 ≈ 1.1 ⋅ 1015 combinations.Mutations within a location are assumed to have no effect on the substitution rates on average. However, when estimating ς converges to nucleotide diversity π when all allele frequencies are equal, and the number of allele types increases. The effectiveness of ς based on spatial samples varied depending on the dispersal ability of the taxon. When the dispersal ability was low enough to be emulated by the stepping stone model, ς was more unbiased than π, but π was more accurate than ς. When the dispersal ability was high enough, ς had better accuracy and almost equal unbiasedness comparing to π. Therefore, we can say that ς is better than π in the island model, but it is not clear that which is better in the stepping stone model. Priority or balance between bias and accuracy may vary depending on the research purpose or the number of the iteration of sampling. Further research is necessary to investigate this problem as well as the statistically strict treatment of bias and accuracy when the statistical parameter is not shared among resampling. Also, this study extracts only one sample per location due to the problem of combination explosion. New theory or calculation method is necessary to utilize information of multiple samples per location for estimation of genetic diversity of a taxon and each location.The genetic diversity According to the result of the simulation experiments, we suggest a practical procedure for applying the tested scheme  as folloe.g., ς as well as π provides insights into phylogenetically distant alleles. When ς >π, there are more phylogenetically close alleles than phylogenetically distant alleles. This situation is expected to be ordinary for taxa with a stable or increasing population size without vicariance. However, if ς >π with population decrease or vicariance, it means that the taxa is preferentially losing phylogenetically distant alleles or that the subpopulations are young, respectively. The situation of π >ς is expected to be ordinary for taxa with a decreasing population size or taxa with vicariance. If endangered taxa are in this situation, conservation breeding should be carefully and rapidly planned to conserve phylogenetically distant alleles before their disappearance by genetic drift.The purpose of this scheme is to estimate genetic diversities of a taxon, which is important for endangered taxa to genetically evaluate the extinction risk e.g., . This sce.g., “collected at Mt. Fuji”), researchers can manually compensate the coordinate of the location. Distribution data can be obtained mainly through GBIF (https://www.gbif.org/) and specimen labels at museums.In the first step, researchers must collect distribution data of the taxon for spatial sampling. Distribution data are geographical coordinates (latitude and longitude) where the taxon was collected or observed. When distribution data is vague . Our analysis is the first study that tested the performance of genetic diversity measures under comprehensive sampling. The new measure of genetic diversity ς is interpreted as the average substitution rate among all allele types in a taxon, and is not based on allele frequencies. The new measure weighs rare and phylogenetically distant alleles which have less weights in nucleotide diversity but are more important in conservation. By comparing nucleotide diversity and the new measure, researchers can judge whether phylogenetically distant alleles are more abundant than phylogenetically closer alleles.The nucleotide diversity, heterozygosity and our new genetic diversity measure, 10.7717/peerj.16027/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16027/supp-2Supplemental Information 2The error bars show the standard deviaitons.Click here for additional data file.10.7717/peerj.16027/supp-3Supplemental Information 3The error bars show the standard deviations.Click here for additional data file.


Oryza sativa L.) breeding programs to understand the advances made to date. We reviewed 29 studies conducted between 1999 and 2023, covering different regions, traits, periods, and estimation methods. The genetic gain for grain yield, in particular, showed significant variation, ranging from 1.5 to 167.6 kg/ha/year, with a mean value of 36.3 kg/ha/year. This translated into a rate of genetic gain for grain yield ranging from 0.1% to over 3.0%. The impact of multi-trait selection on grain yield was clarified by studies that reported genetic gains for other traits, such as plant height, days to flowering, and grain quality. These findings reveal that while breeding programs have achieved significant gains, further improvements are necessary to meet the growing demand for rice. We also highlight the limitations of these studies, which hinder accurate estimations of genetic gain. In conclusion, we offer suggestions for improving the estimation of genetic gain based on quantitative genetic principles and computer simulations to optimize rice breeding strategies.Genetic improvement is crucial for ensuring food security globally. Indeed, plant breeding has contributed significantly to increasing the productivity of major crops, including rice, over the last century. Evaluating the efficiency of breeding strategies necessitates a quantification of this progress. One approach involves assessing the genetic gain achieved through breeding programs based on quantitative traits. This study aims to provide a theoretical understanding of genetic gain, summarize the major results of genetic gain studies in rice breeding, and suggest ways of improving breeding program strategies and future studies on genetic gain. To achieve this, we present the concept of genetic gain and the essential aspects of its estimation. We also provide an extensive literature review of genetic gain studies in rice (The online version contains supplementary material available at 10.1186/s12284-023-00677-6. Plant breeding is the science of improving the genetics of cultivated plants to develop new varieties with desired combinations of traits to meet the needs of users. Breeding programs make use of crosses, evaluations, and strategies of selection in pools of germplasm with interesting attributes to achieve this goal Acquaah . HoweverThe efficiency of a plant breeding program can be evaluated based on several indicators that may be applied in one or more subcomponents of the breeding program: design , engineering (population improvement and product development), and delivery  , the start of modern breeding is generally traced back to the development of IR8  to provide the reader with a theoretical understanding of the genetic gain concept, facilitating identification of the principal factors affecting its estimation in the context of a breeding program; ii) to summarize the major results and conclusions of genetic gain studies in rice breeding over the last six decades, together with the limitations of these studies; and iii) to suggest ways of improving the estimation of genetic gain in breeding programs and for future studies. The first part of this study focuses on the principles and theoretical foundations of genetic gain and presents methods for its estimation in breeding programs. The second part provides an in-depth review of studies on the realized genetic gain in rice, mostly for grain yield and other traits of economic importance. Finally, we describe ways to improve genetic gain with tools to optimize breeding strategies.Genetic gain is a concept that may appear simple. However, its estimation within a breeding program remains complex, and its implementation can be complicated. It is, therefore, important to understand its theoretical basis to improve evaluations of its implications for crop breeding programs. In this section, we provide readers with a definition of genetic gain and a description of the principal methods used for its estimation.Selection leads to many changes in the genetic properties of a population, the most important for the breeder being the change in the average performance of the population. This change is referred to as the response to selection, denoted as Falconer : \documeRutkoski . Geneticmentclass2pt{minimon Baker .Fig. 1DiExpected genetic gain is defined as the predicted change in mean phenotypic value that would be caused by a change in the genetic value of the population under a given breeding strategy Rutkoski . BasicalRealized genetic gain is defined as the change in average population performance observed over at least one cycle of selection. Realized genetic gain can be estimated by a linear regression analysis of the average performance of populations from each selection cycle over the total number of cycles or years Eberhart . The linAssuming that the genetic trend resulting from selection is linear during consecutive early cycles of selection Eberhart , the reaEberhart . More acentclass1pt{minimaith genotype; jth year; kth location; The average phenotype collected directly based on the evaluated genotypes, or genetic means estimated from these phenotypes can be used as the response variable in the regression model for the genetic trend over time. The regression slope corresponding to the realized genetic gain can be estimated in different ways. In general, a concomitant estimation of the slope is performed in a single model with a fixed regression term, in a single-step process Fig. . The regkth year interaction effect, jth location interaction effect, kth year interaction effect, Furthermore, as the observed trend is a result of both genetic effects due to breeding efforts and non-genetic effects due to the improvement of agricultural practices, models have been developed to separate these two components. Piepho et al.  proposedThe terms "genetic gain" or "response to selection" are often used inaccurately to describe the genetic part of a trait's evolution over time, whatever the source of the material. However, this trend is not always the result of selection cycles of a specific breeding program within a relatively closed population Rutkoski . Some stIncreasing numbers of studies in recent years have estimated the genetic gain from rice breeding programs. The key challenges facing breeding programs are evaluating the efficiency of the strategy used to optimize resource allocation, improving the rate of genetic gain, and developing high-yielding varieties. Most studies on genetic gain in rice have focused on grain yield. However, other traits have been studied, albeit to a lesser extent: plant height, days to flowering, grain quality, yield components, disease resistance, and physiological traits. In this section, we provide a detailed overview of the literature and present the genetic gain achieved by rice breeding programs over the last six decades.We reviewed 29 studies on genetic gains in rice, published between 1999 and 2023, with a large number of the studies published recently Fig. A. These Concentrating exclusively on studies which focus on grain yield Table , 25 studTwenty of the 25 studies focusing on yield used historical data. Most studies (60%) focused on the irrigated ecosystem, with fewer dealing with rainfed, upland, salinity-prone, or drought-prone environments. The historical studies included populations of 62  to highly productive semi-dwarf varieties . Most studies focusing on genetic gain in plant height reported a reduction in this trait . Similarly, Streck et al. (−1). For the studies based on early-generation evaluation, a reduction of plant height was also observed even after few cycles of breeding: -0.63 cm yr−1  has been a key breeding target for several decades. Breeding programs designed to improve materials for a wide area typically have multiple maturity groups for a given ecosystem, and several studies have reported the genetic gain for grain yield according to the groups of maturity , appearance , cooking quality (e.g. amylose content and gelatinization temperature), and nutritional qualities   or as a percentage relative to a baseline. The rate of genetic gain per year is considered the best estimate for comparing breeding strategies, which may differ in terms of the number of years per cycle  is also a key factor for obtaining relevant estimates. Coverage of the target environments is greater with historical data, which are mostly obtained in multi-environment trials. Discussions about target environment coverage, therefore, arise principally in the context of era studies. Indeed, the use of a replicated design for the evaluation of cultivars released over a period of time improves the monitoring of genotype-by-environment interactions, thereby providing more accurate estimates. However, most of the era studies reviewed here covered a relatively small number of environments. They were frequently performed at single-site stations over one to four cropping seasons  may contribute to increases in grain yields. In experimental studies, the effect of the environment or, more generally, non-genetic effects may bias the estimation of the genetic effect. Most of the rice studies presented here addressed this issue implicitly by providing estimates of genetic gain incorporating the contributions of these major factors. Only two of the studies considered reported non-genetic trends explicitly . This would also explain the size of the era panels, which were relatively small (less than 50 genotypes for seven out of nine era studies) and spanning a period of up to 60 years of selection. The management and conservation of germplasm in public sector breeding programs is challenging. Nevertheless, a few studies have described rationales for the sample of cultivars for evaluation. In the study by Peng et al. , the 12 There is a significant lack of assessment of genetic gain achieved by hybrid rice breeding programs. A single study on genetic gain incorporating hybrids was conducted by Venkatanagappa et al. . The stuThe efficiency of plant breeding programs is usually evaluated by estimating the rate of genetic gain per unit of time. However, the breeders’ ability to maximize the rate of genetic gain is constrained by limited resources and time. Plant breeders must, therefore, take into account multiple constraints if they are to maximize genetic gain, and the ideal trade-off between genetic gain per unit cost and the maximal rate of genetic gain is not always obvious. Most of the studies reviewed here drew conclusions about the efficiency of their programs based on positive rates of genetic gain, but none actually considered costs in the analysis. This would not, in any case, be feasible for studies covering a very long period or combining information from several programs. However, the integration of costs is relevant when the objective is to evaluate a specific breeding program or the impact of new breeding techniques  was identified as a major driver of low genetic gain in the initial phases of establishment in several rice breeding programs  is one of the most efficient methods for increasing the rate of genetic gain   optimization studies, including the robustness of statistical models, comparisons of alternative GS breeding schemes and assessments of the impact of GS on long-term genetic gain and inbreeding, resource allocation, training population structure, and the updating of models on the maximization of prediction accuracy and genetic gain . Based on a review of the various studies, we highlight the main points on which breeding programs should focus: i) defining clear breeding objectives based on a product concept, ii) use of a data management system to reduce errors and increase the reuse of data, iii) clear separation of breeding and pre-breeding activities to focus on the improvement of elite germplasm and iv) achieving the right balance between cycle length and evaluation steps. The accuracy of genetic gain estimates was most commonly limited by the restricted use of pedigree data, a lack of evaluation of non-genetic trends, or a lack of information regarding the statistical method used for estimation. Improving these elements in future studies should be straightforward, thereby facilitating comparisons.Additional file 1: Table S1. Summary of studies on genetic gain for yield of rice breeding programs; Table S2. Summary of studies on genetic gain for days to heading, plant height, grain quality and disease resistance in rice breeding programs.