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As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06. [ABSTRACT FROM AUTHOR]

Objective: to formulate recommendations and proposals for improving legislation in the field of protection of genomic and genetic information, including those obtained as a result of medical diagnostics.Materials and methods. We analyzed 18 regulatory legal acts, including 4 international, 3 acts of foreign states, 11 domestic legislation and judicial practice in the area under study. In addition, scientific works on this topic were analyzed. In the course of the study, we used the formal-logical, dogmatic, comparative method and axiological approaches.Results. We formulated the concepts of genomic and genetic information and demonstrated differences between these concepts. We showed topical issues of information protection, identified problems in the field of legal regulation of relevant relations, formulated recommendations and proposals for improving legal regulation.Conclusion. Based on performed research results, we recommend: 1. To incorporate in the legislation of the Russian Federation the principle that would allow the use of genetic information for further research depending on certain cases using a criteria-based approach, when such use should meet important public interests, for example, contributes to developing methods for the treatment of serious and socially significant diseases. 2. Regulate relations in the field of obtaining consent for research of biological material for scientific purposes (for example, within the framework of the Federal Law "On Personal Data"). 3. To define in Federal Law No. 86-FZ of July 5, 1996 "On State Regulation in the Field of Genetic Engineering Activities" the cases that require ethical examination in order to comply with the principle of safety of clinical trials of gene diagnostic methods, as well as in other cases. 4. Medical organizations shall ensure compliance with the rules of professional ethics in terms of data confidentiality, carry out their depersonalization, notify patients in writing about compliance with such a regime and, as a result, provide guarantees for the protection of information about patients, as well as about their relatives.

Young adults have increasing genomic testing opportunities; however, little is known about how equipped they feel about making decisions to learn personal genomic information. We conducted qualitative interviews with 19 young adults, ages 18–21 years old, enrolled in a research study where they made decisions about learning personal genomic risk for developing preventable, treatable, and adult‐onset conditions and carrier status for autosomal recessive conditions. Participants had the option to include a parent in their study visit and the decision‐making process. The goal of this project was to explore young adults' reasons for involving or not involving a parent in the study and to assess young adults' perspectives about parental roles in their healthcare. Nine participants included a parent in the study and ten did not include a parent. Eleven participants received genomic test results before the interview, while eight participants had not yet received their results at the time of the interview. The study team developed a coding guide and coded interview transcripts inductively and deductively using an interpretive descriptive‐analytic approach. Logistical issues dominated solo participants' reasons for not involving a parent in the study, whereas those who involved a parent often cited a close relationship with the parent and the parent's previous involvement in the participant's healthcare as reasons for involving them. Both groups of participants described gradually transitioning to independent healthcare decision‐making with age and felt their comfort in medical decision‐making depends on the severity of and their familiarity with the situation. Participants recommended that future genomic researchers or clinicians give young adults the option to involve a parent or friend as a support person in research or clinical visits. Although young adults may have different journeys toward independent healthcare decision‐making, some may benefit from continued parental or peer involvement after reaching the age of legal adulthood. [ABSTRACT FROM AUTHOR]

In this study, a strain, named S5-4, with high esterase activity was obtained from Nongxiangxing Daqu by primary and secondary screening, and subjected to whole genome sequencing, esterase annotation and metabolic pathway analysis. The strain exhibited a maximal ratio of transparent zone diameter to colony diameter (D/d) of 2.27 ± 0.02, and it was identified as Lactococcus garvieae. Its morphological, physiological, and biochemical characteristics were determined, and its esterase activity was measured to be 15.74 U/mL. This strain produced (0.345 0 ± 0.160 0) g/L of ethyl acetate and (0.298 3 ± 0.230 0) g/L of ethyl lactate after anaerobic fermentation at 35 ℃ for 15 days. The genome size of S5-4 was determined to be 2 191 113 bp in length, with a GC content of 37.99%. A total of 1 486 genes, including 4 rRNA genes, 69 tRNA genes, and 34 ncRNA genes were predicted. In total, 11 esterase genes were annotated in the genome of S5-4 including phosphatases (five intracellular ones and two membrane ones), acyl-CoA dehydrogenase (intracellular), triacylglycerol lipase (extracellular), carboxylase (extracellular), and hemicellulose esterase (intracellular). Based on functional genomic annotation, the metabolic pathways to produce ethyl acetate and ethyl lactate from starch and galactose as carbon sources were constructed and found to be consistent with the experimental results. Given its ability to produce esterase, the strain could be used as a biocatalyst for esterification in baijiu fermentation or as a flavor enhancer in Daqu production.

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics. [ABSTRACT FROM AUTHOR]

We identified a new human voltage-gated potassium channel blocker, NnK-1, in the jellyfish Nemopilema nomurai based on its genomic information. The gene sequence encoding NnK-1 contains 5408 base pairs, with five introns and six exons. The coding sequence of the NnK-1 precursor is 894 nucleotides long and encodes 297 amino acids containing five presumptive ShK-like peptides. An electrophysiological assay demonstrated that the fifth peptide, NnK-1, which was chemically synthesized, is an effective blocker of hKv1.3, hKv1.4, and hKv1.5. Multiple-sequence alignment with cnidarian Shk-like peptides, which have Kv1.3-blocking activity, revealed that three residues (3Asp, 25Lys, and 34Thr) of NnK-1, together with six cysteine residues, were conserved. Therefore, we hypothesized that these three residues are crucial for the binding of the toxin to voltage-gated potassium channels. This notion was confirmed by an electrophysiological assay with a synthetic peptide (NnK-1 mu) where these three peptides were substituted with 3Glu, 25Arg, and 34Met. In conclusion, we successfully identified and characterized a new voltage-gated potassium channel blocker in jellyfish that interacts with three different voltage-gated potassium channels. A peptide that interacts with multiple voltage-gated potassium channels has many therapeutic applications in various physiological and pathophysiological contexts. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.

Local chicken breeds play vital roles in rural development and sustainability; therefore, the preservation of local breeds’ genetic diversity is crucial, also, assessing and advancing local breeds is crucial considering the impact of climate change on chicken production. The current study evaluated the differences between a fully feathered local chicken line (CE2) and a naked-neck local chicken line (CE4) at the microsatellite loci level. For this purpose, nine microsatellite primers were used to scan the genomes of both lines. The results generated by different loci between the two lines showed that the lowest allele numbers were 0 and 4 alleles detected at loci LEI0094 and MCW217, respectively, and the highest allele number was 10 detected at MCW328 and ADL299 loci, with an overall mean of 6.69 alleles/locus. The levels of heterozygosity were relatively high and were higher for line CE4 than for line CE2. The genetic distance between males and females of line CE2 was shorter (0.018) than that between males and females of line CE4 (0.104), and the opposite was observed for genetic identity. Also, ΦPT values were 0.088 (P<0.001), and 0.080 (P<0.01) for line CE2 and line CE4, respectively. The association between alleles and body weights revealed different alleles of microsatellites MCW217, MCW328, MCW193, ADL299, and MCW064 contributed significantly to body weight variance at different ages. The study provided an outlook on the genetic diversity of two chicken lines and indicated substantial room for improvement within Egyptian native breeds. [ABSTRACT FROM AUTHOR]

The misuse of genomic information has generated considerable concern in American Indian and Alaska Native communities. To understand how issues of genomic research misuse are understood by the public this article focuses on the case brought against Arizona State University by the Havasupai Indian Tribe. To understand how the Havasupai was being framed in the print media, this article examines newspaper articles (N=59) from when the story broke in 2004 until 2019. Our findings suggest that news media rarely engaged the concept of sovereignty and framed the story as an issue of research and scientific ethics.

We identified a new human voltage-gated potassium channel blocker, NnK-1, in the jellyfish Nemopilema nomurai based on its genomic information. The gene sequence encoding NnK-1 contains 5408 base pairs, with five introns and six exons. The coding sequence of the NnK-1 precursor is 894 nucleotides long and encodes 297 amino acids containing five presumptive ShK-like peptides. An electrophysiological assay demonstrated that the fifth peptide, NnK-1, which was chemically synthesized, is an effective blocker of hKv1.3, hKv1.4, and hKv1.5. Multiple-sequence alignment with cnidarian Shk-like peptides, which have Kv1.3-blocking activity, revealed that three residues (3Asp, 25Lys, and 34Thr) of NnK-1, together with six cysteine residues, were conserved. Therefore, we hypothesized that these three residues are crucial for the binding of the toxin to voltage-gated potassium channels. This notion was confirmed by an electrophysiological assay with a synthetic peptide (NnK-1 mu) where these three peptides were substituted with 3Glu, 25Arg, and 34Met. In conclusion, we successfully identified and characterized a new voltage-gated potassium channel blocker in jellyfish that interacts with three different voltage-gated potassium channels. A peptide that interacts with multiple voltage-gated potassium channels has many therapeutic applications in various physiological and pathophysiological contexts.

ABSTRACT: Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values (PBV¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability PBV¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era.

The article analyzes the issues arising when using human genetic information. The study of the existing legislation governing the use of such information reveals problems in determining the legal nature of genetic information in the personal data system, as well as its adequate legal protection. Purpose: to identify the legal problems of the use of genetic information on the basis of international legislation, judicial practice and the current Russian legislation and to identify possible ways of their solution. Methods: comparison, description and interpretation, dialectical, for-mal-logical, structural-system analysis and synthesis. Results: the study allows to de-termine the current state of legal regulation of genetic information, as well as to out-line ways of solving legal problems in this area.

In the context of the active development of the areas defined by the Concept of Predictive, Preventive and Personalized Medicine, genodiagnostics, applied at the stage of family planning, is of particular importance. The introduction of preimplantation and prenatal genodiagnostics poses not only ethical but also legal problems. Of particular importance are the problems of protecting genetic information obtained using these methods from unauthorized use, which actualizes the need to improve the current regulatory framework for determining and specifying the conditions for carrying out such genodiagnostics. Purpose: to identify actual legal problems arising from the use of genetic information obtained by methods of preimplantation and prenatal genetic diagnosis, to determine the best ways of their solution as well as the main directions of further technological development and improvement of national legislation in the field of genetic diagnosis. Methods: the authors apply empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; specific scientific methods: legal-dogmatic and interpretation of legal norms. Results: the analysis of the current legislation regulating preimplantation and prenatal genetic diagnosis in the Russian Federation reveals a number of pressing legal problems arising from the use of genetic information obtained in diagnostics. It is established that the current legislation does not take into account the specifics of genetic information and cannot provide effective protection against its illegal use in genetic diagnostics. General directions of improving national legislation in order to overcome problems related to the observance of human rights in the use of methods of preimplantation and prenatal genetic diagnostics are outlined.

Abstract Background The emerging viral pandemic worldwide is associated with a novel coronavirus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). This virus is said to emerge from its epidemic center in Wuhan, China, in 2019. Coronaviruses (CoVs) are single-stranded, giant, enveloped RNA viruses that come under the family of coronaviridae and order Nidovirales which are the crucial pathogens for humans and other vertebrates. Main body Coronaviruses are divided into several subfamilies and genera based on the genomic structure and phylogenetic relationship. The name corona is raised due to the presence of spike protein on the envelope of the virus. The structural and genomic study revealed that the total genome size of SARS-CoV-2 is from 29.8 kb to 29.9 kb. The spike protein (S) is a glycoprotein that attaches to the receptor of host cells for entry into the host cell, followed by the attachment of virus RNA to the host ribosome for translation. The phylogenetic analysis of SARS-CoV-2 revealed the similarity (75–88%) with bat SARS-like coronavirus. Conclusion The sign and symptoms of novel severe acute respiratory syndrome coronavirus 2 are also discussed in this paper. The worldwide outbreak and prevention from severe acute respiratory syndrome coronavirus 2 are overviewed in the present article. The latest variant of coronavirus and the status of vaccines are also overviewed in the present article.

Genomic selection increases accuracy and decreases generation interval, accelerating genetic changes in populations. Assumptions of genetic improvement must be addressed to quantify the magnitude and direction of change. Genetic trends of US dairy cattle breeds were examined to determine the genetic gain since the implementation of genomic evaluations in 2009. Inbreeding levels and generation intervals were also investigated. Breeds included Ayrshire, Brown Swiss, Guernsey, Holstein (HO), and Jersey (JE), which were characterized by the evaluation breed the animal received. Mean genomic predicted breeding values ( P B V ¯) were analyzed per year to calculate genetic trends for bulls and cows. The data set contained 154,008 bulls and 33,022,242 cows born since 1975. Breakpoints were estimated using linear regression, and nonlinear regression was used to fit the piecewise model for the small sample number in some years. Generation intervals and inbreeding levels were also investigated since 1975. Milk, fat, and protein yields, somatic cell score, productive life, daughter pregnancy rate, and livability P B V ¯ were documented. In 2017, 100% of bulls in this data set were genotyped. The percentage of genotyped cows has increased 23 percentage points since 2010. Overall, production traits have increased steadily over time, as expected. The HO and JE breeds have benefited most from genomics, with up to 192% increase in genetic gain since 2009. Due to the low number of observations, trends for Ayrshire, Brown Swiss, and Guernsey are difficult to infer from. Trends in fertility are most substantial; particularly, most breeds are trending downwards and daughter pregnancy rate for JE has been decreasing steadily since 1975 for bulls and cows. Levels of genomic inbreeding are increasing in HO bulls and cows. In 2017, genomic inbreeding levels were 12.7% for bulls and 7.9% for cows. A suggestion to control this is to include the genomic inbreeding coefficient with a negative weight to the selection index of bulls with high future genomic inbreeding levels. For sires of bulls, the current generation intervals are 2.2 yr in HO, 3.2 in JE, 4.4 in Brown Swiss, 5.1 in Ayrshire, and 4.3 in Guernsey. The number of colored breed bulls in the United States is currently at an extremely low level, and this number will only increase with a market incentive or additional breed association involvement. Increased education and extension could be beneficial to increase knowledge about inbreeding levels, use of genomics and genetic improvement, and genetic diversity in the genomic selection era. [ABSTRACT FROM AUTHOR]

The effects of inbreeding in livestock species breeds have been well documented and they have a negative impact on profitability. The objective of this study was to evaluate the levels of inbreeding in Sarda (SAR, n = 785) and Valle del Belice (VdB, n = 473) dairy sheep breeds and their impact on milk production traits. Two inbreeding coefficients (F) were estimated: using pedigree (FPED), or runs of homozygosity (ROH; FROH) at different minimum ROH lengths and different ROH classes. After the quality control, 38,779 single nucleotide polymorphisms remained for further analyses. A mixed‐linear model was used to evaluate the impact of inbreeding coefficients on production traits within each breed. VdB showed higher inbreeding coefficients compared to SAR, with both breeds showing lower estimates as the minimum ROH length increased. Significant inbreeding depression was found only for milk yield, with a loss of around 7 g/day (for SAR) and 9 g/day (VdB) for a 1% increase of FROH. The present study confirms how the use of genomic information can be used to manage intra‐breed diversity and to calculate the effects of inbreeding on phenotypic traits. [ABSTRACT FROM AUTHOR]

The author is convinced that a notion genomic information is widespread in sciences thus justifies that a genomic information is already a general scientific notion. The author differentiates internal and external factors that have a great impact on the notion of genomic information. Internal factors are digitalization of sciences, interdisciplinary research, and a new type of rationality in science. External factors are modern economy that is based on scientific and technical progress. The article refers to the theory of notions ranges that was substantiated by A.M. Vasilev. It creates a new notions range concerning the notion of genomic information. It is crucial to understand that this notion is investigated in the aspect of legal information. In the authors opinion, notions range concerning the genomic information notion forms the basis for a fresh interdisciplinary law-and-genome theory which will be developing consequently. The author asserts that it is necessary to use general scientific approaches for law-and-genome informations research. In this sense systematic and informational approaches are the most significant for interdisciplinary law-and-genome theory ones.

The relevance of the research topic is justified by the rapid technological development of genetics and genomics and introduction of genetic technologies and inventions in scientific and medical practice, which is not provided with a proper systematized legal protection in national and international legislation. The legislators problems are caused by the uniqueness and complexity of the genomic legal relationship. The subjects of knowledge in the framework of post-non-classical scientific rationality need to focus on the epistemological philosophical foundations of the knowledge of genomic legal relations. The purpose of the study is to identify and reflect the features of knowledge of the legal protection of genomic legal relations. Research tasks suggest defining the concept and composition of genomic legal relations, identifying the object and subject of legal protection of genomic legal relations, distinguishing between the concepts of "legal safeguard" and "legal protection" of genomic legal relations. The methodology of scientific research includes the dialectical method, general scientific logical operations (deduction and induction, analysis and synthesis), and private scientific methods (formal legal and comparative legal methods). Within the framework of the methodology of post-non-classical scientific rationality, a systematic approach to the study of the object of knowledge is used - the study of the structure, functional and instrumental aspects of the protective genomic legal relationship.

Contemporary scientific and technological process leads to rethinking of human nature and its components including dignified treatment. Identification of the permissible treatment in genomic experiments draws the core discussion about the essence of human dignity. In this regard the article focuses on deep transformation of human dignity nature in terms of new biotechnological challenges and rapid development of genomic technologies. The author analyses main philosophical and legal conceptions of human dignity and concludes about guiding and interdisciplinary role of human dignity in the human rights system and policy. The research goes on to define the somatic rights within the category of human rights and shows new aspects of exercises the somatic rights arisen in increased use of genomic technologies. Author compares philosophical doctrine of inherent generic dignity of every human being and personality theory in legal acts. The article pays special attention on the analyses of recent genomic achievements and genomic manipulation for the last 5 years where the subjects of manipulation are embryos and their tissues. Further, the article deals with philosophical, legal and medical justification of viability and sensitivity of human embryos and fetuses. The author considered the necessity of establishment the concept of moral status of embryo during genomic and genetic manipulations.

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Depending on the patients' genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design. [ABSTRACT FROM AUTHOR]

Background: The emerging viral pandemic worldwide is associated with a novel coronavirus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). This virus is said to emerge from its epidemic center in Wuhan, China, in 2019. Coronaviruses (CoVs) are single-stranded, giant, enveloped RNA viruses that come under the family of coronaviridae and order Nidovirales which are the crucial pathogens for humans and other vertebrates. Main body: Coronaviruses are divided into several subfamilies and genera based on the genomic structure and phylogenetic relationship. The name corona is raised due to the presence of spike protein on the envelope of the virus. The structural and genomic study revealed that the total genome size of SARS-CoV-2 is from 29.8 kb to 29.9 kb. The spike protein (S) is a glycoprotein that attaches to the receptor of host cells for entry into the host cell, followed by the attachment of virus RNA to the host ribosome for translation. The phylogenetic analysis of SARS-CoV-2 revealed the similarity (75–88%) with bat SARS-like coronavirus. Conclusion: The sign and symptoms of novel severe acute respiratory syndrome coronavirus 2 are also discussed in this paper. The worldwide outbreak and prevention from severe acute respiratory syndrome coronavirus 2 are overviewed in the present article. The latest variant of coronavirus and the status of vaccines are also overviewed in the present article. [ABSTRACT FROM AUTHOR]

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine. [ABSTRACT FROM AUTHOR]

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G (⁠ G A P Y ~ - 1 ⁠) with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H−1) also includes the inverse of the pedigree relationship matrix, which can be dense with a long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1–9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G −1 and G A P Y ~ - 1 using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion of G, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates. [ABSTRACT FROM AUTHOR]

To handle genomic information while supporting FAIR principles, we present GIPAMS, a modular architecture. GIPAMS provides security and privacy to manage genomic information by means of several independent services and modules that interact among them in an orchestrated way. The paper analyzes how some security and privacy aspects of the FAIRification process are covered by the GIPAMS platform. [ABSTRACT FROM AUTHOR]

A recent symposium on cancer and evolution has bought many innovative thinkers together to challenge the status quo of current cancer research. Professor Henry Heng's presentation considers cancer as a new system emerging via macro-evolution, where genome chaos-mediated information creation and maintenance plays an important role. This concept departs from the neo-Darwinian influenced somatic mutation theory of cancer. To appreciate his theory, it is helpful to briefly review several of his heterodox findings in the fields of oncology and evolutionary biology. This letter summarizes and highlights these findings and calls for a medical and scientific reckoning as well as integration within and between these fields. [ABSTRACT FROM AUTHOR]

Cancer is traditionally labeled a "cellular growth problem." However, it is fundamentally an issue of macroevolution where new systems emerge from tissue by breaking various constraints. To study this process, we used experimental platforms to "watch evolution in action" by comparing the profiles of karyotypes, transcriptomes, and cellular phenotypes longitudinally before, during, and after key phase transitions. This effort, alongside critical rethinking of current gene-based genomic and evolutionary theory, led to the development of the Genome Architecture Theory. Following a brief historical review, we present four case studies and their takeaways to describe the pattern of genome-based cancer evolution. Our discoveries include 1. The importance of non-clonal chromosome aberrations or NCCAs; 2. Two-phased cancer evolution, comprising a punctuated phase and a gradual phase, dominated by karyotype changes and gene mutation/epigenetic alterations, respectively; 3. How the karyotype codes system inheritance, which organizes gene interactions and provides the genomic basis for physiological regulatory networks; and 4. Stress-induced genome chaos, which creates genomic information by reorganizing chromosomes for macroevolution. Together, these case studies redefine the relationship between cellular macro- and microevolution: macroevolution does not equal microevolution + time. Furthermore, we incorporate genome chaos and gene mutation in a general model: genome reorganization creates new karyotype coding, then diverse cancer gene mutations can promote the dominance of tumor cell populations. Finally, we call for validation of the Genome Architecture Theory of cancer and organismal evolution, as well as the systematic study of genomic information flow in evolutionary processes. [ABSTRACT FROM AUTHOR]

Depending on the patients’ genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

New genome sequencing technologies have decreased the cost of generating genomic data, thus increasing storage needs. The International Organization for Standardization (ISO) working group MPEG has developed a standard for genomic data compression with encryption features. The approach taken in standard MPEG-G (ISO/IEC 23092) to compress genomic information was to group similar data into streams. Taking this into account, one of the protection options considered was to encrypt each stream separately. In this paper, we show that an attacker can use an unencrypted stream to deduce the encrypted content if streams are encrypted separately. To do so, we present two different attacks, one based on signal processing and the other one based on neural networks. The signal-based attack only works with unrealistic settings, whereas the neural network-based one recovers data with realistic settings (regarding read length and coverage). The presented results made MPEG reconsider the encryption strategy, before final publication of the standard, discarding separate streams encryption approach. [ABSTRACT FROM AUTHOR]

The COVID-19 disease for Novel coronavirus (SARS-CoV-2) has turned out to be a global pandemic. The high transmission rate of this pathogenic virus demands an early prediction and proper identification for the subsequent treatment. However, polymorphic nature of this virus allows it to adapt and sustain in different kinds of environment which makes it difficult to predict. On the other hand, there are other pathogens like SARS-CoV-1, MERS-CoV, Ebola, Dengue, and Influenza as well, so that a predictor is highly required to distinguish them with the use of their genomic information. To mitigate this problem, in this work COVID-DeepPredictor is proposed on the framework of deep learning to identify an unknown sequence of these pathogens. COVID-DeepPredictor uses Long Short Term Memory as Recurrent Neural Network for the underlying prediction with an alignment-free technique. In this regard, k-mer technique is applied to create Bag-of-Descriptors (BoDs) in order to generate Bag-of-Unique-Descriptors (BoUDs) as vocabulary and subsequently embedded representation is prepared for the given virus sequences. This predictor is not only validated for the dataset using K-fold cross-validation but also for unseen test datasets of SARS-CoV-2 sequences and sequences from other viruses as well. To verify the efficacy of COVID-DeepPredictor, it has been compared with other state-of-the-art prediction techniques based on Linear Discriminant Analysis, Random Forests, and Gradient Boosting Method. COVID-DeepPredictor achieves 100% prediction accuracy on validation dataset while on test datasets, the accuracy ranges from 99.51 to 99.94%. It shows superior results over other prediction techniques as well. In addition to this, accuracy and runtime of COVID-DeepPredictor are considered simultaneously to determine the value of k in k-mer, a comparative study among k values in k-mer, Bag-of-Descriptors (BoDs), and Bag-of-Unique-Descriptors (BoUDs) and a comparison between COVID-DeepPredictor and Nucleotide BLAST have also been performed. The code, training, and test datasets used for COVID-DeepPredictor are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-DeepPredictor/.

With the establishment of genome sequencing, the influence of genomic information on self-understanding and identity construction has become increasingly important. New sequencing methods far exceed previous genetic tests in terms of scope and quantity. Despite theoretical approaches, however, there are few empirical findings on the identity-relevant influence of genomic information. The present study examines genomic information's identity-relevant influences and considers whether developments in the field of genome sequencing may generate problems that are not yet addressed by existing identity concepts based on traditional genetic tests. The study is based on 10 partially standardized interviews with personally affected persons and four focus groups with medical laypersons as representatives of the public, which were evaluated on the basis of qualitative content analysis. As a result, this paper presents five thematic areas with identity-relevant references within subjective attitudes toward the handling of genomic information, and also derives two basic identity concepts. The results indicate that the lay discourse is still strongly based on older debates about genetic testing and that the view on the complexity of genomic information established in the scientific context has thus far no influence on the perspectives either of those affected or laypersons.

The COVID-19 disease for Novel coronavirus (SARS-CoV-2) has turned out to be a global pandemic. The high transmission rate of this pathogenic virus demands an early prediction and proper identification for the subsequent treatment. However, polymorphic nature of this virus allows it to adapt and sustain in different kinds of environment which makes it difficult to predict. On the other hand, there are other pathogens like SARS-CoV-1, MERS-CoV, Ebola, Dengue, and Influenza as well, so that a predictor is highly required to distinguish them with the use of their genomic information. To mitigate this problem, in this work COVID-DeepPredictor is proposed on the framework of deep learning to identify an unknown sequence of these pathogens. COVID-DeepPredictor uses Long Short Term Memory as Recurrent Neural Network for the underlying prediction with an alignment-free technique. In this regard, k -mer technique is applied to create Bag-of-Descriptors (BoDs) in order to generate Bag-of-Unique-Descriptors (BoUDs) as vocabulary and subsequently embedded representation is prepared for the given virus sequences. This predictor is not only validated for the dataset using K -fold cross-validation but also for unseen test datasets of SARS-CoV-2 sequences and sequences from other viruses as well. To verify the efficacy of COVID-DeepPredictor, it has been compared with other state-of-the-art prediction techniques based on Linear Discriminant Analysis, Random Forests, and Gradient Boosting Method. COVID-DeepPredictor achieves 100% prediction accuracy on validation dataset while on test datasets, the accuracy ranges from 99.51 to 99.94%. It shows superior results over other prediction techniques as well. In addition to this, accuracy and runtime of COVID-DeepPredictor are considered simultaneously to determine the value of k in k -mer, a comparative study among k values in k -mer, Bag-of-Descriptors (BoDs), and Bag-of-Unique-Descriptors (BoUDs) and a comparison between COVID-DeepPredictor and Nucleotide BLAST have also been performed. The code, training, and test datasets used for COVID-DeepPredictor are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-DeepPredictor/. [ABSTRACT FROM AUTHOR]

This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single‐step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single‐step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance–covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis. [ABSTRACT FROM AUTHOR]

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics. [ABSTRACT FROM AUTHOR]

New genome sequencing technologies have simplified the generation of genomic data, making them more common but in turn a likely target of attack. Security strategies have been devised such as restricting the amount of information that can be queried or using new encryption techniques. These solutions might not be enough if the entire file has to be shared, as the recipient might leak the accessible information. This contribution addresses this issue using watermarking. Each read in a genomic file is modified depending on its content and a secret key. This allows generating different watermarked instances of the original file. Each watermark acts as a fingerprint: if a leak occurs, the unique modifications of the instance points to who originated the unauthorized publication. Using the key, the modifications can be undone. This allows sharing a leak-discouraging version with which the relevance of a file can be assessed, and can be reversed to the original if needed. [ABSTRACT FROM AUTHOR]

Abstract Background Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1–8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1–23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information. Results In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands. Conclusions We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.

Abstract Introduction In Japan, there are approximately 300 projects conducting research on rare diseases supported by the Ministry of Health, Labour and Welfare of Japan (MHLW) and the Japan Agency for Medical Research and Development (AMED). Diverse data, including clinical, genomic, and sample‐related data, are generated by these projects. However, at present, such data are managed individually by each project. This makes it difficult for third parties to ascertain the data generated by projects. Methods Again this background, at the beginning of 2017, the AMED started the National Platform for Rare Diseases Data Registry of Japan (RADDAR‐J), whose mission is to construct a cross‐sectional data integration platform incorporating projects supported by the AMED and MHLW. RADDAR‐J promotes data sharing by the projects in accordance with the data‐sharing policy established by the AMED, which classifies data sharing into three categories based on the strategies used to protect the rights of researchers while promoting data sharing. RADDAR‐J integrates and analyzes data shared by each project to add value to the resources and promote secondary use by third parties while protecting the rights of the researchers who shared their data. The platform is designed to provide incentives to projects that shared their data by supporting registry construction or genomic analysis to promote data sharing. RADDAR‐J also has the function of data identification to securely integrate data originating from the same person. RADDAR‐J accelerates clinical research by encouraging each project to utilize a central ethics committee. Results/Conclusion The use of the platform by projects is expected to lead to streamlined data collection, improved quality assurance, improved access to data, and promotion of joint research and the secondary use of shared data. These benefits will accelerate research into diagnosis and treatment technologies and will hopefully lead to improved quality of life for patients with rare diseases.

Accurate genetic parameters are crucial for predicting breeding values and selection responses in breeding programs. Genetic parameters change with selection, reducing additive genetic variance and changing genetic correlations. This study investigates the dynamic changes in genetic parameters for residual feed intake (RFI), gain (GAIN), breast percentage (BP), and femoral head necrosis (FHN) in a broiler population that undergoes selection, both with and without the use of genomic information. Changes in single nucleotide polymorphism (SNP) effects were also investigated when including genomic information. The dataset containing 200,093 phenotypes for RFI, 42,895 for BP, 203,060 for GAIN, and 63,349 for FHN was obtained from 55 mating groups. The pedigree included 1,252,619 purebred broilers, of which 154,318 were genotyped with a 60K Illumina Chicken SNP BeadChip. A Bayesian approach within the GIBBSF90 + software was applied to estimate the genetic parameters for single-, two-, and four-trait models with sliding time intervals. For all models, we used genomic-based (GEN) and pedigree-based approaches (PED), meaning with or without genotypes. For GEN (PED), heritability varied from 0.19 to 0.2 (0.31 to 0.21) for RFI, 0.18 to 0.11 (0.25 to 0.14) for GAIN, 0.45 to 0.38 (0.61 to 0.47) for BP, and 0.35 to 0.24 (0.53 to 0.28) for FHN, across the intervals. Changes in genetic correlations estimated by GEN (PED) were 0.32 to 0.33 (0.12 to 0.25) for RFI-GAIN, -0.04 to -0.27 (-0.18 to -0.27) for RFI-BP, -0.04 to -0.07 (-0.02 to -0.08) for RFI-FHN, -0.04 to 0.04 (0.06 to 0.2) for GAIN-BP, -0.17 to -0.06 (-0.02 to -0.01) for GAIN-FHN, and 0.02 to 0.07 (0.06 to 0.07) for BP-FHN. Heritabilities tended to decrease over time while genetic correlations showed both increases and decreases depending on the traits. Similar to heritabilities, correlations between SNP effects declined from 0.78 to 0.2 for RFI, 0.8 to 0.2 for GAIN, 0.73 to 0.16 for BP, and 0.71 to 0.14 for FHN over the eight intervals with genomic information, suggesting potential epistatic interactions affecting genetic trait architecture. Given rapid genetic architecture changes and differing estimates between genomic and pedigree-based approaches, using more recent data and genomic information to estimate variance components is recommended for populations undergoing genomic selection to avoid potential biases in genetic parameters., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society of Animal Science.)

Increased homozygosity resulted from mating of relatives is considered as one of the challenges faced in dairy farming industry which has attracted the attentions. This research has been conducted to measure homozygosity based on SNP and ROH in high- and low-producing Holstein cows. In current research, both random regression and pedigree index approaches were used to obtain candidate animals for genotyping process. The samples were obtained from 150 Holstein dairy cows (75 by high- and 75 by low-EBV for milk production). We proposed a suitable method, by integrating breeding value estimation calculated by random regression and pedigree index, to select the candidate animals for genotyping. The results showed that putting too much emphasis on production traits in high-producing dairy cows had negative impact on the traits associated with fertility (DPR = -0.55) and productive life (PL = 0.1). The calculated homozygosity based on ROH showed different amount of variation in different chromosomes in the cows with high and low production which may be related to uneven distribution of genes influencing production traits in different chromosomes. [ABSTRACT FROM AUTHOR]

Provision of security and privacy to genomic data is a key issue in current genomic information representation. Existing formats do not give a solution to these issues (or they provide a partial one), so new solutions are demanded. MPEG-G (ISO/IEC 23092, Genomic Information Representation) is an International Standard for the representation of genomic information being defined by the MPEG Committee (Moving Pictures Expert Group, ISO/IEC JTC1 SC29/WG11). We provide flexible protection to the information stored inside the MPEG-G format with a combination of security techniques and privacy rules. [ABSTRACT FROM AUTHOR]