Genetic Research Research Articles

Epidemiological approach to Cryptococcus meningitis and related species in northeast Brazil: a scoping review

Introduction: Cryptococcosis is a fungal infection associated with yeast of the Cryptococcus genus. It is an opportunistic infection, more prevalent in individuals with AIDS, primarily manifesting as meningitis. Although there are numerous cases of cryptococcosis in northeastern Brazil, little is reported in the literature regarding meningitis cases. Objective: This study aimed to conduct a scoping review of meningitis cases associated with the Cryptococcus genus reported in the northeastern region of Brazil. Methodology: Four databases were utilized. Twenty-one studies were selected, totaling 492 cases. All states had publications on cryptococcal meningitis cases. RESULTS: The results revealed few studies on Cryptococcus meningitis in the region, with 492 identified cases. The majority were caused by Cryptococcus neoformans (356 cases), followed by C. gattii (98 cases), and other related species. AIDS was the primary underlying condition, associated with high mortality. Genetic research was underutilized. Conclusion: There is a need to enhance diagnostic methods in conjunction with increased case reporting to obtain a more detailed and comprehensive understanding of the epidemiology of neurocryptococcosis in northeastern Brazil.

Artificial Intelligence and its Role in Forensic Karyotyping: A Systematic Review

Introduction: One of the most important aspects of forensic investigations and genetic research is forensickaryotyping, which involves analyzing a person’s chromosomes to find genetic anomalies and establishidentification. The development of artificial intelligence (AI) technology offers a chance to improve and automate the forensic karyotyping procedure. This study examines the possible advantages and difficulties of artificial intelligence (AI) in forensic karyotyping. In forensic science, forensic karyotyping is essential for providing an accurate interpretation of genetic data for use in legal and investigative processes. It can offer useful details regarding genetic problems, such as chromosomal abnormalities or mutations, which can help with personal identification, paternity determination, or supplying proof in criminal investigations.Aim: To give a general review of how artificial intelligence is used in forensic karyotyping, evaluate its possible advantages, and address any relevant issues. By being aware of Artificial Intelligence’s promise and limitations in this field, we may set the stage for its efficient integration into forensic practitioner.Methods: A database search we did to start the inquiry turned up 582 documents. There were 216 unique records left after duplicates were eliminated. 232 items were subsequently eliminated as a result of download problems. A final sample of 31 research was chosen from the 134 full-text papers that were evaluated (n=134), with 103 being eliminated owing to quality issues.Result: The use of artificial intelligence (AI) in forensic karyotyping has several advantages, including automated chromosomal analysis, quicker abnormality discovery, and increased uniformity. For a successful application, challenges such as a lack of labelled datasets and ethical issues must be resolved.Concussion: By increasing productivity, precision, and uniformity, artificial intelligence has the potential totransform forensic karyotyping. While there are obstacles, continued study and cooperation amongst several fields might help you get through them. The ethical and appropriate use of AI in forensic karyotyping will improve forensic investigations, boost genetic research, and expand the use of genetics in the legal system.

The Promise of Inferring the Past Using the Ancestral Recombination Graph.

The ancestral recombination graph (ARG) is a structure that represents the history of coalescent and recombination events connecting a set of sequences (Hudson RR. In: Futuyma D, Antonovics J, editors. Gene genealogies and the coalescent process. In: Oxford Surveys in Evolutionary Biology; 1991. p. 1 to 44.). The full ARG can be represented as a set of genealogical trees at every locus in the genome, annotated with recombination events that change the topology of the trees between adjacent loci and the mutations that occurred along the branches of those trees (Griffiths RC, Marjoram P. An ancestral recombination graph. In: Donnelly P, Tavare S, editors. Progress in population genetics and human evolution. Springer; 1997. p. 257 to 270.). Valuable insights can be gained into past evolutionary processes, such as demographic events or the influence of natural selection, by studying the ARG. It is regarded as the "holy grail" of population genetics (Hubisz M, Siepel A. Inference of ancestral recombination graphs using ARGweaver. In: Dutheil JY, editors. Statistical population genomics. New York, NY: Springer US; 2020. p. 231-266.) since it encodes the processes that generate all patterns of allelic and haplotypic variation from which all commonly used summary statistics in population genetic research (e.g. heterozygosity and linkage disequilibrium) can be derived. Many previous evolutionary inferences relied on summary statistics extracted from the genotype matrix. Evolutionary inferences using the ARG represent a significant advancement as the ARG is a representation of the evolutionary history of a sample that shows the past history of recombination, coalescence, and mutation events across a particular sequence. This representation in theory contains as much information, if not more, than the combination of all independent summary statistics that could be derived from the genotype matrix. Consistent with this idea, some of the first ARG-based analyses have proven to be more powerful than summary statistic-based analyses (Speidel L, Forest M, Shi S, Myers SR. A method for genome-wide genealogy estimation for thousands of samples. Nat Genet. 2019:51(9):1321 to 1329.; Stern AJ, Wilton PR, Nielsen R. An approximate full-likelihood method for inferring selection and allele frequency trajectories from DNA sequence data. PLoS Genet. 2019:15(9):e1008384.; Hubisz MJ, Williams AL, Siepel A. Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph. PLoS Genet. 2020:16(8):e1008895.; Fan C, Mancuso N, Chiang CWK. A genealogical estimate of genetic relationships. Am J Hum Genet. 2022:109(5):812-824.; Fan C, Cahoon JL, Dinh BL, Ortega-Del Vecchyo D, Huber C, Edge MD, Mancuso N, Chiang CWK. A likelihood-based framework for demographic inference from genealogical trees. bioRxiv. 2023.10.10.561787. 2023.; Hejase HA, Mo Z, Campagna L, Siepel A. A deep-learning approach for inference of selective sweeps from the ancestral recombination graph. Mol Biol Evol. 2022:39(1):msab332.; Link V, Schraiber JG, Fan C, Dinh B, Mancuso N, Chiang CWK, Edge MD. Tree-based QTL mapping with expected local genetic relatedness matrices. bioRxiv. 2023.04.07.536093. 2023.; Zhang BC, Biddanda A, Gunnarsson ÁF, Cooper F, Palamara PF. Biobank-scale inference of ancestral recombination graphs enables genealogical analysis of complex traits. Nat Genet. 2023:55(5):768-776.). As such, there has been significant interest in the field to investigate 2 main problems related to the ARG: (i) How can we estimate the ARG based on genomic data, and (ii) how can we extract information of past evolutionary processes from the ARG? In this perspective, we highlight 3 topics that pertain to these main issues: The development of computational innovations that enable the estimation of the ARG; remaining challenges in estimating the ARG; and methodological advances for deducing evolutionary forces and mechanisms using the ARG. This perspective serves to introduce the readers to the types of questions that can be explored using the ARG and to highlight some of the most pressing issues that must be addressed in order to make ARG-based inference an indispensable tool for evolutionary research.

Polygenic scores for tobacco use provide insights into systemic health risks in a diverse EHR-linked biobank in Los Angeles

Tobacco use is a major risk factor for many diseases and is heavily influenced by environmental factors with significant underlying genetic contributions. Here, we evaluated the predictive performance, risk stratification, and potential systemic health effects of tobacco use disorder (TUD) predisposing germline variants using a European- ancestry-derived polygenic score (PGS) in 24,202 participants from the multi-ancestry, hospital-based UCLA ATLAS biobank. Among genetically inferred ancestry groups (GIAs), TUD-PGS was significantly associated with TUD in European American (EA) (OR: 1.20, CI: [1.16, 1.24]), Hispanic/Latin American (HL) (OR:1.19, CI: [1.11, 1.28]), and East Asian American (EAA) (OR: 1.18, CI: [1.06, 1.31]) GIAs but not in African American (AA) GIA (OR: 1.04, CI: [0.93, 1.17]). Similarly, TUD-PGS offered strong risk stratification across PGS quantiles in EA and HL GIAs and inconsistently in EAA and AA GIAs. In a cross-ancestry phenome-wide association meta-analysis, TUD-PGS was associated with cardiometabolic, respiratory, and psychiatric phecodes (17 phecodes at P < 2.7E-05). In individuals with no history of smoking, the top TUD-PGS associations with obesity and alcohol-related disorders (P = 3.54E-07, 1.61E-06) persist. Mendelian Randomization (MR) analysis provides evidence of a causal association between adiposity measures and tobacco use. Inconsistent predictive performance of the TUD-PGS across GIAs motivates the inclusion of multiple ancestry populations at all levels of genetic research of tobacco use for equitable clinical translation of TUD-PGS. Phenome associations suggest that TUD-predisposed individuals may require comprehensive tobacco use prevention and management approaches to address underlying addictive tendencies.

Unraveling the Secrets of Life: A Comprehensive Overview of Gene Discovery and Gene Mapping

Gene discovery and gene mapping have been key areas of research in genetics and molecular biology for several decades. These processes are fundamental to understanding the genetic basis of numerous diseases and conditions, including cancer, diabetes, and heart disease. Gene discovery involves identifying new genes, while gene mapping involves determining their location within an organism's genome. This essay provides an overview of the methods and technologies used in gene discovery and mapping, including whole-genome sequencing, linkage analysis, association studies, and expression profiling. It also discusses the importance of gene discovery and mapping in advancing our understanding of disease biology and facilitating the development of personalized medicine. Finally, the essay considers the future prospects of these processes, including the potential for new technologies such as single-cell sequencing, CRISPR/Cas9 genome editing, and artificial intelligence to revolutionize the field of genetics research.

Seed Quality and Longevity of Four Cowpea Accessions in Relation to Seed Coat Colour

Background: Cowpea is one of the most important legumes grown as a sole crop or integrated into farming systems in Africa. Seed quality plays a crucial role in plant population establishment in stressed environment. However, the behaviour of cowpea seeds with different seed coat colours at storage is not known. The study therefore aimed to assess the effect of seed coat colour on seed quality and longevity of seeds of four cowpea accessions. Methods: Two experiments (field and laboratory) were conducted at the CSIR-Plant Genetic Resources Research Institute, Bunso, Ghana for a period of 3 years. Seeds of four cowpea accessions dried to a moisture content of 7% and packaged in aluminium foil packets were stored at -20°C in a deep freezer for 30 months. The quality of stored seeds was measured by seed vigour and germination percentage at 6, 12, 18, 24 and 30 months after storage (MAS). Before storage, the 100-seed weight and initial seed quality were assessed. Result: The study showed significant differences in seed vigour and germination percentage at 6 and 12 months after storage (MAS) among the four cowpea accessions. Light-coloured accessions (GH7175 and GH8800) of cowpea seeds showed higher vigour and were of better quality than those of dark colours (GH3677 and GH2323) at 6, 12, 18, 24 and 30 months after storage.

Genome-wide development of intra- and inter-specific transferable SSR markers and construction of a dynamic web resource for yam molecular breeding: Y2MD.

Microsatellite markers are widely used in population genetics and breeding. Despite the economic significance of yams in developing countries, there is a paucity of microsatellite markers, and as of now, no comprehensive microsatellite marker database exists. In this study, we conducted genome-wide microsatellite marker development across four yam species, identified cross-species transferable markers, and designed an easy-to-use web portal for the yam researchers. The screening of Dioscorea alata, Dioscorea rotundata, Dioscorea dumetorum, and Dioscorea zingiberensis genomes resulted in 318,713, 322,501, 307,040, and 253,856 microsatellites, respectively. Mono-, di-, and tri-nucleotides were the most important types of repeats in the different species, and a total of 864,128 primer pairs were designed. Furthermore, we identified 1170 cross-species transferable microsatellite markers. Among them, 17 out of 18 randomly selected were experimentally validated with good discriminatory power, regardless of the species and ploidy levels. Ultimately, we created and deployed a dynamic Yam Microsatellite Markers Database (Y2MD) available at https://y2md.ucad.sn/. Y2MD is embedded with various useful tools such as JBrowse, Blast, insilicoPCR, and SSR Finder to facilitate the exploitation of microsatellite markers in yams. This study represents the first comprehensive microsatellite marker mining across several yam species and will contribute to advancing yam genetic research and marker-assisted breeding. The released user-friendly database constitutes a valuable platform for yam researchers.

Mass spectrometry and split luciferase complementation assays reveal the MecA protein interactome of Streptococcus mutans.

MecA is a highly conserved adaptor protein encoded by prokaryotes from the Bacillota phylum. MecA mutants exhibit similar pleiotropic defects in a variety of organisms, although most of these phenotypes currently lack a mechanistic basis. MecA mediates ClpCP-dependent proteolysis of its substrates, but only several such substrates have been reported in the literature and there are suggestions that proteolysis-independent regulatory mechanisms may also exist. Here, we provide the first comprehensive characterization of the MecA interactome and further assess its regulatory role in Clp-dependent proteolysis. Untargeted coimmunoprecipitation assays coupled with mass spectrometry revealed that the MecA ortholog from the oral pathobiont Streptococcus mutans likely serves as a major protein interaction network hub by potentially complexing with >100 distinct protein substrates, most of which function in highly conserved metabolic pathways. The interactome results were independently verified using a newly developed prokaryotic split luciferase complementation assay (SLCA) to detect MecA protein-protein interactions in vivo. In addition, we further develop a new application of SLCA to support in vivo measurements of MecA relative protein binding affinities. SLCA results were independently verified using targeted coimmunoprecipitation assays, suggesting the general utility of this approach for prokaryotic protein-protein interaction studies. Our results indicate that MecA indeed regulates its interactome through both Clp-dependent proteolysis as well as through an as-yet undefined proteolysis-independent mechanism that may affect more than half of its protein interactome. This suggests a significant aspect of the MecA regulatory function still has yet to be discovered.IMPORTANCEDespite multiple decades of study, the regulatory mechanism and function of MecA have remained largely a mystery. The current study provides the first detailed roadmap to investigate these functions in other medically significant bacteria. Furthermore, this study developed new genetic approaches to assay prokaryotic protein-protein interactions via the split luciferase complementation assay (SLCA). SLCA technology is commonly employed in eukaryotic genetic research but has not yet been established for studies of bacterial protein-protein interactions. The SLCA protein binding affinity assay described here is a new technological advance exclusive to the current study and has not been reported elsewhere.

Genetics and epigenetics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy. Its prevalence is increasing every year with increasing obesity, sedentary lifestyle, poor or unhealthy diet and increasing age of pregnant women. GDM is known to be accompanied by multiple short- and long-term complications for both mother and child. Research in genetics and epigenetics has provided new insights into the pathophysiology of GDM, which provides an opportunity to develop new strategies to improve the diagnosis, prevention and treatment of GDM and its complications.This review describes the role of genetic and epigenetic factors on the risks of developing GDM and its complications. The review also discusses the similarities in the genetic changes underlying type 2 diabetes mellitus and GDM, indicating a multifactorial pathogenesis of GDM.

The causal relationship between immune cells and ankylosing spondylitis: a bidirectional Mendelian randomization study

BackgroundAnkylosing spondylitis (AS) is one of several disorders known as seronegative spinal arthritis (SpA), the origin of which is unknown. Existing epidemiological data show that inflammatory and immunological factors are important in the development of AS. Previous research on the connection between immunological inflammation and AS, however, has shown inconclusive results.MethodsTo evaluate the causal association between immunological characteristics and AS, a bidirectional, two-sample Mendelian randomization (MR) approach was performed in this study. We investigated the causal connection between 731 immunological feature characteristic cells and AS risk using large, publically available genome-wide association studies.ResultsAfter FDR correction, two immunophenotypes were found to be significantly associated with AS risk: CD14 − CD16 + monocyte (OR, 0.669; 95% CI, 0.544 ~ 0.823; P = 1.46 × 10−4; PFDR = 0.043), CD33dim HLA DR + CD11b + (OR, 0.589; 95% CI = 0.446 ~ 0.780; P = 2.12 × 10−4; PFDR = 0.043). AS had statistically significant effects on six immune traits: CD8 on HLA DR + CD8 + T cell (OR, 1.029; 95% CI, 1.015 ~ 1.043; P = 4.46 × 10−5; PFDR = 0.014), IgD on IgD + CD24 + B cell (OR, 0.973; 95% CI, 0.960 ~ 0.987; P = 1.2 × 10−4; PFDR = 0.021), IgD on IgD + CD38 − unswitched memory B cell (OR, 0.962; 95% CI, 0.945 ~ 0.980; P = 3.02 × 10−5; PFDR = 0.014), CD8 + natural killer T %lymphocyte (OR, 0.973; 95% CI, 0.959 ~ 0.987; P = 1.92 × 10−4; PFDR = 0.021), CD8 + natural killer T %T cell (OR, 0.973; 95% CI, 0.959 ~ 0.987; P = 1.65 × 10−4; PFDR = 0.021).ConclusionOur findings extend genetic research into the intimate link between immune cells and AS, which can help guide future clinical and basic research.

Survival Analysis and Prognostic Factors for Peri-ocular Malignancies in Malaysia

Introduction: This study aims to determine the prognostic factors and survival outcomes of patients that underwent this surgery. Method: A retrospective review of 43 patients who underwent exenteration for periocular malignancies over a 14-year period was carried out. Patient demographics, tumour histology, treatment details, surgical margins’ status and post-operative survival were recorded. The survival outcome examined was the overall survival (OS) rate. Cox regression and Kaplan-Meier analysis were used to evaluate post-exenteration survival. Results: In total, 20 females and 23 males with a median age of 62 ± 17.3 years were identified. The most common indication for exenteration was basal cell carcinoma (20.9%), followed by squamous cell carcinoma (18.6%), adenocystic carcinoma (14%), malignant melanoma (14%) and sebaceous gland carcinoma (11.6%). The independent predictors for worse OS on multivariate analysis were Chinese ethnicity (adjusted hazard ratio [aHR] 4.95, p =0.017), sebaceous gland carcinoma (aHR 57.61 p=0.006), adenocystic carcinoma (aHR 45.87, p=0.008), clear surgical margins (aHR 5.41, p=0.025), receiving only chemotherapy (aHR 169.13, p=0.004), and receiving both adjuvant chemotherapy and radiotherapy (aHR 41.51, p=0.010). Conclusion: We recommend targeted public health initiatives for Chinese patients due to their increased mortality risk from peri-ocular malignancies. In addition, we advise comprehensive adjuvant therapy for all patients regardless of whether a clear surgical margin is achieved. Basal cell carcinoma and adenocystic carcinoma may also benefit from genetic research. We advocate more training for ophthalmologists to identify periocular malignancies earlier for better treatment options and increased chances of survival.

Working Ethically with Ancient DNA from Composites in the United States

Abstract This article discusses ethical frameworks for planning and implementing composite research in the United States. Composites, defined here as archaeological materials with multiple genetic sources, include materials such as sediment, coprolites, birch pitch, and dental calculus. Although composites are increasingly used in genetic research, the ethical considerations of their use in ancient DNA studies have not been widely discussed. Here, we consider how composites’ compositions, contexts, and potential to act as proxies can affect research plans and offer an overview of the primary ethical concerns of ancient DNA research. It is our view that ethical principles established for analyses of Ancestral remains and related materials can be used to inform research plans when working with composite evidence. This work also provides a guide to archaeologists unfamiliar with genetics analyses in planning research when using composite evidence from the United States with a focus on collaboration, having a clear research plan, and using lab methods that provide the desired data with minimal destruction. Following the principles discussed in this article and others allows for engaging in composite research while creating and maintaining positive relationships with stakeholders.

Contrasting clinical outcomes and socio-economic impact of young versus elderly-onset oral squamous cell carcinoma, a novel health economic analysis.

The incidence of young-onset oral squamous cell carcinoma (OSCC) is growing, even among non-smokers/drinkers. The effects of adverse histopathological features on long-term oncologic outcomes between the young and old are controversial and confounded by significant heterogeneity. Few studies have evaluated the socio-economic impact of premature mortality from OSCC. Our study seeks to quantify these differences and their economic impact on society. Four hundred and seventy-eight young (<45 years) and 1660 old patients (≥45 years) with OSCC were studied. Logistic regression determined predictors of recurrence and death. Survival analysis was calculated via the Kaplan-Meier method. A separate health economic analysis was conducted for India and Singapore. Years of Potential Productive Life Lost (YPPLL) were estimated with the Human Capital Approach, and premature mortality cost was derived using population-level data. Adverse histopathological features were seen more frequently in young OSCC: PNI (42.9% vs. 35%, p = 0.002), LVI (22.4% vs. 17.3%, p = 0.013) and ENE (36% vs. 24.5%, p < 0.001). Although 5-year OS/DSS were similar, the young cohort had received more intensive adjuvant therapy (CCRT 26.9% vs. 16.6%, p < 0.001). Among Singaporean males, the premature mortality cost per death was US $396,528, and per YPPLL was US $45,486. This was US $397,402 and US $38,458 for females. Among Indian males, the premature mortality cost per death was US $30,641, and per YPPLL was US $595. This was US $ 21,038 and US $305 for females. Young-onset OSCC is an aggressive disease, mitigated by the ability to receive intensive adjuvant treatment. From our loss of productivity analysis, the socio-economic costs from premature mortality are substantial. Early cancer screening and educational outreach campaigns should be tailored to this cohort. Alongside, more funding should be diverted to genetic research, developing novel biomarkers and improving the efficacy of adjuvant treatment in OSCC.

Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.

The imputation of unmeasured genotypes is essential in human genetic research, particularly in enhancing the power of genome-wide association studies and conducting subsequent fine-mapping. Recently, several deep learning-based genotype imputation methods for genome-wide variants with the capability of learning complex linkage disequilibrium patterns have been developed. Additionally, deep learning-based imputation has been applied to a distinct genomic region known as the major histocompatibility complex, referred to as HLA imputation. Despite their various advantages, the current deep learning-based genotype imputation methods do have certain limitations and have not yet become standard. These limitations include the modest accuracy improvement over statistical and conventional machine learning-based methods. However, their benefits include other aspects, such as their "reference-free" nature, which ensures complete privacy protection, and their higher computational efficiency. Furthermore, the continuing evolution of deep learning technologies is expected to contribute to further improvements in prediction accuracy and usability in the future.

Genome-wide versus transcriptome-wide association studies: Prospects and limitations

Genome-wide Association Studies (GWAS) and Transcriptome-wide Association Studies (TWAS) are pivotal tools in genetics research. GWAS, with its comprehensive genome coverage, reveals insights into complex trait variations among different populations. It finds applications beyond gene identification, including historical population studies and linkage disequilibrium pattern assessments. However, GWAS faces challenges like multiple testing corrections and difficulties in pinpointing causal variants. Conversely, TWAS offers higher gene resolution and the potential for deeper insights into genetic mechanisms. Yet, TWAS prediction accuracy is contingent on gene heritability, and its complexity lies in navigating gene regulatory networks and epigenetic factors. Understanding the strengths and limitations of both GWAS and TWAS is crucial for harnessing their full potential in genetic research endeavours.

Analysis of molecular genetic causes of congenital heart defects in children

In 10–20% of cases, congenital heart defects are caused by chromosomal abnormalities and gene mutation. Purpose. The purpose of the study: analysis of the genetic causes of congenital heart defects in children.Materials and Methods. The analysis of the medical history and the results of Genetics and Molecular Research (GMR) in 15 children with congenital heart defects were carried out.Results. Genome-wide DNA sequencing in patients with congenital heart defects revealed deletion 21q22.3 with duplication 4q31.1 -&gt;qter and pathogenic variants in the genes PPP1CB, FN1, PHF6, CITIED2, ARID2, KMT2A, CIT, JAG1, MED13L, ELN, CHRNA3, KCNK9. Conclusion. In the vast majority of cases in the examined children, congenital heart defects were caused by pathogenic gene variants or chromosomal abnormalities. Congenital heart defects are an absolute indication for cytogenetic and molecular genetic examination.

Blood donor biobank as a resource in personalised biomedical genetic research.

Health questionnaires and donation criteria result in accumulation of highly selected individuals in ablood donor population. To understand better the usefulness of ablood donor-based biobank in personalised disease-associated genetic studies, and for possible personalised blood donation policies, we evaluated the occurrence and distributions of common and rare disease-associated genetic variants in Finnish Blood Service Biobank. We analysed among 31,880 blood donors the occurrence and geographical distribution of (i) 53 rare Finnish-enriched disease-associated variants, (ii) mutations assumed to influence blood donation: four Bernard-Soulier syndrome and two hemochromatosis mutations, (iii) type I diabetes risk genotype HLA-DQ2/DQ8. In addition, we analysed the level of consanguinity in Blood Service Biobank. 80.3% of blood donors carried at least one (range 0-9 per donor) of the rare variants, many in homozygous form, as well. Donors carrying multiple rare variants were enriched in Eastern Finland. Haemochromatosis mutation HFE C282Y homozygosity was 43.8% higher than expected, whereas mutations leading to Bernard-Soulier thrombocytopenia were rare. The frequency of HLA-DQ2/DQ8 genotype was slightly lower than expected. First-degree consanguinity was higher in Blood Service Biobank than in the general population. We demonstrate that despite donor selection, the Blood Service Biobank is a valuable resource for personalised medical research and for genotype-selected samples from unaffected individuals. The geographical genetic substructure of Finland enables efficient recruitment of donors carrying rare variants. Furthermore, we show that blood donor biobank material can be utilised for personalised blood donation policies.

Progress and Implications from Genetic Studies of Bipolar Disorder.

With the advancements in gene sequencing technologies, including genome-wide association studies, polygenetic risk scores, and high-throughput sequencing, there has been a tremendous advantage in mapping a detailed blueprint for the genetic model of bipolar disorder (BD). To date, intriguing genetic clues have been identified to explain the development of BD, as well as the genetic association that might be applied for the development of susceptibility prediction and pharmacogenetic intervention. Risk genes of BD, such as CACNA1C, ANK3, TRANK1, and CLOCK, have been found to be involved in various pathophysiological processes correlated with BD. Although the specific roles of these genes have yet to be determined, genetic research on BD will help improve the prevention, therapeutics, and prognosis in clinical practice. The latest preclinical and clinical studies, and reviews of the genetics of BD, are analyzed in this review, aiming to summarize the progress in this intriguing field and to provide perspectives for individualized, precise, and effective clinical practice.

Psychosis superspectrum I: Nosology, etiology, and lifespan development.

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

Stone Pine (Pinus pinea L.) High-Added-Value Genetics: An Overview.

Stone pine (Pinus pinea L.) has received limited attention in terms of genetic research. However, genomic techniques hold promise for decoding the stone pine genome and contributing to developing a more resilient bioeconomy. Retrotransposon and specific genetic markers are effective tools for determining population-specific genomic diversity. Studies on the transcriptome and proteome have identified differentially expressed genes PAS1, CLV1, ATAF1, and ACBF involved in shoot bud formation. The stone pine proteome shows variation among populations and shows the industrial potential of the enzyme pinosylvin. Microsatellite studies have revealed low levels of polymorphism and a unique genetic diversity in stone pine, which may contribute to its environmental adaptation. Transcriptomic and proteomic analyses uncover the genetic and molecular responses of stone pine to fungal infections and nematode infestations, elucidating the defense activation, gene regulation, and the potential role of terpenes in pathogen resistance. Transcriptomics associated with carbohydrate metabolism, dehydrins, and transcription factors show promise as targets for improving stone pine's drought stress response and water retention capabilities. Stone pine presents itself as an important model tree for studying climate change adaptation due to its characteristics. While knowledge gaps exist, stone pine's genetic resources hold significant potential, and ongoing advancements in techniques offer prospects for future exploration.