Prediction and classification are two very active areas in modern data analysis. In this paper, prediction with nonlinear optimal scaling transformations of the variables is reviewed, and extended to the use of multiple additive components, much in the spirit of statistical learning techniques that are currently popular, among other areas, in data mining. Also, a classification/clustering method is described that is particularly suitable for analyzing attribute-value data from systems biology (genomics, proteomics, and metabolomics), and which is able to detect groups of objects that have similar values on small subsets of the attributes.

Elevated levels of IL-6 in plasma are associated with the severity of visceral leishmaniasis (VL). The clinical manifestations of VL vary among patients, influenced by host factors and the virulence of the Leishmania infantum parasite. Considering that severe VL may result from an exaggerated inflammatory response, this study investigated whether IL-6 could serve as a biomarker to identify pro-inflammatory virulence factors. We conducted a genome-wide association study (GWAS) analysis on L. infantum isolates from patients with VL, whose IL-6 concentrations were measured. The analysis revealed that the relationship between IL-6 levels and clinical outcomes (survival vs mortality) had an area under the curve (AUC) of 0.67 (95% CI 0.52–0.81). A cut-off of 391.7 pg mL−1 for IL-6 was established to conduct a logistic regression analysis. We identified 10 029 single nucleotide variants (SNVs) across 62 genomes, resulting in 6,948 SNVs after filtering, of which 6,341 are located in protein-coding regions. The association analysis with PLINK identified 722 variants, of which 35 showed significant associations, with odds ratios ≥3.3, primarily in coding regions. These findings demonstrate that IL-6 levels tended to be associated with the fatal outcome of VL and highlight 35 novel genetic variants that could serve as potential biomarkers for prognosis. Further research into the biological role of these variants may lead to new therapeutic targets and improve the clinical management of VL, especially in identifying high-risk patients.

This guest editorial describes the importance of converging genetics and psychosocial epidemiology research methods to understand the biopsychosocial etiology of psychiatric phenotypes.

Over the past two decades there has been a rapid expansion in our understanding of how human genetic variability impacts susceptibility and severity of disease. Through applications of genome-wide association studies, genome and exome sequencing, researchers have made thousands of discoveries of genetic variants that impact risk of common and rare disorders affecting millions of people. Although these techniques have been primarily applied to highly prevalent chronic disorders such as diabetes1 and cardiovascular disease2, infectious diseases have proven to not be immune to genome-wide association, with studies of Tuberculosis3, HIV4 and SARS-CoV25, to name but a few, identifying host susceptibility loci across the genome. Unlike non-communicable diseases, infectious diseases have the unique element of impacting not only the affected the host, but those who may be most vulnerable to acquiring the infection. Thus, genetic variants that impact one individual’s susceptibility to and severity of an infection may also have broader implications to public health, as was brought into keen focus during the COVID-19 pandemic. Therefore, as we begin to apply the knowledge gained from genomic studies in the clinic or into policy, there are unique ethical, legal, and social implications (ELSI) at the intersection of infectious diseases and human genomics. In this issue of the Journal of Law, Medicine and Ethics, Jose et al attempt to address this need by proposing a research agenda for ELSI studies at what they term the “blurred boundaries” of infectious and genetic diseases.6

The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.

Waterhemp [Amaranthus tuberculatus (Moq.) Sauer] is one of the most troublesome weeds in the United States. An A. tuberculatus population (CHR) was identified in Illinois, USA, as resistant to herbicides from six different site-of-action groups. Recently, the same population was also recognized as dicamba resistant. This study aimed to identify key resistance genes and the putative dicamba resistance mechanism in A. tuberculatus via transcriptomics analysis. Multiple differentially expressed (DE) genes and co-expression gene modules were identified as associated with dicamba resistance. Specifically, genes encoding glutathione S-transferases (GSTs), ATP-binding cassette transporters, peroxidases, and uridine diphosphate (UDP)-glycosyltransferases (UGTs) were identified. Results indicated enhanced oxidative stress tolerance as the primary mechanism for reducing dicamba toxicity. Results also point to potential glycosylation via UGTs and conjugation via GSTs of dicamba and its by-products. This is the first transcriptomics characterization of dicamba resistance in A. tuberculatus. Multiple non-target-site resistance genes were identified, indicating a cross-resistance pattern in the CHR population leading to a putative-enhanced oxidative stress response. Regions of multiple DE genes (i.e., genomic hot spots) across the A. tuberculatus genome corroborate previous results and potentially add to the complexity of non-target-site resistance traits.

Since the emergence of Omicron variant of SARS-CoV-2 in late 2021, a number of sub-lineages have arisen and circulated internationally. Little is known about the relative severity of Omicron sub-lineages BA.2.75, BA.4.6, and BQ.1. We undertook a case–control analysis to determine the clinical severity of these lineages relative to BA.5, using whole genome sequenced, PCR-confirmed infections, between 1 August 2022 and 27 November 2022, among those who presented to emergency care in England 14 days after and up to one day prior to the positive specimen. A total of 10,375 episodes were included in the analysis; of which, 5,207 (50.2%) were admitted to the hospital or died. Multivariable conditional regression analyses found no evidence of greater odds of hospital admission or death among those with BA.2.75 (odds ratio (OR) = 0.96, 95% confidence interval (CI): 0.84–1.09) and BA.4.6 (OR = 1.02, 95% CI: 0.88– 1.17) or BQ.1 (OR = 1.03, 95% CI: 0.94–1.13) compared to BA.5. Future lineages may not follow the same trend and there remains a need for continued surveillance of COVID-19 variants and their clinical outcomes to inform the public health response.

Precision oncology is a rapidly evolving concept that holds great promise in cancer treatment. However, a cancer complexity attributed to genomic and acquired tumour heterogeneity limits treatment effectiveness and increases toxicity. These limitations refer to both systemic therapies and radiotherapy, which are two mainstays of non-invasive cancer treatment. By understanding cancer heterogeneity and utilising advanced tools to personalise treatment strategies, precision oncology has the potential to revolutionise cancer care. In this article, we review the current status of precision oncology in solid tumours, specifically focusing on the impact of tumour heterogeneity and genomic patient features on systemic therapies and radiation. We also discuss the implementation of novel tools, such as next-generation sequencing and liquid biopsies, to overcome this problem.

Trauma exposure is prevalent globally and is a defining event for the development of posttraumatic stress disorder (PTSD), characterised by intrusive thoughts, avoidance behaviours, hypervigilance and negative alterations in cognition and mood. Exposure to trauma elicits a range of physiological responses which can interact with environmental factors to confer relative risk or resilience for PTSD. This systematic review summarises the findings of longitudinal studies examining biological correlates predictive of PTSD symptomology. Databases (Pubmed, Scopus and Web of Science) were systematically searched using relevant keywords for studies published between 1 January 2021 and 31 December 2022. English language studies were included if they were original research manuscripts or meta-analyses of cohort investigations that assessed longitudinal relationships between one or more molecular-level measures and either PTSD status or symptoms. Eighteen of the 1,042 records identified were included. Studies primarily included military veterans/personnel, individuals admitted to hospitals after acute traumatic injury, and women exposed to interpersonal violence or rape. Genomic, inflammation and endocrine measures were the most commonly assessed molecular markers and highlighted processes related to inflammation, stress responding, and learning and memory. Quality assessments were done using the Systematic Appraisal of Quality in Observational Research, and the majority of studies were rated as being of high quality, with the remainder of moderate quality. Studies were predominantly conducted in upper-income countries. Those performed in low- and middle-income countries were not broadly representative in terms of demographic, trauma type and geographic profiles, with three out of the four studies conducted assessing only female participants, rape exposure and South Africa, respectively. They also did not generate multimodal data or use machine learning or multilevel modelling, potentially reflecting greater resource limitations in LMICs. Research examining molecular contributions to PTSD does not adequately reflect the global burden of the disorder.

This article aims to explore the ethical issues arising from attempts to diversify genomic data and include individuals from underserved groups in studies exploring the relationship between genomics and health. We employed a qualitative synthesis design, combining data from three sources: 1) a rapid review of empirical articles published between 2000 and 2022 with a primary or secondary focus on diversifying genomic data, or the inclusion of underserved groups and ethical issues arising from this, 2) an expert workshop and 3) a narrative review. Using these three sources we found that ethical issues are interconnected across structural factors and research practices. Structural issues include failing to engage with the politics of knowledge production, existing inequities, and their effects on how harms and benefits of genomics are distributed. Issues related to research practices include a lack of reflexivity, exploitative dynamics and the failure to prioritise meaningful co-production. Ethical issues arise from both the structure and the practice of research, which can inhibit researcher and participant opportunities to diversify data in an ethical way. Diverse data are not ethical in and of themselves, and without being attentive to the social, historical and political contexts that shape the lives of potential participants, endeavours to diversify genomic data run the risk of worsening existing inequities. Efforts to construct more representative genomic datasets need to develop ethical approaches that are situated within wider attempts to make the enterprise of genomics more equitable.