Decoding a Million Genomes: Unveiling the Protein-coding Landscape and Its Implications for Precision Medicine

Imaging genetics is one of the important keys to precision medicine that leads to personalized treatment based on a patient’s genetics, phenotype, or psychosocial characteristics. It deepens the understanding of the mechanisms through which genetic variations contribute to neurological and psychiatric disorders. This systematic review overviews the methods and applications of imaging genetics in the context of neurological diseases, mentioning its potential role in personalized medicine. Following PRISMA guidelines, this review systematically analyzes 28 studies integrating genetic and neuroimaging data to explore disease mechanisms and their implications for precision medicine. Selected research included multiple neurological disorders, including frontotemporal dementia, Alzheimer’s disease, bipolar disorder, schizophrenia, Parkinson’s disease, and others. Voxel-based morphometry was the most common imaging technique, while frequently examined genetic variants included APOE, C9orf72, MAPT, GRN, COMT, and BDNF. Associations between these variants and regional gray matter loss (e.g., frontal, temporal, or subcortical regions) suggest that genetic risk factors play a key role in disease pathophysiology. Integrating genetic and neuroimaging analyses enhances our understanding of disease mechanisms and supports advancements in precision medicine.

Deciphering the mechanisms of PARP inhibitor resistance in prostate cancer: Implications for precision medicine.

The completion of the Human Genome Project has unleashed a wealth of human genomics information, but it remains unclear how best to implement this information for the benefit of patients. The standard approach of biomedical research, with researchers pursuing advances in knowledge in the laboratory and, separately, clinicians translating research findings into the clinic as much as decades later, will need to give way to new interdisciplinary models for research in genomic medicine. These models should include scientists and clinicians actively working as teams to study patients and populations recruited in clinical settings and communities to make genomics discoveries-through the combined efforts of data scientists, clinical researchers, epidemiologists, and basic scientists-and to rapidly apply these discoveries in the clinic for the prediction, prevention, diagnosis, prognosis, and treatment of cardiovascular diseases and stroke. The highly publicized US Precision Medicine Initiative, also known as All of Us, is a large-scale program funded by the US National Institutes of Health that will energize these efforts, but several ongoing studies such as the UK Biobank Initiative; the Million Veteran Program; the Electronic Medical Records and Genomics Network; the Kaiser Permanente Research Program on Genes, Environment and Health; and the DiscovEHR collaboration are already providing exemplary models of this kind of interdisciplinary work. In this statement, we outline the opportunities and challenges in broadly implementing new interdisciplinary models in academic medical centers and community settings and bringing the promise of genomics to fruition.

The genome continues to fascinate the enthusiasts, and the captivation seems unabating because of the continuous stream of new discoveries. What was once considered a junk DNA has now emerged to contain a large number of important regulatory elements.1 As an example of such discoveries is the recent finding that the genome contains ≈6200 enhancer elements that are operational in the human fetal and adult hearts.2 Genes occupy only ≈1.5% and coding exons only ≈1% of the genome, which make up only ≈60 million nucleotides of the 6.4 billion nucleotides (3.2 billion base pairs) in the genome.3 Yet, ≈5% of the human genome has undergone purifying selection and hence are likely functional.4 It is intriguing that about two third of the evolutionary constrained genomic elements are located in introns and the intergenic regions, suggestive of their regulatory roles in the genome.4 These conserved regions are enriched in loci that have been found to be associated with clinical phenotypes in the genome-wide association studies.4 The initial findings of the ENCODE (Encyclopedia of DNA Elements) project, although preliminary, illustrate the presence of numerous regulatory elements in the genome, including the enhancers.1 The discoveries largely made possible by the recent advances in the high-throughput RNA sequencing point to enormous RNA splicing diversity and the plethora of alternative splicing variants. Approximately 95% of the multiexon genes undergo alternative splicing, resulting in ≈100 000 abundant splice variants in various tissues.5 Moreover, it seems that almost all genomic regions in 1 form or shape are transcribed, which seems perplexing as only ≈1% of the genome codes for proteins, as has been understood to date. Only recently we have started to appreciate the diverse biological functions of these nonprotein coding transcripts, which are referred to as noncoding RNAs (ncRNAs). …

Sequence Kernel Association Tests for the Combined Effect of Rare and Common Variants

Chapter 10 - Lessons Learned From Cohort Studies, and Hospital-Based Studies and Their Implications in Precision Medicine

The majority of the genomic DNA sequence in mammalian and other higher organisms can be transcribed into abundant functional RNA transcripts, especially regulatory non-coding RNAs (ncRNAs) that are expressed in a developmentally and species-specific regulated manner. Here, we review various regulatory non-coding RNAs, including regulatory small non-coding RNAs (sncRNAs) and long non-coding RNAs (lncRNAs), and summarize two and eight kinds of distinct modes of action for sncRNAs and lncRNAs respectively, by which functional ncRNAs mediate the regulation of intracellular events.

This article addresses the challenges solidarity poses for the development of Precision Medicine (PM). Solidarity is invoked in calls for a new ‘social contract’ for PM, seeking to promote participation in PM by emphasizing reciprocity between contributions to and benefits from this new branch of medicine. In this context, there is a need for further conceptualization with regard to what qualifies as solidarity and how solidarity is performed in Precision Medicine initiatives. We address these conceptual gaps that have important practical implications for PM’s development, most notably for agendas of public engagement and trust. We argue that solidarity does not only represent a value but also takes on infrastructural forms, shaping how PM is practiced in, for example, healthcare delivery systems. Next, we empirically probe how solidarity is invoked in PM initiatives in the United States (‘All of Us’-Program) and Europe (the UK 100,000 Genomes Project and the French 2025 Genomics Plan). Based on this analysis, we argue that the infrastructural dimension of solidarity forms a vital precondition to build trust in PM. Echoing the famous motto of The Three Musketeers, ‘One for all, all for one’, PM policies cannot just proclaim solidarity for the gathering of data alone (‘One for all’) without caring about delivery of benefits of PM (‘All for one’). We conclude by proposing an empirical research agenda for studying infrastructural formations to secure solidarity in the implementation of PM practices across national contexts.

Precision medicine and Treat-to-Target approach in atopic dermatitis: enhancing personalized care and outcomes.

Precision medicine means giving patients the right treatment at the right dose at the right time with minimum ill consequences and maximum efficacy. It is medicine personalized to the individual’s genes, environment, and lifestyle and, ultimately, its widespread use will require a deep understanding of the genomic variations that create predispositions or resistances to various diseases. Some of the links between genes and diseases are already known, and more are being discovered every day. Similarly, much is known about which drugs are efficacious for treating which diseases, but there is still more to learn. The issue now is how to extract this information from the biomedical literature in way that can keep pace with today’s rapid discoveries in medical research. Efforts to assemble an organized database of such knowledge to data have focused on mathematical statistic methods, computer-aided methods, etc. Success has been mixed as previous methods usually result in false positive or depend on training sample sets, lacking of generality in different research fields, which have choked advancements in precision medicine. To break through this bottleneck, we need novel methods that can extract and leverage the valuable information locked within the constraints of the data we have. Hence, in this paper, we present a new text-based computational framework for extracting full three-way drug-disease-gene triplet information related to colorectal cancer from biomedical texts. The framework consists of two main steps. The first is to construct an integrated drug-disease-gene network by extracting pair-wise associations between diseases, drugs, and genes, and then store unique drug-disease-gene triplets for further analysis. Since the constructed network is highly likely to be too sparse, the next step is to complete the incomplete links in the network, i.e., to predict novel links from genes to diseases to drugs. To validate our framework, we conducted a case study on colorectal cancer, mining the literature for drug-disease and disease-gene associations. An analysis of the subsequent inferences drawn between the two shows that this approach can help to inform novel research hypotheses and identify new knowledge triplets about various diseases, both of which are significant for the advancement and implementation of precision medicine.

Contribution of heterozygous PCSK1 variants to obesity and implications for precision medicine: a case-control study.

Regulatory RNAs like microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) control vascular and immune cells' phenotype and thus play a crucial role in atherosclerosis. Moreover, the mutual interactions between miRNAs and lncRNAs link both types of regulatory RNAs in a functional network that affects lesion formation. In this review, we deduce novel concepts of atherosclerosis from the analysis of the current data on regulatory RNAs' role in endothelial cells (ECs) and macrophages. In contrast to arterial ECs, which adopt a stable phenotype by adaptation to high shear stress, macrophages are highly plastic and quickly change their activation status. At predilection sites of atherosclerosis, such as arterial bifurcations, ECs are exposed to disturbed laminar flow, which generates a dysadaptive stress response mediated by miRNAs. Whereas the highly abundant miR-126-5p promotes regenerative proliferation of dysadapted ECs, miR-103-3p stimulates inflammatory activation and impairs endothelial regeneration by aberrant proliferation and micronuclei formation. In macrophages, miRNAs are essential in regulating energy and lipid metabolism, which affects inflammatory activation and foam cell formation.Moreover, lipopolysaccharide-induced miR-155 and miR-146 shape inflammatory macrophage activation through their oppositional effects on NF-kB. Most lncRNAs are not conserved between species, except a small group of very long lncRNAs, such as MALAT1, which blocks numerous miRNAs by providing non-functional binding sites. In summary, regulatory RNAs' roles are highly context-dependent, and therapeutic approaches that target specific functional interactions of miRNAs appear promising against cardiovascular diseases.

The landscape of healthcare is undergoing a profound transformation with the emergence of precision medicine, a revolutionary approach that tailors medical interventions to the unique characteristics of each individual. This abstract provides a comprehensive overview of the advancements in precision medicine, exploring its foundations, applications in disease diagnosis and treatment, integration of big data and artificial intelligence, as well as the ethical considerations and challenges associated with this groundbreaking field. Precision medicine is fundamentally rooted in understanding the intricate interplay between an individual's genetic makeup, molecular profile, and environmental factors. Genome sequencing and the identification of genetic markers have become instrumental in predicting, preventing, and treating diseases on a personalized level, moving away from traditional population-based approaches. In the realm of oncology, precision medicine has redefined cancer care by targeting specific genetic mutations, enabling more effective and less toxic therapies.

Bacterial regulatory non-coding RNAs control numerous mRNA targets that direct a plethora of biological processes, such as the adaption to environmental changes, growth and virulence. Recently developed high-throughput techniques, such as genomic tiling arrays and RNA-Seq have allowed investigating prokaryotic cis- and trans-acting regulatory RNAs, including sRNAs, asRNAs, untranslated regions (UTR) and riboswitches. As a result, we obtained a more comprehensive view on the complexity and plasticity of the prokaryotic genome biology. Listeria monocytogenes was utilized as a model system for intracellular pathogenic bacteria in several studies, which revealed the presence of about 180 regulatory RNAs in the listerial genome. A regulatory role of non-coding RNAs in survival, virulence and adaptation mechanisms of L. monocytogenes was confirmed in subsequent experiments, thus, providing insight into a multifaceted modulatory function of RNA/mRNA interference. In this review, we discuss the identification of regulatory RNAs by high-throughput techniques and in their functional role in L. monocytogenes.

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of datasets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.