Precision prevention is a prevention strategy for primary (disease prevention) and secondary (early detection) prevention of disease by combining non-genetic and genetic characteristics of individuals. Many cancers, including hereditary breast cancer and Lynch syndrome, can be detected and risk assessed using genetic testing. It is recommended that carriers of BRCA1 or BRCA2 germline variants undergo prophylactic surgery to mitigate their risk of developing breast cancer, as well as testing relatives at risk and implementing preventive interventions for those who carry variants [15]. An individual who carries cancer susceptibility genes is typically identified based on his family history [16], however, the family-history model has been recognized to have some limitations [17]. Between 50% and 60% of patients miss the opportunity for genetic testing because they do not meet the criteria as assessed by the family history model [18, 19, 20]. Hence, in light of the increasing ease of access to NGS technologies, it has been proposed to incorporate them into a population-based screening approach [21]. With population-based genetic testing, it is possible to significantly improve health outcomes for cancer susceptibility carriers while enabling accurate cancer prevention through risk stratification. Studies have shown that population-based genetic testing reduces cancer incidence without increasing mental stress is cost-effective, and is capable of identifying more mutation carriers [19, 21, 22]. NGS also plays an irreplaceable role in the screening and diagnosis of additional diseases. For example, its use in sudden cardiac death (SCD), which kills millions of people every year [23]. It is estimated that up to 70 percent of SCD cases in people under 50 years of age are potentially due to genetic causes [24]. As the majority of SCD occurs in the general population rather than in those diagnosed with heart disease [25], a high priority should be given to optimising risk stratification and prevention of SCD in the general population as an essential clinical and public health goal. A study has shown that genetic screening using a targeted NGS panel can identify molecular genetic causes in a significant proportion of patients with suspected inherited heart disease and can be applied to cascade screening of family members of genotype-positive pre-diagnosed individuals [26]. It also guides the choice of preventive measures, such as treatment with antiarrhythmic drugs or implantable cardio verterde fibrillators (ICDs) [26]. With additional genetic and phenotypic correlation studies, NGS is expected to be a rapid and cost-effective molecular diagnostic tool for sudden cardiac death [27] that can reduce the probability of sudden cardiac death in patients through population-based screening.

Similarly, NGS has been proposed for newborn screening. Virtual rapid whole-genome sequencing for newborn screening performed in UK Biobank identified 15 disease cases missed by conventional screening. The study concluded that if whole-genome sequencing had been conducted on day 5 of life, symptoms in seven critically ill children could have been completely prevented [28]. Although neonatal genome sequencing is beneficial in detecting many diseases at an early stage, its use as a routine application in NGS requires further research on the long-term impact of preventive genomic screening on families as well as children, the long-term management of genetic sequencing results due to updated knowledge, and the unique ethical challenges that may arise [29]. It is possible to apply genomics to clinical practice as a means of precision prevention through population-based genetic testing, enabling more precise identification of diseased potential groups and earlier clinical intervention, leading to a health strategy that maximizes benefits for more patients.

Information from the molecular level of disease can more accurately characterize the disease, improve the efficiency of disease diagnosis, refine disease typing, and provide information for treatment decisions. There has always been a strong focus on genomic research in cancer. Origins of tissues, types of cells, and morphologies are traditionally used to classify tumors. Cancer diagnostics and prognostics have become increasingly reliant on mutations in the context of cancer’s genomic basis. In the molecular classification of endometrial carcinoma by The Cancer Genome Atlas (TCGA), POLE gene mutation indicates POLE hypermutation, which is associated with a more favorable prognosis. Conversely, the high copy type with TP53 mutations is linked to the poorest prognosis [30]. As well, breast cancers frequently have somatic mutations in TP53 and PIK3C genes. It should be noted that the frequency of these gene mutations varies depending on the subtype of breast cancer. With NGS, mutational variants can be identified, thus making subtype classification easier [31].

It is challenging to collect samples of tissue from patients suffering from cancer during the early stages of their disease so novel non-invasive biomarkers are being explored. It is possible to use plasma cell-free DNA (cfDNA) genotyping instead of tissue genotyping in circumstances where tissue specimens are insufficient or inaccessible [32, 33]. Plasma cfDNA, which is released from cells, contains circulating tumor DNA [34]. NGS technology has been used to detect a variety of mutations associated with cancer in cfDNA from individuals with various types of cancer, including copy number changes, single nucleotide mutations, DNA fragmentation patterns, and methylation changes [35, 36, 37]. Leighl et al. [38] compared tissue genotyping with cfDNA genotyping by NGS in untreated metastatic non-small-cell lung cancer and found highly consistent results and more efficient cfDNA genotyping. Additionally, cfDNA genotyping can enable targeted treatment matching and monitoring of resistance to therapy to provide personalized treatment [39, 40]. In a short time and for a low cost, NGS can quickly identify a wide variety of biomarkers from small biological samples due to its high sensitivity and specificity. As a result, NGS became the technique of choice for analyzing cfDNA, known as liquid biopsy, a new trend in oncology [41].

The implementation of next-generation sequencing in prenatal diagnosis represents a significant achievement in the application of genomics within clinical settings. Conducting noninvasive prenatal screenings (NIPSs) to detect fetal chromosomal defects through the analysis of maternal cfDNA has changed the way chromosomal and other genetic disorders are diagnosed and treated during pregnancy [42, 43]. Furthermore, whole-genome sequencing (WGS) has been shown to assist in determining the cause of rare diseases as well as performing accurate molecular diagnoses. In comparison to traditional diagnostic methods, WGS is more efficient in diagnosing, reducing diagnostic odyssey costs, changing medical management, and ultimately benefiting patients and their families [44, 45, 46]. There are already established guidelines recommending the utilization of clinical whole genome sequencing as either a primary or secondary diagnostic tool for rare disease sufferers [47, 48].

Personalized therapeutic decisions are increasingly being guided by NGS. Genomic analysis of cancer is crucial to selecting drugs that target somatic gene mutations that drive cancer development or progression. According to research, NGS can help identify at least one actionable target for targeted therapy [49, 50, 51]. Further, targeted therapy appears to be associated with higher progression-free survival and response rates [52]. If patients do not benefit from molecularly targeted agents, immunotherapy may also be considered. Patients for whom immunotherapy may be effective can be identified based on genomic information about the tumor obtained through NGS. Recently, a clinical trial demonstrated that patients with HER2-negative progressive gastric cancer achieved significantly improved overall survival with tislelizumab, an immune checkpoint inhibitor, compared to the chemotherapy group [53]. The utilization of immunotherapy has expanded the treatment strategies available to cancer patients, and it is increasingly being employed as a first-line therapy for many types of cancer [54].

With the advent of various biotechnologies, such as variant analysis methods, and gene editing, gene therapy has entered a new era. Genetic analysis through NGS facilitates the identification of target mutations that drive disease progression, furnishes highly precise DNA sequence data, and constitutes a crucial tool for enhancing gene therapy. As a result, many novel gene therapy products have been approved for clinical use after successful laboratory testing [55]. Gene therapy is now used not only for single genetic disorders, and cancer [56] but also for common complex diseases such as osteoarthritis [57] and diabetic neuropathy [58]. Gene therapy uses normally functioning genes to repair or replace defective genes to treat genetic diseases that are generally untreatable by drugs and are less likely to face drug resistance problems [59]. However, the field is still in an immature stage, with challenges that include ineffective delivery systems [60], and the inability to ensure continuous and stable gene expression and host immune responses. To facilitate the comprehensive market penetration of gene therapy, it is necessary to deepen basic research, overcome technical difficulties, precise safety supervision to reduce the harm caused by possible side effects [61], and appropriate pricing to increase the accessibility of treatment.

In summary, genome sequencing technology which is the key to facilitating faster translation of genomic knowledge for clinical practice provides genomic information that provides unprecedented insight into the biology and pathogenesis of many diseases [62, 63, 64] and has been used in the fields of cancer detection and treatment, assisted reproduction, prenatal and perinatal screening, inpatient management of critically ill infants, management of Mendelian diseases, rare diseases, and other clinical aspects [65, 66, 67, 68]. The current NGS still suffers from technical defects such as short long-read and the inability to completely analyze complex repetitive sequences [69]. In addition to the detection and analysis capabilities of sequencing technology itself, there are still problems other than technology that hinder its application in clinical practice. While NGS has become more affordable, its economic viability isn’t always guaranteed in all clinical and research settings [70]. The accumulation of genetic data has produced an increasing proportion of variants of uncertain significance (VUS), which overshadows clinicians’ decision-making. There is a need to classify the VUS found and review them regularly to ensure a beneficial impact on patient health guidance.

Large sample sizes and genomic data from large biobanks have been an important basis for supporting discoveries in GWAS. Among these biobanks, the UK Biobank plays a leading role, containing deep genetic and phenotypic data of 500,000 individuals [74]. Expanding the sample size of GWAS can facilitate the identification of additional risk loci and generate a more comprehensive list for discovering novel therapeutic targets. A genome-wide association analysis with a large sample size of 100,285 subjects revealed novel loci shared by lung function and obesity. However, the complexity of the linkage disequilibrium pattern and deficiencies in the imputation data impeded the identification of true causal variants [81]. Therefore, while we continue to explore novel associations, we must prioritize causal studies of established associations. These studies will undoubtedly provide in-depth insights into the biological mechanisms of disease and enhance clinical translation. In addition, it should be noted that susceptibility loci identified by GWAS do not necessarily correspond to causative genes. Functional genomics studies are required to accurately map these loci to specific variants and genes. The majority of association signals are located in regions of the genome that are not coding for proteins, which poses a challenge for deciphering the functional role of target genes. Moreover, due to the potent capacity of GWAS to detect subtle effects, the identified genes may not necessarily belong to the core pathways that regulate the phenotype [82]. Therefore, identifying causal relationships between variant genes and phenotypes is extremely complex and challenging. A growing number of functional datasets and genomics resources (e.g., ENCODE [83] and the GTEx [84], Epigenome RoadMap [85], FANTOM5 [86]) make it possible to combine GWAS findings with functional genomics data to advance variant functional annotations. What’s more, improved bioinformatics approaches such as computer annotation of gene regulatory regions [87, 88, 89], enrichment of causal variants in epigenome annotation [90, 91], colocalization of GWAS and expression quantitative trait locus (eQTL) signals [92, 93], gene expression prediction [94, 95], and fine mapping of causal variants [96, 97] have provided ideas for downstream analysis of GWAS. Several approaches are currently being studied to establish the connection between regulatory elements and their target genes, such as the 3C-based identification of chromatin loops and the CRISPR/Cas9 system [98, 99]. These innovative approaches with integration with GWAS will further increase the functional understanding of disease-related genes and facilitate biological discoveries translation. As an illustration, the investigators devised a method to decipher the molecular mechanism of disease-linked variants detected by GWAS. In this study, the Activity-by-Contact model was utilized to generate enhancer-gene profiles in cells and tissues, resulting in the identification of 5026 GWAS signals associated with 2249 genes. For inflammatory bowel disease (IBD), this map has revealed the mechanism of genomic regulation by demonstrating that enhancers harboring IBD risk variants alter PPIF expression, thereby altering immune cell mitochondrial function [100].

GWAS is gaining attention in the pharmaceutical industry. Researchers have discovered that there is a likelihood of at least twice as much in receiving approval for drugs supported by GWAS, especially in cases where causal genes have been identified [101, 102]. The prolonged duration and exorbitant cost of novel drug development and clinical trials have prompted researchers to shift their attention towards exploring alternative indications for existing drugs. GWAS has been used successfully to discover drug repurposing opportunities [103, 104]. Although GWAS do not typically provide direct information regarding causative genes or disease mechanisms, the identification of causal variants and biological pathways associated with diseases can be achieved through a combination of GWAS findings, subsequent bioinformatics approaches, and functional experiments. This strategy enables the matching of new drug-disease relationships using a catalog of known drug targets and the disease gene associations identified by GWAS. The high efficiency of drug repurposing satisfies the urgent demand for Corona Virus Disease 2019 (COVID-19) treatment. The TYK2 gene, which has been identified by GWAS as associated with host-driven inflammatory lung injury, is causally linked to critical illness COVID-19 and represents a promising therapeutic target for this disease [105]. TYK2 belongs to the Janus kinase (JAK) family, and baricitinib is a JAK inhibitor that specifically inhibits TYK2 expression [106]. Baricitinib was initially approved for the treatment of rheumatoid arthritis in 2018 [107]. After two phase III clinical trials that were randomized, double-blind, and placebo-controlled confirmed the significant reduction in mortality of patients hospitalized with COVID-19 by baricitinib, it has now been approved by the Food and Drug Administration (FDA) for treating severe cases of COVID-19 [108, 109, 110]. Furthermore, GWAS aids in the prediction of adverse drug reactions and enables the screening of safer drug targets, thereby mitigating foreseeable safety events and enhancing the efficiency of drug development [111].

GWAS is anticipated to offer valuable guidance for precise therapy by identifying genetic variants that are associated with drug response. The observation that patients with the same cancer type and receiving the same chemotherapeutic agent often exhibit varying degrees of response suggests that drug regimens based solely on disease phenotype are not only inefficient in terms of drug utilization but may also exacerbate patient outcomes due to delayed treatment. Accurately identifying patients who are sensitive to a specific chemotherapeutic agent is crucial for improving clinical outcomes. A genome-wide association study (GWAS) has identified two variants in ADCY1 that may affect the responsiveness of non-small cell lung cancer patients to platinum-based chemotherapy. In vitro cellular experiments have confirmed that high expression of ADCY1 is associated with increased sensitivity to cisplatin [112]. More evidence is required to elucidate the mechanisms underlying the modulation of chemotherapeutic drug sensitivity by the two SNPs and to validate the clinical utility of genotype-guided chemotherapeutic drug selection. Additionally, a GWAS study identified a CYP2C19 gene variant associated with poor clopidogrel efficacy [113]. Moreover, clinical trials designed based on this research finding have demonstrated the benefits of antiplatelet therapy regimens formulated according to the CYP2C19 genotype for patients undergoing percutaneous coronary intervention [114]. The Clinical Pharmacogenetics Implementation Consortium (CPIC) has developed a series of dosing guidelines that incorporate genetic variants associated with drug response in an evidence-based and rigorous manner, aimed at assisting clinicians in comprehending the significance of genetic test results and optimizing drug therapy [115]. These guidelines include the clopidogrel regimen based on the CYP2C19 genotype, as previously mentioned. Therefore, GWAS provides a novel approach to achieve precise drug selection in an agnostic manner and allows the integration of research findings into clinical practice.

The genetic variants identified through GWAS may be valuable in discerning individuals who have an elevated risk of specific diseases. According to a study, the LOXL1 gene contains two nonsynonymous SNPs that contribute 99% of the population-attributable risk for exfoliative glaucoma [116]. It is generally the case, however, that individual common variants exhibit low effect sizes and collectively they account for a moderate portion of heritability [117]. The persistent issue of “missing heritability” hinders the clinical applicability of GWAS in predicting diseases. Understanding the origins of deletion heritability is crucial for investigating genetic architecture and phenotypes of complex diseases. This can be attributed to several factors: (1) rare variants serve as the primary source of deletion heritability [118]; (2) neglecting the contribution of small genetic loci that are not captured by GWAS to the phenotype, and (3) intricate gene-environment interactions [119]. When considering associations beyond those of genome-wide significance, polygenic risk scores (PRS), derived from GWAS are anticipated to address the limitations of GWAS in predicting phenotypes through genetic effects.

Variants associated with diseases are typically located in non-coding genes, and it has been challenging to develop methods that can account for the impact of mutations in these genes. Machine learning provides novel insights for predicting the impact of noncoding mutations on disease. In a study, researchers utilized a deep learning model to analyze genome-wide data from 1790 families with autism spectrum disorder (ASD) and identified the contribution of novel noncoding variants on ASD pathogenesis by comparing anticipated transcriptional and post-transcriptional regulatory consequences of these variants in probands versus unaffected matched siblings [144]. In addition, Jaganathan et al. [145] successfully trained a deep learning model to identify non-coding mutations in rare genetic diseases by predicting the splicing of pre-mRNAs from genomic sequences. This shows that machine learning may help increase our understanding of the mechanisms of disease occurrence.

It has been shown that one-third of the genes in the human genome are co-regulated by microRNAs (miRNAs) [146]. MiRNAs are loaded into the Argonaute (AGO) family of proteins to generate miRNA-induced silencing complexes (miRISCs), which are base-paired with target mRNAs and regulate genes through mRNA cleavage or translational repression expression [146]. MiRNAs contribute to the pathogenesis of complex human diseases through post-transcriptional regulation of gene expression, thus serving as potential biomarkers for disease treatment [147]. The first step in clarifying miRNA pathogenesis is to accurately identify miRNA associations with specific diseases through experimental methods; however, this can be time-consuming and expensive given the numerous miRNA disease combinations. Computational modeling addresses this problem by identifying the most likely relevant miRNA-disease associations (MDA) for validation in biological experiments, thereby accelerating new MDA discovery. To fully utilize large-scale multi-source heterogeneous datasets and improve the accuracy of MDA prediction, researchers have started to focus on the potential of machine learning-based methods [148], including various classifiers (e.g., decision trees, support vector machines, plain Bayes, and neural networks) and matrix decomposition techniques, which simplify high-dimensional matrices into a few low-dimensional matrices. Computer-based miRNA analysis tools can predict the association of miRNAs with cellular functions and diseases and predict miRNA target genes and binding sites at the molecular level. For example, experimentally validated reports of miRNA-target interactions can be obtained using MiRTarBase [149]; miRDB, constructed by machine learning algorithms based on support vector machines, is an online resource for miRNA target prediction and functional annotation [150]. ​Numerous studies have previously described the available tools [151, 152]. In recent years, deep learning methods have emerged as promising new approaches to improve miRNA target group prediction. One study reported deep learning-based miRAW, which predicts miRNA targets by analyzing the entire miRNA transcripts and outperforms existing prediction methods in comparisons using independent datasets [153]. Machine learning-based research on the pathogenic mechanism of miRNA has been applied not only to cancer but also shown good prediction performance in other common diseases, such as coronary heart disease and dementia [154]. The investigators analyzed plasma extracellular vesicles (EVs) miRNA sequencing data from SCD patients and normal patients using bioinformatics online tools and statistical methods. The investigators used TargetScan and miRanda to identify EVs-miRNA targets, and functional enrichment analysis using Metascape to screen plasma EVsmiR-208b-3p and miR-143-3p as promising biomarkers for predicting SCD in patients with acute coronary syndrome (ACS) [155]. In a recently published article, the concept of theranomiRNAs, miRNAs for diagnostic and therapeutic purposes, was mentioned for the first time [156], which reaffirms the role of miRNAs in the diagnosis and treatment of diseases, and is a new direction in the translation of basic research into clinical practice, which is expected to play an influential role in precision medicine in the future.

63.4% of the untranslated region (UTR) variants in the ClinVar database are classified as “variants of uncertain significance” (VUS) [157]. In this context, in addition to functional annotation via databases, it is essential to assess the pathogenicity of genetic variants in non-coding regions. Constructing a pathogenicity prediction framework using non-coding variants in the human genome could provide a more comprehensive understanding of disease biology and reveal opportunities to develop new therapeutic targets. Deep learning has become a powerful tool for the functional study of non-coding mutations because genomics research is inherently characterized by features such as sequence local dependence and long-range correlation, and its large-scale and deep data characteristics fit well with the logic of the work of convolutional neural networks algorithms (CNNs). Currently, tools based on the CNN framework for prioritizing mutations in non-coding regions include DeepBind, DeepSEA, Basset, DanQ, Basenji, etc [158]. In 2020, the DeepFun model integrates data from ENCODE and Roadmap on top of existing CNN models to present a dense human-to-human epigenome mapping [159]. Successive upgrades of the model have contributed to increased accuracy in predicting the pathogenicity of non-coding region variants. The recently proposed “Junk” Annotation genome-wide Residual Variation Intolerance Score (JARVIS), which captures previously unavailable human pedigree constraint information, outperforms other human pedigree-specific scores [160]. JARVIS introduces a genome-wide residual variant intolerance score (gwRVIS) and incorporates primary genome sequence information and additional functional genome annotations to prioritize regions in the non-coding genome that may be more likely to be associated with clinically relevant effects when mutated.

Through the integration of genetic data with medical imaging, which is the largest source of data in the healthcare system, we can gain a more profound understanding of how genes influence organ morphology or function. Hallgrímsson et al. [161] used machine learning models to identify 3D facial images and thereby automatically diagnose the syndrome. The approach yielded a balanced accuracy rate of 73% when applied to a sample size of 7057 subjects. ML is capable of assisting in diagnosing diseases by utilizing multi-omics data. Khanna et al. [162] have developed a multivariate multimodal model that utilizes genomic data, imaging data, and biomarkers to predict the time of Alzheimer’s disease diagnosis. In this model, a Bayesian network model (an algorithm of machine learning) aims to reveal the interactions between genetic variants, biological pathways, and imaging-related features across biological scales.