Clinical management is generally non-disease specific; consequently, there is still a lack of biomarkers for early diagnosis or drugs with high efficacy but less adverse effects. Precise and early intervention strategies are urgently needed. It is widely accepted that genetics shape our development and health status across the course of life and recent advances in high-throughput technologies have enabled scientists to generate large genomic data sets using genome-wide association studies (GWASs). A similar trend applies across intermediate molecular traits, including transcriptomic, epigenetic, proteomic, metabolomic, and microbiomic levels. Linking the genotyping and molecular phenotyping data further provides opportunities to identify various types of quantitative trait loci (QTLs): expression QTLs, epigenetic QTLs, proteomic QTLs, metabolic QTLs, as well as microbiome QTLs.
Other emerging genetic methods, such as Mendelian randomization and colocalization approaches, provide a rapid and cost-effective approach to inform the development of biomarkers for disease diagnosis and help prioritize drug targets for disease treatment. Recent studies have also demonstrated the value of proteome-wide and tissue-specific transcriptome-wide studies to promote the development of disciplined pipelines for causal inference and early therapeutic Research and Development for complex diseases. During the current COVID-19 pandemic, the enormous volume of genetic data also provides opportunities for a deeper understanding of SARS-CoV-2 infection in transmission and pathogenesis; disease susceptibility and severity; and mechanism of virus action and therapy. Thus, the growing availability of vast resources of omics data sets as proxies for disease pathogenesis combining with the recently developed methodologies will have very high clinical values for disease prevention and treatment.
In this Research Topic, we wish to highlight the role of integrating multi-omics data sets to support disease diagnosis and treatment from a translational perspective. Authors are encouraged to contribute Original Research, Review, and Mini-Review papers focusing on the promotion of translating genetic findings into a clinical setting. Sub-topics are included but not limited to the following:
1) Exploring the risk factors of disease incidence or progression;
2) Elucidating the molecular mediators underlying the disease pathogenesis;
3) Detecting the potential targets for disease diagnosis;
4) Prioritizing novel drug targets or repositioning existing drugs with efficacy and less adverse effects.
Clinical management is generally non-disease specific; consequently, there is still a lack of biomarkers for early diagnosis or drugs with high efficacy but less adverse effects. Precise and early intervention strategies are urgently needed. It is widely accepted that genetics shape our development and health status across the course of life and recent advances in high-throughput technologies have enabled scientists to generate large genomic data sets using genome-wide association studies (GWASs). A similar trend applies across intermediate molecular traits, including transcriptomic, epigenetic, proteomic, metabolomic, and microbiomic levels. Linking the genotyping and molecular phenotyping data further provides opportunities to identify various types of quantitative trait loci (QTLs): expression QTLs, epigenetic QTLs, proteomic QTLs, metabolic QTLs, as well as microbiome QTLs.
Other emerging genetic methods, such as Mendelian randomization and colocalization approaches, provide a rapid and cost-effective approach to inform the development of biomarkers for disease diagnosis and help prioritize drug targets for disease treatment. Recent studies have also demonstrated the value of proteome-wide and tissue-specific transcriptome-wide studies to promote the development of disciplined pipelines for causal inference and early therapeutic Research and Development for complex diseases. During the current COVID-19 pandemic, the enormous volume of genetic data also provides opportunities for a deeper understanding of SARS-CoV-2 infection in transmission and pathogenesis; disease susceptibility and severity; and mechanism of virus action and therapy. Thus, the growing availability of vast resources of omics data sets as proxies for disease pathogenesis combining with the recently developed methodologies will have very high clinical values for disease prevention and treatment.
In this Research Topic, we wish to highlight the role of integrating multi-omics data sets to support disease diagnosis and treatment from a translational perspective. Authors are encouraged to contribute Original Research, Review, and Mini-Review papers focusing on the promotion of translating genetic findings into a clinical setting. Sub-topics are included but not limited to the following:
1) Exploring the risk factors of disease incidence or progression;
2) Elucidating the molecular mediators underlying the disease pathogenesis;
3) Detecting the potential targets for disease diagnosis;
4) Prioritizing novel drug targets or repositioning existing drugs with efficacy and less adverse effects.
