Noncoding RNAs, including short and long ones, constitute a large proportion of gene transcription transcripts in the genome. However, their functions and disease associations are poorly understood. Genomic variants, including single-nucleotide variants (SNVs), short insertions/deletions (indels) or copy number variants (CNVs), are the underlying genetic contributors to human phenotypic difference, common disease predisposition or some genetic disorders. Vast majority of genome-wide association study (GWAS) hits (>90%) are located in the non-coding regions of the genome, which makes interpretation and causal association establishment difficult. Ongoing large scale population based next generation sequencing projects on whole genomes will find even more “uninterpretable” variant associations. How non-coding variants affect non-coding RNA functions is an unexplored area. Research emphasis and resources are also limited but it is increasingly important to find truly causal non-coding variant associations and their affected target genes so that personalized medicine, particularly for common and complex diseases, can be materialized. Current practice is to overlap variants to the genome (for example regulatory regions) and examine whether they are in the key functional regions such as promoter, enhancer, and transcription factor binding site or to conduct eQTL association by utilizing gene expression data. Computational methods by integrating public resources to predict the impact of noncoding variants are also emerging. However, uncertainty is still a challenge and a reliable knowledge base is lacking. In this Research Topic, we welcome submissions in any of the following topics:
(1) Review or mini-review or practical guidance for interpreting non-coding RNAs and non-coding variants in human diseases
(2) New algorithms and software packages for annotating or prioritizing non-coding variants, which include machine or deep learning to predict epigenetic impact of noncoding variants
(3) Databases with easy access or visualization tools for well-established non-coding variants associated with human diseases.
(4) Reports with good evidence or laboratory support for disease associated non-coding RNAs and their variants, including variants related to genetic disorders.
(5) Variants in non-coding molecules such as miRNAs and lncRNAs and their disease associations.
(6) Statistical models for variant association with integration of multiple data types, gene-gene or gene-environment interaction.
(7) Performance evaluation of existing or novel methods.
(8) Any others not explicitly mentioned but relevant to non-coding variants such as reliable detection, noise filtering.
Noncoding RNAs, including short and long ones, constitute a large proportion of gene transcription transcripts in the genome. However, their functions and disease associations are poorly understood. Genomic variants, including single-nucleotide variants (SNVs), short insertions/deletions (indels) or copy number variants (CNVs), are the underlying genetic contributors to human phenotypic difference, common disease predisposition or some genetic disorders. Vast majority of genome-wide association study (GWAS) hits (>90%) are located in the non-coding regions of the genome, which makes interpretation and causal association establishment difficult. Ongoing large scale population based next generation sequencing projects on whole genomes will find even more “uninterpretable” variant associations. How non-coding variants affect non-coding RNA functions is an unexplored area. Research emphasis and resources are also limited but it is increasingly important to find truly causal non-coding variant associations and their affected target genes so that personalized medicine, particularly for common and complex diseases, can be materialized. Current practice is to overlap variants to the genome (for example regulatory regions) and examine whether they are in the key functional regions such as promoter, enhancer, and transcription factor binding site or to conduct eQTL association by utilizing gene expression data. Computational methods by integrating public resources to predict the impact of noncoding variants are also emerging. However, uncertainty is still a challenge and a reliable knowledge base is lacking. In this Research Topic, we welcome submissions in any of the following topics:
(1) Review or mini-review or practical guidance for interpreting non-coding RNAs and non-coding variants in human diseases
(2) New algorithms and software packages for annotating or prioritizing non-coding variants, which include machine or deep learning to predict epigenetic impact of noncoding variants
(3) Databases with easy access or visualization tools for well-established non-coding variants associated with human diseases.
(4) Reports with good evidence or laboratory support for disease associated non-coding RNAs and their variants, including variants related to genetic disorders.
(5) Variants in non-coding molecules such as miRNAs and lncRNAs and their disease associations.
(6) Statistical models for variant association with integration of multiple data types, gene-gene or gene-environment interaction.
(7) Performance evaluation of existing or novel methods.
(8) Any others not explicitly mentioned but relevant to non-coding variants such as reliable detection, noise filtering.
