Editorial: Identification of phenotypically important genomic variants

Due to significant technological advances and reductions in cost, we are witnessing a rapid increase in Next-Generation Sequencing (NGS) studies including whole exome sequencing, whole genome sequencing, and RNA-seq (transcriptome) together with multi-omics approaches, in all areas of health, disease and research. NGS is not limited to human genomics, but is also having major impacts in the fields of plant, animal and pathogen research. However, these technologies present a significant challenge given the huge number of variants that are being identified and that require interpretation. This can make harnessing the potential of NGS difficult when it comes to analysing this data. Due to the large amount of data, a frequently employed approach in NGS studies is to identify a manageable subset of genomic variations that can be used to further the understanding of the biological underpinnings of the phenotypes of interest. This list is obtained through variant filtering and prioritisation. Variant filtering aims to identify highquality variant calls, removing false positives, with variant prioritisation aiming to identify phenotype-associated or causal variants. For both the variant filtering and prioritisation steps, several public and/or private annotation databases are typically consulted in a single study. Additionally, several tools have been developed (Anderson and Lassmann, 2022)