LAUSR

Advances and reduction of costs in various sequencing technologies allows for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omics data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits, but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omics assays and analytic approaches towards xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic, and metabolomics data. In this review, we first discuss the recent development of xQTL from multi-omics findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.