Interpreting noncoding genetic variation in complex traits and human disease
Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about
... ding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment. Understanding the genetic basis of disease can revolutionize medicine by elucidating relevant biochemical pathways for drug targets and by enabling personalized risk assessments 1,2 . As technologies evolved over the past century, geneticists are no longer limited to studying Mendelian disorders and can tackle complex phenotypes. The resulting discovered associations have broadened from individual variants primarily in coding regions to much richer disease architectures, including non-coding variants, wider allelic spectra, numerous loci, and weak effect sizes (Table 1 ). In the last few years, a new wave of technological advances has intensified the shift towards tackling more complex genetic architectures and uncovering the molecular mechanisms underlying them.