Technological breakthroughs in human genetics have introduced genome sequencing as a viable strategy to explore the diversity of genomic variation across global populations and the genetic underpinnings of human disease. It has also brought the immense challenges of genome-wide discovery and interpretation of genomic variation into clinical genetics. Over a decade ago, our teams and others began to explore balanced chromosomal rearrangements (BCRs) at sequence resolution, revealing the diverse mechanisms and extreme complexity by which they can contribute risk for developmental disorders. Here, we will describe large-scale sequencing across global populations to build reference maps of structural variants (SVs) across the complete size, class, and frequency spectra in the genome aggregation database (gnomAD), and the challenges with SV interpretation in prenatal and pediatric diagnostic practice. We then focus on the functional prediction of SVs and dosage sensitivity metrics to predict haploinsufficiency and triplosensitivity for each gene in the human genome from microarray data in almost a million individuals. Finally, we narrow our focus to sequencing BCRs in the largest study performed to date across consortia studies, encompassing 1,420 breakpoints from cases with developmental disorders and controls, as well as >30,000 simulated breakpoints. These analyses reveal the genomic features predictive of individual genes and long-range positional effects from alterations to three-dimensional chromatin organization that contribute to developmental disorders. These results, and the complex mechanisms by which alterations to 3D genome structure can contribute to disease, will be further discussed by Dr. Tommerup. Overall, these studies emphasize the remarkable insights that have been gained from methods to capture SVs from genome sequencing, and the need for much greater reference resources to comprehensively annotate dosage sensitivity in the human genome for medical and population genetics.

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DOI: https://doi.org/10.1016/j.cancergen.2022.05.015

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