The genomic landscape of balanced cytogenetic abnormalities associated with human congenital anomalies

Despite the clinical significance of balanced chromosomal abnormalities (BCAs), their characterization has largely been restricted to cytogenetic resolution. We explored the landscape of BCAs at nucleotide resolution in 273 subjects with a spectrum of congenital anomalies. Whole-genome sequencing revised 93% of karyotypes and demonstrated complexity that was cryptic to karyotyping in 21% of BCAs, highlighting the limitations of conventional cytogenetic approaches. At least 33.9% of BCAs resulted in gene disruption that likely contributed to the developmental phenotype, 5.2% were associated with pathogenic genomic imbalances, and 7.3% disrupted topologically associated domains (TADs) encompassing known syndromic loci. Remarkably, BCA breakpoints in eight subjects altered a single TAD encompassing MEF2C, a known driver of 5q14.3 microdeletion syndrome, resulting in decreased MEF2C expression. We propose that sequence-level resolution dramatically improves prediction of clinical outcomes for balanced rearrangements and provides insight into new pathogenic mechanisms, such as altered regulation due to changes in chromosome topology

A cross-disorder dosage sensitivity map of the human genome

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the prop-erties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 trip-losensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will pro-vide broad utility for human disease research and clinical genetics

Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts

International audienceSummary Background Cognitive decline is a debilitating manifestation of disease progression in Parkinson’s disease. We aimed to develop a clinical-genetic score to predict global cognitive impairment in patients with the disease. Methods A prediction algorithm for global cognitive impairment (defined as Mini Mental State Exam (MMSE) ≤25) was built using data from 1,350 patients with 5,165 longitudinal visits over 12.8 (median, 2.8) years. Age at onset, MMSE, education, motor exam score, gender, depression and GBA mutations, machine selected through stepwise Cox’ hazards analysis and Akaike’s information criterion, were used to compute the multivariable predictor. Independent validation was achieved in another 1,132 patients with 19,127 visits over 8.6 (median, 6.5) years. Findings The cognitive risk score accurately predicted cognitive impairment within ten years of disease onset with an area under the curve (AUC) of >0.85 in both the discovery (95% CI, 0.821–0.902) and validation populations (95% CI, 0.779 – 0.913). 72.6% of patients scoring in the highest quartile were cognitively impaired by ten years vs. 3.7% in the lowest quartile (hazard ratio, 18.4, 95% CI, 9.4 – 36.1). Dementia or disabling cognitive impairment was predicted with an AUC of 0.877 (95% CI 0.788–0.943) and high negative predictive value (0.920, 95% 0.877–0.954) at the predefined cutoff (0.196). Performance was stable in 10,000 randomly resampled subsets. Interpretation Our predictive algorithm provides a potential test for future cognitive health or impairment in patients with Parkinson’s. It could improve trials of cognitive interventions and inform on prognosis