Allele specific repair of splicing mutations in cystic fibrosis through AsCas12a genome editing.

Funder: Fondazione Fibrosi Cistica - FFC#1/2017Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations in the CFTR gene. The 3272-26A>G and 3849+10kbC>T CFTR mutations alter the correct splicing of the CFTR gene, generating new acceptor and donor splice sites respectively. Here we develop a genome editing approach to permanently correct these genetic defects, using a single crRNA and the Acidaminococcus sp. BV3L6, AsCas12a. This genetic repair strategy is highly precise, showing very strong discrimination between the wild-type and mutant sequence and a complete absence of detectable off-targets. The efficacy of this gene correction strategy is verified in intestinal organoids and airway epithelial cells derived from CF patients carrying the 3272-26A>G or 3849+10kbC>T mutations, showing efficient repair and complete functional recovery of the CFTR channel. These results demonstrate that allele-specific genome editing with AsCas12a can correct aberrant CFTR splicing mutations, paving the way for a permanent splicing correction in genetic diseases

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Biological interpretation of genome-wide association study data frequently involves assessing whether SNPs linked to a biological process, for example, binding of a transcription factor, show unsigned enrichment for disease signal. However, signed annotations quantifying whether each SNP allele promotes or hinders the biological process can enable stronger statements about disease mechanism. We introduce a method, signed linkage disequilibrium profile regression, for detecting genome-wide directional effects of signed functional annotations on disease risk. We validate the method via simulations and application to molecular quantitative trait loci in blood, recovering known transcriptional regulators. We apply the method to expression quantitative trait loci in 48 Genotype-Tissue Expression tissues, identifying 651 transcription factor-tissue associations including 30 with robust evidence of tissue specificity. We apply the method to 46 diseases and complex traits (average n = 290 K), identifying 77 annotation-trait associations representing 12 independent transcription factor-trait associations, and characterize the underlying transcriptional programs using gene-set enrichment analyses. Our results implicate new causal disease genes and new disease mechanisms

Judging the Judiciary by the Numbers: Empirical Research on Judges

Do judges make decisions that are truly impartial? A wide range of experimental and field studies reveal that several extra-legal factors influence judicial decision making. Demographic characteristics of judges and litigants affect judges’ decisions. Judges also rely heavily on intuitive reasoning in deciding cases, making them vulnerable to the use of mental shortcuts that can lead to mistakes. Furthermore, judges sometimes rely on facts outside the record and rule more favorably towards litigants who are more sympathetic or with whom they share demographic characteristics. On the whole, judges are excellent decision makers, and sometimes resist common errors of judgment that influence ordinary adults. The weight of the evidence, however, suggests that judges are vulnerable to systematic deviations from the ideal of judicial impartiality