The activities and genome-wide specificities of CRISPR-Cas Cpf1 nucleases1 are not well defined. We show that two Cpf1 nucleases from Acidaminococcus sp. BV3L6 and Lachnospiraceae bacterium ND2006 (AsCpf1 and LbCpf1, respectively) have on-target efficiencies in human cells comparable with those of the widely used Streptococcus pyogenes Cas9 (SpCas9)2–5. We also report that four to six bases at the 3’ end of the short CRISPR RNA (crRNA) used to program Cpf1 nucleases are insensitive to single base mismatches, but that many of the other bases in this region of the crRNA are highly sensitive to single or double substitutions. Using GUIDE-seq and targeted deep sequencing analyses performed with both Cpf1 nucleases, we were unable to detect off-target cleavage for more than half of 20 different crRNAs. Our results suggest that AsCpf1 and LbCpf1 are highly specific in human cells.

Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN. K E Y W O R D S direct and indirect rank-based inverse normal transformation, nonnormality, omnibus test, quantitative traits, transformation, type I error rate 1262

Dendritic cells (DCs) are the primary leukocytes responsible for priming T cells. To find and activate naïve T cells, DCs must migrate to lymph nodes, yet the cellular programs responsible for this key step remain unclear. DC migration to lymph nodes and the subsequent T-cell response are disrupted in a mouse we recently described lacking the NOD-like receptor NLRP10 (NLR family, pyrin domain containing 10); however, the mechanism by which this pattern recognition receptor governs DC migration remained unknown. Using a proteomic approach, we discovered that DCs from Nlrp10 knockout mice lack the guanine nucleotide exchange factor DOCK8 (dedicator of cytokinesis 8), which regulates cytoskeleton dynamics in multiple leukocyte populations; in humans, loss-of-function mutations in Dock8 result in severe immunodeficiency. Surprisingly, Nlrp10 knockout mice crossed to other backgrounds had normal DOCK8 expression. This suggested that the original Nlrp10 knockout strain harbored an unexpected mutation in Dock8, which was confirmed using whole-exome sequencing. Consistent with our original report, NLRP3 inflammasome activation remained unaltered in NLRP10-deficient DCs even after restoring DOCK8 function; however, these DCs recovered the ability to migrate. Isolated loss of DOCK8 via targeted deletion confirmed its absolute requirement for DC migration. Because mutations in Dock genes have been discovered in other mouse lines, we analyzed the diversity of Dock8 across different murine strains and found that C3H/HeJ mice also harbor a Dock8 mutation that partially impairs DC migration. We conclude that DOCK8 is an important regulator of DC migration during an immune response and is prone to mutations that disrupt its crucial function.D endritic cells (DCs) are crucial for the initiation of an adaptive immune response. Upon acquiring antigens in the periphery, DCs undergo a maturation process that includes antigen processing, cytokine production, and up-regulation of costimulatory molecules. A mature DC must then migrate from peripheral tissues to draining lymph nodes (LNs) to fulfill its role as an antigen-presenting cell that primes naïve T cells (1). Although the signals that induce this maturation process are now well-established (1), relatively little is understood about DC migration aside from the primary chemotactic cue provided by CCR7 that guides DCs to the LN (2, 3).We recently described a genetically modified NLRP10 (NLR family, pyrin domain containing 10) knockout strain in which this migration step was disrupted while leaving the remainder of the DC maturation program, including CCR7 expression, intact (4).NLRP10 is the only NOD-like receptor (NLR) without a leucine-rich repeat domain, the putative pathogen-associated molecular pattern (PAMP)-binding domain. It has been proposed to both positively and negatively regulate other NLRs, such as NOD1 and NLRP3, respectively (5, 6). Although we found that NLRP3 inflammasome activation was unaltered in the absence of NLRP10, we discovered that Nlrp10 −/− mice could ...

Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p % 5 3 10 À8 ) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort.