Non-linear transcriptional responses to gradual modulation of transcription factor dosage.

Genomic loci associated with common traits and diseases are typically non-coding and likely impact gene expression, sometimes coinciding with rare loss-of-function variants in the target gene. However, our understanding of how gradual changes in gene dosage affect molecular, cellular, and organismal traits is currently limited. To address this gap, we induced gradual changes in gene expression of four genes using CRISPR activation and inactivation. Downstream transcriptional consequences of dosage modulation of three master trans-regulators associated with blood cell traits (GFI1B, NFE2, and MYB) were examined using targeted single-cell multimodal sequencing. We showed that guide tiling around the TSS is the most effective way to modulate cis gene expression across a wide range of fold-changes, with further effects from chromatin accessibility and histone marks that differ between the inhibition and activation systems. Our single-cell data allowed us to precisely detect subtle to large gene expression changes in dozens of trans genes, revealing that many responses to dosage changes of these three TFs are non-linear, including non-monotonic behaviours, even when constraining the fold-changes of the master regulators to a copy number gain or loss. We found that the dosage properties are linked to gene constraint and that some of these non-linear responses are enriched for disease and GWAS genes. Overall, our study provides a straightforward and scalable method to precisely modulate gene expression and gain insights into its downstream consequences at high resolution.

Next-generation forward genetic screens: uniting high-throughput perturbations with single-cell analysis.

Programmable genome-engineering technologies, such as CRISPR (clustered regularly interspaced short palindromic repeats) nucleases and massively parallel CRISPR screens that capitalize on this programmability, have transformed biomedical science. These screens connect genes and noncoding genome elements to disease-relevant phenotypes, but until recently have been limited to individual phenotypes such as growth or fluorescent reporters of gene expression. By pairing massively parallel screens with high-dimensional profiling of single-cell types/states, we can now measure how individual genetic perturbations or combinations of perturbations impact the cellular transcriptome, proteome, and epigenome. We review technologies that pair CRISPR screens with single-cell multiomics and the unique opportunities afforded by extending pooled screens using deep multimodal phenotyping.

Large-scale causal discovery using interventional data sheds light on the regulatory network architecture of blood traits.

Inference of directed biological networks is an important but notoriously challenging problem. We introduce inverse sparse regression (inspre), an approach to learning causal networks that leverages large-scale intervention-response data. Applied to 788 genes from the genome-wide perturb-seq dataset, inspre helps elucidate the network architecture of blood traits.

Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis.

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.

Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens.

Most variants associated with complex traits and diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown effects. Using ancestrally diverse, biobank-scale GWAS data, massively parallel CRISPR screens, and single-cell transcriptomic and proteomic sequencing, we discovered 124 cis-target genes of 91 noncoding blood trait GWAS loci. Using precise variant insertion through base editing, we connected specific variants with gene expression changes. We also identified trans-effect networks of noncoding loci when cis target genes encoded transcription factors or microRNAs. Networks were themselves enriched for GWAS variants and demonstrated polygenic contributions to complex traits. This platform enables massively parallel characterization of the target genes and mechanisms of human noncoding variants in both cis and trans.

Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq.

Pooled CRISPR screens coupled with single-cell RNA-sequencing have enabled systematic interrogation of gene function and regulatory networks. Here, we introduce Cas13 RNA Perturb-seq (CaRPool-seq), which leverages the RNA-targeting CRISPR-Cas13d system and enables efficient combinatorial perturbations alongside multimodal single-cell profiling. CaRPool-seq encodes multiple perturbations on a cleavable CRISPR array that is associated with a detectable barcode sequence, allowing for the simultaneous targeting of multiple genes. We compared CaRPool-seq to existing Cas9-based methods, highlighting its unique strength to efficiently profile combinatorially perturbed cells. Finally, we apply CaRPool-seq to perform multiplexed combinatorial perturbations of myeloid differentiation regulators in an acute myeloid leukemia (AML) model system and identify extensive interactions between different chromatin regulators that can enhance or suppress AML differentiation phenotypes.

Anatomy of the Ventricles, Subarachnoid Spaces, and Meninges.

The ventricular system, subarachnoid spaces, and meninges are structures that lend structure, support, and protection to the brain and spinal cord. This article provides a detailed look at the anatomy of the intracranial portions of these structures with a particular focus on neuroimaging methods.

An effector index to predict target genes at GWAS loci.

Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single-nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.

SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis.

Single-cell CRISPR screens are a promising biotechnology for mapping regulatory elements to target genes at genome-wide scale. However, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two single-cell CRISPR screens how these challenges cause calibration issues. We propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling, which infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates very good calibration and sensitivity on CRISPR screen data, yielding hundreds of new regulatory relationships supported by orthogonal biological evidence.

An autoimmune disease risk variant: A trans master regulatory effect mediated by IRF1 under immune stimulation?

Functional mechanisms remain unknown for most genetic loci associated to complex human traits and diseases. In this study, we first mapped trans-eQTLs in a data set of primary monocytes stimulated with LPS, and discovered that a risk variant for autoimmune disease, rs17622517 in an intron of C5ORF56, affects the expression of the transcription factor IRF1 20 kb away. The cis-regulatory effect specific to IRF1 is active under early immune stimulus, with a large number of trans-eQTL effects across the genome under late LPS response. Using CRISPRi silencing, we showed that perturbation of the SNP locus downregulates IRF1 and causes widespread transcriptional effects. Genome editing by CRISPR had suggestive recapitulation of the LPS-specific trans-eQTL signal and lent support for the rs17622517 site being functional. Our results suggest that this common genetic variant affects inter-individual response to immune stimuli via regulation of IRF1. For this autoimmune GWAS locus, our work provides evidence of the functional variant, demonstrates a condition-specific enhancer effect, identifies IRF1 as the likely causal gene in cis, and indicates that overactivation of the downstream immune-related pathway may be the cellular mechanism increasing disease risk. This work not only provides rare experimental validation of a master-regulatory trans-eQTL, but also demonstrates the power of eQTL mapping to build mechanistic hypotheses amenable for experimental follow-up using the CRISPR toolkit.

Osteocyte transcriptome mapping identifies a molecular landscape controlling skeletal homeostasis and susceptibility to skeletal disease.

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this programme is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders (P = 2.4 × 10-22) and are associated with the polygenic diseases osteoporosis (P = 1.8 × 10-13) and osteoarthritis (P = 1.6 × 10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease.

Childhood obesity and multiple sclerosis: A Mendelian randomization study.

BACKGROUND: Higher childhood body mass index (BMI) has been associated with an increased risk of multiple sclerosis (MS). OBJECTIVE: To evaluate whether childhood BMI has a causal influence on MS, and whether this putative effect is independent from early adult obesity and pubertal timing. METHODS: We performed Mendelian randomization (MR) using summary genetic data on 14,802 MS cases and 26,703 controls. Large-scale genome-wide association studies provided estimates for BMI in childhood (n = 47,541) and adulthood (n = 322,154). In multivariable MR, we examined the direct effects of each timepoint and further adjusted for age at puberty. Findings were replicated using the UK Biobank (n = 453,169). RESULTS: Higher genetically predicted childhood BMI was associated with increased odds of MS (odds ratio (OR) = 1.26/SD BMI increase, 95% confidence interval (CI): 1.07-1.50). However, there was little evidence of a direct effect after adjusting for adult BMI (OR = 1.03, 95% CI: 0.70-1.53). Conversely, the effect of adult BMI persisted independent of childhood BMI (OR = 1.43; 95% CI: 1.01-2.03). The addition of age at puberty did not alter the findings. UK Biobank analyses showed consistent results. Sensitivity analyses provided no evidence of pleiotropy. CONCLUSION: Genetic evidence supports an association between childhood obesity and MS susceptibility, mediated by persistence of obesity into early adulthood but independent of pubertal timing.

Automated design of CRISPR prime editors for 56,000 human pathogenic variants.

Prime editors (PEs) are clustered regularly interspaced short palindromic repeats (CRISPR)-based genome engineering tools that can introduce precise base-pair edits. We developed an automated pipeline to correct (therapeutic editing) or introduce (disease modeling) human pathogenic variants from ClinVar that optimizes the design of several RNA constructs required for prime editing and avoids predicted off-targets in the human genome. However, using optimal PE design criteria, we find that only a small fraction of these pathogenic variants can be targeted. Through the use of alternative Cas9 enzymes and extended templates, we increase the number of targetable pathogenic variants from 32,000 to 56,000 variants and make these pre-designed PE constructs accessible through a web-based portal (http://primeedit.nygenome.org). Given the tremendous potential for therapeutic gene editing, we also assessed the possibility of developing universal PE constructs, finding that common genetic variants impact only a small minority of designed PEs.

Comprehensive Analysis of Long-Range Connectivity from and to the Posterior Parietal Cortex of the Mouse.

The posterior parietal cortex (PPC) is a major multimodal association cortex implicated in a variety of higher order cognitive functions, such as visuospatial perception, spatial attention, categorization, and decision-making. The PPC is known to receive inputs from a collection of sensory cortices as well as various subcortical areas and integrate those inputs to facilitate the execution of functions that require diverse information. Although many recent works have been performed with the mouse as a model system, a comprehensive understanding of long-range connectivity of the mouse PPC is scarce, preventing integrative interpretation of the rapidly accumulating functional data. In this study, we conducted a detailed neuroanatomic and bioinformatic analysis of the Allen Mouse Brain Connectivity Atlas data to summarize afferent and efferent connections to/from the PPC. Then, we analyzed variability between subregions of the PPC, functional/anatomical modalities, and species, and summarized the organizational principle of the mouse PPC. Finally, we confirmed key results by using additional neurotracers. A comprehensive survey of the connectivity will provide an important future reference to comprehend the function of the PPC and allow effective paths forward to various studies using mice as a model system.

Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study.

BACKGROUND: Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS: A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS: Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.

Genetic predisposition to increased serum calcium, bone mineral density, and fracture risk in individuals with normal calcium levels: mendelian randomisation study.

OBJECTIVE: To determine if genetically increased serum calcium levels are associated with improved bone mineral density and a reduction in osteoporotic fractures. DESIGN: Mendelian randomisation study. SETTING: Cohorts used included: the UK Biobank cohort, providing genotypic and estimated bone mineral density data; 25 cohorts from UK, USA, Europe, and China, providing genotypic and fracture data; and 17 cohorts from Europe, providing genotypic and serum calcium data (summary level statistics). PARTICIPANTS: A genome-wide association meta-analysis of serum calcium levels in up to 61 079 individuals was used to identify genetic determinants of serum calcium levels. The UK Biobank study was used to assess the association of genetic predisposition to increased serum calcium with estimated bone mineral density derived from heel ultrasound in 426 824 individuals who had, on average, calcium levels in the normal range. A fracture genome-wide association meta-analysis comprising 24 cohorts and the UK Biobank including a total of 76 549 cases and 470 164 controls, who, on average, also had calcium levels in the normal range was then performed. RESULTS: A standard deviation increase in genetically derived serum calcium (0.13 mmol/L or 0.51 mg/dL) was not associated with increased estimated bone mineral density (0.003 g/cm2, 95% confidence interval -0.059 to 0.066; P=0.92) or a reduced risk of fractures (odds ratio 1.01, 95% confidence interval 0.89 to 1.15; P=0.85) in inverse-variance weighted mendelian randomisation analyses. Sensitivity analyses did not provide evidence of pleiotropic effects. CONCLUSIONS: Genetic predisposition to increased serum calcium levels in individuals with normal calcium levels is not associated with an increase in estimated bone mineral density and does not provide clinically relevant protection against fracture. Whether such predisposition mimics the effect of short term calcium supplementation is not known. Given that the same genetically derived increase in serum calcium is associated with an increased risk of coronary artery disease, widespread calcium supplementation in the general population could provide more risk than benefit.

Author Correction: An atlas of genetic influences on osteoporosis in humans and mice.

In the version of this article initially published, in Fig. 5a, the data in the right column of 'DAAM2 gRNA1' were incorrectly plotted as circles indicating 'untreated' rather than as squares indicating 'treated'. The error has been corrected in the HTML and PDF versions of the article.

Effect of age at puberty on risk of multiple sclerosis: A mendelian randomization study.

OBJECTIVE: To investigate the potential for a causal effect of age at puberty on multiple sclerosis (MS) susceptibility using a mendelian randomization (MR) approach. METHODS: We used 372 genetic variants strongly associated with age at menarche in a genome-wide association study (GWAS) involving 329,245 women. The genetic architecture of pubertal timing across both sexes is highly correlated (genetic correlation [r g] = 0.75, p = 1.2 × 10-79), allowing these variants to provide reliable insight into pubertal timing in males as well. The effect of pubertal timing on risk of MS was measured with summary statistics from a GWAS of 14,802 cases with MS and 26,703 controls from the International Multiple Sclerosis Genetics Consortium. Multivariable MR controlling for effects of body mass index (BMI) using genetic data from additional consortia investigated whether pubertal effects on MS were dependent on weight status. RESULTS: A 1-year increase in genetically predicted age at puberty decreased odds of MS by 8% (odds ratio [OR] 0.92, 95% confidence interval [CI] 0.86-0.99, p = 0.03). However, multivariable MR analysis showed that after accounting for effects on adult BMI, the association of age at puberty with MS susceptibility attenuated (OR 0.96, 95% CI 0.88-1.04, p = 0.36). Similar results were obtained when childhood BMI was incorporated. Sensitivity analyses provided no evidence of major bias from genetic pleiotropy. CONCLUSIONS: We found support for an association between higher age at puberty and decreased risk of MS with a magnitude comparable to that reported in observational studies. This effect appears to be largely mediated by the strong association between age at puberty and obesity. A large causal effect of pubertal timing independent of BMI is unlikely.

An atlas of genetic influences on osteoporosis in humans and mice.

Osteoporosis is a common aging-related disease diagnosed primarily using bone mineral density (BMD). We assessed genetic determinants of BMD as estimated by heel quantitative ultrasound in 426,824 individuals, identifying 518 genome-wide significant loci (301 novel), explaining 20% of its variance. We identified 13 bone fracture loci, all associated with estimated BMD (eBMD), in ~1.2 million individuals. We then identified target genes enriched for genes known to influence bone density and strength (maximum odds ratio (OR) = 58, P = 1 × 10-75) from cell-specific features, including chromatin conformation and accessible chromatin sites. We next performed rapid-throughput skeletal phenotyping of 126 knockout mice with disruptions in predicted target genes and found an increased abnormal skeletal phenotype frequency compared to 526 unselected lines (P < 0.0001). In-depth analysis of one gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.

Author Correction: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.

In the version of the article published, the surname of author Aaron Isaacs is misspelled as Issacs.