Estimating Categorical Counterfactuals via Deep Twin Networks

Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be uniquely determined from observations and interventions alone. This raises the question of how to choose the causal mechanisms so that resulting counterfactual inference is trustworthy in a given domain. This question has been addressed in causal models with binary variables, but the case of categorical variables remains unanswered. We address this challenge by introducing for causal models with categorical variables the notion of counterfactual ordering, a principle that posits desirable properties causal mechanisms should posses, and prove that it is equivalent to specific functional constraints on the causal mechanisms. To learn causal mechanisms satisfying these constraints, and perform counterfactual inference with them, we introduce deep twin networks. These are deep neural networks that, when trained, are capable of twin network counterfactual inference -- an alternative to the abduction, action, & prediction method. We empirically test our approach on diverse real-world and semi-synthetic data from medicine, epidemiology, and finance, reporting accurate estimation of counterfactual probabilities while demonstrating the issues that arise with counterfactual reasoning when counterfactual ordering is not enforced

Genome editing for inborn errors of metabolism: advancing towards the clinic

Abstract Inborn errors of metabolism (IEM) include many disorders for which current treatments aim to ameliorate disease manifestations, but are not curative. Advances in the field of genome editing have recently resulted in the in vivo correction of murine models of IEM. Site-specific endonucleases, such as zinc-finger nucleases and the CRISPR/Cas9 system, in combination with delivery vectors engineered to target disease tissue, have enabled correction of mutations in disease models of hemophilia B, hereditary tyrosinemia type I, ornithine transcarbamylase deficiency, and lysosomal storage disorders. These in vivo gene correction studies, as well as an overview of genome editing and future directions for the field, are reviewed and discussed herein

Understanding the impact of an online level 1 coach education award on dodgeball coaches’ learning and practice

Improved internet access and technological advancements have significantly influenced coaches’ learning opportunities, with numerous online coach education courses now available. Despite this, we know little about coaches’ experiences of such provision and how it shapes coach learning. Consequently, the aim of this research is to understand the impact of an online Level 1 coach education award on dodgeball coaches’ learning and practice. Data were collected via an online qualitative survey involving 57 dodgeball coaches who had completed the award, alongside follow-up virtual semistructured interviews with eight coaches. Following a reflexive thematic analysis process drawing upon the theoretical framework of Jennifer Moon, three themes were generated: (a) a surface or deep approach? Understanding dodgeball coaches’ experiences of the Level 1 award, (b) coaches’ preferences and learning styles: a barrier for online coach education, and (c) enhancing the impact of online coach education: assessment and postaward support. Findings indicate that the award’s impact on learning and practice varied depending upon coaches’ cognitive structures, which influenced their perceptions toward the value of online provision. Although coaches’ experiences were generally positive, authentic assessment(s) and mentoring opportunities were proposed to further enhance the award’s impact

Fine-mapping within eQTL credible intervals by expression CROP-seq

The majority of genome-wide association study (GWAS)-identified SNPs are located in noncoding regions of genes and are likely to influence disease risk and phenotypes by affecting gene expression. Since credible intervals responsible for genome-wide associations typically consist of ≥100 variants with similar statistical support, experimental methods are needed to fine map causal variants. We report here a moderate-throughput approach to identifying regulatory GWAS variants, expression CROP-seq, which consists of multiplex CRISPR-Cas9 genome editing combined with single-cell RNAseq to measure perturbation in transcript abundance. Mutations were induced in the HL60/S4 myeloid cell line nearby 57 SNPs in three genes, two of which, rs2251039 and rs35675666, significantly altered CISD1 and PARK7 expression, respectively, with strong replication and validation in single-cell clones. The sites overlap with chromatin accessibility peaks and define causal variants for inflammatory bowel disease at the two loci. This relatively inexpensive approach should be scalable for broad surveys and is also implementable for the fine mapping of individual genes

Tools for experimental and computational analyses of off-target editing by programmable nucleases

Genome editing using programmable nucleases is revolutionizing life science and medicine. Off-target editing by these nucleases remains a considerable concern, especially in therapeutic applications. Here we review tools developed for identifying potential off-target editing sites and compare the ability of these tools to properly analyze off-target effects. Recent advances in both in silico and experimental tools for off-target analysis have generated remarkably concordant results for sites with high off-target editing activity. However, no single tool is able to accurately predict low-frequency off-target editing, presenting a bottleneck in therapeutic genome editing, because even a small number of cells with off-target editing can be detrimental. Therefore, we recommend that at least one in silico tool and one experimental tool should be used together to identify potential off-target sites, and amplicon-based next-generation sequencing (NGS) should be used as the gold standard assay for assessing the true off-target effects at these candidate sites. Future work to improve off-target analysis includes expanding the true off-target editing dataset to evaluate new experimental techniques and to train machine learning algorithms; performing analysis using the particular genome of the cells in question rather than the reference genome; and applying novel NGS techniques to improve the sensitivity of amplicon-based off-target editing quantification.Off-target effects of programmable nucleases remain a critical issue for therapeutic applications of genome editing. This review compares experimental and computational tools for off-target analysis and provides recommendations for better assessments of off-target effects

Do more attractive women show stronger preferences for male facial masculinity?

Researchers have suggested that more attractive women will show stronger preferences for masculine men because such women are better placed to offset the potential costs of choosing a masculine mate. However, evidence for correlations between measures of women’s own attractiveness and preferences for masculine men is mixed. Moreover, the samples used to test this hypothesis are typically relatively small. Consequently, we conducted two large-scale studies that investigated possible associations between women’s preferences for facial masculinity and their own attractiveness as assessed from third-party ratings of their facial attractiveness (Study 1, N = 454, laboratory study) and self-rated attractiveness (Study 2, N = 8972, online study). Own attractiveness was positively correlated with preferences for masculine men in Study 2 (self-rated attractiveness), but not Study 1 (third-party ratings of facial attractiveness). This pattern of results is consistent with the proposal that women’s beliefs about their own attractiveness, rather than their physical condition per se, underpins attractiveness-contingent masculinity preferences

Somatic genome editing with CRISPR/Cas9 generates and corrects a metabolic disease

Germline manipulation using CRISPR/Cas9 genome editing has dramatically accelerated the generation of new mouse models. Nonetheless, many metabolic disease models still depend upon laborious germline targeting, and are further complicated by the need to avoid developmental phenotypes. We sought to address these experimental limitations by generating somatic mutations in the adult liver using CRISPR/Cas9, as a new strategy to model metabolic disorders. As proof-of-principle, we targeted the low-density lipoprotein receptor (Ldlr), which when deleted, leads to severe hypercholesterolemia and atherosclerosis. Here we show that hepatic disruption of Ldlr with AAV-CRISPR results in severe hypercholesterolemia and atherosclerosis. We further demonstrate that co-disruption of Apob, whose germline loss is embryonically lethal, completely prevented disease through compensatory inhibition of hepatic LDL production. This new concept of metabolic disease modeling by somatic genome editing could be applied to many other systemic as well as liver-restricted disorders which are difficult to study by germline manipulation

Pitfalls in single clone crispr-cas9 mutagenesis to fine-map regulatory intervals

The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended

Longitudinal Analysis Between Maternal Feeding Practices and Body Mass Index (BMI): A Study in Asian Singaporean Preschoolers

Bidirectional studies between maternal feeding practices with subsequent child weight are limited, with no studies in Asian populations. In longitudinal analyses, we assessed the directionality of the associations between maternal feeding practices and body mass index (BMI) in preschoolers. Participants were 428 mother child dyads from the GUSTO (Growing Up in Singapore Toward healthy Outcomes) cohort. Feeding practices were assessed using the Comprehensive Feeding Practices Questionnaire (CFPQ) at age 5 y. Child BMI was measured at ages 4 and 6 y. BMI and maternal feeding practices subscales were transformed to SD scores and both directions of their associations examined with multivariable linear regression and pathway modeling. Higher BMI at age 4 was associated with lower encouragement of balance and variety (β = −0.33; 95%CI: −0.53, −0.13), lower pressure to eat (β = −0.49; −0.68, −0.29) and higher restriction (β = 1.10; 0.67, 1.52) at age 5, adjusting for confounders and baseline feeding practices at 3 years. In the reverse direction, only pressure and restriction at age 5 were associated with lower and higher child BMI at age 6 years, respectively. After the adjustment for baseline BMI at age 5, the association with pressure was attenuated to non-significance (β = 0.01 (−0.01, 0.03), while the association with restriction remained significant (β = 0.02; 0.002, 0.03). Overall, associations from child BMI to maternal restriction for weight control and pressure feeding practices was stronger than the association from these maternal feeding practices to child BMI (Wald's statistics = 24.3 and 19.5, respectively; p < 0.001). The strength and directionality suggests that the mothers in the Asian population were likely to adopt these feeding practices in response to their child's BMI, rather than the converse.Clinical Trial Registry Number and Website This study was registered at clinicaltrials.gov as NCT01174875 (www.clinicaltrials.gov, NCT01174875)