Human Epistatic Interaction Controls IL7R Splicing and Increases Multiple Sclerosis Risk.

Multiple sclerosis (MS) is an autoimmune disorder where T cells attack neurons in the central nervous system (CNS) leading to demyelination and neurological deficits. A driver of increased MS risk is the soluble form of the interleukin-7 receptor alpha chain gene (sIL7R) produced by alternative splicing of IL7R exon 6. Here, we identified the RNA helicase DDX39B as a potent activator of this exon and consequently a repressor of sIL7R, and we found strong genetic association of DDX39B with MS risk. Indeed, we showed that a genetic variant in the 5' UTR of DDX39B reduces translation of DDX39B mRNAs and increases MS risk. Importantly, this DDX39B variant showed strong genetic and functional epistasis with allelic variants in IL7R exon 6. This study establishes the occurrence of biological epistasis in humans and provides mechanistic insight into the regulation of IL7R exon 6 splicing and its impact on MS risk.

Like-minded sources on Facebook are prevalent but not polarizing.

Many critics raise concerns about the prevalence of 'echo chambers' on social media and their potential role in increasing political polarization. However, the lack of available data and the challenges of conducting large-scale field experiments have made it difficult to assess the scope of the problem1,2. Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from 'like-minded' sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.

Epitranscriptomic addition of m6A regulates HIV-1 RNA stability and alternative splicing.

Previous work has demonstrated that the epitranscriptomic addition of m6A to viral transcripts can promote the replication and pathogenicity of a wide range of DNA and RNA viruses, including HIV-1, yet the underlying mechanisms responsible for this effect have remained unclear. It is known that m6A function is largely mediated by cellular m6A binding proteins or readers, yet how these regulate viral gene expression in general, and HIV-1 gene expression in particular, has been controversial. Here, we confirm that m6A addition indeed regulates HIV-1 RNA expression and demonstrate that this effect is largely mediated by the nuclear m6A reader YTHDC1 and the cytoplasmic m6A reader YTHDF2. Both YTHDC1 and YTHDF2 bind to multiple distinct and overlapping sites on the HIV-1 RNA genome, with YTHDC1 recruitment serving to regulate the alternative splicing of HIV-1 RNAs. Unexpectedly, while YTHDF2 binding to m6A residues present on cellular mRNAs resulted in their destabilization as previously reported, YTHDF2 binding to m6A sites on HIV-1 transcripts resulted in a marked increase in the stability of these viral RNAs. Thus, YTHDF2 binding can exert diametrically opposite effects on RNA stability, depending on RNA sequence context.

Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California.

The capture-recapture method is a common tool used in epidemiology to estimate the size of "hidden" populations and correct the underascertainment of cases, based on incomplete and overlapping lists of the target population. Log-linear models are often used to estimate the population size yet may produce implausible and unreliable estimates due to model misspecification and small cell sizes. A novel targeted minimum loss-based estimation (TMLE) model developed for capture-recapture makes several notable improvements to conventional modeling: "targeting" the parameter of interest, flexibly fitting the data to alternative functional forms, and limiting bias from small cell sizes. Using simulations and empirical data from the San Francisco, California, Department of Public Health's human immunodeficiency virus (HIV) surveillance registry, we evaluated the performance of the TMLE model and compared results with those of other common models. Based on 2,584 people observed on 3 lists reportable to the surveillance registry, the TMLE model estimated the number of San Francisco residents living with HIV as of December 31, 2019, to be 13,523 (95% confidence interval: 12,222, 14,824). This estimate, compared with a "ground truth" of 12,507, was the most accurate and precise of all models examined. The TMLE model is a significant advancement in capture-recapture studies, leveraging modern statistical methods to improve estimation of the sizes of hidden populations.

Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

Doubly robust nonparametric instrumental variable estimators for survival outcomes.

Instrumental variable (IV) methods allow us the opportunity to address unmeasured confounding in causal inference. However, most IV methods are only applicable to discrete or continuous outcomes with very few IV methods for censored survival outcomes. In this article, we propose nonparametric estimators for the local average treatment effect on survival probabilities under both covariate-dependent and outcome-dependent censoring. We provide an efficient influence function-based estimator and a simple estimation procedure when the IV is either binary or continuous. The proposed estimators possess double-robustness properties and can easily incorporate nonparametric estimation using machine learning tools. In simulation studies, we demonstrate the flexibility and double robustness of our proposed estimators under various plausible scenarios. We apply our method to the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial for estimating the causal effect of screening on survival probabilities and investigate the causal contrasts between the two interventions under different censoring assumptions.

Estimation of the Time-Varying Incremental Effect of Low-dose Aspirin on Incidence of Pregnancy.

BACKGROUND: In many research settings, the intervention implied by the average causal effect of a time-varying exposure is impractical or unrealistic, and we might instead prefer a more realistic target estimand. Instead of requiring all individuals to be always exposed versus unexposed, incremental effects quantify the impact of merely shifting each individual's probability of being exposed. METHODS: We demonstrate the estimation of incremental effects in the time-varying setting, using data from the Effects of Aspirin in Gestation and Reproduction trial, which assessed the effect of preconception low-dose aspirin on pregnancy outcomes. Compliance to aspirin or placebo was summarized weekly and was affected by time-varying confounders such as bleeding or nausea. We sought to estimate what the incidence of pregnancy by 26 weeks postrandomization would have been if we shifted each participant's probability of taking aspirin or placebo each week by odds ratios (OR) between 0.30 and 3.00. RESULTS: Under no intervention (OR = 1), the incidence of pregnancy was 77% (95% CI: 74%, 80%). Decreasing women's probability of complying with aspirin had little estimated effect on pregnancy incidence. When we increased women's probability of taking aspirin, estimated incidence of pregnancy increased, from 83% (95% confidence interval [CI] = 79%, 87%) for OR = 2 to 89% (95% CI = 84%, 93%) for OR=3. We observed similar results when we shifted women's probability of complying with a placebo. CONCLUSIONS: These results estimated that realistic interventions to increase women's probability of taking aspirin would have yielded little to no impact on the incidence of pregnancy, relative to similar interventions on placebo.

Estimation of the Average Causal Effect in Longitudinal Data With Time-Varying Exposures: The Challenge of Nonpositivity and the Impact of Model Flexibility.

There are important challenges to the estimation and identification of average causal effects in longitudinal data with time-varying exposures. Here, we discuss the difficulty in meeting the positivity condition. Our motivating example is the per-protocol analysis of the Effects of Aspirin in Gestation and Reproduction (EAGeR) Trial. We estimated the average causal effect comparing the incidence of pregnancy by 26 weeks that would have occurred if all women had been assigned to aspirin and complied versus the incidence if all women had been assigned to placebo and complied. Using flexible targeted minimum loss-based estimation, we estimated a risk difference of 1.27% (95% CI: -9.83, 12.38). Using a less flexible inverse probability weighting approach, the risk difference was 5.77% (95% CI: -1.13, 13.05). However, the cumulative probability of compliance conditional on covariates approached 0 as follow-up accrued, indicating a practical violation of the positivity assumption, which limited our ability to make causal interpretations. The effects of nonpositivity were more apparent when using a more flexible estimator, as indicated by the greater imprecision. When faced with nonpositivity, one can use a flexible approach and be transparent about the uncertainty, use a parametric approach and smooth over gaps in the data, or target a different estimand that will be less vulnerable to positivity violations.

Performance Evaluation of Parametric and Nonparametric Methods When Assessing Effect Measure Modification.

Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the greatest power to detect effect modification, ranging from 0.42 to 1.00 in the worst and best scenario, respectively. Augmented inverse probability weighting had the lowest power, with an increase of 23% when using sample splitting. For continuous modifiers, the DR-learner was comparable to flexible parametric models in capturing quadratic and nonlinear monotonic functions. However, for nonlinear, nonmonotonic functions, the DR-learner had lower integrated bias than splines and fractional polynomials, with values of 141.3, 251.7, and 209.0, respectively. Our findings suggest comparable performance between nonparametric and correctly specified parametric models in evaluating effect modification.

Use of a Doubly Robust Machine-Learning-Based Approach to Evaluate Body Mass Index as a Modifier of the Association Between Fruit and Vegetable Intake and Preeclampsia.

The Dietary Guidelines for Americans rely on summaries of the effect of dietary pattern on disease risk, independent of other population characteristics. We explored the modifying effect of prepregnancy body mass index (BMI; weight (kg)/height (m)2) on the relationship between fruit and vegetable density (cup-equivalents/1,000 kcal) and preeclampsia using data from a pregnancy cohort study conducted at 8 US medical centers (n = 9,412; 2010-2013). Usual daily periconceptional intake of total fruits and total vegetables was estimated from a food frequency questionnaire. We quantified the effects of diets with a high density of fruits (≥1.2 cups/1,000 kcal/day vs. <1.2 cups/1,000 kcal/day) and vegetables (≥1.3 cups/1,000 kcal/day vs. <1.3 cups/1,000 kcal/day) on preeclampsia risk, conditional on BMI, using a doubly robust estimator implemented in 2 stages. We found that the protective association of higher fruit density declined approximately linearly from a BMI of 20 to a BMI of 32, by 0.25 cases per 100 women per each BMI unit, and then flattened. The protective association of higher vegetable density strengthened in a linear fashion, by 0.3 cases per 100 women for every unit increase in BMI, up to a BMI of 30, where it plateaued. Dietary patterns with a high periconceptional density of fruits and vegetables appear more protective against preeclampsia for women with higher BMI than for leaner women.

Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms.

Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advocated for ML methods to estimate causal effects. Unfortunately, ML algorithmscan perform worse than parametric regression. We demonstrate the performance of ML-based single- and double-robust estimators. We use 100 Monte Carlo samples with sample sizes of 200, 1200, and 5000 to investigate bias and confidence interval coverage under several scenarios. In a simple confounding scenario, confounders were related to the treatment and the outcome via parametric models. In a complex confounding scenario, the simple confounders were transformed to induce complicated nonlinear relationships. In the simple scenario, when ML algorithms were used, double-robust estimators were superior to single-robust estimators. In the complex scenario, single-robust estimators with ML algorithms were at least as biased as estimators using misspecified parametric models. Double-robust estimators were less biased, but coverage was well below nominal. The use of sample splitting, inclusion of confounder interactions, reliance on a richly specified ML algorithm, and use of doubly robust estimators was the only explored approach that yielded negligible bias and nominal coverage. Our results suggest that ML based singly robust methods should be avoided.

AIPW: An R Package for Augmented Inverse Probability-Weighted Estimation of Average Causal Effects.

An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW, a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM, npcausal, tmle, and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies.

Incremental Propensity Score Effects for Time-fixed Exposures.

When causal inference is of primary interest, a range of target parameters can be chosen to define the causal effect, such as average treatment effects (ATEs). However, ATEs may not always align with the research question at hand. Furthermore, the assumptions needed to interpret estimates as ATEs, such as exchangeability, consistency, and positivity, are often not met. Here, we present the incremental propensity score (PS) approach to quantify the effect of shifting each person's exposure propensity by some predetermined amount. Compared with the ATE, incremental PS may better reflect the impact of certain policy interventions and do not require that positivity hold. Using the Nulliparous Pregnancy Outcomes Study: monitoring mothers-to-be (nuMoM2b), we quantified the relationship between total vegetable intake and the risk of preeclampsia and compared it to average treatment effect estimates. The ATE estimates suggested a reduction of between two and three preeclampsia cases per 100 pregnancies for consuming at least half a cup of vegetables per 1,000 kcal. However, positivity violations obfuscate the interpretation of these results. In contrast, shifting each woman's exposure propensity by odds ratios ranging from 0.20 to 5.0 yielded no difference in the risk of preeclampsia. Our analyses show the utility of the incremental PS effects in addressing public health questions with fewer assumptions.

Influenza A virus-derived siRNAs increase in the absence of NS1 yet fail to inhibit virus replication.

While the issue of whether RNA interference (RNAi) ever forms part of the antiviral innate immune response in mammalian somatic cells remains controversial, there is considerable evidence demonstrating that few, if any, viral small interfering RNAs (siRNAs) are produced in infected cells. Moreover, inhibition of RNAi by mutational inactivation of key RNAi factors, such as Dicer or Argonaute 2, fails to enhance virus replication. One potential explanation for this lack of inhibitory effect is that mammalian viruses encode viral suppressors of RNAi (VSRs) that are so effective that viral siRNAs are not produced in infected cells. Indeed, a number of mammalian VSRs have been described, of which the most prominent is the influenza A virus (IAV) NS1 protein, which has not only been reported to inhibit RNAi in plants and insects but also to prevent the production of viral siRNAs in IAV-infected human cells. Here, we confirm that an IAV mutant lacking NS1 indeed differs from wild-type IAV in that it induces the production of readily detectable levels of Dicer-dependent viral siRNAs in infected human cells. However, we also demonstrate that these siRNAs have little if any inhibitory effect on IAV gene expression. This is likely due, at least in part, to their inefficient loading into RNA-induced silencing complexes.

Defining and Identifying Per-protocol Effects in Randomized Trials.

In trials with noncompliance to assigned treatment, researchers might be interested in estimating a per-protocol effect-a comparison of two counterfactual outcomes defined by treatment assignment and (often time-varying) compliance with a well-defined treatment protocol. Here, we provide a general counterfactual definition of a per-protocol effect and discuss examples of per-protocol effects that are of either substantive or methodologic interest. In doing so, we seek to make more concrete what per-protocol effects are and highlight that one can estimate per-protocol effects that are more than just a comparison of always taking treatment in two distinct treatment arms. We then discuss one set of identifiability conditions that allow for identification of a causal per-protocol effect, highlighting some potential violations of those conditions that might arise when estimating per-protocol effects.

Gene Editing: A New Tool for Viral Disease.

The emergence of the CRISPR/Cas system of antiviral adaptive immunity in bacteria as a facile system for gene editing in mammalian cells may well lead to gene editing becoming a novel treatment for a range of human diseases, especially those caused by deleterious germline mutations. Another potential target for gene editing are DNA viruses that cause chronic pathogenic diseases that cannot be cured by using currently available drugs. We review the current state of this field and discuss the potential advantages and problems with using a gene editing approach as a treatment for diseases caused by DNA viruses.

Partial reconstitution of the RNAi response in human cells using Drosophila gene products.

While mammalian somatic cells are incapable of mounting an effective RNA interference (RNAi) response to viral infections, plants and invertebrates are able to generate high levels of viral short interfering RNAs (siRNAs) that can control many infections. In Drosophila, the RNAi response is mediated by the Dicer 2 enzyme (dDcr2) acting in concert with two cofactors called Loqs-PD and R2D2. To examine whether a functional RNAi response could be mounted in human somatic cells, we expressed dDcr2, in the presence or absence of Loqs-PD and/or R2D2, in a previously described human cell line, NoDice/ΔPKR, that lacks functional forms of human Dicer (hDcr) and PKR. We observed significant production of ∼21-nt long siRNAs, derived from a cotransfected double stranded RNA (dsRNA) expression vector, that were loaded into the human RNA-induced silencing complex (RISC) and were able to significantly reduce the expression of a cognate indicator gene. Surprisingly, dDcr2 was able to produce siRNAs even in the absence of Loqs-PD, which is thought to be required for dsRNA cleavage by dDcr2. This result may be explained by our finding that dDcr2 is able to bind the human Loqs-PD homolog TRBP when expressed in human cells in the absence of Loqs-PD. We conclude that it is possible to at least partially rescue the ability of mammalian somatic cells to express functional siRNAs using gene products of invertebrate origin.

Viral Epitranscriptomics.

Although it has been known for over 40 years that eukaryotic mRNAs bear internal base modifications, it is only in the last 5 years that the importance of these modifications has begun to come into focus. The most common mRNA modification, the addition of a methyl group to the N6 position of adenosine (m6A), has been shown to affect splicing, translation, and stability, and m6A is also essential for embryonic development in organisms ranging from plants to mice. While all viral transcripts examined so far have been found to be extensively m6A modified, the role, if any, of m6A in regulating viral gene expression and replication was previously unknown. However, recent data generated using HIV-1 as a model system strongly suggest that sites of m6A addition not only are evolutionarily conserved but also enhance virus replication. It is therefore likely that the field of viral epitranscriptomics, which can be defined as the study of functionally relevant posttranscriptional modifications of viral RNA transcripts that do not change the nucleotide sequence of that RNA, is poised for a major expansion in scientific interest and may well fundamentally change our understanding of how viral replication is regulated.

Production of functional small interfering RNAs by an amino-terminal deletion mutant of human Dicer.

Although RNA interference (RNAi) functions as a potent antiviral innate-immune response in plants and invertebrates, mammalian somatic cells appear incapable of mounting an RNAi response and few, if any, small interfering RNAs (siRNAs) can be detected. To examine why siRNA production is inefficient, we have generated double-knockout human cells lacking both Dicer and protein kinase RNA-activated. Using these cells, which tolerate double-stranded RNA expression, we show that a mutant form of human Dicer lacking the amino-terminal helicase domain can process double-stranded RNAs to produce high levels of siRNAs that are readily detectable by Northern blot, are loaded into RNA-induced silencing complexes, and can effectively and specifically inhibit the expression of cognate mRNAs. Remarkably, overexpression of this mutant Dicer, but not wild-type Dicer, also resulted in a partial inhibition of Influenza A virus-but not poliovirus-replication in human cells.