Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes.

Polygenic prediction of complex trait phenotypes has become important in human genetics, especially in the context of precision medicine. Recently, Morgante et al . introduced mr.mash , a flexible and computationally efficient method that models multiple phenotypes jointly and leverages sharing of effects across such phenotypes to improve prediction accuracy. However, a drawback of mr.mash is that it requires individual-level data, which are often not publicly available. In this work, we introduce mr.mash-rss , an extension of the mr.mash model that requires only summary statistics from Genome-Wide Association Studies (GWAS) and linkage disequilibrium (LD) estimates from a reference panel. By using summary data, we achieve the twin goal of increasing the applicability of the mr.mash model to data sets that are not publicly available and making it scalable to biobank-size data. Through simulations, we show that mr.mash-rss is competitive with, and often outperforms, current state-of-the-art methods for single- and multi-phenotype polygenic prediction in a variety of scenarios that differ in the pattern of effect sharing across phenotypes, the number of phenotypes, the number of causal variants, and the genomic heritability. We also present a real data analysis of 16 blood cell phenotypes in UK Biobank, showing that mr.mash-rss achieves higher prediction accuracy than competing methods for the majority of traits, especially when the data has smaller sample size. Author summary: Polygenic prediction refers to the use of an individual's genetic information ( i.e ., genotypes) to predict traits ( i.e ., phenotypes), which are often of medical relevance. It is known that some phenotypes are related and are affected by the same genotypes. When this is the case, it is possible to improve the accuracy of predictions by using methods that model multiple phenotypes jointly and account for shared effects. mr.mash is a recently developed multi-phenotype method that can learn which effects are shared and has been shown to improve prediction. However, mr.mash requires large data sets of genetic and phenotypic information collected at the individual level. Such data are often unavailable due to privacy concerns, or are difficult to work with due to the computational resources needed to analyze data of this size. Our work extends mr.mash to require only summary statistics from Genome-Wide Association Studies instead of individual-level data, which are usually publicly available. In addition, the computations using summary statistics do not depend on sample size, making the newly developed mr.mash-rss scalable to extremely large data sets. Using simulations and real data analysis, we show that our method is competitive with other methods for polygenic prediction.

Performance of Febrile Infant Decision Tools on Hypothermic Infants Evaluated for Infection.

BACKGROUND: Given the lack of evidence-based guidelines for hypothermic infants, providers may be inclined to use febrile infant decision-making tools to guide management decisions. Our objective was to assess the diagnostic performance of febrile infant decision tools for identifying hypothermic infants at low risk of bacterial infection. METHODS: We conducted a secondary analysis of a retrospective cohort study of hypothermic (≤36.0 C) infants ≤90 days of age presenting to the emergency department or inpatient unit among 9 participating sites between September 1, 2016 and May 5, 2021. Well-appearing infants evaluated for bacterial infections via laboratory testing were included. Infants with complex chronic conditions or premature birth were excluded. Performance characteristics for detecting serious bacterial infection (SBI; urinary tract infection, bacteremia, bacterial meningitis) and invasive bacterial infection (IBI; bacteremia, bacterial meningitis) were calculated for each tool. RESULTS: Overall, 314 infants met the general inclusion criteria, including 14 cases of SBI (4.5%) and 7 cases of IBI (2.2%). The median age was 5 days, and 68.1% of the infants (214/314) underwent a full sepsis evaluation. The Philadelphia, Boston, IBI Score, and American Academy of Pediatrics Clinical Practice Guideline did not misclassify any SBI or IBI as low risk; however, they had low specificity and positive predictive value. Rochester and Pediatric Emergency Care Applied Research Network tools misclassified infants with bacterial infections. CONCLUSIONS: Several febrile infant decision tools were highly sensitive, minimizing missed SBIs and IBIs in hypothermic infants. However, the low specificity of these decision tools may lead to unnecessary testing, antimicrobial exposure, and hospitalization.

The human experience of comprehending source code in virtual reality.

Virtual reality (VR) is an emerging technology used in various domains such as medicine, psychotherapy, architecture, and gaming. Recently, software engineering researchers have started to explore virtual reality as a tool for programmers. However, few studies examine source code comprehension in VR. This paper explores the human experience of comprehending source code in VR and compares it to source code comprehension in a desktop environment. We conducted a study with 26 graduate student programmers. We measured actual productivity, perceived productivity and used the NASA Task Load Index (TLX) survey to measure various factors such as mental demand, physical demand, temporal demand, performance, effort, and frustration. We found that the programmers experienced more physical demand, effort, and overall task load when reading and comprehending code in VR. However, we did not observe any statistically significant differences in the programmers' measured productivity or perceived productivity between VR and desktop comprehension.

Predictive value of clinical symptoms for COVID-19 diagnosis in young adults.

Objective: Assessment of predictive values of clinical symptoms for COVID-19 diagnosis in young adults. Participants: Nonresidential university students (ages 18-25) participating in surveillance testing and mandatory symptom survey between 9/9/2020 and 11/25/2020. Methods: Retrospective study of test results and symptom survey data. Results: Among 6,489 individuals, 288 (4.4%) tested positive for COVID-19, 90 (31.3%) of whom reported symptoms. COVID-19 prevalence among individuals reporting and not reporting symptoms was 17.2% and 3.3%, respectively. The four symptoms with highest positive predictive values (PPVs) were smell/taste loss (PPV = 38.5%), chills (PPV = 31.5%), muscle/joint pain (PPV = 26.0%), and fever (PPV = 25.9%). Conclusions: Institutions should emphasize COVID-19 risk for highly predictive symptoms in public health messaging to inform individuals on when to seek testing or self-isolation. However, low COVID-19 diagnostic accuracy of clinical symptoms and the high pre-symptomatic/asymptomatic rate (69%) highlight the limitations of voluntary testing strategies employed by higher education institutions during the original strain of SARS-CoV-2.

Longitudinal Patterns in Adolescent Intentions to Seek Help for Dating Violence: A Latent Transition Analysis.

As a persistent public health problem affecting thousands of adolescents every year, teen dating violence has been studied extensively. However, gaps remain in the knowledge on what adolescents think about seeking help for violence in a dating relationship and how these attitudes might change over time. This study adopts a longitudinal person-oriented approach to explore configurations of help-seeking preferences in a sample of rural adolescents (N at wave 1 = 580, Mage = 13 years, SD = 1.48; 52.7% female; 46.6% African American, 39.4% White, 14% Hispanic and other minorities), surveyed annually for four years, with each assessment approximately 12 months apart. Latent class analyses uncovered variation in adolescents' willingness to disclose dating violence, captured by six groups: (a) Multi-help-seekers (19%), (b) Reluctant help-seekers (15%), (c) Selective help-seekers (16%), (d) Parent confidants (11%), (e) Friends confidants (22%), and (f) Moderate help-seekers (17%). Follow-up analyses revealed that select sociodemographic characteristics (age, gender, and family income) were unevenly distributed among the identified groups, pointing to the need to account for individual and contextual influences in understanding heterogeneity in help-seeking attitudes. Latent transition models further showed that although individual membership in latent classes was generally stable between middle and high school, transitions between help-seeking classes were common as well. The article concludes by discussing these findings in the context of further research and programming to promote help-seeking among developing adolescents, including targeted strategies to address the needs of adolescents who think differently about disclosing dating abuse.

Is Experience in Hemodialysis Cannulation Related to Expertise? A Metrics-based Investigation for Skills Assessment.

Cannulation is not only one of the most common medical procedures but also fraught with complications. The skill of the clinician performing cannulation directly impacts cannulation outcomes. However, current methods of teaching this skill are deficient, relying on subjective demonstrations and unrealistic manikins that have limited utility for skills training. Furthermore, of the factors that hinders effective continuing medical education is the assumption that clinical experience results in expertise. In this work, we examine if objective metrics acquired from a novel cannulation simulator are able to distinguish between experienced clinicians and established experts, enabling the measurement of true expertise. Twenty-two healthcare professionals, who practiced cannulation with varying experience, performed a simulated arteriovenous fistula cannulation task on the simulator. Four clinicians were peer-identified as experts while the others were designated to the experienced group. The simulator tracked the motion of the needle (via an electromagnetic sensor), rendered blood flashback function (via an infrared light sensor), and recorded pinch forces exerted on the needle (via force sensing elements). Metrics were computed based on motion, force, and other sensor data. Results indicated that, with near 80% of accuracy using both logistic regression and linear discriminant analysis, the objective metrics differentiated between experts and the experienced, including identifying needle motion and finger force as two prominent features that distinguished between the groups. Furthermore, results indicated that expertise was not correlated with years of experience, validating the central hypothesis of the study. These insights contribute to structured and standardized medical skills training by enabling a meaningful definition of expertise and could potentially lead to more effective skills training methods.

Trajectories of Dating Violence Victimization and Perpetration among Rural Adolescents.

Research is inconclusive about the trajectory of dating violence during adolescence and whether there are differences across gender and race/ethnicity. We examined dating victimization and perpetration trajectories among a diverse sample of rural youth (N = 580, 52.7% female, 49% Black, 39% White, 11% Hispanic or other minorities) in middle and high school who were surveyed annually across four years and explored the influences of gender and ethnicity. The results based on cohort-sequential latent growth modeling revealed that for boys, victimization peaked at 11th grade, and then declined. For girls, victimization was stable throughout adolescence. Perpetration was reported less frequently and increased steadily for males and females. For White youth, victimization peaked at grades 9 and 10, followed by a decline. For Black youth, victimization followed a linear increase. Perpetration trajectory followed a linear increase for White and Black but not Hispanic youth. The findings indicate that the developmental progression of dating violence during adolescence varies by demographics. The discussion focuses on future directions for research on teen dating violence among rural youth and implications for prevention and interventions initiatives.

A comparison of existing methods for multiple imputation in individual participant data meta-analysis.

Multiple imputation is a popular method for addressing missing data, but its implementation is difficult when data have a multilevel structure and one or more variables are systematically missing. This systematic missing data pattern may commonly occur in meta-analysis of individual participant data, where some variables are never observed in some studies, but are present in other hierarchical data settings. In these cases, valid imputation must account for both relationships between variables and correlation within studies. Proposed methods for multilevel imputation include specifying a full joint model and multiple imputation with chained equations (MICE). While MICE is attractive for its ease of implementation, there is little existing work describing conditions under which this is a valid alternative to specifying the full joint model. We present results showing that for multilevel normal models, MICE is rarely exactly equivalent to joint model imputation. Through a simulation study and an example using data from a traumatic brain injury study, we found that in spite of theoretical differences, MICE imputations often produce results similar to those obtained using the joint model. We also assess the influence of prior distributions in MICE imputation methods and find that when missingness is high, prior choices in MICE models tend to affect estimation of across-study variability more than compatibility of conditional likelihoods. Copyright © 2017 John Wiley & Sons, Ltd.