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Key Points RNAi has been used for genome-wide screening and other studies that aim to uncover the function of genes and gene networks. Sequence-specific RNAi off-target effects (OTEs) must be taken into consideration when interpreting RNAi data. New experimental and computational strategies such as the detection of microRNA-like seed sequence matches and the use of C911 RNAi controls help to address OTEs and improve data quality. Innovations in RNAi screening include new applications for high-content imaging, screens for synthetic interactions using sensitized cell backgrounds, screening in three-dimensional tissue cultures, parallel screening in different species or using different approaches followed by result integration, and new strategies for in vivo RNAi screening. RNAi and the genome-editing CRISPR (clustered regularly interspaced short palindromic repeats)–Cas9 system are complementary technologies, and using these two techniques together should result in improved assay development, screening and validation of screen results. With careful attention to reagent and assay design, data analysis and experimental follow-ups, improved genome-wide RNAi screens are uncovering gene function in all areas of biology. RNAi is used for genome-wide functional screens in cultured cells and animals. New experimental and bioinformatics approaches, including the combination of RNAi with genome-editing strategies, has improved the efficacy of RNAi screening and follow-up experiments, and enhanced our understanding of gene function and regulatory networks. Gene silencing through sequence-specific targeting of mRNAs by RNAi has enabled genome-wide functional screens in cultured cells and in vivo in model organisms. These screens have resulted in the identification of new cellular pathways and potential drug targets. Considerable progress has been made to improve the quality of RNAi screen data through the development of new experimental and bioinformatics approaches. The recent availability of genome-editing strategies, such as the CRISPR (clustered regularly interspaced short palindromic repeats)–Cas9 system, when combined with RNAi, could lead to further improvements in screen data quality and follow-up experiments, thus promoting our understanding of gene function and gene regulatory networks.

Supplementary feeding, the intentional provision of food to wild birds is a common activity in developed nations during the winter. The energy inputs represented by supplementary feeding are vast, and thus it is likely an important mechanism shaping bird communities in urban areas. However, research in this regard has mainly occurred in temperate and non-urban settings. Moreover, few studies have been informed by supplementary feeding habits of local community members limiting their inference. We evaluated the effects of two commonly provided wild bird foods on the abundance and species diversity of birds in yards over two winters in San Antonio, Texas, United States, a city located in a subtropical region. We used a reversed Before-After-Control-Impact experimental design in which yards were randomly allocated either mixed seed, Nyjer, or no food (control) between November 2019 and March 2020 (Year One). Between November 2020 and March 2021 (Year Two) supplementary food was not provided in any yards. Point counts conducted during both years of the study revealed that overall bird abundance was consistent between years in control yards and yards provided with Nyjer. In contrast, overall bird abundance was statistically significantly higher when supplementary food was present in mixed seed yards, driven by an increase in granivorous and omnivorous species. In contrast, supplementary feeding had no statistically significant effect on the abundance of insectivorous species or on species diversity, although species diversity tended to be higher in the presence of mixed seed. Our study demonstrates that wild bird food commonly provided by community members influences measures of avian community structure during the winter in urban yards in a subtropical city. However, these results depend on the type of bird food provided. Our results provide insight into the processes underlying the effects of urbanization on bird communities, and thus have implications for the management of urban birds more broadly.

Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect measurements for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases.

Owls (Strigiformes) provide myriad ecosystem services and are sentinels for ecosystem health. However, they are at continued risk from anthropogenic threats such as vehicle collisions, entanglement with human-made materials, and exposure to anticoagulant rodenticides (ARs), a widespread pesticide known to affect owls. Texas is an important region for numerous migratory and non-migratory owl species in the United States (US), yet assessments of threats owls face here are lacking preventing the development of informed conservation strategies. This study coupled assessment of admittance data from two wildlife rehabilitation centers in Texas with AR liver screening to (1) identify which species of owls are commonly admitted, (2) evaluate seasonality of admittance, and (3) assess causes of admittance for owls in Texas. Between 2010 and 2021, 1,620 owls were admitted into rehabilitation, representing eight species of which the Great-horned Owl ( Bubo virginianus ) was the most common. For all owls combined admittance rates were highest in the spring, driven by an influx of juveniles (n = 703, 43.40%). The leading cause of admittance amongst species was ‘no apparent injury’ (n = 567, 34.94%). Where clear diagnoses could be made, the leading causes of admittances were ‘entrapment in human infrastructure’ (n = 100, 6.11%) and ‘collision with vehicles’ (n = 74, 4.56%). While the admittance data did not reveal any cases of AR poisoning, liver screening demonstrated high incidences of AR exposure; of 53 owls screened for ARs, 50.94% (n = 27) tested positive with 18 showing exposure to multiple ARs. Brodifacoum was the most frequently detected AR (n = 19, 43.18%) and seven owls (25.93%) tested positive within lethal ranges. Our results suggest that owls in Texas are at risk from myriad anthropogenic threats and face high exposure rates to ARs. In doing so, our results can inform conservation strategies that mitigate anthropogenic threats faced by owls in Texas and beyond.

Epigenetic researchers often evaluate DNA methylation as a potential mediator of the effect of social/environmental exposures on a health outcome. Modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large multi-ethnic cohort in the United States, while providing an R package for their seamless implementation and adoption. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model (BSLMM) and high-dimensional mediation analysis (HDMA); while the preferred methods for estimating the global mediation effect are high-dimensional linear mediation analysis (HILMA) and principal component mediation analysis (PCMA). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.

Accelerated biological aging due to differences in socially patterned exposures has been proposed as a mechanism underlying racial and ethnic disparities in morbidity and mortality. Research exploring this hypothesis has been limited by a lack of consensus regarding the measurement of biological aging. The goal of this study is to examine self-reported race and ethnicity as a predictor of 13 measures of epigenetic aging. Data are from the National Health and Nutrition Examination Survey (1999-2002), a nationally representative study of US residents aged two months and older. The analytic sample includes 2,402 adults aged 50-84 with epigenetic data. The exposure is self-reported race and ethnicity, and the outcomes are 13 measures of epigenetic aging trained on different aging phenotypes. In linear regression models controlling for age, age-squared, gender, and nativity, White respondents had higher epigenetic aging than Black respondents (the reference group) for six out of seven measures trained on chronological age (Hannum: b = 1.98, 95% CI = 1.43, 2.54; Horvath: b = 0.75, 95% CI = 0.09, 1.40; Weidner: b = 1.15, 95% CI = 0.30, 2.01; Vidal-Bralo: b = 2.30, 95% CI = 1.76, 2.84; SkinBlood: b = 0.85, 95% CI = 0.28, 1.43; Zhang: b = 0.58, 95% CI = 0.40, 0.76) and for one measure trained on telomere length (b = -0.17, 95% CI = -0.20, -0.14). In contrast, White respondents had lower epigenetic aging than Black respondents for three out of four measures trained on physiological age (GrimAge: b = -1.33, 95% CI = -2.01, -0.64; DunedinPoAm: b = -0.03, 95% CI = -0.04, -0.01; GrimAge2: b = -1.97, 95% CI = -2.74, -1.20) and for one measure trained on stem cell divisions (b = -0.01, 95% CI = -0.01, -0.01). Fewer differences in epigenetic aging were observed when comparing Mexican American, other Hispanic, and another race or ethnicity respondents to Black respondents. White respondents had higher epigenetic aging than Black respondents for measures trained on chronological age, whereas the opposite was true for measures trained on physiological age. More work is needed to validate measures of epigenetic aging in non-White populations and to determine whether these measures are associated with health-related outcomes similarly across racial and ethnic groups.

Microplastic pollution has become a global concern and understanding its impact on wildlife requires effective sampling techniques that quantify exposure. In particular, non-lethal sampling techniques are needed for passerines for which microplastic exposure is poorly understood. In this study, we evaluated whether non-lethal proventricular gastric lavage can provide a representative sample of total microplastic ingestion in passerine birds. We sampled Brown-headed Cowbirds ( Molothrus ater ) (n = 105) from Government Canyon State Natural Area in San Antonio, Texas, United States (US). We performed gastric lavage to recover microplastics from each bird, before euthanizing them and dissecting gastrointestinal tracts. We recovered microplastics from 99% of birds. Gastric lavage recovered an average of 50.4% of ingested microplastics although recovery rate was highly variable (range: 0–100%, coefficient of variation: 59.52%), indicating much uncertainty in estimating individual total microplastic loads from gastric lavage. Sampling date influenced microplastic loads and recovery rates, which may be due to untested microplastic-environment interactions or may be an artifact of sampling conditions. Recovery rate was unaffected by time of day, bird age, sex, or body condition, or microplastic shape. Overall, our findings suggest that gastric lavage provides highly variable estimates of total gastrointestinal microplastics, and may be more appropriate for studies of recently ingested microplastics, only, that should be contained within the proventriculus.

Identifying genetic variants that are associated with variation in DNA methylation, an analysis commonly referred to as methylation quantitative trait locus (meQTL) mapping, is an important first step towards understanding the genetic architecture underlying epigenetic variation. Most existing meQTL mapping studies have focused on individuals of European ancestry and are underrepresented in other populations, with a particular absence of large studies in populations with African ancestry. We fill this critical knowledge gap by performing a large-scale cis-meQTL mapping study in 961 African Americans from the Genetic Epidemiology Network of Arteriopathy (GENOA) study. We identify a total of 4,565,687 cis-acting meQTLs in 320,965 meCpGs. We find that 45% of meCpGs harbor multiple independent meQTLs, suggesting potential polygenic genetic architecture underlying methylation variation. A large percentage of the cis-meQTLs also colocalize with cis-expression QTLs (eQTLs) in the same population. Importantly, the identified cis-meQTLs explain a substantial proportion (median = 24.6%) of methylation variation. In addition, the cis-meQTL associated CpG sites mediate a substantial proportion (median = 24.9%) of SNP effects underlying gene expression. Overall, our results represent an important step toward revealing the co-regulation of methylation and gene expression, facilitating the functional interpretation of epigenetic and gene regulation underlying common diseases in African Americans. Here, the authors performed a large-scale in-depth cis-meQTL mapping study in 961 African Americans from the GENOA study, investigating the co-regulation of methylation and gene expression in African Americans.

Since the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic, a focus of research has been to identify risk factors associated with COVID-19-related outcomes, such as testing and diagnosis, and use them to build prediction models. Existing studies have used data from digital surveys or electronic health records (EHRs), but very few have linked the two sources to build joint predictive models. In this study, we used survey data on 7,054 patients from the Michigan Genomics Initiative biorepository to evaluate how well self-reported data could be integrated with electronic records for the purpose of modeling COVID-19-related outcomes. We observed that among survey respondents, self-reported COVID-19 diagnosis captured a larger number of cases than the corresponding EHRs, suggesting that self-reported outcomes may be better than EHRs for distinguishing COVID-19 cases from controls. In the modeling context, we compared the utility of survey- and EHR-derived predictor variables in models of survey-reported COVID-19 testing and diagnosis. We found that survey-derived predictors produced uniformly stronger models than EHR-derived predictors—likely due to their specificity, temporal proximity, and breadth—and that combining predictors from both sources offered no consistent improvement compared to using survey-based predictors alone. Our results suggest that, even though general EHRs are useful in predictive models of COVID-19 outcomes, they may not be essential in those models when rich survey data are already available. The two data sources together may offer better prediction for COVID severity, but we did not have enough severe cases in the survey respondents to assess that hypothesis in in our study.