Using Protection Motivation Theory to Predict Intentions for Breast Cancer Risk Management: Intervention Mechanisms from a Randomized Controlled Trial

The purpose of this study is to evaluate the direct and indirect effects of a web-based, Protection Motivation Theory (PMT)-informed breast cancer education and decision support tool on intentions for risk-reducing medication and breast MRI among high-risk women. Women with ≥ 1.67% 5-year breast cancer risk (N = 995) were randomized to (1) control or (2) the PMT-informed intervention. Six weeks post-intervention, 924 (93% retention) self-reported PMT constructs and behavioral intentions. Bootstrapped mediations evaluated the direct effect of the intervention on behavioral intentions and the mediating role of PMT constructs. There was no direct intervention effect on intentions for risk-reducing medication or MRI (p's ≥ 0.12). There were significant indirect effects on risk-reducing medication intentions via perceived risk, self-efficacy, and response efficacy, and on MRI intentions via perceived risk and response efficacy (p's ≤ 0.04). The PMT-informed intervention effected behavioral intentions via perceived breast cancer risk, self-efficacy, and response efficacy. Future research should extend these findings from intentions to behavior. ClinicalTrials.gov Identifier: NCT03029286 (date of registration: January 24, 2017).

CD47 interactions with exportin-1 limit the targeting of m7G-modified RNAs to extracellular vesicles

CD47 is a marker of self and a signaling receptor for thrombospondin-1 that is also a component of extracellular vesicles (EVs) released by various cell types. Previous studies identified CD47-dependent functional effects of T cell EVs on target cells, mediated by delivery of their RNA contents, and enrichment of specific subsets of coding and noncoding RNAs in CD47+ EVs. Mass spectrometry was employed here to identify potential mechanisms by which CD47 regulates the trafficking of specific RNAs to EVs. Specific interactions of CD47 and its cytoplasmic adapter ubiquilin-1 with components of the exportin-1/Ran nuclear export complex were identified and confirmed by coimmunoprecipitation. Exportin-1 is known to regulate nuclear to cytoplasmic trafficking of 5'-7-methylguanosine (m7G)-modified microRNAs and mRNAs that interact with its cargo protein EIF4E. Interaction with CD47 was inhibited following alkylation of exportin-1 at Cys528 by its covalent inhibitor leptomycin B. Leptomycin B increased levels of m7G-modified RNAs, and their association with exportin-1 in EVs released from wild type but not CD47-deficient cells. In addition to perturbing nuclear to cytoplasmic transport, transcriptomic analyses of EVs released by wild type and CD47-deficient Jurkat T cells revealed a global CD47-dependent enrichment of m7G-modified microRNAs and mRNAs in EVs released by CD47-deficient cells. Correspondingly, decreasing CD47 expression in wild type cells or treatment with thrombospondin-1 enhanced levels of specific m7G-modified RNAs released in EVs, and re-expressing CD47 in CD47-deficient T cells decreased their levels. Therefore, CD47 signaling limits the trafficking of m7G-modified RNAs to EVs through physical interactions with the exportin-1/Ran transport complex.

LGBTQ+ Perspectives on Conducting Genomic Research on Sexual Orientation and Gender Identity

We conducted in-depth, semi-structured interviews with LGBTQ+-identified individuals (n = 31) to explore the range of LGBTQ+ perspectives on genomic research using either sexual orientation or gender identity (SOGI) data. Most interviewees presumed that research would confirm genetic contributions to sexual orientation and gender identity. Primary hopes for such confirmation included validating LGBTQ+ identities, improved access to and quality of healthcare and other resources, and increased acceptance in familial, socio-cultural, and political environments. Areas of concern included threats of pathologizing and medicalizing LGBTQ+ identities and experiences, undermining reproductive rights, gatekeeping of health or social systems, and malicious testing or misuse of genetic results, particularly for LGBTQ+ youth. Overall, interviewees were divided on the acceptability of genomic research investigating genetic contributions to sexual orientation and gender identity. Participants emphasized researchers' ethical obligations to LGBTQ+ individuals and endorsed engagement with LGBTQ+ communities throughout all aspects of genomic research using SOGI data.

Rare coding variants in RCN3 are associated with blood pressure

BACKGROUND

While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries.

RESULTS

Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7).

CONCLUSIONS

Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

Comparison and evaluation of statistical error models for scRNA-seq

BACKGROUND

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate.

RESULTS

Here, we analyze 59 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation.

CONCLUSIONS

Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

Transcription factor regulation of eQTL activity across individuals and tissues

Tens of thousands of genetic variants associated with gene expression (cis-eQTLs) have been discovered in the human population. These eQTLs are active in various tissues and contexts, but the molecular mechanisms of eQTL variability are poorly understood, hindering our understanding of genetic regulation across biological contexts. Since many eQTLs are believed to act by altering transcription factor (TF) binding affinity, we hypothesized that analyzing eQTL effect size as a function of TF level may allow discovery of mechanisms of eQTL variability. Using GTEx Consortium eQTL data from 49 tissues, we analyzed the interaction between eQTL effect size and TF level across tissues and across individuals within specific tissues and generated a list of 10,098 TF-eQTL interactions across 2,136 genes that are supported by at least two lines of evidence. These TF-eQTLs were enriched for various TF binding measures, supporting with orthogonal evidence that these eQTLs are regulated by the implicated TFs. We also found that our TF-eQTLs tend to overlap genes with gene-by-environment regulatory effects and to colocalize with GWAS loci, implying that our approach can help to elucidate mechanisms of context-specificity and trait associations. Finally, we highlight an interesting example of IKZF1 TF regulation of an APBB1IP gene eQTL that colocalizes with a GWAS signal for blood cell traits. Together, our findings provide candidate TF mechanisms for a large number of eQTLs and offer a generalizable approach for researchers to discover TF regulators of genetic variant effects in additional QTL datasets.

Barriers and facilitators to qualitative data sharing in the United States: A survey of qualitative researchers

Qualitative health data are rarely shared in the United States (U.S.). This is unfortunate because gathering qualitative data is labor and time-intensive, and data sharing enables secondary research, training, and transparency. A new U.S. federal policy mandates data sharing by 2023, and is agnostic to data type. We surveyed U.S. qualitative researchers (N = 425) on the barriers and facilitators of sharing qualitative health or sensitive research data. Most researchers (96%) have never shared qualitative data in a repository. Primary concerns were lack of participant permission to share data, data sensitivity, and breaching trust. Researcher willingness to share would increase if participants agreed and if sharing increased the societal impact of their research. Key resources to increase willingness to share were funding, guidance, and de-identification assistance. Public health and biomedical researchers were most willing to share. Qualitative researchers need to prepare for this new reality as sharing qualitative data requires unique considerations.

Multivariable association discovery in population-scale meta-omics studies

It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.

De novo variants in CACNA1E found in patients with intellectual disability, developmental regression and social cognition deficit but no seizures

BACKGROUND

De novo variants in the voltage-gated calcium channel subunit α1 E gene (CACNA1E) have been described as causative of epileptic encephalopathy with contractures, macrocephaly and dyskinesias.

METHODS

Following the observation of an index patient with developmental delay and autism spectrum disorder (ASD) without seizures who had a de novo deleterious CACNA1E variant, we screened GeneMatcher for other individuals with CACNA1E variants and neurodevelopmental phenotypes without epilepsy. The spectrum of pathogenic CACNA1E variants was compared to the mutational landscape of variants in the gnomAD control population database.

RESULTS

We identified seven unrelated individuals with intellectual disability, developmental regression and ASD-like behavioral profile, and notably without epilepsy, who had de novo heterozygous putatively pathogenic variants in CACNA1E. Age of onset of clinical manifestation, presence or absence of regression and degree of severity were variable, and no clear-cut genotype-phenotype association could be recognized. The analysis of disease-associated variants and their comparison to benign variants from the control population allowed for the identification of regions in the CACNA1E protein that seem to be intolerant to substitutions and thus more likely to harbor pathogenic variants. As in a few reported cases with CACNA1E variants and epilepsy, one patient showed a positive clinical behavioral response to topiramate, a specific calcium channel modulator.

LIMITATIONS

The significance of our study is limited by the absence of functional experiments of the effect of identified variants, the small sample size and the lack of systematic ASD assessment in all participants. Moreover, topiramate was given to one patient only and for a short period of time.

CONCLUSIONS

Our results indicate that CACNA1E variants may result in neurodevelopmental disorders without epilepsy and expand the mutational and phenotypic spectrum of this gene. CACNA1E deserves to be included in gene panels for non-specific developmental disorders, including ASD, and not limited to patients with seizures, to improve diagnostic recognition and explore the possible efficacy of topiramate.

APOC3 genetic variation, serum triglycerides, and risk of coronary artery disease in Asian Indians, Europeans, and other ethnic groups

BACKGROUND

Hypertriglyceridemia has emerged as a critical coronary artery disease (CAD) risk factor. Rare loss-of-function (LoF) variants in apolipoprotein C-III have been reported to reduce triglycerides (TG) and are cardioprotective in American Indians and Europeans. However, there is a lack of data in other Europeans and non-Europeans. Also, whether genetically increased plasma TG due to ApoC-III is causally associated with increased CAD risk is still unclear and inconsistent. The objectives of this study were to verify the cardioprotective role of earlier reported six LoF variants of APOC3 in South Asians and other multi-ethnic cohorts and to evaluate the causal association of TG raising common variants for increasing CAD risk.

METHODS

We performed gene-centric and Mendelian randomization analyses and evaluated the role of genetic variation encompassing APOC3 for affecting circulating TG and the risk for developing CAD.

RESULTS

One rare LoF variant (rs138326449) with a 37% reduction in TG was associated with lowered risk for CAD in Europeans (p = 0.007), but we could not confirm this association in Asian Indians (p = 0.641). Our data could not validate the cardioprotective role of other five LoF variants analysed. A common variant rs5128 in the APOC3 was strongly associated with elevated TG levels showing a p-value 2.8 × 10- 424. Measures of plasma ApoC-III in a small subset of Sikhs revealed a 37% increase in ApoC-III concentrations among homozygous mutant carriers than the wild-type carriers of rs5128. A genetically instrumented per 1SD increment of plasma TG level of 15 mg/dL would cause a mild increase (3%) in the risk for CAD (p = 0.042).

CONCLUSIONS

Our results highlight the challenges of inclusion of rare variant information in clinical risk assessment and the generalizability of implementation of ApoC-III inhibition for treating atherosclerotic disease. More studies would be needed to confirm whether genetically raised TG and ApoC-III concentrations would increase CAD risk.

2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.