MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Public Health Experts Ask CDC Director to Broaden Input on Revisions to Key Infection Control Guidelines.

On July 20, 2023 a letter was sent to the Director of the Centers for Disease Control and Prevention requesting the agency's Healthcare Infection Control Practice Advisory Committee seek input from more stakeholders and the public, recognize the importance of infectious disease transmission by inhalation of human-generated aerosols, and ensure the application of interventions from all levels of the control hierarchy.

Cell-Type Composition Affects Adipose Gene Expression Associations With Cardiometabolic Traits.

Understanding differences in adipose gene expression between individuals with different levels of clinical traits may reveal the genes and mechanisms leading to cardiometabolic diseases. However, adipose is a heterogeneous tissue. To account for cell-type heterogeneity, we estimated cell-type proportions in 859 subcutaneous adipose tissue samples with bulk RNA sequencing (RNA-seq) using a reference single-nuclear RNA-seq data set. Cell-type proportions were associated with cardiometabolic traits; for example, higher macrophage and adipocyte proportions were associated with higher and lower BMI, respectively. We evaluated cell-type proportions and BMI as covariates in tests of association between >25,000 gene expression levels and 22 cardiometabolic traits. For >95% of genes, the optimal, or best-fit, models included BMI as a covariate, and for 79% of associations, the optimal models also included cell type. After adjusting for the optimal covariates, we identified 2,664 significant associations (P ≤ 2e-6) for 1,252 genes and 14 traits. Among genes proposed to affect cardiometabolic traits based on colocalized genome-wide association study and adipose expression quantitative trait locus signals, 25 showed a corresponding association between trait and gene expression levels. Overall, these results suggest the importance of modeling cell-type proportion when identifying gene expression associations with cardiometabolic traits.

Rare tandem repeat expansions associate with genes involved in synaptic and neuronal signaling functions in schizophrenia.

Tandem repeat expansions (TREs) are associated with over 60 monogenic disorders and have recently been implicated in complex disorders such as cancer and autism spectrum disorder. The role of TREs in schizophrenia is now emerging. In this study, we have performed a genome-wide investigation of TREs in schizophrenia. Using genome sequence data from 1154 Swedish schizophrenia cases and 934 ancestry-matched population controls, we have detected genome-wide rare (<0.1% population frequency) TREs that have motifs with a length of 2-20 base pairs. We find that the proportion of individuals carrying rare TREs is significantly higher in the schizophrenia group. There is a significantly higher burden of rare TREs in schizophrenia cases than in controls in genic regions, particularly in postsynaptic genes, in genes overlapping brain expression quantitative trait loci, and in brain-expressed genes that are differentially expressed between schizophrenia cases and controls. We demonstrate that TRE-associated genes are more constrained and primarily impact synaptic and neuronal signaling functions. These results have been replicated in an independent Canadian sample that consisted of 252 schizophrenia cases of European ancestry and 222 ancestry-matched controls. Our results support the involvement of rare TREs in schizophrenia etiology.

Gangs, violence, and fear: punitive Darwinism in El Salvador.

This article evaluates the factors impacting support for tough on crime policies in El Salvador. Examining theoretical and empirical scholarly work, we look at how fear, together with social and political contexts drive public appetite for punitive policies towards criminals. We show that President Nayib Bukele is responding to public opinion and has implemented tough on crime policies at the expense of human rights violations and democratic institutions. Society favors candidates who are the "toughest" against criminal actors. Political candidates from all sides of the ideological spectrum tap into the fear of the populace to win votes, leading to punitive Darwinism. We provide an empirical assessment of which theoretically relevant factors are statistically associated with punitivism in the Salvadoran context, using multiple regression analysis of high-quality public opinion survey data from LAPOP.

HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets.

High-throughput chromatin conformation capture technologies, such as Hi-C and Micro-C, have enabled genome-wide view of chromatin spatial organization. Most recently, Hi-C-derived enrichment-based technologies, including HiChIP and PLAC-seq, offer attractive alternatives due to their high signal-to-noise ratio and low cost. While a series of computational tools have been developed for Hi-C data, methods tailored for HiChIP and PLAC-seq data are still under development. Here we present HPTAD, a computational method to identify topologically associating domains (TADs) from HiChIP and PLAC-seq data. We performed comprehensive benchmark analysis to demonstrate its superior performance over existing TAD callers designed for Hi-C data. HPTAD is freely available at https://github.com/yunliUNC/HPTAD.

MagicalRsq: Machine-learning-based genotype imputation quality calibration.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

TOP-LD: A tool to explore linkage disequilibrium with TOPMed whole-genome sequence data.

Current publicly available tools that allow rapid exploration of linkage disequilibrium (LD) between markers (e.g., HaploReg and LDlink) are based on whole-genome sequence (WGS) data from 2,504 individuals in the 1000 Genomes Project. Here, we present TOP-LD, an online tool to explore LD inferred with high-coverage (∼30×) WGS data from 15,578 individuals in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. TOP-LD provides a significant upgrade compared to current LD tools, as the TOPMed WGS data provide a more comprehensive representation of genetic variation than the 1000 Genomes data, particularly for rare variants and in the specific populations that we analyzed. For example, TOP-LD encompasses LD information for 150.3, 62.2, and 36.7 million variants for European, African, and East Asian ancestral samples, respectively, offering 2.6- to 9.1-fold increase in variant coverage compared to HaploReg 4.0 or LDlink. In addition, TOP-LD includes tens of thousands of structural variants (SVs). We demonstrate the value of TOP-LD in fine-mapping at the GGT1 locus associated with gamma glutamyltransferase in the African ancestry participants in UK Biobank. Beyond fine-mapping, TOP-LD can facilitate a wide range of applications that are based on summary statistics and estimates of LD. TOP-LD is freely available online.

A systematic evaluation of Hi-C data enhancement methods for enhancing PLAC-seq and HiChIP data.

The three-dimensional organization of chromatin plays a critical role in gene regulation. Recently developed technologies, such as HiChIP and proximity ligation-assisted ChIP-Seq (PLAC-seq) (hereafter referred to as HP for brevity), can measure chromosome spatial organization by interrogating chromatin interactions mediated by a protein of interest. While offering cost-efficiency over genome-wide unbiased high-throughput chromosome conformation capture (Hi-C) data, HP data remain sparse at kilobase (Kb) resolution with the current sequencing depth in the order of 108 reads per sample. Deep learning models, including HiCPlus, HiCNN, HiCNN2, DeepHiC and Variationally Encoded Hi-C Loss Enhancer (VEHiCLE), have been developed to enhance the sequencing depth of Hi-C data, but their performance on HP data has not been benchmarked. Here, we performed a comprehensive evaluation of HP data sequencing depth enhancement using models developed for Hi-C data. Specifically, we analyzed various HP data, including Smc1a HiChIP data of the human lymphoblastoid cell line GM12878, H3K4me3 PLAC-seq data of four human neural cell types as well as of mouse embryonic stem cells (mESC), and mESC CCCTC-binding factor (CTCF) PLAC-seq data. Our evaluations lead to the following three findings: (i) most models developed for Hi-C data achieve reasonable performance when applied to HP data (e.g. with Pearson correlation ranging 0.76-0.95 for pairs of loci within 300 Kb), and the enhanced datasets lead to improved statistical power for detecting long-range chromatin interactions, (ii) models trained on HP data outperform those trained on Hi-C data and (iii) most models are transferable across cell types. Our results provide a general guideline for HP data enhancement using existing methods designed for Hi-C data.

Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients.

Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped approximately 8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (approximately 30×) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among patients with CF. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the approximately 8,000 CF samples with GWAS array genotype using the Trans-Omics for Precision Medicine (TOPMed) freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for patients with CF, boosting genomic coverage from approximately 0.3-4.2 million genotyped markers to approximately 11-43 million well-imputed markers, and significantly improving polygenic risk score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of patients with CF. We demonstrate that despite having approximately 3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely owing to allele and haplotype differences between patients with CF and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program. Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$= 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

A large-scale transcriptome-wide association study (TWAS) of 10 blood cell phenotypes reveals complexities of TWAS fine-mapping.

Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases and are highly heritable. Although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) to systematically investigate the association between genetically predicted gene expression and hematological measures in 54,542 Europeans from the Genetic Epidemiology Research on Aging cohort. We found 239 significant gene-trait associations with hematological measures; we replicated 71 associations at p < 0.05 in a TWAS meta-analysis consisting of up to 35,900 Europeans from the Women's Health Initiative, Atherosclerosis Risk in Communities Study, and BioMe Biobank. Additionally, we attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with hematological measures, and performed further fine-mapping of TWAS loci. To facilitate interpretation of our findings, we designed an R Shiny application to interactively visualize our TWAS results by integrating them with additional genetic data sources (GWAS, TWAS from multiple reference panels, conditional analyses, known GWAS variants, etc.). Our results and application highlight frequently overlooked TWAS challenges and illustrate the complexity of TWAS fine-mapping.

Obesity in people living with type 1 diabetes.

Although type 1 diabetes is traditionally considered a disease of lean people, overweight and obesity are becoming increasingly more common in individuals with type 1 diabetes. Non-physiological insulin replacement that causes peripheral hyperinsulinaemia, insulin profiles that do not match basal and mealtime insulin needs, defensive snacking to avoid hypoglycaemia, or a combination of these, are believed to affect body composition and drive excessive accumulation of body fat in people with type 1 diabetes. The consequences of overweight or obesity in people with type 1 diabetes are of particular concern, as they increase the risk of both diabetes-related and obesity-related complications, including cardiovascular disease, stroke, and various types of cancer. In this Review, we summarise the current understanding of the aetiology and consequences of excessive bodyweight in people with type 1 diabetes and highlight the need to optimise future prevention and treatment strategies in this population.

The Intersectionality of Activism and Public Health in New York State: Can Labor/Health and Safety Move Addiction Recovery Forward?

People affected by overdose deaths are advocating for prevention and increased access to treatment. Activist coalitions challenged the deadly impact of stigma, discrimination, and inadequate access to life-saving substance use disorder (SUD) and mental health care. Advocacy by coalitions resulted in federal and state funding and legislation, improving access to care. New York State is a model for these reforms. Occupational safety and health activists have largely been absent from this critical policy work even though 70% of people who are struggling with substance use are working. Antiquated workplace policies discipline workers who have substance use problems, silencing those who need support. Pain related to hazardous and stressful work are drivers of the crisis. Prevention and recovery-friendly workplace programs are part of the solution. Partnerships among employers, unions and safety and health activists with the recovery movement can prevent SUD and help affected workers build and sustain their recovery.

Introduction to the Special Issue: Opioids and the Workplace - Risk Factors and Solutions.

The workplace has been a neglected element in the national response to the opioid crisis. This ignores that workplace safety and health and drug policies have become important factors in opioid use disorder among workers. This results from physical or emotional pain related to workplace injuries, illnesses, and stress, and through punitive workplace drug policies, failure to address stigma, and inadequate access to treatment and recovery resources. This comprehensive New Solutions special issue encompasses timely cutting-edge research, commentaries, activism, and calls for action on primary prevention in the workplace and intervention research. It also addresses the convergence of the COVID-19 and the opioid crises, high-risk occupations and industries, health inequalities, employer and union programs, peer advocacy and member assistance programs, worker training, health parity for addiction treatment and recovery services, protection of first responders and site clean-up workers, working conditions of substance use treatment workers, and calls for necessary funding.

HPRep: Quantifying Reproducibility in HiChIP and PLAC-Seq Datasets.

HiChIP and PLAC-Seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by the protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-Seq data. Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-Seq data. We applied HPRep to multiple real datasets and demonstrate that HPRep outperforms existing reproducibility measures developed for Hi-C data. Specifically, we applied HPRep to H3K4me3 PLAC-Seq data from mouse embryonic stem cells and mouse brain tissues as well as H3K27ac HiChIP data from human lymphoblastoid cell line GM12878 and leukemia cell line K562, showing that HPRep can more clearly separate among pseudo-replicates, real replicates, and non-replicates. Furthermore, in an H3K4me3 PLAC-Seq dataset consisting of 11 samples from four human brain cell types, HPRep demonstrated the expected clustering of data that could not be achieved by existing methods developed for Hi-C data, highlighting the need for a reproducibility metric tailored to HiChIP and PLAC-Seq data.

Instructor Training on Opioids and the Workplace, Prevention and Response in the Plumbing and Pipe-Fitting Industry: An Interview With Cheryl Ambrose.

Workers in the plumbing and pipe-fitting industry experience a wide variety of physical and emotional pain related to job hazards and lifestyle issues. Pain treatment and stress can lead to prescription or illicit substance use. The United Association of Journeymen and Apprentices of the Plumbing and Pipe-Fitting Industry of the United States and Canada has taken on these issues by adapting training developed by the National Institute of Environmental Health Sciences, Opioids and the Workplace, Prevention and Response Training. Under the leadership of Cheryl Ambrose, Health, Safety, and Environmental Administrator, the United Association of Journeymen and Apprentices of the Plumbing and Pipe-Fitting Industry of the United States and Canada has added an instructor training course and is tailoring the National Institute of Environmental Health Sciences curriculum to industry and union needs.

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations.

BACKGROUND: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. METHODS: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. RESULTS: Our results revealed 24 suggestive signals (p < 1 × 10-4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. CONCLUSIONS: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

It Is Time to Implement Primary Prevention in the Workplace to Ameliorate the Ongoing U.S. Opioid Epidemic.

The United States' opioid public health crisis continues having disastrous consequences on communities, including workers and employers. From May 2019 to May 2020, the largest number of drug overdose deaths was recorded over a twelve-month period. The "twindemics" of COVID-19 and opioids underscore the urgent need to address workers' physical and mental health. Although much has been written about the negative impacts of the opioid epidemic on the workplace, few initiatives have focused on primary prevention, addressing work-related root causes of opioid use disorders (e.g., injury, stress) that may lead to prescription or illicit opioid use. We suggest primary prevention efforts to address the connection between workplace hazards and opioid misuse, dependence, and addiction such as examining patterns of work injury and stress with records of opioid prescription. Government funding should be expanded to support primary prevention and research efforts to strengthen the evidence-base to support workplace primary prevention endeavors.