Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits.

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

Effectiveness of Covid-19 Vaccines over a 9-Month Period in North Carolina.

BACKGROUND: The duration of protection afforded by coronavirus disease 2019 (Covid-19) vaccines in the United States is unclear. Whether the increase in postvaccination infections during the summer of 2021 was caused by declining immunity over time, the emergence of the B.1.617.2 (delta) variant, or both is unknown. METHODS: We extracted data regarding Covid-19-related vaccination and outcomes during a 9-month period (December 11, 2020, to September 8, 2021) for approximately 10.6 million North Carolina residents by linking data from the North Carolina Covid-19 Surveillance System and the Covid-19 Vaccine Management System. We used a Cox regression model to estimate the effectiveness of the BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), and Ad26.COV2.S (Johnson & Johnson-Janssen) vaccines in reducing the current risks of Covid-19, hospitalization, and death, as a function of time elapsed since vaccination. RESULTS: For the two-dose regimens of messenger RNA (mRNA) vaccines BNT162b2 (30 µg per dose) and mRNA-1273 (100 µg per dose), vaccine effectiveness against Covid-19 was 94.5% (95% confidence interval [CI], 94.1 to 94.9) and 95.9% (95% CI, 95.5 to 96.2), respectively, at 2 months after the first dose and decreased to 66.6% (95% CI, 65.2 to 67.8) and 80.3% (95% CI, 79.3 to 81.2), respectively, at 7 months. Among early recipients of BNT162b2 and mRNA-1273, effectiveness decreased by approximately 15 and 10 percentage points, respectively, from mid-June to mid-July, when the delta variant became dominant. For the one-dose regimen of Ad26.COV2.S (5 × 1010 viral particles), effectiveness against Covid-19 was 74.8% (95% CI, 72.5 to 76.9) at 1 month and decreased to 59.4% (95% CI, 57.2 to 61.5) at 5 months. All three vaccines maintained better effectiveness in preventing hospitalization and death than in preventing infection over time, although the two mRNA vaccines provided higher levels of protection than Ad26.COV2.S. CONCLUSIONS: All three Covid-19 vaccines had durable effectiveness in reducing the risks of hospitalization and death. Waning protection against infection over time was due to both declining immunity and the emergence of the delta variant. (Funded by a Dennis Gillings Distinguished Professorship and the National Institutes of Health.).

EPIC: Inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing.

More than a decade of genome-wide association studies (GWASs) have identified genetic risk variants that are significantly associated with complex traits. Emerging evidence suggests that the function of trait-associated variants likely acts in a tissue- or cell-type-specific fashion. Yet, it remains challenging to prioritize trait-relevant tissues or cell types to elucidate disease etiology. Here, we present EPIC (cEll tyPe enrIChment), a statistical framework that relates large-scale GWAS summary statistics to cell-type-specific gene expression measurements from single-cell RNA sequencing (scRNA-seq). We derive powerful gene-level test statistics for common and rare variants, separately and jointly, and adopt generalized least squares to prioritize trait-relevant cell types while accounting for the correlation structures both within and between genes. Using enrichment of loci associated with four lipid traits in the liver and enrichment of loci associated with three neurological disorders in the brain as ground truths, we show that EPIC outperforms existing methods. We apply our framework to multiple scRNA-seq datasets from different platforms and identify cell types underlying type 2 diabetes and schizophrenia. The enrichment is replicated using independent GWAS and scRNA-seq datasets and further validated using PubMed search and existing bulk case-control testing results.

Mediation analysis of multiple mediators with incomplete omics data.

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.

Fitting the Cox proportional hazards model to big data.

The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects. Specifically, we perform maximum partial likelihood estimation on a small subset of the whole data and improve the initial estimator by incorporating the remaining data through one-step estimation with estimated efficient score functions. We show that the final estimator has the same asymptotic distribution as the conventional maximum partial likelihood estimator using the whole dataset but requires only a small fraction of computation time. We demonstrate the usefulness of the proposed method through extensive simulation studies and an application to the UK Biobank data.

Proportional rates models for multivariate panel count data.

Multivariate panel count data arise when there are multiple types of recurrent events, and the observation for each study subject consists of the number of recurrent events of each type between two successive examinations. We formulate the effects of potentially time-dependent covariates on multiple types of recurrent events through proportional rates models, while leaving the dependence structures of the related recurrent events completely unspecified. We employ nonparametric maximum pseudo-likelihood estimation under the working assumptions that all types of events are independent and each type of event is a nonhomogeneous Poisson process, and we develop a simple and stable EM-type algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that can be estimated consistently by a sandwich estimator. In addition, we develop a class of graphical and numerical methods for checking the adequacy of the fitted model. Finally, we evaluate the performance of the proposed methods through simulation studies and analysis of a skin cancer clinical trial.

Evaluating treatment efficacy in hospitalized COVID-19 patients, with applications to Adaptive COVID-19 Treatment Trials.

BACKGROUND: The current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness. METHODS: We specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials. RESULTS: For remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23-1.79; p < 0.001), and the common hazard ratio was 1.27 (95% CI = 1.09-1.47; p = 0.002). For baricitinib plus remdesivir versus remdesivir alone, the common odds ratio was 1.32 (95% CI = 1.10-1.57; p = 0.002), and the common hazard ratio was 1.30 (95% CI = 1.13-1.49; p < 0.001). For interferon beta-1a plus remdesivir versus remdesivir alone, the common odds ratio was 0.95 (95% CI = 0.79-1.14; p = 0.56), and the common hazard ratio was 0.98 (95% CI = 0.85-1.12; p = 0.74). CONCLUSIONS: The proposed methods comprehensively characterize the treatment effects on the entire clinical course of a hospitalized COVID-19 patient.

Impact of Vaccination, Prior Infection, and Therapy on Omicron Infection and Mortality.

BACKGROUND: Understanding immunity against Omicron infection and severe outcomes conferred by coronavirus disease 2019 (Covid-19) vaccination, prior severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and monoclonal antibody therapy will inform intervention strategies. METHODS: We considered 295 691 patients tested for SARS-CoV-2 at Cleveland Clinic between 1 October 2021 and 31 January 2022. We used logistic regression to investigate the association of vaccination and prior infection with the risk of SARS-CoV-2 infection and used Cox regression to investigate the association of vaccination, prior infection, and monoclonal antibody therapy with the risks of intensive care unit (ICU) stay and death. RESULTS: Vaccination and prior infection were less effective against Omicron than Delta infection but provided strong protection against ICU admission and death. Boosting greatly increased vaccine effectiveness against Omicron infection and severe outcomes, although effectiveness waned rapidly over time. Monoclonal antibody therapy considerably reduced risks of ICU admission and death. The relatively low mortality of the Omicron variant was due to both reduced lethality of this variant and increased population immunity acquired from booster vaccination and previous infection. CONCLUSIONS: Booster vaccination and prior SARS-CoV-2 infection provide strong protection against ICU admission and death from Omicron infection. Monoclonal antibody therapy is also beneficial.

α-Synuclein Induces Neuroinflammation Injury through the IL6ST-AS/STAT3/HIF-1α Axis.

The aggregation of α-synuclein (α-syn) promotes neuroinflammation and neuronal apoptosis, which eventually contribute to the pathogenesis of Parkinson's disease (PD). Our microarray analysis and experimental data indicated a significant expression difference of the long noncoding RNA IL6ST-AS and its anti-sense strand, IL6ST, in α-synuclein-induced microglia, compared with unstimulated microglia. IL6ST is a key component of the IL6R/IL6ST complex in the microglial membrane, which recognizes extracellular inflammatory factors, such as IL6. Studies have shown that the binding of IL6 to the IL6R/IL6ST complex could activate the JAK2-STAT3 pathway and promote an excessive immune response in glia cells. Meanwhile, the phosphorylation and activation of STAT3 could increase the transcription of HIF1A, encoding a hypoxia-inducible factor related to cytotoxic damage. Our results indicated that the overexpression of IL6ST-AS induced by exogenous α-synuclein could inhibit the expression of IL6ST and the activation of JAK2-STAT3 pathway in HMC3 cells. In addition, a reduction in STAT3 resulted in the transcription inhibition of HIF1A and the acceleration of oxidative stress injury in SH-SY5Y cells co-cultured with α-synuclein-induced HMC3 cells. Our findings indicate that IL6ST-AS is an important factor that regulates microglia activation and neuronal necrosis in the progression of PD. In the HMC3 and SH-SY5Y cell co-culture system, the overexpression of IL6ST-AS led to microglial dysfunction and neurotoxicology through the IL6ST-AS/STAT3/HIF-1α axis. Our research revealed the relationships among α-synuclein, IL6ST, STAT3, and HIF-1α in the pathological process of PD and provided a new inflammation hypothesis for the pathogenesis of PD.

Reliably Assessing Duration of Protection for Coronavirus Disease 2019 Vaccines.

Decision making about vaccination and boosting schedules for coronavirus disease 2019 (COVID-19) hinges on reliable methods for evaluating the longevity of vaccine protection. We show that modeling of protection as a piecewise linear function of time since vaccination for the log hazard ratio of the vaccine effect provides more reliable estimates of vaccine effectiveness at the end of an observation period and also detects plateaus in protective effectiveness more reliably than the standard method of estimating a constant vaccine effect over each time period. This approach will be useful for analyzing data pertaining to COVID-19 vaccines and other vaccines for which rapid and reliable understanding of vaccine effectiveness over time is desired.

Evaluating Vaccine Efficacy Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Although interim results from several large, placebo-controlled, phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic coronavirus disease 2019 (COVID-19), it is unknown how effective the vaccines are in preventing people from becoming asymptomatically infected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between 2 antibody or reverse-transcription polymerase chain reaction (RT-PCR) tests. Additional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment of participants or crossover of placebo recipients to the vaccine arm before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, staggered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies that mimic the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates.

Genome-wide association studies of survival in 1520 cancer patients treated with bevacizumab-containing regimens.

Germline variants might predict cancer progression. Bevacizumab improves overall survival (OS) in patients with advanced cancers. No biomarkers are available to identify patients that benefit from bevacizumab. A meta-analysis of genome-wide association studies (GWAS) was conducted in 1,520 patients from Phase III trials (CALGB 80303, 40503, 80405 and ICON7), where bevacizumab was randomized to treatment without bevacizumab. We aimed to identify genes and single nucleotide polymorphisms (SNPs) associated with survival independently of bevacizumab treatment or through interaction with bevacizumab. A cause-specific Cox model was used to test the SNP-OS association in both arms combined (prognostic), and the effect of SNPs-bevacizumab interaction on OS (predictive) in each study. The SNP effects across studies were combined using inverse variance. Findings were tested for replication in advanced colorectal and ovarian cancer patients from The Cancer Genome Atlas (TGCA). In the GWAS meta-analysis, patients with rs680949 in PRUNE2 experienced shorter OS compared to patients without it (P = 1.02 × 10-7 , hazard ratio [HR] = 1.57, 95% confidence interval [CI] 1.33-1.86), as well as in TCGA (P = .0219, HR = 1.58, 95% CI 1.07-2.35). In the GWAS meta-analysis, patients with rs16852804 in BARD1 experienced shorter OS compared to patients without it (P = 1.40 × 10-5 , HR = 1.51, 95% CI 1.25-1.82) as well as in TCGA (P = 1.39 × 10-4 , HR = 3.09, 95% CI 1.73-5.51). Patients with rs3795897 in AGAP1 experienced shorter OS in the bevacizumab arm compared to the nonbevacizumab arm (P = 1.43 × 10-5 ). The largest GWAS meta-analysis of bevacizumab treated patients identified PRUNE2 and BARD1 (tumor suppressor genes) as prognostic genes of colorectal and ovarian cancer, respectively, and AGAP1 as a potentially predictive gene that interacts with bevacizumab with respect to patient survival.

Bevacizumab-induced hypertension and proteinuria: a genome-wide study of more than 1000 patients.

BACKGROUND: Hypertension and proteinuria are common bevacizumab-induced toxicities. No validated biomarkers are available for identifying patients at risk of these toxicities. METHODS: A genome-wide association study (GWAS) meta-analysis was performed in 1039 bevacizumab-treated patients of European ancestry in four clinical trials (CALGB 40502, 40503, 80303, 90401). Grade ≥2 hypertension and proteinuria were recorded (CTCAE v.3.0). Single-nucleotide polymorphism (SNP)-toxicity associations were determined using a cause-specific Cox model adjusting for age and sex. RESULTS: The most significant SNP associated with hypertension with concordant effect in three out of the four studies (p-value <0.05 for each study) was rs6770663 (A > G) in KCNAB1, with the G allele increasing the risk of hypertension (p-value = 4.16 × 10-6). The effect of the G allele was replicated in ECOG-ACRIN E5103 in 582 patients (p-value = 0.005). The meta-analysis of all five studies for rs6770663 led to p-value = 7.73 × 10-8, close to genome-wide significance. The most significant SNP associated with proteinuria was rs339947 (C > A, between DNAH5 and TRIO), with the A allele increasing the risk of proteinuria (p-value = 1.58 × 10-7). CONCLUSIONS: The results from the largest study of bevacizumab toxicity provide new markers of drug safety for further evaluations. SNP in KCNAB1 validated in an independent dataset provides evidence toward its clinical applicability to predict bevacizumab-induced hypertension. ClinicalTrials.gov Identifier: NCT00785291 (CALGB 40502); NCT00601900 (CALGB 40503); NCT00088894 (CALGB 80303) and NCT00110214 (CALGB 90401).

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

Consistency and overfitting of multi-omics methods on experimental data.

Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.

PIK3R5 genetic predictors of hypertension induced by VEGF-pathway inhibitors.

No biomarkers are available to predict patients at risk of developing hypertension induced by VEGF-pathway inhibitors. This study aimed to identify predictive biomarkers of hypertension induced by these drugs using a discovery-replication approach. The discovery set included 140 sorafenib-treated patients (TARGET study) genotyped for 973 SNPs in 56 genes. The most statistically significant SNPs associated with grade ≥2 hypertension were tested for association with grade ≥2 hypertension in the replication set of a GWAS of 1039 bevacizumab-treated patients from four clinical trials (CALGB/Alliance). In the discovery set, rs444904 (G > A) in PIK3R5 was associated with an increased risk of sorafenib-induced hypertension (p = 0.006, OR = 3.88 95% CI 1.54-9.81). In the replication set, rs427554 (G > A) in PIK3R5 (in complete linkage disequilibrium with rs444904) was associated with an increased risk of bevacizumab-induced hypertension (p = 0.008, OR = 1.39, 95% CI 1.09-1.78). This study identified a predictive marker of drug-induced hypertension that should be evaluated for other VEGF-pathway inhibitors.ClinicalTrials.gov Identifier:NCT00073307 (TARGET).

Association of Primary and Booster Vaccination and Prior Infection With SARS-CoV-2 Infection and Severe COVID-19 Outcomes.

Importance: Data about the association of COVID-19 vaccination and prior SARS-CoV-2 infection with risk of SARS-CoV-2 infection and severe COVID-19 outcomes may guide prevention strategies. Objective: To estimate the time-varying association of primary and booster COVID-19 vaccination and prior SARS-CoV-2 infection with subsequent SARS-CoV-2 infection, hospitalization, and death. Design, Setting, and Participants: Cohort study of 10.6 million residents in North Carolina from March 2, 2020, through June 3, 2022. Exposures: COVID-19 primary vaccine series and boosters and prior SARS-CoV-2 infection. Main Outcomes and Measures: Rate ratio (RR) of SARS-CoV-2 infection and hazard ratio (HR) of COVID-19-related hospitalization and death. Results: The median age among the 10.6 million participants was 39 years; 51.3% were female, 71.5% were White, and 9.9% were Hispanic. As of June 3, 2022, 67% of participants had been vaccinated. There were 2 771 364 SARS-CoV-2 infections, with a hospitalization rate of 6.3% and mortality rate of 1.4%. The adjusted RR of the primary vaccine series compared with being unvaccinated against infection became 0.53 (95% CI, 0.52-0.53) for BNT162b2, 0.52 (95% CI, 0.51-0.53) for mRNA-1273, and 0.51 (95% CI, 0.50-0.53) for Ad26.COV2.S 10 months after the first dose, but the adjusted HR for hospitalization remained at 0.29 (95% CI, 0.24-0.35) for BNT162b2, 0.27 (95% CI, 0.23-0.32) for mRNA-1273, and 0.35 (95% CI, 0.29-0.42) for Ad26.COV2.S and the adjusted HR of death remained at 0.23 (95% CI, 0.17-0.29) for BNT162b2, 0.15 (95% CI, 0.11-0.20) for mRNA-1273, and 0.24 (95% CI, 0.19-0.31) for Ad26.COV2.S. For the BNT162b2 primary series, boosting in December 2021 with BNT162b2 had the adjusted RR relative to primary series of 0.39 (95% CI, 0.38-0.40) and boosting with mRNA-1273 had the adjusted RR of 0.32 (95% CI, 0.30-0.34) against infection after 1 month and boosting with BNT162b2 had the adjusted RR of 0.84 (95% CI, 0.82-0.86) and boosting with mRNA-1273 had the adjusted RR of 0.60 (95% CI, 0.57-0.62) after 3 months. Among all participants, the adjusted RR of Omicron infection compared with no prior infection was estimated at 0.23 (95% CI, 0.22-0.24) against infection, and the adjusted HRs were 0.10 (95% CI, 0.07-0.14) against hospitalization and 0.11 (95% CI, 0.08-0.15) against death after 4 months. Conclusions and Relevance: Receipt of primary COVID-19 vaccine series compared with being unvaccinated, receipt of boosters compared with primary vaccination, and prior infection compared with no prior infection were all significantly associated with lower risk of SARS-CoV-2 infection (including Omicron) and resulting hospitalization and death. The associated protection waned over time, especially against infection.

A general framework for integrative analysis of incomplete multiomics data.

There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative omics measurements, such as metabolite levels and protein expressions, are subject to detection limits in that the measurements below (or above) certain thresholds are not detectable. In this article, we propose a rigorous and powerful approach to handle missing values and detection limits in integrative analysis of multiomics data. We relate quantitative omics variables to genetic variants and other variables through linear regression models and relate phenotypes to quantitative omics variables and other variables through generalized linear models. We derive the joint-likelihood for the two sets of models by allowing arbitrary patterns of missing values and detection limits for quantitative omics variables. We carry out maximum-likelihood estimation through computationally fast and stable algorithms. The resulting estimators are approximately unbiased and statistically efficient. An application to a major study on chronic obstructive lung disease yielded new biological insights.

Evaluating the Long-term Efficacy of Coronavirus Disease 2019 (COVID-19) Vaccines.

Large-scale deployment of safe and durably effective vaccines can curtail the coronavirus disease-2019 (COVID-19) pandemic. However, the high vaccine efficacy (VE) reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about 2 months, and thus does not pertain to long-term efficacy. To evaluate the duration of protection while allowing trial participants timely access to efficacious vaccine, investigators can sequentially cross participants over from the placebo arm to the vaccine arm. Here, we show how to estimate potentially time-varying placebo-controlled VE in this type of staggered vaccination of participants. In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term VE.