Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics.

Understanding the causal genetic architecture of complex phenotypes is essential for future research into disease mechanisms and potential therapies. Here, we present a novel framework for genome-wide detection of sets of variants that carry non-redundant information on the phenotypes and are therefore more likely to be causal in a biological sense. Crucially, our framework requires only summary statistics obtained from standard genome-wide marginal association testing. The described approach, implemented in open-source software, is also computationally efficient, requiring less than 15 minutes on a single CPU to perform genome-wide analysis. Through extensive genome-wide simulation studies, we show that the method can substantially outperform usual two-stage marginal association testing and fine-mapping procedures in precision and recall. In applications to a meta-analysis of ten large-scale genetic studies of Alzheimer's disease (AD), we identified 82 loci associated with AD, including 37 additional loci missed by conventional GWAS pipeline. The identified putative causal variants achieve state-of-the-art agreement with massively parallel reporter assays and CRISPR-Cas9 experiments. Additionally, we applied the method to a retrospective analysis of 67 large-scale GWAS summary statistics since 2013 for a variety of phenotypes. Results reveal the method's capacity to robustly discover additional loci for polygenic traits and pinpoint potential causal variants underpinning each locus beyond conventional GWAS pipeline, contributing to a deeper understanding of complex genetic architectures in post-GWAS analyses.

Structure-based prediction and characterization of photo-crosslinking in native protein-RNA complexes.

UV-crosslinking of protein and RNA in direct contacts has been widely used to study protein-RNA complexes while our understanding of the photo-crosslinking mechanisms remains poor. This knowledge gap is due to the challenge of precisely mapping the crosslink sites in protein and RNA simultaneously in their native sequence and structural contexts. Here we systematically analyze protein-RNA interactions and photo-crosslinking by bridging crosslinked nucleotides and amino acids mapped using different assays with protein-RNA complex structures. We developed a computational method PxR3D-map which reliably predicts crosslink sites using structural information characterizing protein-RNA interaction interfaces. Analysis of the informative features revealed that photo-crosslinking is facilitated by base stacking with not only aromatic residues, but also dipeptide bonds that involve glycine, and distinct mechanisms are utilized by different RNA-binding domains. Our work suggests protein-RNA photo-crosslinking is highly selective in the cellular environment, which can guide data interpretation and further technology development for UV-crosslinking-based assays.

Results of Immediate Postpartum Long Acting Reversible Contraception Provision After Expanded Reimbursement Policy Implementation at an Academic Medical Institution.

INTRODUCTION: Immediate postpartum (IPP) Long Acting Reversible Contraception (LARC) is effective in reducing short birth spacing, which is highest among minoritized and younger women with lower socioeconomic status. The structural barrier of cost for pregnant people who desire IPP LARC insertion was alleviated in 2016 when New York State provided statewide reimbursement for Medicaid recipients. METHODS: Analyses of existing electronic medical records (EMR) were conducted on women who received IPP LARC between 3/2/17 and 9/2/19 at two hospitals after a term delivery, defined as gestational age 37 0/7 weeks or greater. Descriptive and bivariate statistics, including chi-square tests and Fischer's exact tests, based on cell sizes, were calculated using SAS (version9.4). RESULTS: Prior to the study period, IPP LARC was not placed in these hospitals. After reimbursement policy changes, electronic medical record data identified 501 women with full term delivery and IPP LARC placed, of which the majority were single (82.8%), Black (49.1%), and had public insurance (Medicaid and Medicaid Managed Care) (79.2%). DISCUSSION: Removing structural economic barriers for people using public insurance may increase health equity in contraceptive access and choice.

CARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses.

Fine-mapping is commonly used to identify putative causal variants at genome-wide significant loci. Here we propose a Bayesian model for fine-mapping that has several advantages over existing methods, including flexible specification of the prior distribution of effect sizes, joint modeling of summary statistics and functional annotations and accounting for discrepancies between summary statistics and external linkage disequilibrium in meta-analyses. Using simulations, we compare performance with commonly used fine-mapping methods and show that the proposed model has higher power and lower false discovery rate (FDR) when including functional annotations, and higher power, lower FDR and higher coverage for credible sets in meta-analyses. We further illustrate our approach by applying it to a meta-analysis of Alzheimer's disease genome-wide association studies where we prioritize putatively causal variants and genes.

BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data.

We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at [Formula: see text] of the associated loci.

GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies.

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer's disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests.

KnockoffTrio: A knockoff framework for the identification of putative causal variants in genome-wide association studies with trio design.

Family-based designs can eliminate confounding due to population substructure and can distinguish direct from indirect genetic effects, but these designs are underpowered due to limited sample sizes. Here, we propose KnockoffTrio, a statistical method to identify putative causal genetic variants for father-mother-child trio design built upon a recently developed knockoff framework in statistics. KnockoffTrio controls the false discovery rate (FDR) in the presence of arbitrary correlations among tests and is less conservative and thus more powerful than the conventional methods that control the family-wise error rate via Bonferroni correction. Furthermore, KnockoffTrio is not restricted to family-based association tests and can be used in conjunction with more powerful, potentially nonlinear models to improve the power of standard family-based tests. We show, using empirical simulations, that KnockoffTrio can prioritize causal variants over associations due to linkage disequilibrium and can provide protection against confounding due to population stratification. In applications to 14,200 trios from three study cohorts for autism spectrum disorders (ASDs), including AGP, SPARK, and SSC, we show that KnockoffTrio can identify multiple significant associations that are missed by conventional tests applied to the same data. In particular, we replicate known ASD association signals with variants in several genes such as MACROD2, NRXN1, PRKAR1B, CADM2, PCDH9, and DOCK4 and identify additional associations with variants in other genes including ARHGEF10, SLC28A1, ZNF589, and HINT1 at FDR 10%.

A Systematical Evaluation of the Crystallographic Orientation Relationship between MC Precipitates and Ferrite Matrix in HSLA Steels.

Here we systematically investigate the crystallographic orientation relationship (OR) between MC-type precipitates (M, metal; C, carbon) and ferrite matrix in the Ti-Mo microalloyed steel with different processing. In the specimens without austenite deformation, the interphase precipitation can be obtained, and the precipitates obey Baker-Nutting (BN) OR with ferrite matrix. By contrast, in the specimens with austenite deformation, the supersaturated precipitates were formed in ferrite grains, which can obey BN, Nishiyama-Wasserman (NW), Kurdjumov-Sachs (KS) and Pitsch (P) ORs simultaneously. The cooling rate after austenite deformation can influence the OR between carbides and ferrite in the MC/ferrite system. At the cooling rate of 80 °C/s, carbides and ferrite can roughly satisfy these OR with the deviation ≥ 10°, while at the cooling rate of 20 °C/s, carbides and ferrite can strictly obey the specific OR. The energy accumulated in the deformation process and maintained in the fast-cooling process (80 °C/s) can offset the formation energy of the carbides. Thus, the carbides formed in the specimen with the cooling rate of 80 °C/s do not strictly satisfy the specific ORs to meet the rule of lowest energy, and then deviate by a small angle based on the specific ORs.

Comprehensive analysis of lncRNA-mRNAs co-expression network identifies potential lncRNA biomarkers in cutaneous squamous cell carcinoma.

BACKGROUND: Cutaneous squamous cell carcinoma (cSCC) is the second most common type of skin cancer, the prognosis for patients with metastatic cSCC remains relatively poor. Thus, there is an urgent need to identify new diagnostic, prognostic, and therapeutic targets and pathways in cSCC. RESULTS: It detected a total of 37,507 lncRNA probes and 32,825 mRNA probes and found 3593 differentially expressed lncRNAs and 3236 differentially expressed mRNAs. It has been found that mRNAs ACY3, NR1D1, MZB1 has co-expression relationship with six lncRNAs, GXYLT1P3, LINC00348, LOC101928131, A-33-p3340852, A-21-p0003442 and LOC644838. CONCLUSIONS: The aim of this study is to identify cSCC-specific lncRNAs and indicated that six unstudied lncRNAs may serve an important role in endoplasmic reticulum stress apoptosis, autophagy and the progression of cSCC by modulating ACY3, NR1D1 and MZB1.

A rare case of self-healing giant condyloma acuminatum.

Giant condyloma acuminatum (GCA) which is also called Buschke-Lowenstein tumor. It is a rare tumor of the anorectal area and external genitalia associated with low-risk HPV types 6 or 11. GCA has a high-rate of recurrence (66%) and malignant transformation (56%). The clinical features of GCA are progression of exophytic, ulcerative, and cauliflower-shaped tumors, it has significant dimensions and may undergo malignant transformation such as squamous cell carcinoma or cervical cancer. It is difficult to treat GCA, and it may be impossible for GCA to self-healing, but we herein report a rare case of a 19-year-old female with self-healing GCA.

Genome-wide analysis of common and rare variants via multiple knockoffs at biobank scale, with an application to Alzheimer disease genetics.

Knockoff-based methods have become increasingly popular due to their enhanced power for locus discovery and their ability to prioritize putative causal variants in a genome-wide analysis. However, because of the substantial computational cost for generating knockoffs, existing knockoff approaches cannot analyze millions of rare genetic variants in biobank-scale whole-genome sequencing and whole-genome imputed datasets. We propose a scalable knockoff-based method for the analysis of common and rare variants across the genome, KnockoffScreen-AL, that is applicable to biobank-scale studies with hundreds of thousands of samples and millions of genetic variants. The application of KnockoffScreen-AL to the analysis of Alzheimer disease (AD) in 388,051 WG-imputed samples from the UK Biobank resulted in 31 significant loci, including 14 loci that are missed by conventional association tests on these data. We perform replication studies in an independent meta-analysis of clinically diagnosed AD with 94,437 samples, and additionally leverage single-cell RNA-sequencing data with 143,793 single-nucleus transcriptomes from 17 control subjects and AD-affected individuals, and proteomics data from 735 control subjects and affected indviduals with AD and related disorders to validate the genes at these significant loci. These multi-omics analyses show that 79.1% of the proximal genes at these loci and 76.2% of the genes at loci identified only by KnockoffScreen-AL exhibit at least suggestive signal (p < 0.05) in the scRNA-seq or proteomics analyses. We highlight a potentially causal gene in AD progression, EGFR, that shows significant differences in expression and protein levels between AD-affected individuals and healthy control subjects.

Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes.

Gene-based tests are valuable techniques for identifying genetic factors in complex traits. Here, we propose a gene-based testing framework that incorporates data on long-range chromatin interactions, several recent technical advances for region-based tests, and leverages the knockoff framework for synthetic genotype generation for improved gene discovery. Through simulations and applications to genome-wide association studies (GWAS) and whole-genome sequencing data for multiple diseases and traits, we show that the proposed test increases the power over state-of-the-art gene-based tests in the literature, identifies genes that replicate in larger studies, and can provide a more narrow focus on the possible causal genes at a locus by reducing the confounding effect of linkage disequilibrium. Furthermore, our results show that incorporating genetic variation in distal regulatory elements tends to improve power over conventional tests. Results for UK Biobank and BioBank Japan traits are also available in a publicly accessible database that allows researchers to query gene-based results in an easy fashion.

Identification of putative causal loci in whole-genome sequencing data via knockoff statistics.

The analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

Acute myocardial infarction associated with unconventional natural gas development: A natural experiment.

BACKGROUND: Whereas it is plausible that unconventional natural gas development (UNGD) may adversely affect cardiovascular health, little is currently known. We investigate whether UNGD is associated with acute myocardial infarction (AMI). METHODS: In this observational study leveraging the natural experiment generated by New York's ban on hydraulic fracturing, we analyzed the relationship between age- and sex-specific county-level AMI hospitalization and mortality rates and three UNGD drilling measures. This longitudinal panel analysis compares Pennsylvania and New York counties on the Marcellus Shale observed over 2005-2014 (N = 2840 county-year-quarters). RESULTS: A hundred cumulative wells is associated with 0.26 more hospitalizations per 10,000 males 45-54y.o. (95% CI 0.07,0.46), 0.40 more hospitalizations per 10,000 males 65-74y.o. (95% CI 0.09,0.71), 0.47 more hospitalizations per 10,000 females 65-74y.o. (95% CI 0.18,0.77) and 1.11 more hospitalizations per 10,000 females 75y.o.+ (95% CI 0.39,1.82), translating into 1.4-2.8% increases. One additional well per square mile is associated with 2.63 more hospitalizations per 10,000 males 45-54y.o. (95% CI 0.67,4.59) and 9.7 hospitalizations per 10,000 females 75y.o.+ (95% CI 1.92,17.42), 25.8% and 24.2% increases, respectively. As for mortality rates, a hundred cumulative wells is associated with an increase of 0.09 deaths per 10,000 males 45-54y.o. (95% CI 0.02,0.16), a 5.3% increase. CONCLUSIONS: Cumulative UNGD is associated with increased AMI hospitalization rates among middle-aged men, older men and older women as well as with increased AMI mortality among middle-aged men. Our findings lend support for increased awareness about cardiovascular risks of UNGD and scaled-up AMI prevention as well as suggest that bans on hydraulic fracturing can be protective for public health.

Time to treatment initiation and outcomes in high-grade glioma patients in rehabilitation: a retrospective cohort study.

Aims: To investigate wait time (WT) for chemoradiation and survival in post-op high-grade glioma (HGG) patients admitted to inpatient rehabilitation compared with those discharged home. Materials & methods: A total of 291 HGG patients (14.4% grade III and 84.9% grade IV) were included in this retrospective cohort study. Patients were grouped by disposition following surgery. Results: Median length of stay was longer in acute inpatient rehabilitation facility (AIRF) patients (10d) compared with patients discharged home (3d). AIRF admission was associated with higher odds of excessive treatment delay. Median survival for AIRF patients less than for patients discharged home (42.9 vs 72.71 weeks). WT was not associated with survival even after adjusting for prognostic factors. Conclusion: HGG patients discharged to rehabilitation facilities have longer length of stay, longer WT and shorter survival compared with patients discharged home.

Predicting Risk for Incident Heart Failure With Omega-3 Fatty Acids: From MESA.

OBJECTIVES: The aim of this study was to determine if plasma eicosapentaenoic acid (EPA) abundance (%EPA) is associated with reduced hazard for primary heart failure (HF) events in the MESA (Multi-Ethnic Study of Atherosclerosis) trial. BACKGROUND: Clinical trials suggest that omega-3 polyunsaturated fatty acids (ω3 PUFAs) prevent sudden death in coronary heart disease and HF, but this is controversial. In mice, the authors demonstrated that the ω3 PUFA EPA prevents contractile dysfunction and fibrosis in an HF model, but whether this extends to humans is unclear. METHODS: In the MESA cohort, the authors tested if plasma phospholipid EPA predicts primary HF incidence, including HF with reduced ejection fraction (EF) (EF <45%) and HF with preserved EF (EF ≥45%) using Cox proportional hazards modeling. RESULTS: A total of 6,562 participants 45 to 84 years of age had EPA measured at baseline (1,794 black, 794 Chinese, 1,442 Hispanic, and 2,532 white; 52% women). Over a median follow-up period of 13.0 years, 292 HF events occurred: 128 HF with reduced EF, 110 HF with preserved EF, and 54 with unknown EF status. %EPA in HF-free participants was 0.76% (0.75% to 0.77%) but was lower in participants with HF at 0.69% (0.64% to 0.74%) (p = 0.005). Log %EPA was associated with lower HF incidence (hazard ratio: 0.73 [95% confidence interval: 0.60 to 0.91] per log-unit difference in %EPA; p = 0.001). Adjusting for age, sex, race, body mass index, smoking, diabetes mellitus, blood pressure, lipids and lipid-lowering drugs, albuminuria, and the lead fatty acid for each cluster did not change this relationship. Sensitivity analyses showed no dependence on HF type. CONCLUSIONS: Higher plasma EPA was significantly associated with reduced risk for HF, with both reduced and preserved EF. (Multi-Ethnic Study of Atherosclerosis [MESA]; NCT00005487).

The Impact of Timing of Concurrent Chemoradiation in Patients With High-Grade Glioma in the Era of the Stupp Protocol.

Background: The purpose of this study is to provide a critical review of current evidence for the impact of time to initiation of chemoradiation on overall survival in patients with newly diagnosed high-grade gliomas treated with radiation and concurrent temozolomide chemotherapy. Methods: A literature search was conducted using PubMed/MEDLINE and EMBASE databases. Studies were included if they provided separate analysis for patients treated with current standard of care: radiation and concurrent temozolomide. Bias assessment was performed for each included study using the Newcastle-Ottawa Assessment Scale, with Karnofsky Performance Status (KPS) and extent of resection used for comparability. Results: The initial search yielded 575 citations. Based on the inclusion/exclusion criteria, a total of 10 retrospective cohort studies were included in this review for a total of 30,298 patients. Of these, one study described an indirect relationship between time to initiation of treatment and overall survival. One study found decreased survival only with patients with significantly longer time to treatment. Four studies found no significant effect of time to treatment on overall survival. The four remaining studies found that patients with moderate time to initiation had the best overall survival. Conclusion: This review provides evidence that moderate time to initiation of chemoradiotherapy in patients with high-grade gliomas does not lead to a significant decrease in overall survival, though the effect of significant delays in treatment initiation remains unclear.

A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs.

Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type specific epigenetic annotations to predict functional consequences of non-coding variants. Through the application to several experimental datasets, we demonstrate that the proposed method significantly improves prediction accuracy compared to existing functional prediction methods at the tissue/cell type level, but especially so at the organism level. Importantly, we illustrate how the GenoNet scores can help in fine-mapping at GWAS loci, and in the discovery of disease associated genes in sequencing studies. As more comprehensive lists of experimentally validated variants become available over the next few years, semi-supervised methods like GenoNet can be used to provide increasingly accurate functional predictions for variants genome-wide and across a variety of cell/tissue types.

Convergence rates of a partition based Bayesian multivariate density estimation method.

We study a class of non-parametric density estimators under Bayesian settings. The estimators are obtained by adaptively partitioning the sample space. Under a suitable prior, we analyze the concentration rate of the posterior distribution, and demonstrate that the rate does not directly depend on the dimension of the problem in several special cases. Another advantage of this class of Bayesian density estimators is that it can adapt to the unknown smoothness of the true density function, thus achieving the optimal convergence rate without artificial conditions on the density. We also validate the theoretical results on a variety of simulated data sets.