Organ aging signatures in the plasma proteome track health and disease.

Animal studies show aging varies between individuals as well as between organs within an individual1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

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%.

Second-order group knockoffs with applications to genome-wide association studies.

MOTIVATION: Conditional testing via the knockoff framework allows one to identify-among a large number of possible explanatory variables-those that carry unique information about an outcome of interest and also provides a false discovery rate guarantee on the selection. This approach is particularly well suited to the analysis of genome-wide association studies (GWAS), which have the goal of identifying genetic variants that influence traits of medical relevance. RESULTS: While conditional testing can be both more powerful and precise than traditional GWAS analysis methods, its vanilla implementation encounters a difficulty common to all multivariate analysis methods: it is challenging to distinguish among multiple, highly correlated regressors. This impasse can be overcome by shifting the object of inference from single variables to groups of correlated variables. To achieve this, it is necessary to construct "group knockoffs." While successful examples are already documented in the literature, this paper substantially expands the set of algorithms and software for group knockoffs. We focus in particular on second-order knockoffs, for which we describe correlation matrix approximations that are appropriate for GWAS data and that result in considerable computational savings. We illustrate the effectiveness of the proposed methods with simulations and with the analysis of albuminuria data from the UK Biobank. AVAILABILITY AND IMPLEMENTATION: The described algorithms are implemented in an open-source Julia package Knockoffs.jl. R and Python wrappers are available as knockoffsr and knockoffspy packages.

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.

Temporal tau asymmetry spectrum influences divergent behavior and language patterns in Alzheimer's disease.

Understanding the psychiatric symptoms of Alzheimer s disease (AD) is crucial for advancing precision medicine and therapeutic strategies. The relationship between AD behavioral symptoms and asymmetry in spatial tau PET patterns is not well-known. Braak tau progression implicates the temporal lobes early. However, the clinical and pathological implications of temporal tau laterality remain unexplored. This cross-sectional study investigated the correlation between temporal tau PET asymmetry and behavior assessed using the neuropsychiatric inventory and composite scores for memory, executive function, and language, using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. In the entire cohort, continuous right and left temporal tau contributions to behavior and cognition were evaluated, controlling for age, sex, education, and tau burden on the contralateral side. Additionally, a temporal tau laterality index was calculated to define "asymmetry-extreme" groups (individuals with laterality indices greater than two standard deviations from the mean). 695 individuals (age = 73.9 ± 7.6 years, 372 (53.5 %) females) were included, comprising 281 (40%) cognitively unimpaired (CU) amyloid negative, 185 (27%) CU amyloid positive, and 229 (33%) impaired (CI) amyloid positive participants. In the full cohort analysis, right temporal tau was associated with worse behavior (B = 8.14, p-value = 0.007), and left temporal tau was associated with worse language (B = 1.4, p-value < 0.001). Categorization into asymmetry-extreme groups revealed 20 right- and 27 left-asymmetric participants. Within these extreme groups, there was additional heterogeneity along the anterior-posterior dimension. Asymmetrical tau burden is associated with distinct behavioral and cognitive profiles. Wide multi-cultural implementation of social cognition measures is needed to understand right-sided asymmetry in AD.

Summary statistics knockoffs inference with family-wise error rate control.

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.

The introduction of the CGRP monoclonal antibodies and their effect on the prescription patterns of chronic migraine preventive medications in a tertiary headache center: A retrospective, observational analysis.

OBJECTIVE: To determine the effect of the introduction of the calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs) in 2018 on the prescribing of older medications for the prevention of chronic migraine. BACKGROUND: Prior to 2018, the preventive treatment of migraine borrowed from medications intended to treat other illnesses with the last medication, onabotulinumtoxinA, receiving Food and Drug Administration (FDA) approval for the prevention of chronic migraine in 2010. The FDA approval of three CGRP mAbs in 2018 provided the ideal natural experiment to assess how the introduction of these medications, and a fourth in 2020, affected the generally stable migraine preventive medications market. METHODS: We performed a retrospective cohort analysis using the aggregated de-identified data of 6595 patients. The percentage of patients with chronic migraine who had been prescribed one of ten most prescribed oral preventive medications or onabotulinumtoxinA, or any of the four CGRP mAbs, were calculated relative to the total number of patients with chronic migraine who received a prescription for any medication from our clinic during the pre-CGRP mAb years of 2015-2017 and post-approval years of 2019-2021. RESULTS: We observed a statistically significant decrease in the prescription of the top 10 most prescribed medications after the introduction of the CGRP mAbs overall (1456/3144, 46.3%, to 1995/4629, 43.1%, p = 0.001), as well as with most individual medications, including large decreases in verapamil (230/3144, 7.3%, to 125/4629, 2.7%; p < 0.001), the tricyclic antidepressants (494/3144, 15.7%, to 532/4629, 11.5%; p < 0.001), topiramate (566/3144, 18.0%, to 653/4629, 14.1%; p < 0.001), and onabotulinumtoxinA (861/3144, 27.4%, to 1134/4629, 24.5%; p = 0.001). CONCLUSION: The introduction of the CGRP mAbs during 2018 resulted in a decrease in utilization of most oral medications and onabotulinumtoxinA for the prevention of migraine. Future work should continue to observe how the prescription patterns of these medications evolve with time.

Long-term persistence to onabotulinumtoxinA to prevent chronic migraine: results from 11 years of patient data from a tertiary headache center.

OBJECTIVE: To determine if patients with chronic migraine continue onabotulinumtoxinA (onabotA) long-term. METHODS: We performed a retrospective cohort analysis using aggregated, de-identified patient data from the Stanford Headache Center. We included patients in California who received at least one prescription for onabotA during the years of 2011-2021. The primary outcome was the number of onabotA treatments each patient received. Secondary outcomes included sex, age, race, ethnicity, body mass index (BMI), distance to the treatment facility, and zip code income quartile. RESULTS: A total of 1551 patients received a mean of 7.60 ± 7.26 treatments and a median of 5 treatments, with 16.2% of patients receiving only one treatment and 10.6% receiving at least 19. Time-to-event survival analysis suggested 26.0% of patients would complete at least 29 treatments if able. Younger age and female sex were associated with statistically significant differences between quartile groups of number of onabotA treatments (P = .007, P = .015). BMI, distance to treatment facility, and zip code income quartile were not statistically significantly different between quartile groups (P > .500 for all). Prescriptions of both triptans and non-onabotA preventive medications showed a statistically significant increase with each higher quartile of number of onabotA treatments (P < .001; P < .001). DISCUSSION: We show long-term persistence to onabotA is high and that distance to treatment facility and income are not factors in continuation. Our work also demonstrates that as patients continue onabotA over time, there may be an increased need for adjunctive or alternative treatments.

Deep learning-assisted genome-wide characterization of massively parallel reporter assays.

Massively parallel reporter assay (MPRA) is a high-throughput method that enables the study of the regulatory activities of tens of thousands of DNA oligonucleotides in a single experiment. While MPRA experiments have grown in popularity, their small sample sizes compared to the scale of the human genome limits our understanding of the regulatory effects they detect. To address this, we develop a deep learning model, MpraNet, to distinguish potential MPRA targets from the background genome. This model achieves high discriminative performance (AUROC = 0.85) at differentiating MPRA positives from a set of control variants that mimic the background genome when applied to the lymphoblastoid cell line. We observe that existing functional scores represent very distinct functional effects, and most of them fail to characterize the regulatory effect that MPRA detects. Using MpraNet, we predict potential MPRA functional variants across the genome and identify the distributions of MPRA effect relative to other characteristics of genetic variation, including allele frequency, alternative functional annotations specified by FAVOR, and phenome-wide associations. We also observed that the predicted MPRA positives are not uniformly distributed across the genome; instead, they are clumped together in active regions comprising 9.95% of the genome and inactive regions comprising 89.07% of the genome. Furthermore, we propose our model as a screen to filter MPRA experiment candidates at genome-wide scale, enabling future experiments to be more cost-efficient by increasing precision relative to that observed from previous MPRAs.

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.

Multitrait GWAS to connect disease variants and biological mechanisms.

Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.

Post-translational modifications linked to preclinical Alzheimer's disease-related pathological and cognitive changes.

INTRODUCTION: In this study, we leverage proteomic techniques to identify communities of proteins underlying Alzheimer's disease (AD) risk among clinically unimpaired (CU) older adults. METHODS: We constructed a protein co-expression network using 3869 cerebrospinal fluid (CSF) proteins quantified by SomaLogic, Inc., in a cohort of participants along the AD clinical spectrum. We then replicated this network in an independent cohort of CU older adults and related these modules to clinically-relevant outcomes. RESULTS: We discovered modules enriched for phosphorylation and ubiquitination that were associated with abnormal amyloid status, as well as p-tau181 (M4: ß = 2.44, p < 0.001, M7: ß = 2.57, p < 0.001) and executive function performance (M4: ß = -2.00, p = 0.005, M7: ß = -2.39, p < 0.001). DISCUSSION: In leveraging CSF proteomic data from individuals spanning the clinical spectrum of AD, we highlight the importance of post-translational modifications for early cognitive and pathological changes.

Inequities in Therapy for Infantile Spasms: A Call to Action.

OBJECTIVE: The aim of this study was to determine whether selection of treatment for children with infantile spasms (IS) varies by race/ethnicity. METHODS: The prospective US National Infantile Spasms Consortium database includes children with IS treated from 2012 to 2018. We examined the relationship between race/ethnicity and receipt of standard IS therapy (prednisolone, adrenocorticotropic hormone, vigabatrin), adjusting for demographic and clinical variables using logistic regression. Our primary outcome was treatment course, which considered therapy prescribed for the first and, when needed, the second IS treatment together. RESULTS: Of 555 children, 324 (58%) were non-Hispanic white, 55 (10%) non-Hispanic Black, 24 (4%) non-Hispanic Asian, 80 (14%) Hispanic, and 72 (13%) other/unknown. Most (398, 72%) received a standard treatment course. Insurance type, geographic location, history of prematurity, prior seizures, developmental delay or regression, abnormal head circumference, hypsarrhythmia, and IS etiologies were associated with standard therapy. In adjusted models, non-Hispanic Black children had lower odds of receiving a standard treatment course compared with non-Hispanic white children (odds ratio [OR], 0.42; 95% confidence interval [CI], 0.20-0.89; p = 0.02). Adjusted models also showed that children with public (vs. private) insurance had lower odds of receiving standard therapy for treatment 1 (OR, 0.42; CI, 0.21-0.84; p = 0.01). INTERPRETATION: Non-Hispanic Black children were more often treated with non-standard IS therapies than non-Hispanic white children. Likewise, children with public (vs. private) insurance were less likely to receive standard therapies. Investigating drivers of inequities, and understanding the impact of racism on treatment decisions, are critical next steps to improve care for patients with IS. ANN NEUROL 2022;92:32-44.

An evolutionarily acquired microRNA shapes development of mammalian cortical projections.

The corticospinal tract is unique to mammals and the corpus callosum is unique to placental mammals (eutherians). The emergence of these structures is thought to underpin the evolutionary acquisition of complex motor and cognitive skills. Corticospinal motor neurons (CSMN) and callosal projection neurons (CPN) are the archetypal projection neurons of the corticospinal tract and corpus callosum, respectively. Although a number of conserved transcriptional regulators of CSMN and CPN development have been identified in vertebrates, none are unique to mammals and most are coexpressed across multiple projection neuron subtypes. Here, we discover 17 CSMN-enriched microRNAs (miRNAs), 15 of which map to a single genomic cluster that is exclusive to eutherians. One of these, miR-409-3p, promotes CSMN subtype identity in part via repression of LMO4, a key transcriptional regulator of CPN development. In vivo, miR-409-3p is sufficient to convert deep-layer CPN into CSMN. This is a demonstration of an evolutionarily acquired miRNA in eutherians that refines cortical projection neuron subtype development. Our findings implicate miRNAs in the eutherians' increase in neuronal subtype and projection diversity, the anatomic underpinnings of their complex behavior.

Major Adverse Dystrophinopathy Events (MADE) Score as Marker of Cumulative Morbidity and Risk for Mortality in Boys with Duchenne Muscular Dystrophy.

Background: Overlapping symptoms from cardiomyopathy, respiratory insufficiency, and skeletal myopathy confound assessment of heart failure in Duchenne Muscular Dystrophy. We developed an ordinal scale of multiorgan clinical variables that reflect cumulative disease burden-the Major Adverse Dystrophinopathy Event (MADE) Score. We hypothesized that a higher MADE score would be associated with increased mortality in boys with Duchenne Muscular Dystrophy. The Cooperative International Neuromuscular Research Group Duchenne Natural History Study dataset was utilized for validation. Methods: Duchenne Natural History Study variables were selected based on clinical relevance to prespecified domains: Cardiac, Pulmonary, Myopathy, Nutrition. Severity points (0-4) were assigned and summed for study visits. MADE score for cohorts defined by age, ambulatory status, and survival were compared at enrollment and longitudinally.Associations between MADE score and mortality were examined. Results: Duchenne Natural History Study enrolled 440 males, 12.6 ±6.1 years old, with 3,559 visits over 4.6 ±2.8 years, 45 deaths. MADE score increased with age and nonambulatory status. Mean MADE score per visit was 19 ±10 for those who died vs. 9.8 ±9.3 in survivors p=0.03. Baseline MADE score >12 predicted mortality independent of age (78% sensitivity, CPE.70). Rising MADE score trajectory was associated with mortality in models adjusted for enrollment age, follow-up time, and ambulatory status, all p<.001. Conclusion: A multiorgan severity score, MADE, was developed to track cumulative morbidities that impact heart failure in Duchenne muscular dystrophy. MADE score predicted Duchenne Natural History Study mortality. MADE score can be used for serial heart failure assessment in males and may serve as an endpoint for Duchenne muscular dystrophy clinical research.

Association of African Ancestry-Specific APOE Missense Variant R145C With Risk of Alzheimer Disease.

Importance: Numerous studies have established the association of the common APOE ε2 and APOE ε4 alleles with Alzheimer disease (AD) risk across ancestries. Studies of the interaction of these alleles with other amino acid changes on APOE in non-European ancestries are lacking and may improve ancestry-specific risk prediction. Objective: To determine whether APOE amino acid changes specific to individuals of African ancestry modulate AD risk. Design, Setting, and Participants: Case-control study including 31 929 participants and using a sequenced discovery sample (Alzheimer Disease Sequencing Project; stage 1) followed by 2 microarray imputed data sets derived from the Alzheimer Disease Genetic Consortium (stage 2, internal replication) and the Million Veteran Program (stage 3, external validation). This study combined case-control, family-based, population-based, and longitudinal AD cohorts, which recruited participants (1991-2022) in primarily US-based studies with 1 US/Nigerian study. Across all stages, individuals included in this study were of African ancestry. Exposures: Two APOE missense variants (R145C and R150H) were assessed, stratified by APOE genotype. Main Outcomes and Measures: The primary outcome was AD case-control status, and secondary outcomes included age at AD onset. Results: Stage 1 included 2888 cases (median age, 77 [IQR, 71-83] years; 31.3% male) and 4957 controls (median age, 77 [IQR, 71-83] years; 28.0% male). In stage 2, across multiple cohorts, 1201 cases (median age, 75 [IQR, 69-81] years; 30.8% male) and 2744 controls (median age, 80 [IQR, 75-84] years; 31.4% male) were included. In stage 3, 733 cases (median age, 79.4 [IQR, 73.8-86.5] years; 97.0% male) and 19 406 controls (median age, 71.9 [IQR, 68.4-75.8] years; 94.5% male) were included. In ε3/ε4-stratified analyses of stage 1, R145C was present in 52 individuals with AD (4.8%) and 19 controls (1.5%); R145C was associated with an increased risk of AD (odds ratio [OR], 3.01; 95% CI, 1.87-4.85; P = 6.0 × 10-6) and was associated with a reported younger age at AD onset (ß, -5.87 years; 95% CI, -8.35 to -3.4 years; P = 3.4 × 10-6). Association with increased AD risk was replicated in stage 2 (R145C was present in 23 individuals with AD [4.7%] and 21 controls [2.7%]; OR, 2.20; 95% CI, 1.04-4.65; P = .04) and was concordant in stage 3 (R145C was present in 11 individuals with AD [3.8%] and 149 controls [2.7%]; OR, 1.90; 95% CI, 0.99-3.64; P = .051). Association with earlier AD onset was replicated in stage 2 (ß, -5.23 years; 95% CI, -9.58 to -0.87 years; P = .02) and stage 3 (ß, -10.15 years; 95% CI, -15.66 to -4.64 years; P = 4.0 × 10-4). No significant associations were observed in other APOE strata for R145C or in any APOE strata for R150H. Conclusions and Relevance: In this exploratory analysis, the APOE ε3[R145C] missense variant was associated with an increased risk of AD among individuals of African ancestry with the ε3/ε4 genotype. With additional external validation, these findings may inform AD genetic risk assessment in individuals of African ancestry.

Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach.

BACKGROUND: Most trials in critical care have been neutral, in part because between-patient heterogeneity means not all patients respond identically to the same treatment. The Precision Care in Cardiac Arrest: Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (PRECICECAP) study will apply machine learning to high-resolution, multimodality data collected from patients resuscitated from out-of-hospital cardiac arrest. We aim to discover novel biomarker signatures to predict the optimal duration of therapeutic hypothermia and 90-day functional outcomes. In parallel, we are developing a freely available software platform for standardized curation of intensive care unit-acquired data for machine learning applications. METHODS: The Influence of Cooling duration on Efficacy in Cardiac Arrest Patients (ICECAP) study is a response-adaptive, dose-finding trial testing different durations of therapeutic hypothermia. Twelve ICECAP sites will collect data for PRECICECAP from multiple modalities routinely used after out-of-hospital cardiac arrest, including ICECAP case report forms, detailed medication data, cardiopulmonary and electroencephalographic waveforms, and digital imaging and communications in medicine files (DICOMs). We partnered with Moberg Analytics to develop a freely available software platform to allow high-resolution critical care data to be used efficiently and effectively. We will use an autoencoder neural network to create low-dimensional representations of all raw waveforms and derivative features, censored at rewarming to ensure clinical usability to guide optimal duration of hypothermia. We will also consider simple features that are historically considered to be important. Finally, we will create a supervised deep learning neural network algorithm to directly predict 90-day functional outcome from large sets of novel features. RESULTS: PRECICECAP is currently enrolling and will be completed in late 2025. CONCLUSIONS: Cardiac arrest is a heterogeneous disease that causes substantial morbidity and mortality. PRECICECAP will advance the overarching goal of titrating personalized neurocritical care on the basis of robust measures of individual need and treatment responsiveness. The software platform we develop will be broadly applicable to hospital-based research after acute illness or injury.

FUN-LDA: A Latent Dirichlet Allocation Model for Predicting Tissue-Specific Functional Effects of Noncoding Variation: Methods and Applications.

We describe a method based on a latent Dirichlet allocation model for predicting functional effects of noncoding genetic variants in a cell-type- and/or tissue-specific way (FUN-LDA). Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome in 127 different tissues and cell types. We demonstrate the usefulness of our predictions by using several validation experiments. Using eQTL data from several sources, including the GTEx project, Geuvadis project, and TwinsUK cohort, we show that eQTLs in specific tissues tend to be most enriched among the predicted functional variants in relevant tissues in Roadmap. We further show how these integrated functional scores can be used for (1) deriving the most likely cell or tissue type causally implicated for a complex trait by using summary statistics from genome-wide association studies and (2) estimating a tissue-based correlation matrix of various complex traits. We found large enrichment of heritability in functional components of relevant tissues for various complex traits, and FUN-LDA yielded higher enrichment estimates than existing methods. Finally, using experimentally validated functional variants from the literature and variants possibly implicated in disease by previous studies, we rigorously compare FUN-LDA with state-of-the-art functional annotation methods and show that FUN-LDA has better prediction accuracy and higher resolution than these methods. In particular, our results suggest that tissue- and cell-type-specific functional prediction methods tend to have substantially better prediction accuracy than organism-level prediction methods. Scores for each position in the human genome and for each ENCODE and Roadmap tissue are available online (see Web Resources).

Treatment Practices and Outcomes in Continuous Spike and Wave during Slow Wave Sleep: A Multicenter Collaboration.

OBJECTIVES: To determine how continuous spike and wave during slow wave sleep (CSWS) is currently managed and to compare the effectiveness of current treatment strategies using a database from 11 pediatric epilepsy centers in the US. STUDY DESIGN: This retrospective study gathered information on baseline clinical characteristics, CSWS etiology, and treatment(s) in consecutive patients seen between 2014 and 2016 at 11 epilepsy referral centers. Treatments were categorized as benzodiazepines, steroids, other antiseizure medications (ASMs), or other therapies. Two measures of treatment response (clinical improvement as noted by the treating physician; and electroencephalography improvement) were compared across therapies, controlling for baseline variables. RESULTS: Eighty-one children underwent 153 treatment trials during the study period (68 trials of benzodiazepines, 25 of steroids, 45 of ASMs, 14 of other therapies). Children most frequently received benzodiazepines (62%) or ASMs (27%) as first line therapy. Treatment choice did not differ based on baseline clinical variables, nor did these variables correlate with outcome. After adjusting for baseline variables, children had a greater odds of clinical improvement with benzodiazepines (OR 3.32, 95%CI 1.57-7.04, P = .002) or steroids (OR 4.04, 95%CI 1.41-11.59, P = .01) than with ASMs and a greater odds of electroencephalography improvement after steroids (OR 3.36, 95% CI 1.09-10.33, P = .03) than after ASMs. CONCLUSIONS: Benzodiazepines and ASMs are the most frequent initial therapy prescribed for CSWS in the US. Our data suggests that ASMs are inferior to benzodiazepines and steroids and support earlier use of these therapies. Multicenter prospective studies that rigorously assess treatment protocols and outcomes are needed.

Unified Sequence-Based Association Tests Allowing for Multiple Functional Annotations and Meta-analysis of Noncoding Variation in Metabochip Data.

Substantial progress has been made in the functional annotation of genetic variation in the human genome. Integrative analysis that incorporates such functional annotations into sequencing studies can aid the discovery of disease-associated genetic variants, especially those with unknown function and located outside protein-coding regions. Direct incorporation of one functional annotation as weight in existing dispersion and burden tests can suffer substantial loss of power when the functional annotation is not predictive of the risk status of a variant. Here, we have developed unified tests that can utilize multiple functional annotations simultaneously for integrative association analysis with efficient computational techniques. We show that the proposed tests significantly improve power when variant risk status can be predicted by functional annotations. Importantly, when functional annotations are not predictive of risk status, the proposed tests incur only minimal loss of power in relation to existing dispersion and burden tests, and under certain circumstances they can even have improved power by learning a weight that better approximates the underlying disease model in a data-adaptive manner. The tests can be constructed with summary statistics of existing dispersion and burden tests for sequencing data, therefore allowing meta-analysis of multiple studies without sharing individual-level data. We applied the proposed tests to a meta-analysis of noncoding rare variants in Metabochip data on 12,281 individuals from eight studies for lipid traits. By incorporating the Eigen functional score, we detected significant associations between noncoding rare variants in SLC22A3 and low-density lipoprotein and total cholesterol, associations that are missed by standard dispersion and burden tests.