Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Heterogeneity of treatment effects by risk in pulmonary arterial hypertension.

BACKGROUND: It is currently unknown if disease severity modifies response to therapy in pulmonary arterial hypertension (PAH). We aimed to explore if disease severity, as defined by established risk-prediction algorithms, modified response to therapy in randomised clinical trials in PAH. METHODS: We performed a meta-analysis using individual participant data from 18 randomised clinical trials of therapy for PAH submitted to the United States Food and Drug Administration to determine if predicted risk of 1-year mortality at randomisation modified the treatment effect on three outcomes: change in 6-min walk distance (6MWD), clinical worsening at 12 weeks and time to clinical worsening. RESULTS: Of 6561 patients with a baseline US Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL 2.0) score, we found that individuals with higher baseline risk had higher probabilities of clinical worsening but no difference in change in 6MWD. We detected a significant interaction of REVEAL 2.0 risk and treatment assignment on change in 6MWD. For every 3-point increase in REVEAL 2.0 score, there was a 12.49 m (95% CI 5.86-19.12 m; p=0.001) greater treatment effect in change in 6MWD. We did not detect a significant risk by treatment interaction on clinical worsening with most of the risk-prediction algorithms. CONCLUSIONS: We found that predicted risk of 1-year mortality in PAH modified treatment effect as measured by 6MWD, but not clinical worsening. Our findings highlight the importance of identifying sources of treatment heterogeneity by predicted risk to tailor studies to patients most likely to have the greatest treatment response.

HLA amino acid Mismatch-Based risk stratification of kidney allograft failure using a novel Machine learning algorithm.

OBJECTIVE: While associations between HLA antigen-level mismatches (Ag-MM) and kidney allograft failure are well established, HLA amino acid-level mismatches (AA-MM) have been less explored. Ag-MM fails to consider the substantial variability in the number of MMs at polymorphic amino acid (AA) sites within any given Ag-MM category, which may conceal variable impact on allorecognition. In this study we aim to develop a novel Feature Inclusion Bin Evolver for Risk Stratification (FIBERS) and apply it to automatically discover bins of HLA amino acid mismatches that stratify donor-recipient pairs into low versus high graft survival risk groups. METHODS: Using data from the Scientific Registry of Transplant Recipients, we applied FIBERS on a multiethnic population of 166,574 kidney transplants between 2000 and 2017. FIBERS was applied (1) across all HLA-A, B, C, DRB1, and DQB1 locus AA-MMs with comparison to 0-ABDR Ag-MM risk stratification, (2) on AA-MMs within each HLA locus individually, and (3) using cross validation to evaluate FIBERS generalizability. The predictive power of graft failure risk stratification was evaluated while adjusting for donor/recipient characteristics and HLA-A, B, C, DRB1, and DQB1 Ag-MMs as covariates. RESULTS: FIBERS's best-performing bin (on AA-MMs across all loci) added significant predictive power (hazard ratio = 1.10, Bonferroni adj. p < 0.001) in stratifying graft failure risk (where low-risk is defined as zero AA-MMs and high-risk is one or more AA-MMs) even after adjusting for Ag-MMs and donor/recipient covariates. The best bin also categorized more than twice as many patients to the low-risk category, compared to traditional 0-ABDR Ag mismatching (∼24.4% vs âˆ¼ 9.1%). When HLA loci were binned individually, the bin for DRB1 exhibited the strongest risk stratification; relative to zero AA-MM, one or more MMs in the bin yielded HR = 1.11, p < 0.005 in a fully adjusted Cox model. AA-MMs at HLA-DRB1 peptide contact sites contributed most to incremental risk of graft failure. Additionally, FIBERS points to possible risk associated with HLA-DQB1 AA-MMs at positions that determine specificity of peptide anchor residues and HLA-DQ heterodimer stability. CONCLUSION: FIBERS's performance suggests potential for discovery of HLA immunogenetics-based risk stratification of kidney graft failure that outperforms traditional assessment.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Embracing study heterogeneity for finding genetic interactions in large-scale research consortia.

Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.

STatistical Inference Relief (STIR) feature selection.

MOTIVATION: Relief is a family of machine learning algorithms that uses nearest-neighbors to select features whose association with an outcome may be due to epistasis or statistical interactions with other features in high-dimensional data. Relief-based estimators are non-parametric in the statistical sense that they do not have a parameterized model with an underlying probability distribution for the estimator, making it difficult to determine the statistical significance of Relief-based attribute estimates. Thus, a statistical inferential formalism is needed to avoid imposing arbitrary thresholds to select the most important features. We reconceptualize the Relief-based feature selection algorithm to create a new family of STatistical Inference Relief (STIR) estimators that retains the ability to identify interactions while incorporating sample variance of the nearest neighbor distances into the attribute importance estimation. This variance permits the calculation of statistical significance of features and adjustment for multiple testing of Relief-based scores. Specifically, we develop a pseudo t-test version of Relief-based algorithms for case-control data. RESULTS: We demonstrate the statistical power and control of type I error of the STIR family of feature selection methods on a panel of simulated data that exhibits properties reflected in real gene expression data, including main effects and network interaction effects. We compare the performance of STIR when the adaptive radius method is used as the nearest neighbor constructor with STIR when the fixed-k nearest neighbor constructor is used. We apply STIR to real RNA-Seq data from a study of major depressive disorder and discuss STIR's straightforward extension to genome-wide association studies. AVAILABILITY AND IMPLEMENTATION: Code and data available at http://insilico.utulsa.edu/software/STIR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Benchmarking relief-based feature selection methods for bioinformatics data mining.

Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e.g. 'omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e.g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e.g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable. To that end, this work examines a set of filter-style feature selection algorithms inspired by the 'Relief' algorithm, i.e. Relief-Based algorithms (RBAs). We implement and expand these RBAs in an open source framework called ReBATE (Relief-Based Algorithm Training Environment). We apply a comprehensive genetic simulation study comparing existing RBAs, a proposed RBA called MultiSURF, and other established feature selection methods, over a variety of problems. The results of this study (1) support the assertion that RBAs are particularly flexible, efficient, and powerful feature selection methods that differentiate relevant features having univariate, multivariate, epistatic, or heterogeneous associations, (2) confirm the efficacy of expansions for classification vs. regression, discrete vs. continuous features, missing data, multiple classes, or class imbalance, (3) identify previously unknown limitations of specific RBAs, and (4) suggest that while MultiSURF∗ performs best for explicitly identifying pure 2-way interactions, MultiSURF yields the most reliable feature selection performance across a wide range of problem types.

Relief-based feature selection: Introduction and review.

Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations. Specifically, there is a need for feature selection methods that are computationally efficient, yet sensitive to complex patterns of association, e.g. interactions, so that informative features are not mistakenly eliminated prior to downstream modeling. This paper focuses on Relief-based algorithms (RBAs), a unique family of filter-style feature selection algorithms that have gained appeal by striking an effective balance between these objectives while flexibly adapting to various data characteristics, e.g. classification vs. regression. First, this work broadly examines types of feature selection and defines RBAs within that context. Next, we introduce the original Relief algorithm and associated concepts, emphasizing the intuition behind how it works, how feature weights generated by the algorithm can be interpreted, and why it is sensitive to feature interactions without evaluating combinations of features. Lastly, we include an expansive review of RBA methodological research beyond Relief and its popular descendant, ReliefF. In particular, we characterize branches of RBA research, and provide comparative summaries of RBA algorithms including contributions, strategies, functionality, time complexity, adaptation to key data characteristics, and software availability.

Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing.

According to the World Stroke Organization, 12.2 million people world-wide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-based classifiers are rigid, resulting in inadequate performance. We report findings from a pilot study using NLP to improve relation detection for stroke assertion detection to support research studies and healthcare operations.

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA.

Plasma cell-free DNA (cfDNA) is a promising source of gene mutations for cancer detection by liquid biopsy. However, no current tests interrogate chromosomal structural variants (SVs) genome-wide. Here, we report a simple molecular and sequencing workflow called Genome-wide Analysis of Palindrome Formation (GAPF-seq) to probe DNA palindromes, a type of SV that often demarcates gene amplification. With low-throughput next-generation sequencing and automated machine learning, tumor DNA showed skewed chromosomal distributions of high-coverage 1-kb bins (HCBs), which differentiated 39 breast tumors from matched normal DNA with an average Area Under the Curve (AUC) of 0.9819. A proof-of-concept liquid biopsy study using cfDNA from prostate cancer patients and healthy individuals yielded an average AUC of 0.965. HCBs on the X chromosome emerged as a determinant feature and were associated with androgen receptor gene amplification. As a novel agnostic liquid biopsy approach, GAPF-seq could fill the technological gap offering unique cancer-specific SV profiles.

Advancing LGBTQ+ inclusion in STEM education and AI research.

The authors emphasize diversity, equity, and inclusion in STEM education and artificial intelligence (AI) research, focusing on LGBTQ+ representation. They discuss the challenges faced by queer scientists, educational resources, the implementation of National AI Campus, and the notion of intersectionality. The authors hope to ensure supportive and respectful engagement across all communities.

Health activism, vaccine, and mpox discourse: BERTopic based mixed-method analyses of tweets from sexual minority men and gender diverse (SMMGD) individuals in the U.S.

Objectives: To synthesize discussions among sexual minority men and gender diverse (SMMGD) individuals on mpox, given limited representation of SMMGD voices in existing mpox literature. Methods: BERTopic (a topic modeling technique) was employed with human validations to analyze mpox-related tweets (n = 8,688; October 2020-September 2022) from 2,326 self-identified SMMGD individuals in the U.S.; followed by content analysis and geographic analysis. Results: BERTopic identified 11 topics: health activism (29.81%); mpox vaccination (25.81%) and adverse events (0.98%); sarcasm, jokes, emotional expressions (14.04%); COVID-19 and mpox (7.32%); government/public health response (6.12%); mpox symptoms (2.74%); case reports (2.21%); puns on the virus' naming (i.e., monkeypox; 0.86%); media publicity (0.68%); mpox in children (0.67%). Mpox health activism negatively correlated with LGB social climate index at U.S. state level, ρ = -0.322, p = 0.031. Conclusions: SMMGD discussions on mpox encompassed utilitarian (e.g., vaccine access, case reports, mpox symptoms) and emotionally-charged themes-advocating against homophobia, misinformation, and stigma. Mpox health activism was more prevalent in states with lower LGB social acceptance. Public Health Implications: Findings illuminate SMMGD engagement with mpox discourse, underscoring the need for more inclusive health communication strategies in infectious disease outbreaks to control associated stigma.

A Practical Approach for Targeting Structural Variants Genome-wide in Plasma Cell-free DNA.

Interrogating plasma cell-free DNA (cfDNA) to detect cancer offers promise; however, no current tests scan structural variants (SVs) throughout the genome. Here, we report a simple molecular workflow to enrich a tumorigenic SV (DNA palindromes/fold-back inversions) that often demarcates genomic amplification and its feasibility for cancer detection by combining low-throughput next-generation sequencing with automated machine learning (Genome-wide Analysis of Palindrome Formation, GAPF-seq). Tumor DNA signal manifested as skewed chromosomal distributions of high-coverage 1-kb bins (HCBs), differentiating 39 matched breast tumor DNA from normal DNA with an average AUC of 0.9819. In a proof-of-concept liquid biopsy study, cfDNA from 0.5 mL plasma from prostate cancer patients was sufficient for binary classification against matched buffy coat DNA with an average AUC of 0.965. HCBs on the X chromosome emerged as a determinant feature and were associated with AR amplification. GAPF-seq could generate unique cancer-specific SV profiles in an agnostic liquid biopsy setting.

Comparing Amyloid Imaging Normalization Strategies for Alzheimer's Disease Classification using an Automated Machine Learning Pipeline.

Amyloid imaging has been widely used in Alzheimer's disease (AD) diagnosis and biomarker discovery through detecting the regional amyloid plaque density. It is essential to be normalized by a reference region to reduce noise and artifacts. To explore an optimal normalization strategy, we employ an automated machine learning (AutoML) pipeline, STREAMLINE, to conduct the AD diagnosis binary classification and perform permutation-based feature importance analysis with thirteen machine learning models. In this work, we perform a comparative study to evaluate the prediction performance and biomarker discovery capability of three amyloid imaging measures, including one original measure and two normalized measures using two reference regions (i.e., the whole cerebellum and the composite reference region). Our AutoML results indicate that the composite reference region normalization dataset yields a higher balanced accuracy, and identifies more AD-related regions based on the fractioned feature importance ranking.

Exploring Automated Machine Learning for Cognitive Outcome Prediction from Multimodal Brain Imaging using STREAMLINE.

STREAMLINE is a simple, transparent, end-to-end automated machine learning (AutoML) pipeline for easily conducting rigorous machine learning (ML) modeling and analysis. The initial version is limited to binary classification. In this work, we extend STREAMLINE through implementing multiple regression-based ML models, including linear regression, elastic net, group lasso, and L21 norm. We demonstrate the effectiveness of the regression version of STREAMLINE by applying it to the prediction of Alzheimer's disease (AD) cognitive outcomes using multimodal brain imaging data. Our empirical results demonstrate the feasibility and effectiveness of the newly expanded STREAMLINE as an AutoML pipeline for evaluating AD regression models, and for discovering multimodal imaging biomarkers.

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors.

PURPOSE: Predicting 30-day readmission risk is paramount to improving the quality of patient care. In this study, we compare sets of patient-, provider-, and community-level variables that are available at two different points of a patient's inpatient encounter (first 48 hours and the full encounter) to train readmission prediction models and identify possible targets for appropriate interventions that can potentially reduce avoidable readmissions. METHODS: Using electronic health record data from a retrospective cohort of 2,460 oncology patients and a comprehensive machine learning analysis pipeline, we trained and tested models predicting 30-day readmission on the basis of data available within the first 48 hours of admission and from the entire hospital encounter. RESULTS: Leveraging all features, the light gradient boosting model produced higher, but comparable performance (area under receiver operating characteristic curve [AUROC]: 0.711) with the Epic model (AUROC: 0.697). Given features in the first 48 hours, the random forest model produces higher AUROC (0.684) than the Epic model (AUROC: 0.676). Both models flagged patients with a similar distribution of race and sex; however, our light gradient boosting and random forest models were more inclusive, flagging more patients among younger age groups. The Epic models were more sensitive to identifying patients with an average lower zip income. Our 48-hour models were powered by novel features at various levels: patient (weight change over 365 days, depression symptoms, laboratory values, and cancer type), hospital (winter discharge and hospital admission type), and community (zip income and marital status of partner). CONCLUSION: We developed and validated models comparable with the existing Epic 30-day readmission models with several novel actionable insights that could create service interventions deployed by the case management or discharge planning teams that may decrease readmission rates over time.

ChatGPT and large language models in academia: opportunities and challenges.

The introduction of large language models (LLMs) that allow iterative "chat" in late 2022 is a paradigm shift that enables generation of text often indistinguishable from that written by humans. LLM-based chatbots have immense potential to improve academic work efficiency, but the ethical implications of their fair use and inherent bias must be considered. In this editorial, we discuss this technology from the academic's perspective with regard to its limitations and utility for academic writing, education, and programming. We end with our stance with regard to using LLMs and chatbots in academia, which is summarized as (1) we must find ways to effectively use them, (2) their use does not constitute plagiarism (although they may produce plagiarized text), (3) we must quantify their bias, (4) users must be cautious of their poor accuracy, and (5) the future is bright for their application to research and as an academic tool.

Baseline Sex Differences in Pulmonary Arterial Hypertension Randomized Clinical Trials.

Rationale: Sex-based differences in pulmonary arterial hypertension (PAH) are known, but the contribution to disease measures is understudied. Objectives: We examined whether sex was associated with baseline 6-minute-walk distance (6MWD), hemodynamics, and functional class. Methods: We conducted a secondary analysis of participant-level data from randomized clinical trials of investigational PAH therapies conducted between 1998 and 2014 and provided by the U.S. Food and Drug Administration. Outcomes were modeled as a function of an interaction between sex and age or sex and body mass index (BMI), respectively, with generalized mixed modeling. Results: We included a total of 6,633 participants from 18 randomized clinical trials. A total of 5,197 (78%) were female, with a mean age of 49.1 years and a mean BMI of 27.0 kg/m2. Among 1,436 males, the mean age was 49.7 years, and the mean BMI was 26.4 kg/m2. The most common etiology of PAH was idiopathic. Females had shorter 6MWD. For every 1 kg/m2 increase in BMI for females, 6MWD decreased 2.3 (1.6-3.0) meters (P < 0.001), whereas 6MWD did not significantly change with BMI in males (0.31 m [-0.30 to 0.92]; P = 0.32). Females had lower right atrial pressure (RAP) and mean pulmonary artery pressure, and higher cardiac index than males (all P < 0.03). Age significantly modified the sex by RAP and mean pulmonary artery pressure relationships. For every 10-year increase in age, RAP was lower in males (0.5 mm Hg [0.3-0.7]; P < 0.001), but not in females (0.13 [-0.03 to 0.28]; P = 0.10). There was a significant decrease in pulmonary vascular resistance (PVR) with increasing age regardless of sex (P < 0.001). For every 1 kg/m2 increase in BMI, there was a 3% decrease in PVR for males (P < 0.001), compared with a 2% decrease in PVR in females (P < 0.001). Conclusions: Sexual dimorphism in subjects enrolled in clinical trials extends to 6MWD and hemodynamics; these relationships are modified by age and BMI. Sex, age, and body size should be considered in the evaluation and interpretation of surrogate outcomes in PAH.

Is low-risk status a surrogate outcome in pulmonary arterial hypertension? An analysis of three randomised trials.

BACKGROUND: Targeting short-term improvements in multicomponent risk scores for mortality in patients with pulmonary arterial hypertension (PAH) could result in improved long-term outcomes. We aimed to determine whether PAH risk scores were adequate surrogates for clinical worsening or mortality outcomes in PAH randomised clinical trials (RCTs). METHODS: We performed an individual participant data meta-analysis of RCTs selected from PAH trials provided by the US Food and Drug Administration (FDA). We calculated predicted risk using the COMPERA, COMPERA 2.0, non-invasive FPHR, REVEAL 2.0, and REVEAL Lite 2 risk scores. The primary outcome of interest was time to clinical worsening, a composite endpoint composed of any of the following events: all-cause death, hospitalisation for worsening PAH, lung transplantation, atrial septostomy, discontinuation of study treatment (or study withdrawal) for worsening PAH, initiation of parenteral prostacyclin analogue therapy, or decrease of at least 15% in 6-min walk distance from baseline, combined with either worsening of WHO functional class from baseline or the addition of an approved PAH treatment. The secondary outcome of interest was time to all-cause mortality. We assessed the surrogacy of these risk scores, parameterised as attainment of low-risk status by 16 weeks, for improvement in long-term clinical worsening and survival using mediation and meta-analysis frameworks. FINDINGS: Of 28 trials received from the FDA, three RCTs (AMBITION, GRIPHON, and SERAPHIN; n=2508) had the data necessary to assess long-term surrogacy. The mean age was 49 years (SD 16), 1956 (78%) participants were women, 1704 (68%) were classified as White, and 280 (11%) were Hispanic or Latino. 1388 (55%) of 2503 participants with available data had idiopathic PAH and 776 (31%) of 2503 had PAH associated with connective tissue disease. In a mediation analysis, the proportions of treatment effects explained by attainment of low-risk status ranged only from 7% to 13%. In a meta-analysis of trial-regions, the treatment effects on low-risk status were not predictive of the treatment effects on time to clinical worsening (R2 values 0·01-0·19) nor the treatment effects on time to all-cause mortality (R2 values 0-0·2). A leave-one-out analysis suggested that the use of these risk scores as surrogates might lead to biased inferences regarding the effect of therapies on clinical outcomes in PAH RCTs. Results were similar when using absolute risk scores at 16 weeks as the potential surrogates. INTERPRETATION: Multicomponent risk scores have utility for the prediction of outcomes in patients with PAH. Clinical surrogacy for long-term outcomes cannot be inferred from observational studies of outcomes. Our analyses of three PAH trials with long-term follow-up suggest that further study is necessary before using these or other scores as surrogate outcomes in PAH RCTs or clinical care. FUNDING: Cardiovascular Medical Research and Education Fund, US National Institutes of Health.

Gene-Interaction-Sensitive enrichment analysis in congenital heart disease.

BACKGROUND: Gene set enrichment analysis (GSEA) uses gene-level univariate associations to identify gene set-phenotype associations for hypothesis generation and interpretation. We propose that GSEA can be adapted to incorporate SNP and gene-level interactions. To this end, gene scores are derived by Relief-based feature importance algorithms that efficiently detect both univariate and interaction effects (MultiSURF) or exclusively interaction effects (MultiSURF*). We compare these interaction-sensitive GSEA approaches to traditional χ2 rankings in simulated genome-wide array data, and in a target and replication cohort of congenital heart disease patients with conotruncal defects (CTDs). RESULTS: In the simulation study and for both CTD datasets, both Relief-based approaches to GSEA captured more relevant and significant gene ontology terms compared to the univariate GSEA. Key terms and themes of interest include cell adhesion, migration, and signaling. A leading edge analysis highlighted semaphorins and their receptors, the Slit-Robo pathway, and other genes with roles in the secondary heart field and outflow tract development. CONCLUSIONS: Our results indicate that interaction-sensitive approaches to enrichment analysis can improve upon traditional univariate GSEA. This approach replicated univariate findings and identified additional and more robust support for the role of the secondary heart field and cardiac neural crest cell migration in the development of CTDs.