Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine

Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry

Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.

Developmental psychopathology as a meta-paradigm: From zero-sum science to epistemological pluralism in theory and research

In a thoughtful commentary in this journal a decade ago, Michael Rutter reviewed 25 years of progress in the field before concluding that developmental psychopathology (DP) initiated a paradigm shift in clinical science. This deduction requires that DP itself be a paradigm. According to Thomas Kuhn, canonical paradigms in the physical sciences serve unifying functions by consolidating scientists' thinking and scholarship around single, closed sets of discipline-defining epistemological assumptions and methods. Paradigm shifts replace these assumptions and methods with a new field-defining framework. In contrast, the social sciences are multiparadigmatic, with thinking and scholarship unified locally around open sets of epistemological assumptions and methods with varying degrees of inter-, intra-, and subdisciplinary reach. DP challenges few if any of these local paradigms. Instead, DP serves an essential pluralizing function, and is therefore better construed as a metaparadigm. Seen in this way, DP holds tremendous untapped potential to move the field from zero-sum thinking and scholarship to positive-sum science and epistemological pluralism. This integrative vision, which furthers Dante Cicchetti's legacy of interdisciplinarity, requires broad commitment among scientists to reject zero-sum scholarship in which portending theories, useful principles, and effective interventions are jettisoned based on confirmation bias, errors in logic, and ideology.

A mouse model of the 3-hit effects of stress: Genotype controls the effects of life adversities in females

Helplessness is a dysfunctional coping response to stressors associated with different psychiatric conditions. The present study tested the hypothesis that early and adult adversities cumulate to produce helplessness depending on the genotype (3-hit hypothesis of psychopathology). To this aim, we evaluated whether Chronic Unpredictable Stress (CUS) differently affected coping and mesoaccumbens dopamine (DA) responses to stress challenge by adult mice of the C57BL/6J (B6) and DBA/2J (D2) inbred strains depending on early life experience (Repeated Cross Fostering, RCF). Three weeks of CUS increased the helplessness expressed in the Forced Swimming Test (FST) and the Tail Suspension Test by RCF-exposed female mice of the D2 strain. Moreover, female D2 mice with both RCF and CUS experiences showed inhibition of the stress-induced extracellular DA outflow in the Nucleus Accumbens, as measured by in vivo microdialysis, during and after FST. RCF-exposed B6 mice, instead, showed reduced helplessness and increased mesoaccumbens DA release. The present results support genotype-dependent additive effects of early experiences and adult adversities on behavioral and neural responses to stress by female mice. To our knowledge, this is the first report of a 3-hit effect in an animal model. Finally, the comparative analyses of behavioral and neural phenotypes expressed by B6 and D2 mice suggest some translationally relevant hypotheses of genetic risk factors for psychiatric disorders.

Predicting genotype-specific gene regulatory networks

Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.

Novel EDGE encoding method enhances ability to identify genetic interactions

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes.

Circulating non-coding RNAs in biomarker-guided cardiovascular therapy: a novel tool for personalized medicine?

Current clinical guidelines emphasize the unmet need for technological innovations to guide physician decision-making and to transit from conventional care to personalized cardiovascular medicine. Biomarker-guided cardiovascular therapy represents an interesting approach to inform tailored treatment selection and monitor ongoing efficacy. However, results from previous publications cast some doubts about the clinical applicability of biomarkers to direct individualized treatment. In recent years, the non-coding human transcriptome has emerged as a new opportunity for the development of novel therapeutic strategies and biomarker discovery. Non-coding RNA (ncRNA) signatures may provide an accurate molecular fingerprint of patient phenotypes and capture levels of information that could complement traditional markers and established clinical variables. Importantly, ncRNAs have been identified in body fluids and their concentrations change with physiology and pathology, thus representing promising non-invasive biomarkers. Previous publications highlight the translational applicability of circulating ncRNAs for diagnosis and prognostic stratification within cardiology. Numerous independent studies have also evaluated the potential of the circulating non-coding transcriptome to predict and monitor response to cardiovascular treatment. However, this field has not been reviewed in detail. Here, we discuss the state-of-the-art research into circulating ncRNAs, specifically microRNAs and long non-coding RNAs, to support clinical decision-making in cardiovascular therapy. Furthermore, we summarize current methodological and conceptual limitations and propose future steps for their incorporation into personalized cardiology. Despite the lack of robust population-based studies and technical barriers, circulating ncRNAs emerge as a promising tool for biomarker-guided therapy.

Genetics of Equine Endocrine and Metabolic Disease

A role for a genetic contribution to equine metabolic syndrome (EMS) and pars pituitary intermedia dysfunction (PPID) has been hypothesized. Heritability estimates of EMS biochemical measurements were consistent with moderately to highly heritable traits. Further, genome-wide association analyses have identified hundreds of regions of the genome contributing to EMS and candidate variants have been identified. The genetics of PPID has not yet been proven. Continued research for the specific genetic risk factors for both EMS and PPID is crucial for gaining a better understanding of the pathophysiology of both conditions and allowing development of genetic tests.

Circulating microRNAs in suspected stable coronary artery disease: A coronary computed tomography angiography study

OBJECTIVES

To explore the diagnostic performance of circulating microRNAs (miRNAs) as biomarkers in patients with suspected stable coronary artery disease (CAD).

METHODS

Plasma samples were collected from 237 consecutive patients referred for coronary computed tomography angiography (CCTA). Presence, extension and severity of coronary stenosis were evaluated using the indexes: presence of diameter stenosis ≥ 50%, segment involvement score (SIS), segment stenosis score (SSS) and 3-vessel plaque score. A panel of 10 miRNAs previously associated with CAD was analysed using RT-qPCR. Multivariate analyses were used to analyse the associations between biomarkers and indexes. Discrimination was evaluated using the area under the ROC curve (AUC). Decision trees were generated using chi-squared Automatic Interaction Detector (CHAID) prediction models.

RESULTS

After comprehensive adjustment including cardiovascular risk factors, medication use, confounding factors and protein-based biomarkers (hs-TnT and hs-CRP), several circulating miRNAs were inversely associated with coronary atherosclerosis extension (SIS and 3-vessel plaque score) and severity (SSS). In the whole population, circulating miRNAs showed a poor discrimination value for all indexes (AUC = 0.539-0.644) and did not increase the discrimination capacity of a clinical model of coronary stenosis presence, extension and severity based on conventional cardiovascular risk factors. Conversely, the inclusion of circulating miRNAs in decision trees produces models that improve the classification of cases and controls in specific patient subgroups.

CONCLUSIONS

This study identifies a group of circulating miRNAs that failed to improve the discrimination capacity of cardiovascular risk factors but that has the potential to define specific subpopulations of patients with suspected stable CAD.

Thyroid and Cardiovascular Disease: Research Agenda for Enhancing Knowledge, Prevention, and Treatment

Thyroid hormones have long been known to have a range of effects on the cardiovascular system. However, significant knowledge gaps exist concerning the precise molecular and biochemical mechanisms governing these effects and the optimal strategies for management of abnormalities in thyroid function in patients with and without preexisting cardiovascular disease. In September 2017, the National Heart, Lung, and Blood Institute convened a Working Group with the goal of developing priorities for future scientific research relating thyroid dysfunction to the progression of cardiovascular disease. The Working Group reviewed and discussed the roles of normal thyroid physiology, the consequences of thyroid dysfunction, and the effects of therapy in 3 cardiovascular areas: cardiac electrophysiology and arrhythmias, the vasculature and atherosclerosis, and the myocardium and heart failure. This report describes the current state of the field, outlines barriers and challenges to progress, and proposes research opportunities to advance the field, including strategies for leveraging novel approaches using omics and big data. The Working Group recommended research in 3 broad areas: (1) investigation into the fundamental biology relating thyroid dysfunction to the development of cardiovascular disease and into the identification of novel biomarkers of thyroid hormone action in cardiovascular tissues; (2) studies that define subgroups of patients with thyroid dysfunction amenable to specific preventive strategies and interventional therapies related to cardiovascular disease; and (3) clinical trials focused on improvement in cardiovascular performance and cardiovascular outcomes through treatment with thyroid hormone or thyromimetic drugs.

Thyroid and Cardiovascular Disease: Research Agenda for Enhancing Knowledge, Prevention, and Treatment

Thyroid hormones have long been known to have a range of effects on the cardiovascular system. However, significant knowledge gaps exist concerning the precise molecular and biochemical mechanisms governing these effects and the optimal strategies for management of abnormalities in thyroid function in patients with and without preexisting cardiovascular disease. In September 2017, The National Heart, Lung, and Blood Institute convened a Working Group with the goal of developing priorities for future scientific research relating thyroid dysfunction to the progression of cardiovascular disease. The Working Group reviewed and discussed the roles of normal thyroid physiology, the consequences of thyroid dysfunction, and the effects of therapy in three cardiovascular areas: cardiac electrophysiology and arrhythmias, the vasculature and atherosclerosis, and the myocardium and heart failure. This report describes the current state of the field, outlines barriers and challenges to progress, and proposes research opportunities to advance the field, including strategies for leveraging novel approaches using omics and big data. The Working Group recommended research in three broad areas: 1) investigation into the fundamental biology relating thyroid dysfunction to the development of cardiovascular disease and into the identification of novel biomarkers of thyroid hormone action in cardiovascular tissues; 2) studies that define subgroups of patients with thyroid dysfunction amenable to specific preventive strategies and interventional therapies related to cardiovascular disease; and 3) clinical trials focused on improvement in cardiovascular performance and cardiovascular outcomes through treatment with thyroid hormone or thyromimetic drugs.

Catalyzing Transcriptomics Research in Cardiovascular Disease: The CardioRNA COST Action CA17129

Cardiovascular disease (CVD) remains the leading cause of death worldwide and, despite continuous advances, better diagnostic and prognostic tools, as well as therapy, are needed. The human transcriptome, which is the set of all RNA produced in a cell, is much more complex than previously thought and the lack of dialogue between researchers and industrials and consensus on guidelines to generate data make it harder to compare and reproduce results. This European Cooperation in Science and Technology (COST) Action aims to accelerate the understanding of transcriptomics in CVD and further the translation of experimental data into usable applications to improve personalized medicine in this field by creating an interdisciplinary network. It aims to provide opportunities for collaboration between stakeholders from complementary backgrounds, allowing the functions of different RNAs and their interactions to be more rapidly deciphered in the cardiovascular context for translation into the clinic, thus fostering personalized medicine and meeting a current public health challenge. Thus, this Action will advance studies on cardiovascular transcriptomics, generate innovative projects, and consolidate the leadership of European research groups in the field.COST (European Cooperation in Science and Technology) is a funding organization for research and innovation networks (www.cost.eu).

Large-Scale Analysis of Genetic and Clinical Patient Data

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

Genome-wide, imputed, sequence, and structural data are now available for exceedingly large sample sizes. The needs for data management, handling population structure and related samples, and performing associations have largely been met. However, the infrastructure to support analyses involving complexity beyond genome-wide association studies is not standardized or centralized. We provide the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO), a software tool equipped to handle multi-omic data for hundreds of thousands of samples to explore complexity using genetic interactions, environment-wide association studies and gene–environment interactions, phenome-wide association studies, as well as copy number and rare variant analyses. Using the data from the Marshfield Personalized Medicine Research Project, a site in the electronic Medical Records and Genomics Network, we apply each feature of PLATO to type 2 diabetes and demonstrate how PLATO can be used to uncover the complex etiology of common traits.

Centralized infrastructure to support analyses involving complexity beyond genome-wide association studies is broadly needed. Here, Ritchie and colleagues develop PLATO, a software tool to process and integrate various methods for this task.

Panic disorder and cardiovascular diseases: an overview

The association between panic disorder (PD) and cardiovascular diseases (CVD) has been extensively studied in recent years and, although some studies have shown anxiety disorders co-existing or increasing the risk of heart disease, no causal hypothesis has been well established. Thus, a critical review was performed of the studies that evaluated the association between PD and cardiovascular diseases; synthesizing the evidence on the mechanisms mediators that theoretically would be the responsible for the causal pathway between PD and CVD, specifically. This overview shows epidemiological studies, and discusses biological mechanisms that could link PD to CVD, such as pleiotropy, heart rate variability, unhealthy lifestyle, atherosclerosis, mental stress, and myocardial perfusion defects. This study tried to provide a comprehensive narrative synthesis of previously published information regarding PD and CVD and open new possibilities of clinical management and pathophysiological understanding. Some epidemiological studies have indicated that PD could be a risk factor for CVD, raising morbidity and mortality in PD, suggesting an association between them. These studies argue that PD pathophysiology could cause or potentiate CVD. However, there is no evidence in favour of a causal relationship between PD and CVD. Therefore, PD patients with suspicions of cardiovascular symptoms need redoubled attention.

Prioritizing single-nucleotide polymorphisms and variants associated with clinical mastitis

Next-generation sequencing technology has provided resources to easily explore and identify candidate single-nucleotide polymorphisms (SNPs) and variants. However, there remains a challenge in identifying and inferring the causal SNPs from sequence data. A problem with different methods that predict the effect of mutations is that they produce false positives. In this hypothesis, we provide an overview of methods known for identifying causal variants and discuss the challenges, fallacies, and prospects in discerning candidate SNPs. We then propose a three-point classification strategy, which could be an additional annotation method in identifying causalities.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes.

Genetic studies of alcohol dependence in the context of the addiction cycle

Family, twin and adoption studies demonstrate clearly that alcohol dependence and alcohol use disorders are phenotypically complex and heritable. The heritability of alcohol use disorders is estimated at approximately 50-60% of the total phenotypic variability. Vulnerability to alcohol use disorders can be due to multiple genetic or environmental factors or their interaction which gives rise to extensive and daunting heterogeneity. This heterogeneity makes it a significant challenge in mapping and identifying the specific genes that influence alcohol use disorders. Genetic linkage and (candidate gene) association studies have been used now for decades to map and characterize genomic loci and genes that underlie the genetic vulnerability to alcohol use disorders. These approaches have been moderately successful in identifying several genes that contribute to the complexity of alcohol use disorders. Recently, genome-wide association studies have become one of the major tools for identifying genes for alcohol use disorders by examining correlations between millions of common single-nucleotide polymorphisms with diagnosis status. Genome-wide association studies are just beginning to uncover novel biology; however, the functional significance of results remains a matter of extensive debate and uncertainty. In this review, we present a select group of genome-wide association studies of alcohol dependence, as one example of a way to generate functional hypotheses, within the addiction cycle framework. This analysis may provide novel directions for validating the functional significance of alcohol dependence candidate genes. This article is part of the Special Issue entitled "Alcoholism".