Using genetic markers to orient the edges in quantitative trait networks: The NEO software

<p>Abstract</p> <p>Background</p> <p>Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers.</p> <p>Results</p> <p>We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue.</p> <p>Conclusion</p> <p>The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: <url>http://www.genetics.ucla.edu/labs/horvath/aten/NEO</url>.</p

The transcriptional landscape of Alzheimer’s and Parkinson’s diseases

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the two most common neurodegenerative disorders worldwide. Although the aetiology, affected brain region and clinical features are particular to each of these diseases, they nevertheless share common mechanisms such as mitochondria dysfunction, neuronal loss and tau protein accumulation. The major risk factor for those disorders is ageing, the age of onset of both AD or PD being around 65 years old. Together, they account for 50 million cases worldwide, a number expected to increase due to the fact that the world population is living longer than ever. Most of AD and PD cases are sporadic and, despite all the research during the last centuries to better understand their molecular nature, current treatments are still symptomatic. Therefore, the development of effective therapies requires a better comprehension of the diseases’ aetiology and underlying mechanisms as well as finding disease-specific targets for drug discovery. A common strategy to identify biological pathways and cellular processes altered in neurodegenerative disorders is to compare gene expression profiles between age-matched diseased and non-diseased post-mortem brain tissues. However, the expression profiles derived from whole brain tissue mRNA highly reflect alterations in cellular composition, namely the well-known AD- or PD-associated loss of neurons, but not necessarily the disease-related molecular changes in brain cells. The advent of single-cell transcriptomes has made it possible to tackle this limitation, enabling the determination of reference gene expression profiles for each major brain cell type (namely neurons, astrocytes, microglia and oligodendrocytes) that can then be used to computationally estimate the cell type-specific content of bulk brain sample’s in healthy and diseased conditions, decoupling the neurodegeneration effect (i.e. the relative loss of neurons) from the intrinsic systemic or cell type-specific disease effects. This approach has already been applied in determining the effects of age and psychiatric disorders on the cellular composition of human brain, or the contribution of each cell type in shaping the pathological autism transcriptome. The same principle was applied in AD by modelling the expression of its risk genes as a function of estimated cellular composition of brain samples. For instance, APP, PSEN1, APOE and TREM2 had their expression levels associated with the relative abundance of respectively neurons, oligodendrocytes, astrocytes and microglia. Additionally, two recent studies profiled single nuclei of major brain cell types in AD and non-AD post-mortem brain samples, unveiling cell type-specific transcriptional changes. All these studies highlight the importance of charactering disease-associated cell type-specific phenotypes that can not only unveil the cellular and molecular bases of pathological mechanisms but also be therapeutically targeted. However, some of these studies still lack independent validation and have not fully dissected the nature of transcriptomic alterations in AD brains. Moreover, to our knowledge, similar approaches have not yet been applied to PD, despite increasing evidence regarding the importance of modelling cellular composition in neurodegenerative disorders. We therefore used scRNA-seq data to derive gene expression signatures for the major human brain cell types and estimate the cellular composition of idiopathic AD and PD post-mortem brain samples from their bulk transcriptomes, investigating whether neuronal loss could be confounding or masking the intrinsic disease effects on gene expression, and validating the results in independent datasets. Additionally, since AD and PD might share the same mechanisms of disease progression, we also investigated the similarities between the transcriptomic alterations induced by AD and PD in human brain tissues. This approach allowed the novel identification of genes and pathways whose activity in the brain is intrinsically altered by AD and PD in systemic and cell type-specific ways. Additionally, we pinpoint the genes that are commonly altered by these major neurodegenerative disorders as well as those specifically perturbed in each illness. Moreover, using chemical perturbagen data, we computationally identified candidate small molecules for specifically targeting the profiled AD/PD-associated molecular alterations. Thus, we unveil a set of novel candidates that can potentially be targeted in AD and PD therapeutics. Moreover, we herein demonstrate the potential of modelling cellular composition in transcriptomics analyses in the discovery of therapeutic targets for other neurodegenerative diseases

The role of air pollution in the aetiology of type 2 diabetes

Background. The public health burden of type 2 diabetes cannot be overestimated. Prevalence of type 2 diabetes is continuously increasing and has caused a great number of deaths and economic losses. Optimal prevention measures for type 2 diabetes entail that more risk factors need to be identified. Air pollution is one of the modifiable environmental risk factors causing health problems, most notably respiratory diseases. Recently there have been indications for a spill-over of its effects into the cardio-metabolic systems. Short-term exposure to air pollution may exert acute or sub-acute inflammatory cardio-metabolic responses which on long-term, sustained exposure could lead to overt cardiovascular diseases and type 2 diabetes. However, it is unclear if long-term exposure to pollutants in the air contributes to the development of type 2 diabetes. This work generates evidence to fill knowledge gaps on the impact of air pollutants on the development of type 2 diabetes and on how different susceptibilities in the general population could contribute to the understanding of the mechanisms involved in this relationship. Methods. First, this work summarized the existing evidence on the possible relationship between long-term exposure to air pollutants and type 2 diabetes. Furthermore, in the framework of the first follow-up of SAPALDIA- the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults, this work used indices for long-term exposure air pollution – 10-year mean particulate matter <10μm in diameter [PM10] and nitrogen dioxide [NO2] - assigned to participants’ residences using a combination of Gaussian dispersion and Land-use regression models, participants residential histories and pollutant trends at monitoring stations. It identified diabetes and metabolic syndrome cases in a comprehensive way considering self-reports, blood tests and other physical measures. It additionally identified genetic variants through genotyping on two different arrays – the Human Illumina610quad Bead Chip and the Taqman PCR assay - for 63 type 2 diabetes genetic polymorphisms [towards a diabetes gene score] and a functional polymorphism on the IL6 gene respectively. Based on the above and detailed health socio-demographic and lifestyle characteristics including smoking habits, occupational exposures, alcohol, nutrition, physical activity, body measurements and additional data collected in SAPALDIA, it was ideal to investigate the cross-sectional relationships between air pollutants and diabetes and to explore interactions [based on various susceptibilities] to understand mechanisms involved in the relationship between long-term exposure to air pollutants and type 2 diabetes. Results. In this work, we found a positive relationship between PM2.5 and NO2 and the risk of T2D in the pooled evidence synthesized from electronic databases. In the frame of SAPALDIA biobank, we found a moderate positive association between long-term exposure to PM10 [and NO2] and prevalent diabetes, and demonstrated a sustained effect of PM10 independent of NO2, while NO2 lost its association on accounting for PM10 in multi-pollutant models. Among the measures of cardio-metabolic function, PM10 impacted most on impairment of glucose homeostasis and least on blood lipoproteins and triglycerides. The relationship between PM10 and impaired fasting glycaemia was more apparent among the physically active. Age also appeared to influence the relationship between PM10 and impaired fasting glycaemia. People at higher polygenic risk for type 2 diabetes were more susceptible to PM10. Genetic risk for insulin resistance and obesity appeared to be more relevant than those for beta-cell function in modifying the effects of PM10, especially among those with some background inflammatory conditions. Carriers of the pro-inflammatory major ‘G’ allele of IL6-572GC, with allele frequency of 93%, were also more susceptible to PM10 in relation to diabetes. Conclusions. This work has greatly contributed to evidence suggesting the possible role of air pollutants in diabetes aetiology. The reported associations were observed at mean concentrations below current air quality guidelines. PM10 may be a good marker for aspects of air pollution [rather than NO2] relevant for the development of diabetes. In particular, PM10 might act through sub-clinical inflammation and resultant impaired insulin sensitivity. Impairment of insulin secretion may be a less relevant pathway for PM10 action. Physical activity, though beneficial, presented another likely pathway for PM10 effects. These findings, if confirmed, call for the strengthening of air quality policies and adaptation of physical activity promotion to environmental contrasts. Future studies should explore the totality of environmental exposures – exposomics –in a life-course fashion. The mediating role of DNA methylation influencing genetic expression should be further explored. For global generalizability, there is a strong need for evidence replication in developing countries where outdoor and indoor air pollution is quite high and mostly unregulated, and the burden of non-communicable diseases is rapidly growing

A comprehensive and comparative phenotypic analysis of the collaborative founder strains identifies new and known phenotypes.

The collaborative cross (CC) is a large panel of mouse-inbred lines derived from eight founder strains (NOD/ShiLtJ, NZO/HILtJ, A/J, C57BL/6J, 129S1/SvImJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Here, we performed a comprehensive and comparative phenotyping screening to identify phenotypic differences and similarities between the eight founder strains. In total, more than 300 parameters including allergy, behavior, cardiovascular, clinical blood chemistry, dysmorphology, bone and cartilage, energy metabolism, eye and vision, immunology, lung function, neurology, nociception, and pathology were analyzed; in most traits from sixteen females and sixteen males. We identified over 270 parameters that were significantly different between strains. This study highlights the value of the founder and CC strains for phenotype-genotype associations of many genetic traits that are highly relevant to human diseases. All data described here are publicly available from the mouse phenome database for analyses and downloads

The rearing environment persistently modulates mouse phenotypes from the molecular to the behavioural level.

The phenotype of an organism results from its genotype and the influence of the environment throughout development. Even when using animals of the same genotype, independent studies may test animals of different phenotypes, resulting in poor replicability due to genotype-by-environment interactions. Thus, genetically defined strains of mice may respond differently to experimental treatments depending on their rearing environment. However, the extent of such phenotypic plasticity and its implications for the replicability of research findings have remained unknown. Here, we examined the extent to which common environmental differences between animal facilities modulate the phenotype of genetically homogeneous (inbred) mice. We conducted a comprehensive multicentre study, whereby inbred C57BL/6J mice from a single breeding cohort were allocated to and reared in 5 different animal facilities throughout early life and adolescence, before being transported to a single test laboratory. We found persistent effects of the rearing facility on the composition and heterogeneity of the gut microbial community. These effects were paralleled by persistent differences in body weight and in the behavioural phenotype of the mice. Furthermore, we show that environmental variation among animal facilities is strong enough to influence epigenetic patterns in neurons at the level of chromatin organisation. We detected changes in chromatin organisation in the regulatory regions of genes involved in nucleosome assembly, neuronal differentiation, synaptic plasticity, and regulation of behaviour. Our findings demonstrate that common environmental differences between animal facilities may produce facility-specific phenotypes, from the molecular to the behavioural level. Furthermore, they highlight an important limitation of inferences from single-laboratory studies and thus argue that study designs should take environmental background into account to increase the robustness and replicability of findings

Observational causality from -omics

Some human traits like disease are heritable, which means that they run in families. This indicates that there must be something on the DNA that affects an individual’s susceptibility to developing a trait. In the last 15 years, scientists from around the world have been very successful in mapping the locations on the DNA that are associated to traits like disease, finding thousands of loci, and hundreds of DNA locations per trait, making them truly complex traits. So, we have a very good understanding about which locations on the DNA are important for developing complex traits like disease. Unfortunately, it’s still unclear how these locations on the DNA affect an individual’s trait. In this thesis I investigate how we can best understand the DNA locations that affect trait susceptibility and in doing so, identify the causes for human traits like disease. One important technique that we have used to test for finding these causal relationships is called Mendelian randomization. Mendelian randomization identifies naturally occurring experiments that have happened in observational data. In principle, Mendelian randomization can conclude the same things from observational data as from an experimental study. So called `observational causality` has many benefits as it’s cheaper than an experiment, and is less burdensome on the subjects, as they are not subjected to any intervention. The causes that I’m interested in are so called `-omics` traits. -omics traits are molecular measurements that are usually strongly regulated by the DNA. This strong DNA regulation makes -omics traits interesting candidates to understand the mechanism behind the genetic loci of other traits. In this thesis we have investigated gene expression, protein levels and microbiome measurements as our -omics traits of interest for a wide variety of traits including celiac disease and LDL-cholesterol levels

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: r(g) = 0.098, p = 2.3 x 10(-8)) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: r(g) = 0.137, p = 2.0 x 10(-12)). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets

Human spermatogenic failure purges deleterious mutation load from the autosomes and both sex chromosomes, including the gene DMRT1

Gonadal failure, along with early pregnancy loss and perinatal death, may be an important filter that limits the propagation of harmful mutations in the human population. We hypothesized that men with spermatogenic impairment, a disease with unknown genetic architecture and a common cause of male infertility, are enriched for rare deleterious mutations compared to men with normal spermatogenesis. After assaying genomewide SNPs and CNVs in 323 Caucasian men with idiopathic spermatogenic impairment and more than 1,100 controls, we estimate that each rare autosomal deletion detected in our study multiplicatively changes a man’s risk of disease by 10% (OR 1.10 [1.04–1.16], p,261023), rare X-linked CNVs by 29%, (OR 1.29 [1.11–1.50], p,161023), and rare Y-linked duplications by 88% (OR 1.88 [1.13–3.13], p,0.03). By contrasting the properties of our case-specific CNVs with those of CNV callsets from cases of autism, schizophrenia, bipolar disorder, and intellectual disability, we propose that the CNV burden in spermatogenic impairment is distinct from the burden of large, dominant mutations described for neurodevelopmental disorders. We identified two patients with deletions of DMRT1, a gene on chromosome 9p24.3 orthologous to the putative sex determination locus of the avian ZW chromosome system. In an independent sample of Han Chinese men, we identified 3 more DMRT1 deletions in 979 cases of idiopathic azoospermia and none in 1,734 controls, and found none in an additional 4,519 controls from public databases. The combined results indicate that DMRT1 loss-of-function mutations are a risk factor and potential genetic cause of human spermatogenic failure (frequency of 0.38% in 1306 cases and 0% in 7,754 controls, p = 6.261025). Our study identifies other recurrent CNVs as potential causes of idiopathic azoospermia and generates hypotheses for directing future studies on the genetic basis of male infertility and IVF outcomes.This work was partially funded by the Portuguese Foundation for Science and Technology FCT/MCTES (PIDDAC) and co-financed by European funds (FEDER) through the COMPETE program, research grant PTDC/SAU-GMG/101229/2008. IPATIMUP is an Associate Laboratory of the Portuguese Ministry of Science, Technology, and Higher Education and is partially supported by FCT. AML is the recipient of a postdoctoral fellowship from FCT (SFRH/BPD/73366/2010). CO is supported by a grant from the United States National Institutes of Health (R01 HD21244), JDS is supported by Damon Runyon Clinical Investigator Award, Alex's Lemonade Stand Foundation Epidemiology Award, and the Eunice Kennedy Shriver Children's Health Research Career Development Award NICHD 5K12HD001410. Support for humans studies and specimens were provided by the NIH/NIDDK George M. O'Brien Center for Kidney Disease Kidney Translational Research Core (P30DK079333) grant to Washington University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Heart to Heart: Exploring Heart Rate Variability in Insomnia Patient Subtypes

Insomnia is one of the most common complaints in medical practice and the sleep disorder of highest prevalence. At least 10% of the worldwide population has chronic insomnia, which has been associated with a range of negative health outcomes. Within the clinical setting, patient subtypes have been defined according to symptomology. More recently insomnia researchers have proposed phenotypes based on total sleep time during overnight polysomnography (PSG). Short-sleeping insomnia patients are purported to be a biologically severe phenotype at higher risk of cardiovascular morbidity, poor mental health, and obesity (compared to healthy controls). Heart rate variability (HRV) is an objective marker that provides insight into autonomic nervous system dynamics. The overarching aim of my research was to explore a large clinical sample of patients with Insomnia Disorder to determine whether differences in HRV exist during sleep in empirically-derived insomnia patient subtypes. The aim of the work presented within Chapter 2 was to identify all previous insomnia-HRV research to determine if HRV was impaired in adult patients with insomnia, and whether treatments altered HRV. A systematic review of five web databases located 22 relevant articles; 17 case-control studies and 5 interventions studies. Results were difficult to synthesise due to incomparable methodology and reporting. There was a high risk of bias in the majority of studies. It was concluded that although HRV impairment in insomnia may be a widely-accepted concept, it is not supported by research nor has it been determined if it varies after treatment or according to patient subtype. The aim of the first empirical study of the thesis (Chapter 3) was to objectively-derive insomnia patient subtypes and evaluate their physiological signals (HRV and electroencephalography [EEG]) during sleep onset. Patients (n = 96) with clinically-diagnosed Insomnia Disorder underwent overnight PSG to determine sleep metrics for cluster analysis using Ward’s method: Total Sleep Time (TST), Wake After Sleep Onset (WASO) and Sleep Onset Latency (SOL). Electrocardiogram (ECG) from the PSG was extracted in the 10 minutes before and after sleep onset. After R-wave detection, the ECG was visually checked and manually corrected as required. Six time and frequency-domain HRV measures were analyzed; heart rate (HR), standard deviation of all N-N intervals (SDNN), root mean square of successive R-R intervals (RMSSD), percentage of successive R-R intervals that differ by > 50 ms (PNN50), high frequency (HF), and low frequency (LF)/HF ratio. Cluster analysis derived two solutions; one comprising two subtypes and another with three subtypes. The two cluster solution consisted of insomnia with short-sleep duration (I-SSD: n = 43) and insomnia with normal objective sleep duration (I-NSD: n = 53). At sleep onset, between-group HRV analysis revealed reduced parasympathetic activity (PNN50 and RMSSD) in the short-sleeping subtype. This was not mirrored by significant increases in HR and/or the LF/HF ratio. These findings suggested that reduced parasympathetic activity during sleep onset might contribute to poor cardiometabolic health outcomes previously reported in short-sleeping insomnia patients. The final component of this thesis was a case-control study (Chapter 4) which examined whether HRV measures differed between insomnia subtypes across the nocturnal period. It was hypothesized that short-sleeping insomnia patients would have impaired HRV compared to normal-sleep duration insomnia patients, consistent with differences observed at sleep onset (Chapter 3). Insomnia patients underwent overnight PSG, which provided sleep metrics for cluster analysis and ECG for HRV analysis. ECG was visually checked for accurate R-wave detection, and manually corrected as required. HRV analysis was performed from lights-off to lights-on (and separately by sleep/wake stage) using time and frequency-domain measures. Differences in HRV measures (HR, SDNN, RMSSD, LF, HF, LF/HF) were tested between the subtypes using General Linear Models controlling for age as a core confounder. Short-sleeping insomnia patients (I-SSD: n = 34; 45.5 ± 10.5 years) and normal-sleep duration insomnia patients (I-NSD: n = 41; 37.6 ± 10.9 years) were included in the HRV analysis. There were no statistically significant nocturnal HRV differences between subtypes after controlling for age. As such, short-sleeping insomnia patients did not have statistically significant reductions in HRV measures representative of parasympathetic activity.«br /» In summary, there was a lack of persistent nocturnal HRV disparities (between empirically-derived insomnia patient subtypes) that extended beyond sleep onset in this large clinical sample of patients with Insomnia Disorder. The central tenet of 24-hour hyperarousal amongst short-sleep duration insomnia patients cannot be supported by the combined findings of these two empirical studies. Post-hoc calculations revealed larger sample sizes would be required to determine a small to medium effect size difference in nocturnal HRV between insomnia patient subtypes. Until this time, the directional relationship between insomnia, heart rate variability, hyperarousal and cardiovascular disease remains unclear in the heterogeneous insomnia population