Integrating lipidomics and genomics : emerging tools to understand cardiovascular diseases

Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity worldwide leading to 31% of all global deaths. Early prediction and prevention could greatly reduce the enormous socio-economic burden posed by CVDs. Plasma lipids have been at the center stage of the prediction and prevention strategies for CVDs that have mostly relied on traditional lipids (total cholesterol, total triglycerides, HDL-C and LDL-C). The tremendous advancement in the field of lipidomics in last two decades has facilitated the research efforts to unravel the metabolic dysregulation in CVDs and their genetic determinants, enabling the understanding of pathophysiological mechanisms and identification of predictive biomarkers, beyond traditional lipids. This review presents an overview of the application of lipidomics in epidemiological and genetic studies and their contributions to the current understanding of the field. We review findings of these studies and discuss examples that demonstrates the potential of lipidomics in revealing new biology not captured by traditional lipids and lipoprotein measurements. The promising findings from these studies have raised new opportunities in the fields of personalized and predictive medicine for CVDs. The review further discusses prospects of integrating emerging genomics tools with the high-dimensional lipidome to move forward from the statistical associations towards biological understanding, therapeutic target development and risk prediction. We believe that integrating genomics with lipidome holds a great potential but further advancements in statistical and computational tools are needed to handle the high-dimensional and correlated lipidome.Peer reviewe

MetaPhat : Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics

Background: Multivariate testing tools that integrate multiple genome-wide association studies (GWAS) have become important as the number of phenotypes gathered from study cohorts and biobanks has increased. While these tools have been shown to boost statistical power considerably over univariate tests, an important remaining challenge is to interpret which traits are driving the multivariate association and which traits are just passengers with minor contributions to the genotype-phenotypes association statistic. Results: We introduce MetaPhat, a novel bioinformatics tool to conduct GWAS of multiple correlated traits using univariate GWAS results and to decompose multivariate associations into sets of central traits based on intuitive trace plots that visualize Bayesian Information Criterion (BIC) andP-value statistics of multivariate association models. We validate MetaPhat with Global Lipids Genetics Consortium GWAS results, and we apply MetaPhat to univariate GWAS results for 21 heritable and correlated polyunsaturated lipid species from 2,045 Finnish samples, detecting seven independent loci associated with a cluster of lipid species. In most cases, we are able to decompose these multivariate associations to only three to five central traits out of all 21 traits included in the analyses. We release MetaPhat as an open source tool written in Python with built-in support for multi-processing, quality control, clumping and intuitive visualizations using the R software. Conclusion: MetaPhat efficiently decomposes associations between multivariate phenotypes and genetic variants into smaller sets of central traits and improves the interpretation and specificity of genome-phenome associations. MetaPhat is freely available under the MIT license at:.Peer reviewe

Polygenic Score for Physical Activity Is Associated with Multiple Common Diseases

Introduction Genetic pleiotropy, in which the same genes affect two or more traits, may partially explain the frequently observed associations between high physical activity (PA) and later reduced morbidity or mortality. This study investigated associations between PA polygenic risk scores (PRS) and cardiometabolic diseases among the Finnish population. Methods PRS for device-measured overall PA were adapted to a FinnGen study cohort of 218,792 individuals with genomewide genotyping and extensive digital longitudinal health register data. Associations between PA PRS and body mass index, diseases, and mortality were analyzed with linear and logistic regression models. Results A high PA PRS predicted a lower body mass index (beta = -0.025 kg center dot m(-2) per one SD change in PA PRS, SE = 0.013, P = 1.87 x 10(-80)). The PA PRS also predicted a lower risk for diseases that typically develop later in life or not at all among highly active individuals. A lower disease risk was systematically observed for cardiovascular diseases (odds ratio [OR] per 1 SD change in PA PRS = 0.95, P = 9.5 x 10(-19)) and, for example, hypertension [OR = 0.93, P = 2.7 x 10(-44)), type 2 diabetes (OR = 0.91, P = 4.1 x 10(-42)), and coronary heart disease (OR = 0.95, P = 1.2 x 10(-9)). Participants with high PA PRS had also lower mortality risk (OR = 0.97, P = 0.0003). Conclusions Genetically less active persons are at a higher risk of developing cardiometabolic diseases, which may partly explain the previously observed associations between low PA and higher disease and mortality risk. The same inherited physical fitness and metabolism-related mechanisms may be associated both with PA levels and with cardiometabolic disease risk.Peer reviewe

The relation of severe malocclusion to patients’ mental and behavioral disorders, growth, and speech problems

cjaa028Severe malocclusions appear in up to 20 per cent of the population. Many neuropsychiatric diseases are likely to have a neurodevelopmental, partially genetic background with their origins as early as fetal life. However, the possible relationship between neurodevelopmental disorders and severe malocclusions is unclear. The aim of this study was in a population-based setting (270 000 inhabitants) to investigate whether patients with severe malocclusions have more mental and behavioural disorders and growth or speech problems than controls without severe malocclusion.The study group consisted of patients from the Espoo Health Care Center, Finland, born in year 2000, who were retrospectively screened for their medical and dental records, including their possible mental and behavioural disorders (i.e. attention deficit hyperactivity disorder, Asperger’s syndrome, autism, mood disorder, or broadly defined behavioural abnormalities, learning problems, mental disorders, sleep disturbances, anxiety symptoms, depressive symptoms, and eating-related symptoms) and their need of orthodontic treatment according to the Treatment Priority Index (TPI). The study group consisted of a severe malocclusion group (n =1008; TPI 8–10) and a control group (n = 1068) with no severe malocclusion (TPI 0–7).Patients with severe mandibular retrognatia (P \lt; 0.000), lip incompetence (P = 0.006), or neurodevelopmental disorders (mental and behavioural; P = 0.002) were found to have significantly more speech problems than the controls. The patients with severe malocclusions were leaner, that is, body mass index (kg/m2) \lt;17, underweight; 17–25, normal weight; \gt;25, overweight) than controls (P = 0.003), and underweight patients had a significant association with retrognathic maxilla (P \lt; 0.000) compared to normal or overweight patients. No significant relationship between neurodevelopmental disorders and severe malocclusions, that is, retrognatia of maxilla, hypodontia, and severe dental crowding was observed.Our results indicate that patients with severe mandibular retrognatia, lip incompetence, or neurodevelopmental disorders were found to have significantly more speech problems than controls. During orthodontic treatment of patients with severe malocclusion, special attention should be paid to patients with severe mandibular retrognatia, lip incompetence, and speech problems to detect signs of possible neurodevelopmental disorders and record if potential follow-up measures are in place.Peer reviewe

Regularized Machine Learning in the Genetic Prediction of Complex Traits

Compared to univariate analysis of genome-wide association (GWA) studies, machine learning&ndash;based models have been shown to provide improved means of learning such multilocus panels of genetic variants and their interactions that are most predictive of complex phenotypic traits. Many applications of predictive modeling rely on effective variable selection, often implemented through model regularization, which penalizes the model complexity and enables predictions in individuals outside of the training dataset. However, the different regularization approaches may also lead to considerable differences, especially in the number of genetic variants needed for maximal predictive accuracy, as illustrated here in examples from both disease classification and quantitative trait prediction. We also highlight the potential pitfalls of the regularized machine learning models, related to issues such as model overfitting to the training data, which may lead to over-optimistic prediction results, as well as identifiability of the predictive variants, which is important in many medical applications. While genetic risk prediction for human diseases is used as a motivating use case, we argue that these models are also widely applicable in nonhuman applications, such as animal and plant breeding, where accurate genotype-to-phenotype modeling is needed. Finally, we discuss some key future advances, open questions and challenges in this developing field, when moving toward low-frequency variants and cross-phenotype interactions.</p

Immune system-wide Mendelian randomization and triangulation analyses support autoimmunity as a modifiable component in dementia-causing diseases

Publisher Copyright: © 2022, The Author(s).Immune system and blood–brain barrier dysfunction are implicated in the development of Alzheimer’s and other dementia-causing diseases, but their causal role remains unknown. We performed Mendelian randomization for 1,827 immune system- and blood–brain barrier-related biomarkers and identified 127 potential causal risk factors for dementia-causing diseases. Pathway analyses linked these biomarkers to amyloid-β, tau and α-synuclein pathways and to autoimmunity-related processes. A phenome-wide analysis using Mendelian randomization-based polygenic risk score in the FinnGen study (n = 339,233) for the biomarkers indicated shared genetic background for dementias and autoimmune diseases. This association was further supported by human leukocyte antigen analyses. In inverse-probability-weighted analyses that simulate randomized controlled drug trials in observational data, anti-inflammatory methotrexate treatment reduced the incidence of Alzheimer’s disease in high-risk individuals (hazard ratio compared with no treatment, 0.64, 95% confidence interval 0.49–0.88, P = 0.005). These converging results from different lines of human research suggest that autoimmunity is a modifiable component in dementia-causing diseases.Peer reviewe

Rintasyöpäriskin arviointiin uusia genomityökaluja

Vertaisarvioitu.Tutkimukset viimeksi kuluneen vuosikymmenen ajalta ovat osoittaneet rintasyövän perinnöllisen alttiuden monimuotoisuuden. Yksittäisten merkittävien rintasyövän alttiusgeenien mutaatioita seulotaan ja valikoitujen potilaiden osalta myös hyödynnetään, mutta testauskriteerit täyttävistäkin vain noin kuudennekselta löydetään tällainen mutaatio. Sairastumisriskin arvioimiseksi on viime vuosina kehitetty moniin tauteihin polygeenisiä riskisummia - tehokkaita algoritmeja, jotka huomioivat riskitekijöitä perimänlaajuisesti. Suuri rintasyövän polygeeninen riski lisää rintasyöpäriskiä huomattavasti ja rikastuu yksittäisten mutaatioiden tapaan sukuihin. Siksi rintasyövän geneettisen riskinarvioinnin tulisi olla nykyistä kokonaisvaltaisempaa. Polygeenisen riskiarvion lupaavimmat käyttökohteet ovat naisten, joiden rintasyöpäriski on suuri, tunnistaminen rintasyöpäpotilaiden lähisukulaisten joukosta, sekä yksittäisten merkittävien mutaatioiden kantajien riskiarvion tarkentaminen.Peer reviewe

Integration of questionnaire-based risk factors improves polygenic risk scores for human coronary heart disease and type 2 diabetes

Max Tamlander et al. combine polygenic risk scores and clinical assessments to improve prediction of coronary artery disease and type 2 diabetes in European cohorts. Taken together, their results provide a useful method for preliminary cardiometabolic risk assessment in patients. Large-scale biobank initiatives and commercial repositories store genomic data collected from millions of individuals, and tools to leverage the rapidly growing pool of health and genomic data in disease prevention are needed. Here, we describe the derivation and validation of genomics-enhanced risk tools for two common cardiometabolic diseases, coronary heart disease and type 2 diabetes. Data used for our analyses include the FinnGen study (N = 309,154) and the UK Biobank project (N = 343,672). The risk tools integrate contemporary genome-wide polygenic risk scores with simple questionnaire-based risk factors, including demographic, lifestyle, medication, and comorbidity data, enabling risk calculation across resources where genome data is available. Compared to routinely used clinical risk scores for coronary heart disease and type 2 diabetes prevention, the risk tools show at least equivalent risk discrimination, improved risk reclassification (overall net reclassification improvements ranging from 3.7 [95% CI 2.8-4.6] up to 6.2 [4.6-7.8]), and capacity to be improved even further with standard lipid and blood pressure measurements. Without the need for blood tests or evaluation by a health professional, the risk tools provide a powerful yet simple method for preliminary cardiometabolic risk assessment for individuals with genome data available.Peer reviewe

Towards a Molecular Systems Model of Coronary Artery Disease

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