Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention

Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention. Multi-ancestry meta-analyses of genome-wide association studies for self-reported physical activity during leisure time, leisure screen time, sedentary commuting and sedentary behavior at work identify 99 loci associated with at least one of these traits

Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention.publishedVersionPeer reviewe

Integrative Bioinformatics in Post-GWAS cardiovascular genomics

Cardiovascular diseases (CVDs) are the leading cause of death in the world. Genome-wide association (GWAS) studies have identified many genetic loci robustly associated to CVDs. Because most CVD-associated loci are non-coding, one of the main challenges in the post-GWAS era is interpretation of these statistical signals. This thesis presents bioinformatics applications that integrate genome, regulome and transcriptome information to address this challenge. Integrative approaches such as the ones presented in this thesis can help expand our knowledge of the biological mechanisms involved in CVDs, which in turn can be translated into better prevention and treatment

Spatio-temporal data mining in palaeogeographic data with a density-based clustering algorithm

Made available in DSpace on 2015-04-14T14:50:12Z (GMT). No. of bitstreams: 1 458539.pdf: 3705446 bytes, checksum: de3d802acba0f10f03298ee0277b51b1 (MD5) Previous issue date: 2014-03-20The usefulness of data mining and the process of Knowledge Discovery in Databases (KDD) has increased its importance as grows the volume of data stored in large repositories. A promising area for knowledge discovery concerns oil prospection, in which data used differ both from traditional and geographical data. In palaeogeographic data, temporal dimension is treated according to the geologic time scale, while the spatial dimension is related to georeferenced data, i.e., latitudes and longitudes on Earth s surface. This approach differs from that presented by spatio-temporal data mining algorithms found in literature, arising the need to evolve the existing ones to the context of this research. This work presents the development of a solution to employ a density-based spatio-temporal algorithm for mining palaeogeographic data on the Earth s surface. An evolved version of the ST-DBSCAN algorithm was implemented in Java language making use of Weka API, where improvements were carried out in order to allow the data mining algorithm to solve a variety of research problems identified. A set of experiments that validate the proposed implementations on the algorithm are presented in this work. The experiments show that the solution developed allow palaeogeographic data mining by applying appropriate formulas for calculating distances over the Earth s surface and, at the same time, treating the temporal dimension according to the geologic time scaleO uso da minera??o de dados e do processo de descoberta de conhecimento em banco de dados (Knowledge Discovery in Databases (KDD)) vem crescendo em sua import?ncia conforme cresce o volume de dados armazenados em grandes reposit?rios. Uma ?rea promissora para descoberta do conhecimento diz respeito ? prospec??o de petr?leo, onde os dados usados diferem tanto de dados tradicionais como de dados geogr?ficos. Nesses dados, a dimens?o temporal ? tratada de acordo com a escala de tempo geol?gico, enquanto a escala espacial ? relacionada a dados georeferenciados, ou seja, latitudes e longitudes projetadas na superf?cie terrestre. Esta abordagem difere da adotada em algoritmos de minera??o espa?o-temporal presentes na literatura, surgindo assim a necessidade de evolu??o dos algoritmos existentes a esse contexto de pesquisa. Este trabalho apresenta o desenvolvimento de uma solu??o para uso do algoritmo de minera??o de dados espa?o-temporais baseado em densidade ST-DBSCAN para minera??o de dados paleogeogr?ficos na superf?cie terrestre. O algoritmo foi implementado em linguagem de programa??o Java utilizando a API Weka, onde aperfei?oamentos foram feitos a fim de permitir o uso de minera??o de dados na solu??o de problemas de pesquisa identificados. Como resultados, s?o apresentados conjuntos de experimentos que validam as implementa??es propostas no algoritmo. Os experimentos demonstram que a solu??o desenvolvida permite a minera??o de dados paleogeogr?ficos com a aplica??o de f?rmulas apropriadas para c?lculo de dist?ncias sobre a superf?cie terrestre e, ao mesmo tempo, tratando a dimens?o temporal de acordo com a escala de tempo geol?gic

Integrative Bioinformatics in Post-GWAS cardiovascular genomics

Cardiovascular diseases (CVDs) are the leading cause of death in the world. Genome-wide association (GWAS) studies have identified many genetic loci robustly associated to CVDs. Because most CVD-associated loci are non-coding, one of the main challenges in the post-GWAS era is interpretation of these statistical signals. This thesis presents bioinformatics applications that integrate genome, regulome and transcriptome information to address this challenge. Integrative approaches such as the ones presented in this thesis can help expand our knowledge of the biological mechanisms involved in CVDs, which in turn can be translated into better prevention and treatment

Integrative Bioinformatics Approaches for Identification of Drug Targets in Hypertension

High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets

Integrative Bioinformatics Approaches for Identification of Drug Targets in Hypertension

High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets

Integrative Bioinformatics Approaches for Identification of Drug Targets in Hypertension

High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets

Table1.xlsx

<p>High blood pressure or hypertension is an established risk factor for a myriad of cardiovascular diseases. Genome-wide association studies have successfully found over nine hundred loci that contribute to blood pressure. However, the mechanisms through which these loci contribute to disease are still relatively undetermined as less than 10% of hypertension-associated variants are located in coding regions. Phenotypic cell-type specificity analyses and expression quantitative trait loci show predominant vascular and cardiac tissue involvement for blood pressure-associated variants. Maps of chromosomal conformation and expression quantitative trait loci (eQTL) in critical tissues identified 2,424 genes interacting with blood pressure-associated loci, of which 517 are druggable. Integrating genome, regulome and transcriptome information in relevant cell-types could help to functionally annotate blood pressure associated loci and identify drug targets.</p