Identification of rare loss-of-function genetic variation regulating body fat distribution

This is the final version. Available on open access from Oxford University Press via the DOI in this recordData Availability: This research was conducted using the UK Biobank resource (application Nos. 44448 and 9905). Access to the UK Biobank genotype and phenotype data is open to all approved health researchers (http://www.ukbiobank.ac.uk/).CONTEXT: Biological and translational insights from large-scale, array-based genetic studies of fat distribution, a key determinant of metabolic health, have been limited by the difficulty in linking predominantly non-coding variants to specific gene targets. Rare coding variant analyses provide greater confidence that a specific gene is involved, but do not necessarily indicate whether gain or loss-of-function (LoF) would be of most therapeutic benefit. OBJECTIVE, DESIGN AND SETTING: To identify genes/proteins involved in determining fat distribution, we combined the power of genome-wide analysis of array-based rare, non-synonymous variants in 450,562 individuals of UK Biobank with exome-sequence-based rare loss of function gene burden testing in 184,246 individuals. RESULTS: The data indicates that loss-of-function of four genes (PLIN1 [LoF variants, p=5.86×10 -7], INSR [LoF variants, p=6.21×10 -7], ACVR1C [LoF + Moderate impact variants, p=1.68×10 -7; Moderate impact variants, p=4.57×10 -7] and PDE3B [LoF variants, p=1.41×10 -6]) is associated with a beneficial impact on WHRadjBMI and increased gluteofemoral fat mass, whereas LoF of PLIN4 [LoF variants, p=5.86×10 -7] adversely affects these parameters. Phenotypic follow-up suggests that LoF of PLIN1, PDE3B and ACVR1C favourably affects metabolic phenotypes (e.g. triglyceride [TG] and HDL cholesterol concentrations) and reduces the risk of cardiovascular disease, whereas PLIN4 LoF has adverse health consequences. INSR LoF is associated with lower TG and HDL levels but may increase the risk of type 2 diabetes. CONCLUSION: This study robustly implicates these genes in the regulation of fat distribution, providing new and in some cases somewhat counter-intuitive insight into the potential consequences of targeting these molecules therapeutically.Medical Research Council (MRC)National Institute for Health Research (NIHR)Wellcome TrustResearch Englan

The genetic underpinnings of body fat distribution

Introduction Obesity, defined as a body mass index (BMI) > 30 kilograms/meter^2 , has reached epidemic proportions: people who are overweight (BMI > 25 kg/m^2 ) or obese now comprise more than 25% of the world’s population. Obese individuals have a higher risk of comorbidity development including type 2 diabetes, cardiovascular disease, cancer, and fertility complications. Areas covered The study of monogenic and syndromic forms of obesity have revealed a small number of genes key to metabolic perturbations. Further, obesity and body shape in the general population are highly heritable phenotypes. Study of obesity at the population level, through genome-wide association studies of BMI and waist-to-hip ratio (WHR), have revealed > 150 genomic loci that associate with these traits, and highlight the role of adipose tissue and the central nervous system in obesity related traits. Studies in animal models and cell lines have helped further elucidate the potential biological mechanisms underlying obesity. In particular, these studies implicate adipogenesis and expansion of adipose tissue as key biological pathways in obesity and weight gain. Expert commentary Further work, including a focus on integrating genetic and additional genomic data types, as well as modeling obesity-like features in vitro , will be crucial in translating genome-wide association signals to the causal mechanisms driving disease. </p

The genetic underpinnings of body fat distribution

Introduction Obesity, defined as a body mass index (BMI) &gt; 30 kilograms/meter^2 , has reached epidemic proportions: people who are overweight (BMI &gt; 25 kg/m^2 ) or obese now comprise more than 25% of the world’s population. Obese individuals have a higher risk of comorbidity development including type 2 diabetes, cardiovascular disease, cancer, and fertility complications. Areas covered The study of monogenic and syndromic forms of obesity have revealed a small number of genes key to metabolic perturbations. Further, obesity and body shape in the general population are highly heritable phenotypes. Study of obesity at the population level, through genome-wide association studies of BMI and waist-to-hip ratio (WHR), have revealed &gt; 150 genomic loci that associate with these traits, and highlight the role of adipose tissue and the central nervous system in obesity related traits. Studies in animal models and cell lines have helped further elucidate the potential biological mechanisms underlying obesity. In particular, these studies implicate adipogenesis and expansion of adipose tissue as key biological pathways in obesity and weight gain. Expert commentary Further work, including a focus on integrating genetic and additional genomic data types, as well as modeling obesity-like features in vitro , will be crucial in translating genome-wide association signals to the causal mechanisms driving disease. </p

Count-ception: counting by fully convolutional redundant counting

Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and Zisserman, to instead predict a count map which contains redundant counts based on the receptive field of a smaller regression network. The regression network predicts a count of the objects that exist inside this frame. By processing the image in a fully convolutional way each pixel is going to be accounted for some number of times, the number of windows which include it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true count we take the average over the redundant predictions. Our contribution is redundant counting instead of predicting a density map in order to average over errors. We also propose a novel deep neural network architecture adapted from the Inception family of networks called the Count-ception network. Together our approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state of the art method by Xie, Noble, and Zisserman in 2016

Count-ception: counting by fully convolutional redundant counting

Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and Zisserman, to instead predict a count map which contains redundant counts based on the receptive field of a smaller regression network. The regression network predicts a count of the objects that exist inside this frame. By processing the image in a fully convolutional way each pixel is going to be accounted for some number of times, the number of windows which include it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true count we take the average over the redundant predictions. Our contribution is redundant counting instead of predicting a density map in order to average over errors. We also propose a novel deep neural network architecture adapted from the Inception family of networks called the Count-ception network. Together our approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state of the art method by Xie, Noble, and Zisserman in 2016

Towards deep cellular phenotyping in placental histology

The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep convolutional neural network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes

Towards deep cellular phenotyping in placental histology

The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep convolutional neural network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes

Deep long period seismicity preceding and during the 2021 Fagradalsfjall eruption, Iceland

AbstractWe use a dense seismic network on the Reykjanes Peninsula, Iceland, to image a group of earthquakes at 10–12 km depth, 2 km north-east of 2021 Fagradalsfjall eruption site. These deep earthquakes have a lower frequency content compared to earthquakes located in the upper, brittle crust and are similar to deep long period (DLP) seismicity observed at other volcanoes in Iceland and around the world. We observed several swarms of DLP earthquakes between the start of the study period (June 2020) and the initiation of the 3-week-long dyke intrusion that preceded the eruption in March 2021. During the eruption, DLP earthquake swarms returned 1 km SW of their original location during periods when the discharge rate or fountaining style of the eruption changed. The DLP seismicity is therefore likely to be linked to the magma plumbing system beneath Fagradalsfjall. However, the DLP seismicity occurred ~ 5 km shallower than where petrological modelling places the near-Moho magma storage region in which the Fagradalsfjall lava was stored. We suggest that the DLP seismicity was triggered by the exsolution of CO2-rich fluids or the movement of magma at a barrier to the transport of melt in the lower crust. Increased flux through the magma plumbing system during the eruption likely adds to the complexity of the melt migration process, thus causing further DLP seismicity, despite a contemporaneous magma channel to the surface.</jats:p

Deep long period seismicity preceding and during the 2021 Fagradalsfjall eruption, Iceland

AbstractWe use a dense seismic network on the Reykjanes Peninsula, Iceland, to image a group of earthquakes at 10–12 km depth, 2 km north-east of 2021 Fagradalsfjall eruption site. These deep earthquakes have a lower frequency content compared to earthquakes located in the upper, brittle crust and are similar to deep long period (DLP) seismicity observed at other volcanoes in Iceland and around the world. We observed several swarms of DLP earthquakes between the start of the study period (June 2020) and the initiation of the 3-week-long dyke intrusion that preceded the eruption in March 2021. During the eruption, DLP earthquake swarms returned 1 km SW of their original location during periods when the discharge rate or fountaining style of the eruption changed. The DLP seismicity is therefore likely to be linked to the magma plumbing system beneath Fagradalsfjall. However, the DLP seismicity occurred ~ 5 km shallower than where petrological modelling places the near-Moho magma storage region in which the Fagradalsfjall lava was stored. We suggest that the DLP seismicity was triggered by the exsolution of CO2-rich fluids or the movement of magma at a barrier to the transport of melt in the lower crust. Increased flux through the magma plumbing system during the eruption likely adds to the complexity of the melt migration process, thus causing further DLP seismicity, despite a contemporaneous magma channel to the surface.</jats:p