scholarly journals Genetic architecture of 11 abdominal organ traits derived from abdominal MRI using deep learning

2020 ◽  
Author(s):  
Yi Liu ◽  
Nicolas Basty ◽  
Brandon Whitcher ◽  
Jimmy D Bell ◽  
Elena Sorokin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Population Based ◽  
Therapeutic Interventions ◽  
Liver Fat ◽  
Liver Iron ◽  
Abdominal Mri ◽  
Mri Scans

AbstractCardiometabolic diseases are an increasing global health burden. While well established socioeconomic, environmental, behavioural, and genetic risk factors have been identified, our understanding of the drivers and mechanisms underlying these complex diseases remains incomplete. A better understanding is required to develop more effective therapeutic interventions. Magnetic resonance imaging (MRI) has been used to assess organ health in a number of studies, but large-scale population-based studies are still in their infancy. Using 38,683 abdominal MRI scans in the UK Biobank, we used deep learning to systematically quantify parameters from individual organs (liver, pancreas, spleen, kidneys, lungs and adipose depots), and demonstrate that image derived phenotypes (volume, fat and iron content) reflect organ health and disease. We show that these traits have a substantial heritable component (8%-44%), and identify 93 independent genome-wide significant associations, including 3 associations with liver fat and one with liver iron that have not previously been reported, and 73 in traits that have not previously been studied. Overall our work demonstrates the utility of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues of the abdomen, and to generate new insights into the genetic architecture of complex traits.

scholarly journals PS-202-Genome-wide association studies of abdominal MRI scans identifies loci associated with liver fat and liver iron in the UK Biobank

2019 ◽  
Vol 70 (1) ◽  
pp. e135
Author(s):  
Henry Wilman ◽  
Constantinos Parisinos ◽  
Matt Kelly ◽  
Stefan Neubauer ◽  
Louise Thomas ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Liver Fat ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Liver Iron ◽  
Genome Wide ◽  
Abdominal Mri ◽  
Mri Scans ◽  
The Uk

scholarly journals Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Yi Liu ◽  
Nicolas Basty ◽  
Brandon Whitcher ◽  
Jimmy D Bell ◽  
Elena P Sorokin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Genetic Architecture ◽  
Resonance Imaging ◽  
Effective Interventions ◽  
Genome Wide ◽  
Abdominal Mri ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Underlying Mechanisms ◽  
The Uk

Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8–44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.

scholarly journals Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

2018 ◽  
Author(s):  
Doug Speed ◽  
David J Balding
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Confounding Bias ◽  
Conserved Regions ◽  
Genome Wide ◽  
Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

scholarly journals Sexually dimorphic genetic architecture of complex traits in a large-scale F2 cross in pigs

2014 ◽  
Vol 46 (1) ◽  
Author(s):  
Leilei Cui ◽  
Junjie Zhang ◽  
Junwu Ma ◽  
Yuanmei Guo ◽  
Lin Li ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Sexually Dimorphic

scholarly journals Impact of the Analytical Approach on the Reliability of MRI-Based Assessment of Hepatic Fat Content

2020 ◽  
Vol 4 (12) ◽  
Author(s):  
Maggie S Burhans ◽  
Niranjan Balu ◽  
Kelsey A Schmidt ◽  
Gail Cromer ◽  
Kristina M Utzschneider ◽  
...  
Keyword(s):  
Gold Standard ◽  
Accurate Estimate ◽  
Fat Content ◽  
Normal Weight ◽  
Liver Fat ◽  
Liver Fat Content ◽  
Abdominal Mri ◽  
Mri Scans ◽  
Hepatic Fat ◽  
The Mean

ABSTRACT MRI is a popular noninvasive method for the assessment of liver fat content. After MRI scan acquisition, there is currently no standardized image analysis procedure for the most accurate estimate of liver fat content. We determined intraindividual reliability of MRI-based liver fat measurement using 10 different MRI slice analysis methods in normal-weight, overweight, and obese individuals who underwent 2 same-day abdominal MRI scans. We also compared the agreement in liver fat content between analytical methods and assessed the variability in fat content across the entire liver. Our results indicate that liver fat content varies across the liver, with some slices averaging 54% lower and others 75% higher fat content than the mean of all slices (gold standard). Our data suggest that the entire liver should be contoured on at least every 10th slice to achieve close agreement with the gold standard.

scholarly journals A powerful approach to estimating annotation-stratified genetic covariance using GWAS summary statistics

2017 ◽  
Author(s):  
Qiongshi Lu ◽  
Boyang Li ◽  
Derek Ou ◽  
Margret Erlendsdottir ◽  
Ryan L. Powles ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Genetic Architecture ◽  
Large Scale ◽  
Late Onset ◽  
Association Studies ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genetic Covariance

AbstractDespite the success of large-scale genome-wide association studies (GWASs) on complex traits, our understanding of their genetic architecture is far from complete. Jointly modeling multiple traits’ genetic profiles has provided insights into the shared genetic basis of many complex traits. However, large-scale inference sets a high bar for both statistical power and biological interpretability. Here we introduce a principled framework to estimate annotation-stratified genetic covariance between traits using GWAS summary statistics. Through theoretical and numerical analyses we demonstrate that our method provides accurate covariance estimates, thus enabling researchers to dissect both the shared and distinct genetic architecture across traits to better understand their etiologies. Among 50 complex traits with publicly accessible GWAS summary statistics (Ntotal ≈ 4.5 million), we identified more than 170 pairs with statistically significant genetic covariance. In particular, we found strong genetic covariance between late-onset Alzheimer’s disease (LOAD) and amyotrophic lateral sclerosis (ALS), two major neurodegenerative diseases, in single-nucleotide polymorphisms (SNPs) with high minor allele frequencies and in SNPs located in the predicted functional genome. Joint analysis of LOAD, ALS, and other traits highlights LOAD’s correlation with cognitive traits and hints at an autoimmune component for ALS.

scholarly journals An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles

2021 ◽  
Author(s):  
Frank Niemeyer ◽  
Annika Zanker ◽  
René Jonas ◽  
Youping Tao ◽  
Fabio Galbusera ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Deep Neural Network ◽  
External Validation ◽  
Erector Spinae ◽  
Published Data ◽  
Spinal Disorders ◽  
Paravertebral Muscles ◽  
Mri Scans

Purpose. Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset. Methods. A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Psoas major, quadratus lumborum, erector spinae, and multifidus were manually segmented in all available slices. The dataset was used to train and validate a deep neural network able to segment muscles automatically. Subsequently, the network was externally validated on images purposely acquired from 22 healthy volunteers. Results. The Jaccard index for the individual muscles calculated for the 22 subjects of the external validation set ranged between 0.862 and 0.935, demonstrating a generally excellent performance of the network. Cross-sectional area and fat fraction of the muscles were in agreement with published data. Conclusions. The externally validated deep neural network was able to perform the segmentation of the paravertebral muscles in axial MRI scans in an accurate and fully automated manner, and is therefore a suitable tool to perform large-scale studies in the field of spinal disorders and sarcopenia, overcoming the limitations of non-automated methods.

scholarly journals Contrasting the genetic architecture of 30 complex traits from summary association data

2016 ◽  
Author(s):  
Huwenbo Shi ◽  
Gleb Kichaev ◽  
Bogdan Pasaniuc
Keyword(s):  
Complex Traits ◽  
Variance Components ◽  
Genetic Architecture ◽  
Large Scale ◽  
Multiple Traits ◽  
New Methods ◽  
Large Sets ◽  
High Contribution ◽  
Association Data ◽  
Summary Data

Variance components methods that estimate the aggregate contribution of large sets of variants to the heritability of complex traits have yielded important insights into the disease architecture of common diseases. Here, we introduce new methods that estimate the total variance in trait explained by a single locus in the genome (local heritability) from summary GWAS data while accounting for linkage disequilibrium (LD) among variants. We apply our new estimator to ultra large-scale GWAS summary data of 30 common traits and diseases to gain insights into their local genetic architecture. First, we find that common SNPs have a high contribution to the heritability of all studied traits. Second, we identify traits for which the majority of the SNP heritability can be confined to a small percentage of the genome. Third, we identify GWAS risk loci where the entire locus explains significantly more variance in the trait than the GWAS reported variants. Finally, we identify 55 loci that explain a large proportion of heritability across multiple traits.

scholarly journals Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications

2018 ◽  
Author(s):  
Simon Haworth ◽  
Ruth Mitchell ◽  
Laura Corbin ◽  
Kaitlin H Wade ◽  
Tom Dudding ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Data ◽  
Population Based ◽  
Risk Scores ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Common Genetic Variants ◽  
The Uk

Introductory paragraphThe inclusion of genetic data in large studies has enabled the discovery of genetic contributions to complex traits and their application in applied analyses including those using genetic risk scores (GRS) for the prediction of phenotypic variance. If genotypes show structure by location and coincident structure exists for the trait of interest, analyses can be biased. Having illustrated structure in an apparently homogeneous collection, we aimed to a) test for geographical stratification of genotypes in UK Biobank and b) assess whether stratification might induce bias in genetic association analysis.We found that single genetic variants are associated with birth location within UK Biobank and that geographic structure in genetic data could not be accounted for using routine adjustment for study centre and principal components (PCs) derived from genotype data. We found that GRS for complex traits do appear geographically structured and analysis using GRS can yield biased associations. We discuss the likely origins of these observations and potential implications for analysis within large-scale population based genetic studies.

scholarly journals Deep Learning for Predicting Complex Traits in Spring Wheat Breeding Program

2021 ◽  
Vol 11 ◽  
Author(s):  
Karansher S. Sandhu ◽  
Dennis N. Lozada ◽  
Zhiwu Zhang ◽  
Michael O. Pumphrey ◽  
Arron H. Carter
Keyword(s):  
Deep Learning ◽  
Spring Wheat ◽  
Complex Traits ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Recombinant Inbred Lines ◽  
Wheat Breeding ◽  
Snp Markers ◽  
Breeding Program ◽  
Wheat Breeding Program

Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy in GS. Deep learning (DL) is a branch of machine learning (ML) which focuses on densely connected networks using artificial neural networks for training the models. The objective of this research was to evaluate the potential of DL models in the Washington State University spring wheat breeding program. We compared the performance of two DL algorithms, namely multilayer perceptron (MLP) and convolutional neural network (CNN), with ridge regression best linear unbiased predictor (rrBLUP), a commonly used GS model. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat nested association mapping (NAM) population planted from 2014–2016 growing seasons. We predicted five different quantitative traits with varying genetic architecture using cross-validations (CVs), independent validations, and different sets of SNP markers. Hyperparameters were optimized for DL models by lowering the root mean square in the training set, avoiding model overfitting using dropout and regularization. DL models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Furthermore, MLP produces 5% higher prediction accuracy than CNN for grain yield and grain protein content. Altogether, DL approaches obtained better prediction accuracy for each trait, and should be incorporated into a plant breeder’s toolkit for use in large scale breeding programs.

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