scholarly journals Multidimensional Heritability Analysis of Neuroanatomical Shape

2015 ◽  
Author(s):  
Tian Ge ◽  
Martin Reuter ◽  
Anderson M. Winkler ◽  
Avram J. Holmes ◽  
Phil H. Lee ◽  
...  
Keyword(s):  
Brain Structure ◽  
Large Scale ◽  
Brain Mri ◽  
Brain Structures ◽  
Biomedical Data ◽  
Genetic Determinants ◽  
Genetic Studies ◽  
Genome Wide ◽  
Human Connectome Project ◽  
Mri Scans

In the dawning era of large-scale biomedical data, multidimensional phenotype vectors will play an increasing role in examining the genetic underpinnings of brain features, behavior and disease. For example, shape measurements derived from brain MRI scans are multidimensional geometric descriptions of brain structure and provide an alternate class of phenotypes that remains largely unexplored in genetic studies. Here we extend the concept of heritability to multidimensional traits, and present the first comprehensive analysis of the heritability of neuroanatomical shape measurements across an ensemble of brain structures based on genome-wide SNP and MRI data from 1,320 unrelated, young and healthy individuals. We replicate our findings in an extended twin sample from the Human Connectome Project (HCP). Our results demonstrate that neuroanatomical shape can be significantly heritable, above and beyond volume, and can serve as a complementary phenotype to study the genetic determinants and clinical relevance of brain structure.

scholarly journals Harmonization of large multi-site imaging datasets: Application to 10,232 MRIs for the analysis of imaging patterns of structural brain change throughout the lifespan

2019 ◽  
Author(s):  
Raymond Pomponio ◽  
Guray Erus ◽  
Mohamad Habes ◽  
Jimit Doshi ◽  
Dhivya Srinivasan ◽  
...  
Keyword(s):  
Brain Structure ◽  
Large Scale ◽  
Brain Mri ◽  
Big Data Analytics ◽  
Imaging Data ◽  
Mri Scans ◽  
Age Trends ◽  
Development And Aging ◽  
Structural Brain Change ◽  
Structural Brain

AbstractAs medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,232 structural brain MRI scans from participants without known neuropsychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive normative age trends of brain structure through the lifespan (3 to 96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this normative reference of brain development and aging, and to examine deviations from normative ranges, potentially related to disease.

scholarly journals Polygenic scores for handedness and their association with asymmetries in brain structure

2021 ◽  
Author(s):  
Sebastian Ocklenburg ◽  
Dorothea Metzen ◽  
Caroline Schlüter ◽  
Christoph Fraenz ◽  
Larissa Arning ◽  
...  
Keyword(s):  
Brain Structure ◽  
Large Scale ◽  
Validation Cohort ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Independent Validation ◽  
Genome Wide ◽  
Polygenic Scores ◽  
First Time

AbstractHandedness is the most widely investigated motor preference in humans. The genetics of handedness and especially the link between genetic variation, brain structure, and right-left preference have not been investigated in detail. Recently, several well-powered genome-wide association studies (GWAS) on handedness have been published, significantly advancing the understanding of the genetic determinants of left and right-handedness. In the present study, we estimated polygenic scores (PGS) of handedness-based on the GWAS by de Kovel and Francks (Sci Rep 9: 5986, 2019) in an independent validation cohort (n = 296). PGS reflect the sum effect of trait-associated alleles across many genetic loci. For the first time, we could show that these GWAS-based PGS are significantly associated with individual handedness lateralization quotients in an independent validation cohort. Additionally, we investigated whether handedness-derived polygenic scores are associated with asymmetries in gray matter macrostructure across the whole brain determined using magnetic resonance imaging. None of these associations reached significance after correction for multiple comparisons. Our results implicate that PGS obtained from large-scale handedness GWAS are significantly associated with individual handedness in smaller validation samples with more detailed phenotypic assessment.

scholarly journals Opportunities for Artificial Intelligence in Advancing Precision Medicine

2019 ◽  
Vol 7 (4) ◽  
pp. 208-213 ◽  
Author(s):  
Fabian V. Filipp
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Large Scale ◽  
Digital Health ◽  
Disease Classification ◽  
Biomedical Data ◽  
Cell Clustering ◽  
Genome Wide ◽  
Definition Of ◽  
Disease Signatures

Abstract Purpose of Review We critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. Recent Findings High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Summary Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.

An overlapping pattern of cerebral cortical thinning is associated with both positive symptoms and aggression in schizophrenia via the ENIGMA consortium

2019 ◽  
Vol 50 (12) ◽  
pp. 2034-2045 ◽  
Author(s):  
Ting Yat Wong ◽  
Joaquim Radua ◽  
Edith Pomarol-Clotet ◽  
Raymond Salvador ◽  
Anton Albajes-Eizagirre ◽  
...  
Keyword(s):  
Brain Mri ◽  
Brain Structures ◽  
Primary Diagnosis ◽  
Positive Symptoms ◽  
Formal Thought Disorder ◽  
Midcingulate Cortex ◽  
Neuroimaging Data ◽  
Mri Scans ◽  
Cortical Gray Matter ◽  
Abnormal Brain

AbstractBackgroundPositive symptoms are a useful predictor of aggression in schizophrenia. Although a similar pattern of abnormal brain structures related to both positive symptoms and aggression has been reported, this observation has not yet been confirmed in a single sample.MethodTo study the association between positive symptoms and aggression in schizophrenia on a neurobiological level, a prospective meta-analytic approach was employed to analyze harmonized structural neuroimaging data from 10 research centers worldwide. We analyzed brain MRI scans from 902 individuals with a primary diagnosis of schizophrenia and 952 healthy controls.ResultsThe result identified a widespread cortical thickness reduction in schizophrenia compared to their controls. Two separate meta-regression analyses revealed that a common pattern of reduced cortical gray matter thickness within the left lateral temporal lobe and right midcingulate cortex was significantly associated with both positive symptoms and aggression.ConclusionThese findings suggested that positive symptoms such as formal thought disorder and auditory misperception, combined with cognitive impairments reflecting difficulties in deploying an adaptive control toward perceived threats, could escalate the likelihood of aggression in schizophrenia.

scholarly journals NRXN1 is associated with enlargement of the temporal horns of the lateral ventricles in psychosis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ney Alliey-Rodriguez ◽  
Tamar A. Grey ◽  
Rebecca Shafee ◽  
Huma Asif ◽  
Olivia Lutz ◽  
...  
Keyword(s):  
Brain Mri ◽  
Bipolar Disorders ◽  
Brain Structures ◽  
Genome Wide Association ◽  
Association Analyses ◽  
1000 Genomes ◽  
Lateral Ventricles ◽  
Intermediate Phenotypes ◽  
Genome Wide ◽  
Structural Brain

Abstract Schizophrenia, Schizoaffective, and Bipolar disorders share behavioral and phenomenological traits, intermediate phenotypes, and some associated genetic loci with pleiotropic effects. Volumetric abnormalities in brain structures are among the intermediate phenotypes consistently reported associated with these disorders. In order to examine the genetic underpinnings of these structural brain modifications, we performed genome-wide association analyses (GWAS) on 60 quantitative structural brain MRI phenotypes in a sample of 777 subjects (483 cases and 294 controls pooled together). Genotyping was performed with the Illumina PsychChip microarray, followed by imputation to the 1000 genomes multiethnic reference panel. Enlargement of the Temporal Horns of Lateral Ventricles (THLV) is associated with an intronic SNP of the gene NRXN1 (rs12467877, P = 6.76E–10), which accounts for 4.5% of the variance in size. Enlarged THLV is associated with psychosis in this sample, and with reduction of the hippocampus and enlargement of the choroid plexus and caudate. Eight other suggestively significant associations (P < 5.5E–8) were identified with THLV and 5 other brain structures. Although rare deletions of NRXN1 have been previously associated with psychosis, this is the first report of a common SNP variant of NRXN1 associated with enlargement of the THLV in psychosis.

scholarly journals Large-scale genome-wide association study of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits.

2021 ◽  
Author(s):  
Sebastian May-Wilson ◽  
Nana Matoba ◽  
Kaitlin H Wade ◽  
Jouke-Jan Hottenga ◽  
Maria Pina Concas ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Correlation ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Taste Receptor ◽  
Genome Wide Association ◽  
Genetic Determinants ◽  
Genome Wide ◽  
Food Liking

Variable preferences for different foods are among the main determinants of their intake and are influenced by many factors, including genetics. Despite considerable twins' heritability, studies aimed at uncovering food-liking genetics have focused mostly on taste receptors. Here, we present the first results of a large-scale genome-wide association study of food liking conducted on 161,625 participants from UK Biobank. Liking was assessed over 139 specific foods using a 9-point hedonic scale. After performing GWAS, we used genetic correlations coupled with structural equation modelling to create a multi-level hierarchical map of food liking. We identified three main dimensions: high caloric foods defined as "Highly palatable", strong-tasting foods ranging from alcohol to pungent vegetables, defined as "Learned" and finally "Low caloric" foods such as fruit and vegetables. The "Highly palatable" dimension was genetically uncorrelated from the other two, suggesting that two independent processes underlie liking high reward foods and the Learned/Low caloric ones. Genetic correlation analysis with the corresponding food consumption traits revealed a high correlation, while liking showed twice the heritability compared to consumption. For example, fresh fruit liking and consumption showed a genetic correlation of 0.7 with heritabilities of 0.1 and 0.05, respectively. GWAS analysis identified 1401 significant food-liking associations located in 173 genomic loci, with only 11 near taste or olfactory receptors. Genetic correlation with morphological and functional brain data (33,224 UKB participants) uncovers associations of the three food-liking dimensions with non-overlapping, distinct brain areas and networks, suggestive of separate neural mechanisms underlying the liking dimensions. In conclusion, we created a comprehensive and data-driven map of the genetic determinants and associated neurophysiological factors of food liking beyond taste receptor genes.

Aetiology and pathogenesis

2018 ◽  
Author(s):  
Joanna E. Parkes ◽  
Simon Rothwell ◽  
Janine A. Lamb
Keyword(s):  
Immune Responses ◽  
Large Scale ◽  
Adaptive Immune Response ◽  
Infectious Agents ◽  
Inflammatory Myopathies ◽  
Genetic Studies ◽  
Adaptive Immune ◽  
Histocompatibility Complex ◽  
Genome Wide ◽  
Models Of Disease

The aetiology and pathogenesis of idiopathic inflammatory myopathies (IIM) is poorly understood; IIM are thought to result from exposure to environmental factors in genetically susceptible individuals. Both innate and adaptive immune responses are involved in IIM, and there is increasing evidence that non-inflammatory mechanisms play an important role in disease pathology. Several environmental risk factors, including infectious agents, ultraviolet radiation, cigarette smoking, and exposure to statins, have been implicated. Genetic studies have identified the major histocompatibility complex as the most strongly associated region, while recent large scale genome-wide studies have implicated genes that commonly regulate the adaptive immune response, which overlap with other seropositive autoimmune diseases. Integrating data across these various fields should facilitate refined models of disease pathogenesis.

scholarly journals Morphometricity as a measure of the neuroanatomical signature of a trait

2016 ◽  
Vol 113 (39) ◽  
pp. E5749-E5756 ◽  
Author(s):  
Mert R. Sabuncu ◽  
Tian Ge ◽  
Avram J. Holmes ◽  
Jordan W. Smoller ◽  
Randy L. Buckner ◽  
...  
Keyword(s):  
Large Scale ◽  
Anatomical Variation ◽  
Brain Mri ◽  
Mixed Effects ◽  
Brain Morphology ◽  
Linear Mixed Effects Model ◽  
Linear Mixed Effects ◽  
Behavioral Traits ◽  
Clinical Diagnoses ◽  
Mri Scans

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.

scholarly journals Drone phenotyping and machine learning enable discovery of loci regulating daily floral opening in lettuce

2021 ◽  
Vol 72 (8) ◽  
pp. 2979-2994 ◽  
Author(s):  
Rongkui Han ◽  
Andy J Y Wong ◽  
Zhehan Tang ◽  
Maria J Truco ◽  
Dean O Lavelle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Course ◽  
Image Data ◽  
Support Vector ◽  
Genetic Determinants ◽  
Flower Opening ◽  
Genetic Studies ◽  
Marker Data ◽  
Genome Wide ◽  
Bayesian Inference Method

Abstract Flower opening and closure are traits of reproductive importance in all angiosperms because they determine the success of self- and cross-pollination. The temporal nature of this phenotype rendered it a difficult target for genetic studies. Cultivated and wild lettuce, Lactuca spp., have composite inflorescences that open only once. An L. serriola×L. sativa F6 recombinant inbred line (RIL) population differed markedly for daily floral opening time. This population was used to map the genetic determinants of this trait; the floral opening time of 236 RILs was scored using time-course image series obtained by drone-based phenotyping on two occasions. Floral pixels were identified from the images using a support vector machine with an accuracy &gt;99%. A Bayesian inference method was developed to extract the peak floral opening time for individual genotypes from the time-stamped image data. Two independent quantitative trait loci (QTLs; Daily Floral Opening 2.1 and qDFO8.1) explaining &gt;30% of the phenotypic variation in floral opening time were discovered. Candidate genes with non-synonymous polymorphisms in coding sequences were identified within the QTLs. This study demonstrates the power of combining remote sensing, machine learning, Bayesian statistics, and genome-wide marker data for studying the genetics of recalcitrant phenotypes.

scholarly journals The Genetic Basis of Hypertension

2020 ◽  
pp. 1-6
Author(s):  
Michalopoulou Helena ◽  
◽  
Ligga Georgia ◽  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Hormone Metabolism ◽  
Genome Wide ◽  
Small Effect Size ◽  
Renal Sodium Handling ◽  
Artery Disease ◽  
Almost All

Hypertension (HTN) is one of the major risk factors for almost all cardiovascular diseases including coronary artery disease, stroke, heart failure and renal failure. Nonetheless , blood pressure (BP) regulation is insufficient due to its multifactorial nature involving interactions among genetic, environmental, mechanistic and neuroendocrine factors. Essential hypertension is the most frequent diagnosis indicating that a monocausal etiology has not been identified. The identification of causal genetic determinants has been unfulfilling. Analyses of rare monogenic syndromes of HTN focusing on renal sodium handling and steroid hormone metabolism have proved the well-defined genetic frame of hypertension though they do not affect the normal distribution of BP in the general population. Genome-wide association studies (GWAS) have revealed genetic variants that are associated with BP with small effect size which cumulatively explain to a very small extend the variability of BP. New large-scale studies in the genomic arena will clarify the polygenic determinants of BP and open a perspective on translation of the progression in BP genetics to clinical use.

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