scholarly journals A unified framework for cross-population trait prediction by leveraging the genetic correlation of polygenic traits

2021 ◽  
Author(s):  
Mingxuan Cai ◽  
Jiashun Xiao ◽  
Shunkang Zhang ◽  
Xiang Wan ◽  
Hongyu Zhao ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Unified Framework ◽  
Polygenic Traits ◽  
Trait Prediction

scholarly journals Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

2020 ◽  
Author(s):  
Yiliang Zhang ◽  
Qiongshi Lu ◽  
Yixuan Ye ◽  
Kunling Huang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Accurate Estimation ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Genomic Regions

AbstractLocal genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies. We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with significant local genetic correlations. In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations. We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.

A unified framework for automated person re-indentification

2020 ◽  
Vol 71 (7) ◽  
pp. 868-880
Author(s):  
Nguyen Hong-Quan ◽  
Nguyen Thuy-Binh ◽  
Tran Duc-Long ◽  
Le Thi-Lan
Keyword(s):  
Deep Learning ◽  
Video Analysis ◽  
Camera Network ◽  
Unified Framework ◽  
Person Detection ◽  
Practical Applications ◽  
Detection And Tracking ◽  
Analysis System ◽  
Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

A Unified Model for Photophysical and Electro-Optical Properties of Green Fluorescent Proteins

2019 ◽  
Author(s):  
Chi-Yun Lin ◽  
Matthew Romei ◽  
Luke Oltrogge ◽  
Irimpan Mathews ◽  
Steven Boxer
Keyword(s):  
Driving Force ◽  
Fluorescent Protein ◽  
Charge Transfer Band ◽  
Photophysical Properties ◽  
Polymethine Dyes ◽  
Emission Rates ◽  
Unified Framework ◽  
Underlying Factor ◽  
Green Fluorescent ◽  
Protein Environment

Green fluorescent protein (GFPs) have become indispensable imaging and optogenetic tools. Their absorption and emission properties can be optimized for specific applications. Currently, no unified framework exists to comprehensively describe these photophysical properties, namely the absorption maxima, emission maxima, Stokes shifts, vibronic progressions, extinction coefficients, Stark tuning rates, and spontaneous emission rates, especially one that includes the effects of the protein environment. In this work, we study the correlations among these properties from systematically tuned GFP environmental mutants and chromophore variants. Correlation plots reveal monotonic trends, suggesting all these properties are governed by one underlying factor dependent on the chromophore's environment. By treating the anionic GFP chromophore as a mixed-valence compound existing as a superposition of two resonance forms, we argue that this underlying factor is defined as the difference in energy between the two forms, or the driving force, which is tuned by the environment. We then introduce a Marcus-Hush model with the bond length alternation vibrational mode, treating the GFP absorption band as an intervalence charge transfer band. This model explains all the observed strong correlations among photophysical properties; related subtopics are extensively discussed in Supporting Information. Finally, we demonstrate the model's predictive power by utilizing the additivity of the driving force. The model described here elucidates the role of the protein environment in modulating photophysical properties of the chromophore, providing insights and limitations for designing new GFPs with desired phenotypes. We argue this model should also be generally applicable to both biological and non-biological polymethine dyes.<br>

Genetic and Environmental Influences on Executive Functions and Intelligence in the ABCD Study

2020 ◽  
Author(s):  
Samantha M Freis ◽  
Claire Morrison ◽  
Jeffrey M. Lessem ◽  
John K. Hewitt ◽  
Naomi P. Friedman
Keyword(s):  
Cognitive Development ◽  
Executive Functions ◽  
Genetic Correlation ◽  
Middle Childhood ◽  
Middle Age ◽  
Environmental Influences ◽  
Genetic Correlations ◽  
Twin Studies

Executive functions (EFs) and intelligence (IQ) are phenotypically correlated and heritable; however, they show variable genetic correlations in twin studies spanning childhood to middle age. We analyzed data from over 11,000 children (9-10-year-olds, including 749 twin pairs) in the Adolescent Brain Cognitive Development (ABCD) Study to examine the phenotypic and genetic relations between EFs and IQ in childhood. We identified two EF factors – Common EF and Updating-Specific, which were both related to IQ (rs = .64-.81). Common EF and IQ were heritable (53-67%), and their genetic correlation (rG = .86) was not significantly different than 1. These results suggest that EFs and IQ are phenotypically but not genetically separable in middle childhood.

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