scholarly journals Genomic evaluation and variance component estimation of additive and dominance effects using single nucleotide polymorphism markers in heterogeneous stock mice

2018 ◽  
Vol 63 (No. 12) ◽  
pp. 492-506
Author(s):  
M. Mahdavi ◽  
G.R. Dashab ◽  
M. Vafaye Valeh ◽  
M. Rokouei ◽  
M. Sargolzaei
Keyword(s):  
Genomic Prediction ◽  
Variance Component ◽  
Estimation Method ◽  
Additive Models ◽  
Variance Component Estimation ◽  
Additive Effects ◽  
Pedigree Data ◽  
Single Nucleotide ◽  
Heterogeneous Stock ◽  
Component Estimation

Exploration of genetic variance has mostly been limited to additive effects estimated using pedigree data and non-additive effects have been ignored. This study aimed to evaluate the performance of single nucleotide polymorphisms (SNPs) marker models in the mixed and orthogonal framework including both additive and non-additive effects for estimating variances and genomic prediction in four diabetes-related traits in heterogeneous stock mice. Models have performed differently in detecting SNPs affecting traits. Dominance variances explained over 14.7 and 3.8% of genetic and phenotype variance in a Genomic prediction and variance component estimation method (GVCBLUP) framework. Reliabilities of additive Genomic best linear unbiased prediction model (GBLUP) in different traits ranged from 44.8 to 66.6%, for GVCBLUPs framework including both additive and dominance effects (MAD), and 46.1 to 69% for the model including additive effect (MA). Dominance GBLUP reliabilities ranged from 6 to 26.4% for MAD and from 22.5 to 50.5% in the model including dominance (MD). MA and MD had higher reliability for additive and dominance GBLUPs compared to MAD. Reliabilities of GBLUPs in MAD and MA for all traits were not significant except for growth slope (P < 0.01). In orthogonal framework models, epistasis variances accounted for a greater proportion (87.3, 89.1, 95.5, and 77.2%) of genetic variation for end weight, growth slope, body mass index, and body length, respectively. Heritability in a broad sense was estimated at 1.12, 1.67, 3.64, and 2.0%, in which non-additive heritability had a significant contribution. Genetic variances explained by dominance using GVCBLUPs were 16.8, 29.4, 14.6, and 14.9% for the traits. Generally, the non-additive models had a lower value of deviance information criterion (DIC) and performed better in estimating the variance component. Comparing the estimated variance by orthogonal framework models confirmed the results previously estimated by GVCBLUPs, with the difference that the estimates were shrinking. Following significant SNPs affecting diabetes-related traits by post-genome-wide studies could reveal unknown aspects and contribute to genetic control of the disease.

scholarly journals Detecting Epistasis with the Marginal Epistasis Test in Genetic Mapping Studies of Quantitative Traits

2016 ◽  
Author(s):  
Lorin Crawford ◽  
Ping Zeng ◽  
Sayan Mukherjee ◽  
Xiang Zhou
Keyword(s):  
Variance Component ◽  
Statistical Power ◽  
Estimation Method ◽  
Pairwise Interaction ◽  
P Value ◽  
Variance Component Estimation ◽  
Parameter Inference ◽  
Epistatic Interactions ◽  
Epistatic Effects ◽  
Component Estimation

AbstractEpistasis, commonly defined as the interaction between multiple genes, is an important genetic component underlying phenotypic variation. Many statistical methods have been developed to model and identify epistatic interactions between genetic variants. However, because of the large combinatorial search space of interactions, most epistasis mapping methods face enormous computational challenges and often suffer from low statistical power due to multiple test correction. Here, we present a novel, alternative strategy for mapping epistasis: instead of directly identifying individual pairwise or higher-order interactions, we focus on mapping variants that have non-zero marginal epistatic effects — the combined pairwise interaction effects between a given variant and all other variants. By testing marginal epistatic effects, we can identify candidate variants that are involved in epistasis without the need to identify the exact partners with which the variants interact, thus potentially alleviating much of the statistical and computational burden associated with standard epistatic mapping procedures. Our method is based on a variance component model, and relies on a recently developed variance component estimation method for efficient parameter inference and p-value computation. We refer to our method as the “MArginal ePIstasis Test”, or MAPIT. With simulations, we show how MAPIT can be used to estimate and test marginal epistatic effects, produce calibrated test statistics under the null, and facilitate the detection of pairwise epistatic interactions. We further illustrate the benefits of MAPIT in a QTL mapping study by analyzing the gene expression data of over 400 individuals from the GEUVADIS consortium.Author SummaryEpistasis is an important genetic component that underlies phenotypic variation and is also a key mechanism that accounts for missing heritability. Identifying epistatic interactions in genetic association studies can help us better understand the genetic architecture of complex traits and diseases. However, the ability to identify epistatic interactions in practice faces important statistical and computational challenges. Standard statistical methods scan through all-pairs (or all high-orders) of interactions, and the large number of interaction combinations results in slow computation time and low statistical power. We propose an alternative mapping strategy and a new variance component method for identifying epistasis. Our method examines one variant at a time, and estimates and tests its marginal epistatic effect — the combined pairwise interaction effects between a given variant and all other variants. By testing for marginal epistatic effects, we can identify variants that are involved in epistasis without the need of explicitly searching for interactions. Our method also relies on a recently developed variance component estimation method for efficient and robust parameter inference, and accurate p-value computation. We illustrate the benefits of our method using simulations and real data applications.

scholarly journals Improved Point–Line Visual–Inertial Odometry System Using Helmert Variance Component Estimation

2020 ◽  
Vol 12 (18) ◽  
pp. 2901
Author(s):  
Bo Xu ◽  
Yu Chen ◽  
Shoujian Zhang ◽  
Jingrong Wang
Keyword(s):  
Visual Information ◽  
Variance Component ◽  
Estimation Method ◽  
Visual Image ◽  
Variance Component Estimation ◽  
Localization Accuracy ◽  
Component Estimation ◽  
Line Features ◽  
Pixel Correlation ◽  
Point Line

Mobile platform visual image sequence inevitably has large areas with various types of weak textures, which affect the acquisition of accurate pose in the subsequent platform moving process. The visual–inertial odometry (VIO) with point features and line features as visual information shows a good performance in weak texture environments, which can solve these problems to a certain extent. However, the extraction and matching of line features are time consuming, and reasonable weights between the point and line features are hard to estimate, which makes it difficult to accurately track the pose of the platform in real time. In order to overcome the deficiency, an improved effective point–line visual–inertial odometry system is proposed in this paper, which makes use of geometric information of line features and combines with pixel correlation coefficient to match the line features. Furthermore, this system uses the Helmert variance component estimation method to adjust weights between point features and line features. Comprehensive experimental results on the two datasets of EuRoc MAV and PennCOSYVIO demonstrate that the point–line visual–inertial odometry system developed in this paper achieved significant improvements in both localization accuracy and efficiency compared with several state-of-the-art VIO systems.

scholarly journals GVCBLUP: a computer package for genomic prediction and variance component estimation of additive and dominance effects

2014 ◽  
Vol 15 (1) ◽  
pp. 270 ◽  
Author(s):  
Chunkao Wang ◽  
Dzianis Prakapenka ◽  
Shengwen Wang ◽  
Sujata Pulugurta ◽  
Hakizumwami Runesha ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Variance Component ◽  
Variance Component Estimation ◽  
Component Estimation ◽  
Computer Package

GPS-BDS-Galileo double-differenced stochastic model refinement based on least-squares variance component estimation

2021 ◽  
pp. 1-16
Author(s):  
Hong Hu ◽  
Xuefeng Xie ◽  
Jingxiang Gao ◽  
Shuanggen Jin ◽  
Peng Jiang
Keyword(s):  
Stochastic Model ◽  
Least Squares ◽  
Stochastic Models ◽  
Variance Component ◽  
Satellite System ◽  
Variance Component Estimation ◽  
Short Baseline ◽  
Component Estimation ◽  
Zero Baseline ◽  
Global Navigation Satellite

Abstract Stochastic models are essential for precise navigation and positioning of the global navigation satellite system (GNSS). A stochastic model can influence the resolution of ambiguity, which is a key step in GNSS positioning. Most of the existing multi-GNSS stochastic models are based on the GPS empirical model, while differences in the precision of observations among different systems are not considered. In this paper, three refined stochastic models, namely the variance components between systems (RSM1), the variances of different types of observations (RSM2) and the variances of observations for each satellite (RSM3) are proposed based on the least-squares variance component estimation (LS-VCE). Zero-baseline and short-baseline GNSS experimental data were used to verify the proposed three refined stochastic models. The results show that, compared with the traditional elevation-dependent model (EDM), though the proposed models do not significantly improve the ambiguity resolution success rate, the positioning precision of the three proposed models has been improved. RSM3, which is more realistic for the data itself, performs the best, and the precision at elevation mask angles 20°, 30°, 40°, 50° can be improved by 4⋅6%, 7⋅6%, 13⋅2%, 73⋅0% for L1-B1-E1 and 1⋅1%, 4⋅8%, 16⋅3%, 64⋅5% for L2-B2-E5a, respectively.

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