scholarly journals Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Keyan Zhao ◽  
Chih-Wei Tung ◽  
Georgia C. Eizenga ◽  
Mark H. Wright ◽  
M. Liakat Ali ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Association Mapping ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genome Wide Association ◽  
Genome Wide

scholarly journals Genome-wide association mapping of leaf metabolic profiles for dissecting complex traits in maize

2012 ◽  
Vol 109 (23) ◽  
pp. 8872-8877 ◽  
Author(s):  
C. Riedelsheimer ◽  
J. Lisec ◽  
A. Czedik-Eysenberg ◽  
R. Sulpice ◽  
A. Flis ◽  
...  
Keyword(s):  
Association Mapping ◽  
Complex Traits ◽  
Genome Wide Association ◽  
Metabolic Profiles ◽  
Genome Wide

scholarly journals Genome-wide association mapping for grain manganese in rice (Oryza sativa L.) using a multi-experiment approach

Heredity ◽  
2020 ◽  
Author(s):  
Panthita Ruang-areerate ◽  
Anthony J. Travis ◽  
Shannon R. M. Pinson ◽  
Lee Tarpley ◽  
Georgia C. Eizenga ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Association Mapping ◽  
Oryza Sativa L ◽  
Genome Wide Association ◽  
Genome Wide

scholarly journals Genetic architecture of plant stress resistance: multi-trait genome-wide association mapping

2016 ◽  
Vol 213 (3) ◽  
pp. 1346-1362 ◽  
Author(s):  
Manus P. M. Thoen ◽  
Nelson H. Davila Olivas ◽  
Karen J. Kloth ◽  
Silvia Coolen ◽  
Ping-Ping Huang ◽  
...  
Keyword(s):  
Association Mapping ◽  
Stress Resistance ◽  
Genetic Architecture ◽  
Plant Stress ◽  
Genome Wide Association ◽  
Genome Wide

scholarly journals Genome-wide association mapping of salinity tolerance in rice (Oryza sativa)

DNA Research ◽  
2015 ◽  
Vol 22 (2) ◽  
pp. 133-145 ◽  
Author(s):  
V. Kumar ◽  
A. Singh ◽  
S. V. A. Mithra ◽  
S. L. Krishnamurthy ◽  
S. K. Parida ◽  
...  
Keyword(s):  
Oryza Sativa ◽  
Association Mapping ◽  
Salinity Tolerance ◽  
Genome Wide Association ◽  
Genome Wide

scholarly journals Multiplex Confounding Factor Correction for Genomic Association Mapping with Squared Sparse Linear Mixed Model

2017 ◽  
Author(s):  
Haohan Wang ◽  
Xiang Liu ◽  
Yunpeng Xiao ◽  
Ming Xu ◽  
Eric P. Xing
Keyword(s):  
Population Structure ◽  
Association Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Phenotypic Variability ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Confounding Factors ◽  
Genetic Loci ◽  
Genome Wide

AbstractGenome-wide Association Study has presented a promising way to understand the association between human genomes and complex traits. Many simple polymorphic loci have been shown to explain a significant fraction of phenotypic variability. However, challenges remain in the non-triviality of explaining complex traits associated with multifactorial genetic loci, especially considering the confounding factors caused by population structure, family structure, and cryptic relatedness. In this paper, we propose a Squared-LMM (LMM2) model, aiming to jointly correct population and genetic confounding factors. We offer two strategies of utilizing LMM2 for association mapping: 1) It serves as an extension of univariate LMM, which could effectively correct population structure, but consider each SNP in isolation. 2) It is integrated with the multivariate regression model to discover association relationship between complex traits and multifactorial genetic loci. We refer to this second model as sparse Squared-LMM (sLMM2). Further, we extend LMM2/sLMM2 by raising the power of our squared model to the LMMn/sLMMn model. We demonstrate the practical use of our model with synthetic phenotypic variants generated from genetic loci of Arabidopsis Thaliana. The experiment shows that our method achieves a more accurate and significant prediction on the association relationship between traits and loci. We also evaluate our models on collected phenotypes and genotypes with the number of candidate genes that the models could discover. The results suggest the potential and promising usage of our method in genome-wide association studies.

scholarly journals Bench Research Informed by GWAS Results

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3184
Author(s):  
Nikolay V. Kondratyev ◽  
Margarita V. Alfimova ◽  
Arkadiy K. Golov ◽  
Vera E. Golimbet
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Reverse Genetics ◽  
Genetic Factors ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually ‘highly polygenic’. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise ‘wet biologists’ with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.

scholarly journals Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models

2018 ◽  
Author(s):  
Ping Zeng ◽  
Xinjie Hao ◽  
Xiang Zhou
Keyword(s):  
Association Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Penalized Regression ◽  
Genome Wide Association ◽  
Accurate Estimation ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Snp Association ◽  
Genome Wide

AbstractMotivationGenome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci are associated with multiple traits – a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide SNPs together.ResultsWe develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling, and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially noninformative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP both in terms of high association mapping power and in terms of accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. iMAP is freely available at www.xzlab.org/software.html.

scholarly journals Dissecting the genetic architecture of seed-cotton and lint yields in Upland cotton using genome-wide association mapping

2019 ◽  
Vol 69 (4) ◽  
pp. 611-620
Author(s):  
Yuanyuan Wang ◽  
Guirong Li ◽  
Xinlei Guo ◽  
Runrun Sun ◽  
Tao Dong ◽  
...  
Keyword(s):  
Association Mapping ◽  
Upland Cotton ◽  
Genetic Architecture ◽  
Genome Wide Association ◽  
Seed Cotton ◽  
Genome Wide
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