scholarly journals Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model

2019 ◽  
Vol 102 (3) ◽  
pp. 2378-2383 ◽  
Author(s):  
Dan Wang ◽  
Chao Ning ◽  
Jian-Feng Liu ◽  
Qin Zhang ◽  
Li Jiang
Keyword(s):  
Milk Production ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Short Communication ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Genome Wide

scholarly journals Genome‐wide association studies for milk production traits in Valle del Belice sheep using repeated measures

2019 ◽  
Vol 50 (3) ◽  
pp. 311-314 ◽  
Author(s):  
A. M. Sutera ◽  
V. Riggio ◽  
S. Mastrangelo ◽  
R. Di Gerlando ◽  
M. T. Sardina ◽  
...  
Keyword(s):  
Milk Production ◽  
Repeated Measures ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Genome Wide

scholarly journals Genome Wide Association Studies for Milk Production Traits in Chinese Holstein Population

PLoS ONE ◽  
2010 ◽  
Vol 5 (10) ◽  
pp. e13661 ◽  
Author(s):  
Li Jiang ◽  
Jianfeng Liu ◽  
Dongxiao Sun ◽  
Peipei Ma ◽  
Xiangdong Ding ◽  
...  
Keyword(s):  
Milk Production ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Production Traits ◽  
Milk Production Traits ◽  
Chinese Holstein ◽  
Genome Wide ◽  
Holstein Population

scholarly journals GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

2019 ◽  
Author(s):  
Jan A. Freudenthal ◽  
Markus J. Ankenbrand ◽  
Dominik G. Grimm ◽  
Arthur Korte
Keyword(s):  
Complex Traits ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Large Datasets ◽  
Genome Wide Association ◽  
Small Data ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Non Gaussian

AbstractMotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naïve Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.ResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.AvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

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