Longitudinal Data Analysis in Genome-Wide Association Studies

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

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Efficient multivariate analysis algorithms for longitudinal genome-wide association studies

Bioinformatics ◽

10.1093/bioinformatics/btz304 ◽

2019 ◽

Vol 35 (23) ◽

pp. 4879-4885 ◽

Cited By ~ 4

Author(s):

Chao Ning ◽

Dan Wang ◽

Lei Zhou ◽

Julong Wei ◽

Yuanxin Liu ◽

...

Keyword(s):

Longitudinal Data ◽

Software Package ◽

Mixed Model ◽

Linear Mixed Model ◽

Association Studies ◽

Genome Wide Association ◽

Supplementary Information ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Computational Speed

Abstract Motivation Current dynamic phenotyping system introduces time as an extra dimension to genome-wide association studies (GWAS), which helps to explore the mechanism of dynamical genetic control for complex longitudinal traits. However, existing methods for longitudinal GWAS either ignore the covariance among observations of different time points or encounter computational efficiency issues. Results We herein developed efficient genome-wide multivariate association algorithms for longitudinal data. In contrast to existing univariate linear mixed model analyses, the proposed method has improved statistic power for association detection and computational speed. In addition, the new method can analyze unbalanced longitudinal data with thousands of individuals and more than ten thousand records within a few hours. The corresponding time for balanced longitudinal data is just a few minutes. Availability and implementation A software package to implement the efficient algorithm named GMA (https://github.com/chaoning/GMA) is available freely for interested users in relevant fields. Supplementary information Supplementary data are available at Bioinformatics online.

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SNP data analysis in genome-wide association studies

10.14711/thesis-b1146292 ◽

2011 ◽

Author(s):

Can Yang

Keyword(s):

Data Analysis ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Snp Data ◽

Genome Wide

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Data analysis of Genome-Wide Association studies (GWAS) concerning rheumatoid arthritis and multiple sclerosis

Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine ◽

10.1109/itab.2010.5687817 ◽

2010 ◽

Author(s):

Konstantina Nikolaou ◽

Fanis G. Kalatzis ◽

Nikolaos Giannakeas ◽

Themis P. Exarchos ◽

Sofia Markoula ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Multiple Sclerosis ◽

Data Analysis ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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Genome-Wide Association Studies for Bivariate Sparse Longitudinal Data

Human Heredity ◽

10.1159/000330781 ◽

2011 ◽

Vol 72 (2) ◽

pp. 110-120 ◽

Cited By ~ 9

Author(s):

Kiranmoy Das ◽

Jiahan Li ◽

Guifang Fu ◽

Zhong Wang ◽

Rongling Wu

Keyword(s):

Longitudinal Data ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

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LMM-22: An Enhanced Linear Mixed Model (LMM) Approach for Genome-Wide Association Studies (GWAS) for the Prediction of Diseases and Traits among Humans from Genomics Data

10.20944/preprints202005.0154.v1 ◽

2020 ◽

Author(s):

Siddharth Sharma

Keyword(s):

Data Analysis ◽

Graphics Processing Units ◽

Mixed Model ◽

Linear Mixed Model ◽

Association Studies ◽

Analysis Data ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Quality Of Data ◽

Genome Wide

Increasingly, genomics is being used for the prediction of specific traits and diseases (phenotypes) among humans. Wider availability of genomics data through multiple research projects (such as International HapMap Project1 and 1000 Genomes2) has been a catalyst in that direction. With the recent advances in machine learning and big data analysis, data computation resources and data models needed for genomics data analysis are readily available. However, the prediction of traits and diseases has its own challenges in terms of computational requirements and computational analysis, statistical analysis (example: confounding variables), and limited quality of data collection. Linear Mixed Models (LMM, a type of linear regression) is a common approach for Genome-wide Association Studies (GWAS) for the prediction of common traits among humans using genomics. This paper researches the existing LMM-based approaches for Genome-wide Association Studies (GWAS), describes the experiment performed on FaST-LMM approach from Microsoft Research, and then proposes an enhanced approach (called LMM-22) on how to address computational and statistical issues. LMM-22 focuses on the parallelization of LMM computations and execution of LMM-22 on General Purpose Graphics Processing Units (GPU) as against CPUs to accelerate the LMM approach for GWAS studies.

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