Common statistical issues in genome-wide association studies: a review on power, data quality control, genotype calling and population structure

2008 ◽  
Vol 19 (2) ◽  
pp. 133-143 ◽  
Author(s):  
Yik Y Teo
Keyword(s):  
Quality Control ◽  
Population Structure ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Data Quality Control ◽  
Genotype Calling ◽  
Genome Wide ◽  
Statistical Issues ◽  
Control Genotype

scholarly journals Non-random error in genotype calling procedures: Implications for family-based and case-control genome-wide association studies

2008 ◽  
Vol 147B (8) ◽  
pp. 1379-1386 ◽  
Author(s):  
Richard J.L. Anney ◽  
Elaine Kenny ◽  
Colm T. O'Dushlaine ◽  
Jessica Lasky-Su ◽  
Barbara Franke ◽  
...  
Keyword(s):  
Random Error ◽  
Association Studies ◽  
Case Control ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genotype Calling ◽  
Genome Wide ◽  
Family Based

scholarly journals GWASTools: an R/Bioconductor package for quality control and analysis of genome-wide association studies

2012 ◽  
Vol 28 (24) ◽  
pp. 3329-3331 ◽  
Author(s):  
S. M. Gogarten ◽  
T. Bhangale ◽  
M. P. Conomos ◽  
C. A. Laurie ◽  
C. P. McHugh ◽  
...  
Keyword(s):  
Quality Control ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Bioconductor Package ◽  
Genome Wide

scholarly journals Impacts of Population Structure and Analytical Models in Genome-Wide Association Studies of Complex Traits in Forest Trees: A Case Study in Eucalyptus globulus

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e81267 ◽  
Author(s):  
Eduardo P. Cappa ◽  
Yousry A. El-Kassaby ◽  
Martín N. Garcia ◽  
Cintia Acuña ◽  
Nuno M. G. Borralho ◽  
...  
Keyword(s):  
Population Structure ◽  
Complex Traits ◽  
Eucalyptus Globulus ◽  
Association Studies ◽  
Genome Wide Association ◽  
Analytical Models ◽  
Genome Wide Association Studies ◽  
Forest Trees ◽  
Genome Wide

scholarly journals A multi-marker association method for genome-wide association studies without the need for population structure correction

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Jonas R. Klasen ◽  
Elke Barbez ◽  
Lukas Meier ◽  
Nicolai Meinshausen ◽  
Peter Bühlmann ◽  
...  
Keyword(s):  
Population Structure ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Association Method

scholarly journals Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies

2017 ◽  
Author(s):  
Haohan Wang ◽  
Bryon Aragam ◽  
Eric P. Xing
Keyword(s):  
Population Structure ◽  
Variable Selection ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Genome Wide Association ◽  
Low Rank ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Genome Wide

AbstractA fundamental and important challenge in modern datasets of ever increasing dimensionality is variable selection, which has taken on renewed interest recently due to the growth of biological and medical datasets with complex, non-i.i.d. structures. Naïvely applying classical variable selection methods such as the Lasso to such datasets may lead to a large number of false discoveries. Motivated by genome-wide association studies in genetics, we study the problem of variable selection for datasets arising from multiple subpopulations, when this underlying population structure is unknown to the researcher. We propose a unified framework for sparse variable selection that adaptively corrects for population structure via a low-rank linear mixed model. Most importantly, the proposed method does not require prior knowledge of sample structure in the data and adaptively selects a covariance structure of the correct complexity. Through extensive experiments, we illustrate the effectiveness of this framework over existing methods. Further, we test our method on three different genomic datasets from plants, mice, and human, and discuss the knowledge we discover with our method.

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.

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