TY - JOUR
T1 - Principals about principal components in statistical genetics
AU - Abegaz, Fentaw
AU - Chaichoompu, Kridsadakorn
AU - Génin, Emmanuelle
AU - Fardo, David W
AU - König, Inke R
AU - Mahachie John, Jestinah M
AU - Van Steen, Kristel
PY - 2018/9/14
Y1 - 2018/9/14
N2 - Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.
AB - Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.
U2 - 10.1093/bib/bby081
DO - 10.1093/bib/bby081
M3 - Journal articles
C2 - 30219892
SN - 1467-5463
SP - 1
EP - 17
JO - Briefings in bioinformatics
JF - Briefings in bioinformatics
ER -