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In brief, the PCA method extracts factors (LD groups) to capture ≥ 90% of the genetic diversity.
Principal Component Analysis extracts factors and retains the maximum amount of common variance possible in the first factor.
ML-EFA extracts factors step-by-step and assesses with a χ2 test whether the model fits the postulated structure across the entire population.
Each LD group is considered separately and the PCA method again extracts factors; tSNPs are chosen as the SNPs with the highest factor loading.
Since factor analysis extracts factors due to the interrelatedness of measured variables, using two or more measures for the same trait (e.g. SBP and DBP, TG and HDL-C) would increase the number of factors found [ 24, 33].
The Appendix Table 4 lists the questions and the results of an exploratory factor analysis which applies the usual Kaiser criterion, i.e. extracts factors with initial eigenvalues above 1.00.
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Secondly, PCA, a factorization method, was utilized to extract factors explaining maximum variance of data.
There are multiple ways to extract factors.
Normalized varimax rotation was applied to the extracted factors.
Below, we present the profiles of the four extracted factors.
Table 5 displays the factor loadings of the 65 items under the extracted factors.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com