Hi!! I am using EIGENSTRAT to detect if my samples contains clusters of sub-populations. The p-value along each eigenvector for population differences are insignificant, p values are more than 0.01.

     eigenvector_1_Control_Case_      0.107068 
     eigenvector_2_Control_Case_      0.158401 
     eigenvector_3_Control_Case_      0.619718 
     eigenvector_4_Control_Case_      0.372473 
     eigenvector_5_Control_Case_      0.740483 
     eigenvector_6_Control_Case_      0.672963 
     eigenvector_7_Control_Case_       0.91454 
     eigenvector_8_Control_Case_      0.492866 
     eigenvector_9_Control_Case_       0.39202 
    eigenvector_10_Control_Case_      0.288796

Following are the values for co-rrelation between eigenvector and case-control status

Correlation between eigenvector 1 (of 10) and Case/Control status is 0.045
Correlation between eigenvector 2 (of 10) and Case/Control status is 0.040
Correlation between eigenvector 3 (of 10) and Case/Control status is 0.014
Correlation between eigenvector 4 (of 10) and Case/Control status is 0.025
Correlation between eigenvector 5 (of 10) and Case/Control status is 0.009
Correlation between eigenvector 6 (of 10) and Case/Control status is 0.012
Correlation between eigenvector 7 (of 10) and Case/Control status is -0.003
Correlation between eigenvector 8 (of 10) and Case/Control status is 0.019
Correlation between eigenvector 9 (of 10) and Case/Control status is 0.024
Correlation between eigenvector 10 (of 10) and Case/Control status is 0.030

Does this conclude that the spread of samples seen in the plot is not due to the population stratification?? Please help me understand this concept.