%0 Generic %A Jin, Yumi %A Schaffer, Alejandro %A Feolo, Michael %A Holmes, J. Bradley %A Kattman, Brandi %D 2019 %T Supplemental Material for Jin et al., 2019 %U https://gsajournals.figshare.com/articles/dataset/Supplemental_Material_for_Jin_et_al_2019/8061485 %R 10.25387/g3.8061485.v1 %2 https://gsajournals.figshare.com/ndownloader/files/15023051 %2 https://gsajournals.figshare.com/ndownloader/files/15023057 %2 https://gsajournals.figshare.com/ndownloader/files/15023045 %2 https://gsajournals.figshare.com/ndownloader/files/15023060 %2 https://gsajournals.figshare.com/ndownloader/files/15023048 %2 https://gsajournals.figshare.com/ndownloader/files/15023054 %K Population Structure %K ancestry inference %K admixture mapping %K Genome wide association studies %K Bioinformatics %K Population, Ecological and Evolutionary Genetics %K Genomics %K Genetics %K Computational Biology %X Figure S1 shows distribution of GRAF-pop scores of subjects with different study-reported, continental populations across all dbGaP studies. Figure S2 shows distribution of GRAF-pop scores of subjects included in 1000 Genomes Project. Table S1 summarizes study-reported populations and the GRAF-calculated populations of dbGaP subjects. Table S2 describes dbGaP studies selected to evaluate GRAF-pop and other ancestry-inference software packages.
%I GSA Journals