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Chunk #30 — Methods — Data. — Genome-wide association data.

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Improving the trans-ancestry portability of polygenic risk scores by prioritizing variants in predicted cell-type-specific regulatory elements.
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We collected publicly available summary statistics data for 111 GWAS across separate cohorts of East Asian and European individuals3,24,35. East Asian GWAS data were collected from BBJ while European GWAS data were collected from either UKBB or the GWAS catalog, referred to as publicly available summary statistics (PASS) (Supplementary Table 3). Since our analysis utilized S-LDSC which is based on the polygenic inheritance model, it is crucial to include summary statistics of GWAS conducted in large-scale samples3. First, we included summary statistics of EUR GWAS in which biologically plausible polygenic signals were confirmed in previous studies (Supplementary Table 3), beginning with the set of summary statistics (n = 42) we had previously downloaded from the Price Lab and used in our previous work31. Next, we included additional diseases/traits for which both EAS (specifically BBJ) and EUR GWAS summary statistics are available. We chose to focus this study on EUR and EAS populations, as there is a very limited number of large GWAS in populations other than EUR and EAS1,40,41. As blood quantitative trait GWAS and disease GWAS were available from