PMID- 37932343
OWN - NLM
STAT- MEDLINE
DCOM- 20231108
LR  - 20231111
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 13
IP  - 1
DP  - 2023 Nov 6
TI  - Evaluation of optimal methods and ancestries for calculating polygenic risk 
      scores in East Asian population.
PG  - 19195
LID - 10.1038/s41598-023-45859-w [doi]
LID - 19195
AB  - Polygenic risk scores (PRSs) have been studied for predicting human diseases, and 
      various methods for PRS calculation have been developed. Most PRS studies to date 
      have focused on European ancestry, and the performance of PRS has not been 
      sufficiently assessed in East Asia. Herein, we evaluated the predictive 
      performance of PRSs for East Asian populations under various conditions. 
      Simulation studies using data from the Korean cohort, Health Examinees (HEXA), 
      demonstrated that SBayesRC and PRS-CS outperformed other PRS methods (lassosum, 
      LDpred-funct, and PRSice) in high fixed heritability (0.3 and 0.7). In addition, 
      we generated PRSs using real-world data from HEXA for ten diseases: asthma, 
      breast cancer, cataract, coronary artery disease, gastric cancer, glaucoma, 
      hyperthyroidism, hypothyroidism, osteoporosis, and type 2 diabetes (T2D). We 
      utilized the five previous PRS methods and genome-wide association study (GWAS) 
      data from two biobank-scale datasets [European (UK Biobank) and East Asian 
      (BioBank Japan) ancestry]. Additionally, we employed PRS-CSx, a PRS method that 
      combines GWAS data from both ancestries, to generate a total of 110 PRS for ten 
      diseases. Similar to the simulation results, SBayesRC showed better predictive 
      performance for disease risk than the other methods. Furthermore, the East Asian 
      GWAS data outperformed those from European ancestry for breast cancer, cataract, 
      gastric cancer, and T2D, but neither of the two GWAS ancestries showed a 
      significant advantage on PRS performance for the remaining six diseases. Based on 
      simulation data and real data studies, it is expected that SBayesRC will offer 
      superior performance for East Asian populations, and PRS generated using GWAS 
      from non-East Asian may also yield good results.
CI  - (c) 2023. The Author(s).
FAU - Kim, Dong Jun
AU  - Kim DJ
AD  - Genoplan Korea, Seoul, Korea.
FAU - Kang, Joon Ho
AU  - Kang JH
AD  - Genoplan Korea, Seoul, Korea.
FAU - Kim, Ji-Woong
AU  - Kim JW
AD  - Genoplan Korea, Seoul, Korea.
FAU - Cheon, Myeong Jae
AU  - Cheon MJ
AD  - Genoplan Korea, Seoul, Korea.
FAU - Kim, Sun Bin
AU  - Kim SB
AD  - Genoplan Korea, Seoul, Korea.
FAU - Lee, Young Kee
AU  - Lee YK
AD  - Genoplan Korea, Seoul, Korea.
FAU - Lee, Byung-Chul
AU  - Lee BC
AD  - Genoplan Korea, Seoul, Korea. io@genoplan.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20231106
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Humans
MH  - Female
MH  - Genome-Wide Association Study
MH  - *Diabetes Mellitus, Type 2
MH  - East Asian People
MH  - *Stomach Neoplasms
MH  - Genetic Predisposition to Disease
MH  - Risk Factors
MH  - Multifactorial Inheritance
MH  - *Breast Neoplasms/epidemiology
MH  - *Cataract
PMC - PMC10628155
COIS- The authors declare no competing interests.
EDAT- 2023/11/07 00:42
MHDA- 2023/11/08 06:44
PMCR- 2023/11/06
CRDT- 2023/11/06 23:29
PHST- 2023/01/18 00:00 [received]
PHST- 2023/10/25 00:00 [accepted]
PHST- 2023/11/08 06:44 [medline]
PHST- 2023/11/07 00:42 [pubmed]
PHST- 2023/11/06 23:29 [entrez]
PHST- 2023/11/06 00:00 [pmc-release]
AID - 10.1038/s41598-023-45859-w [pii]
AID - 45859 [pii]
AID - 10.1038/s41598-023-45859-w [doi]
PST - epublish
SO  - Sci Rep. 2023 Nov 6;13(1):19195. doi: 10.1038/s41598-023-45859-w.