Browsing by Department "A-Star, Genome Institute of Singapore"
  • Institution Publication
    Genome-wide association analyses identify two susceptibility loci for pachychoroid disease central serous chorioretinopathy
    ( 2019-12-01)
    Hosoda Y.
    ;
    Hosoda Y.
    ;
    Miyake M.
    ;
    Miyake M.
    ;
    Schellevis R.L.
    ;
    Boon C.J.F.
    ;
    Hoyng C.B.
    ;
    Miki A.
    ;
    Meguro A.
    ;
    Sakurada Y.
    ;
    Yoneyama S.
    ;
    Takasago Y.
    ;
    Hata M.
    ;
    Muraoka Y.
    ;
    Nakanishi H.
    ;
    Oishi A.
    ;
    Ooto S.
    ;
    Tamura H.
    ;
    Uji A.
    ;
    Miyata M.
    ;
    Takahashi A.
    ;
    Ueda-Arakawa N.
    ;
    Tajima A.
    ;
    Sato T.
    ;
    Mizuki N.
    ;
    Shiragami C.
    ;
    Iida T.
    ;
    Khor C.C.
    ;
    Khor C.C.
    ;
    Wong T.Y.
    ;
    Wong T.Y.
    ;
    Wong T.Y.
    ;
    Yamada R.
    ;
    Honda S.
    ;
    Honda S.
    ;
    de Jong E.K.
    ;
    Hollander A.I.d.
    ;
    Matsuda F.
    ;
    Yamashiro K.
    ;
    Yamashiro K.
    ;
    Tsujikawa A.
    The recently emerged pachychoroid concept has changed the understanding of age-related macular degeneration (AMD), which is a major cause of blindness; recent studies attributed AMD in part to pachychoroid disease central serous chorioretinopathy (CSC), suggesting the importance of elucidating the CSC pathogenesis. Our large genome-wide association study followed by validation studies in three independent Japanese and European cohorts, consisting of 1546 CSC samples and 13,029 controls, identified two novel CSC susceptibility loci: TNFRSF10A-LOC389641 and near GATA5 (rs13278062, odds ratio = 1.35, P = 1.26 × 10−13; rs6061548, odds ratio = 1.63, P = 5.36 × 10−15). A T allele at TNFRSF10A-LOC389641 rs13278062, a risk allele for CSC, is known to be a risk allele for AMD. This study not only identified new susceptibility genes for CSC, but also improves the understanding of the pathogenesis of AMD.
  • Institution Publication
    Insights into ancestral diversity in Parkinson’s disease risk: a comparative assessment of polygenic risk scores
    ( 2025-12-01)
    Saffie-Awad P.
    ;
    Saffie-Awad P.
    ;
    Saffie-Awad P.
    ;
    Grant S.M.
    ;
    Makarious M.B.
    ;
    Makarious M.B.
    ;
    Elsayed I.
    ;
    Sanyaolu A.O.
    ;
    Crea P.W.
    ;
    Schumacher Schuh A.F.
    ;
    Schumacher Schuh A.F.
    ;
    Schumacher Schuh A.F.
    ;
    Levine K.S.
    ;
    Levine K.S.
    ;
    Vitale D.
    ;
    Vitale D.
    ;
    Koretsky M.J.
    ;
    Kim J.
    ;
    Kim J.
    ;
    Peixoto Leal T.
    ;
    Periñán M.T.
    ;
    Periñán M.T.
    ;
    Periñán M.T.
    ;
    Dey S.
    ;
    Noyce A.J.
    ;
    Reyes-Palomares A.
    ;
    Rodriguez-Losada N.
    ;
    Foo J.N.
    ;
    Foo J.N.
    ;
    Mohamed W.
    ;
    Heilbron K.
    ;
    Norcliffe-Kaufmann L.
    ;
    Wong C.D.
    ;
    Wilton P.
    ;
    Weldon C.H.
    ;
    Wang W.
    ;
    Wang X.
    ;
    Tung J.Y.
    ;
    Tran V.
    ;
    Tchakouté C.T.
    ;
    Tat S.A.
    ;
    Su Q.J.
    ;
    Shringarpure S.
    ;
    Shi J.
    ;
    Shelton J.F.
    ;
    Shastri A.J.
    ;
    Schumacher M.
    ;
    Schloetter M.
    ;
    Reynoso A.
    ;
    Poznik G.D.
    ;
    Petrakovitz A.A.
    ;
    O’Connell J.
    ;
    Noblin E.S.
    ;
    Nguyen D.T.
    ;
    Nandakumar P.
    ;
    Moreno M.E.
    ;
    Micheletti S.J.
    ;
    McIntyre M.H.
    ;
    McCreight J.C.
    ;
    Lowe M.
    ;
    Llamas B.A.
    ;
    Lin K.H.
    ;
    Kwong A.
    ;
    Kukar K.
    ;
    Jiang Y.
    ;
    Jewett E.M.
    ;
    Hinds D.A.
    ;
    Hicks B.
    ;
    Hernandez A.
    ;
    Granka J.M.
    ;
    Freyman W.
    ;
    Fontanillas P.
    ;
    Fletez-Brant K.
    ;
    Fitch A.
    ;
    Filshtein T.
    ;
    Eriksson N.
    ;
    Elson S.L.
    ;
    Das S.
    ;
    Dhamija D.
    ;
    Partida G.C.
    ;
    Coker D.
    ;
    Cannon P.
    ;
    Bullis E.
    ;
    Bryc K.
    ;
    Bielenberg J.
    ;
    Bell R.K.
    ;
    Babalola E.
    ;
    Auton A.
    ;
    Aslibekyan S.
    ;
    Rizig M.
    ;
    Rizig M.
    ;
    Okubadejo N.
    ;
    Nalls M.A.
    ;
    Nalls M.A.
    ;
    Nalls M.A.
    ;
    Blauwendraat C.
    ;
    Blauwendraat C.
    ;
    Singleton A.
    ;
    Singleton A.
    ;
    Leonard H.
    ;
    Leonard H.
    ;
    Leonard H.
    ;
    Leonard H.
    ;
    Atadzhanov M.
    ;
    Nguyen T.
    ;
    Nguyen D.
    ;
    Song Y.
    ;
    Sherer T.
    ;
    Beach T.
    ;
    Foroud T.
    ;
    Xie T.
    ;
    Lubbe S.
    ;
    Dumanis S.
    ;
    Chowdhury S.
    ;
    Walker R.
    ;
    Alcalay R.
    ;
    Albin R.
    ;
    Mencacci N.E.
    ;
    Louie N.
    Risk prediction models play a crucial role in advancing healthcare by enabling early detection and supporting personalized medicine. Nonetheless, polygenic risk scores (PRS) for Parkinson’s disease (PD) have not been extensively studied across diverse populations, contributing to health disparities. In this study, we constructed 105 PRS using individual-level data from seven ancestries and compared two different models. Model 1 was based on the cumulative effect of 90 known European PD risk variants, weighted by summary statistics from four independent ancestries (European, East Asian, Latino/Admixed American, and African/Admixed). Model 2 leveraged multi-ancestry summary statistics using a p-value thresholding approach to improve prediction across diverse populations. Our findings provide a comprehensive assessment of PRS performance across ancestries and highlight the limitations of a “one-size-fits-all” approach to genetic risk prediction. We observed variability in predictive performance between models, underscoring the need for larger sample sizes and ancestry-specific approaches to enhance accuracy. These results establish a foundation for future research aimed at improving generalizability in genetic risk prediction for PD.