Phenome-wide analysis of Taiwan Biobank reveals novel glycemia-related loci and genetic risks for diabetes

Chia Jung Lee; Ting Huei Chen; Aylwin Ming Wee Lim; Chien Ching Chang; Jia Jyun Sie; Pei Lung Chen; Su Wei Chang; Shang Jung Wu; Chia Lin Hsu; Ai Ru Hsieh; Wei Shiung Yang; Cathy S.J. Fann

doi:10.1038/s42003-022-04168-0

Phenome-wide analysis of Taiwan Biobank reveals novel glycemia-related loci and genetic risks for diabetes

Chia Jung Lee, Ting Huei Chen, Aylwin Ming Wee Lim, Chien Ching Chang, Jia Jyun Sie, Pei Lung Chen, Su Wei Chang, Shang Jung Wu, Chia Lin Hsu, Ai Ru Hsieh^*, Wei Shiung Yang^*, Cathy S.J. Fann^*

^*Corresponding author for this work

Department of Artificial Intelligence

Research output: Contribution to journal › Journal Article › peer-review

10 Scopus citations

Abstract

To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA_1c) was performed and identified 115 significant loci with four novel genetic variants (HACL1, RAD21, ASH1L and GAK). Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.

Original language	English
Article number	1175
Journal	Communications Biology
Volume	5
Issue number	1
DOIs	https://doi.org/10.1038/s42003-022-04168-0
State	Published - 12 2022

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title = "Phenome-wide analysis of Taiwan Biobank reveals novel glycemia-related loci and genetic risks for diabetes",

abstract = "To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA1c) was performed and identified 115 significant loci with four novel genetic variants (HACL1, RAD21, ASH1L and GAK). Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.",

author = "Lee, {Chia Jung} and Chen, {Ting Huei} and Lim, {Aylwin Ming Wee} and Chang, {Chien Ching} and Sie, {Jia Jyun} and Chen, {Pei Lung} and Chang, {Su Wei} and Wu, {Shang Jung} and Hsu, {Chia Lin} and Hsieh, {Ai Ru} and Yang, {Wei Shiung} and Fann, {Cathy S.J.}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s42003-022-04168-0",

language = "英语",

volume = "5",

journal = "Communications Biology",

issn = "2399-3642",

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T1 - Phenome-wide analysis of Taiwan Biobank reveals novel glycemia-related loci and genetic risks for diabetes

AU - Lee, Chia Jung

AU - Chen, Ting Huei

AU - Lim, Aylwin Ming Wee

AU - Chang, Chien Ching

AU - Sie, Jia Jyun

AU - Chen, Pei Lung

AU - Chang, Su Wei

AU - Wu, Shang Jung

AU - Hsu, Chia Lin

AU - Hsieh, Ai Ru

AU - Yang, Wei Shiung

AU - Fann, Cathy S.J.

PY - 2022/12

Y1 - 2022/12

N2 - To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA1c) was performed and identified 115 significant loci with four novel genetic variants (HACL1, RAD21, ASH1L and GAK). Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.

AB - To explore the complex genetic architecture of common diseases and traits, we conducted comprehensive PheWAS of ten diseases and 34 quantitative traits in the community-based Taiwan Biobank (TWB). We identified 995 significantly associated loci with 135 novel loci specific to Taiwanese population. Further analyses highlighted the genetic pleiotropy of loci related to complex disease and associated quantitative traits. Extensive analysis on glycaemic phenotypes (T2D, fasting glucose and HbA1c) was performed and identified 115 significant loci with four novel genetic variants (HACL1, RAD21, ASH1L and GAK). Transcriptomics data also strengthen the relevancy of the findings to metabolic disorders, thus contributing to better understanding of pathogenesis. In addition, genetic risk scores are constructed and validated for absolute risks prediction of T2D in Taiwanese population. In conclusion, our data-driven approach without a priori hypothesis is useful for novel gene discovery and validation on top of disease risk prediction for unique non-European population.

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AN - SCOPUS:85141211531

SN - 2399-3642

VL - 5

JO - Communications Biology

JF - Communications Biology

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M1 - 1175

ER -

Phenome-wide analysis of Taiwan Biobank reveals novel glycemia-related loci and genetic risks for diabetes

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