Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits

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Summary

Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.

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Highlights

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The Taiwan Biobank (TWB) is a prospective study with health and multi-omics data
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Genome-wide analysis in TWB for 36 quantitative traits identified 968 novel loci
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Differences in genetic architecture were observed in TWB, Biobank Japan, and UK Biobank
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Polygenic prediction for disease onset was enhanced by cross-population biomarker GWAS

Abstract

Chen, Chen, Feng, Yu, Lin, et al. reported findings from genome-wide analysis in 102,900 Taiwan Biobank (TWB) participants across 36 human quantitative traits. A total of 1,986 significant loci were identified, and comparative analyses with Biobank Japan, UK Biobank, and previous GWASs revealed 968 novel loci. We also showed the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results highlight the potential to advance gene discovery with diverse populations.

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Most cited references 52

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A global reference for human genetic variation

Lachlan Coin, Robert Garry, Oleksyk Taras (2018)

The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

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ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

Kai Wang, Mingyao Li, Hakon Hakonarson (2010)

High-throughput sequencing platforms are generating massive amounts of genetic variation data for diverse genomes, but it remains a challenge to pinpoint a small subset of functionally important variants. To fill these unmet needs, we developed the ANNOVAR tool to annotate single nucleotide variants (SNVs) and insertions/deletions, such as examining their functional consequence on genes, inferring cytogenetic bands, reporting functional importance scores, finding variants in conserved regions, or identifying variants reported in the 1000 Genomes Project and dbSNP. ANNOVAR can utilize annotation databases from the UCSC Genome Browser or any annotation data set conforming to Generic Feature Format version 3 (GFF3). We also illustrate a ‘variants reduction’ protocol on 4.7 million SNVs and indels from a human genome, including two causal mutations for Miller syndrome, a rare recessive disease. Through a stepwise procedure, we excluded variants that are unlikely to be causal, and identified 20 candidate genes including the causal gene. Using a desktop computer, ANNOVAR requires ∼4 min to perform gene-based annotation and ∼15 min to perform variants reduction on 4.7 million variants, making it practical to handle hundreds of human genomes in a day. ANNOVAR is freely available at http://www.openbioinformatics.org/annovar/ .

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Second-generation PLINK: rising to the challenge of larger and richer datasets

Christopher Chang, Carson Chow, Laurent Tellier … (2015)

PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

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Author and article information

Contributors

Chia-Yen Chen

Yen-Chen Anne Feng

Hailiang Huang

Tian Ge

Yen-Feng Lin

Journal

Journal ID (nlm-ta): Cell Genom

Journal ID (iso-abbrev): Cell Genom

Title: Cell Genomics

Publisher: Elsevier

ISSN (Electronic): 2666-979X

Publication date PMC-release: 16 November 2023

Publication date Collection: 13 December 2023

Publication date (Electronic): 16 November 2023

Volume: 3

Issue: 12

Electronic Location Identifier: 100436

Affiliations

[1 ]Biogen, Cambridge, MA 02142, USA

[2 ]Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA

[3 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA

[4 ]Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

[5 ]Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan

[6 ]Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100025, Taiwan

[7 ]Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei 100025, Taiwan

[8 ]National Center for Geriatrics and Welfare Research, National Health Research Institutes, Miaoli 35053, Taiwan

[9 ]Department of Public Health, College of Public Health, China Medical University, Taichung 40678, Taiwan

[10 ]Marcus Institute for Aging Research and Harvard Medical School, Boston, MA 02131, USA

[11 ]Beth Israel Deaconess Medical Center, Boston, MA 02215, USA

[12 ]Harvard School of Public Health, Boston, MA 02115, USA

[13 ]Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

[14 ]Genomics Research Center, Academia Sinica, Taipei 115201, Taiwan

[15 ]Institute of Clinical Medicine, National Yang-Ming University, Taipei 112304, Taiwan

[16 ]Doctoral Program of Clinical and Experimental Medicine, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan

[17 ]Biomedical Translation Research Center, Academia Sinica, Taipei 115021, Taiwan

[18 ]Department of Psychiatry, College of Medicine and National Taiwan University Hospital, Taipei 106319, Taiwan

[19 ]Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, Finland

[20 ]Department of Medicine, Harvard Medical School, Boston, MA 02114, USA

[21 ]Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA

[22 ]Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan

[23 ]Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan

Author notes

[∗ ]Corresponding author chiayenc@ 123456gmail.com

[∗∗ ]Corresponding author ajfeng@ 123456ntu.edu.tw

[∗∗∗ ]Corresponding author hhuang@ 123456atgu.mgh.harvard.edu

[∗∗∗∗ ]Corresponding author tge1@ 123456mgh.harvard.edu

[∗∗∗∗∗ ]Corresponding author yflin@ 123456nhri.edu.tw

[24]

These authors contributed equally

[25]

Lead contact

Article

Publisher Item ID: S2666-979X(23)00271-9 Publisher ID: 100436

DOI: 10.1016/j.xgen.2023.100436

PMC ID: 10726425

PubMed ID: 38116116

SO-VID: a7beab01-54cc-4d85-9a1d-3a482419ca84

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 5 August 2021

Date revision received : 21 November 2021

Date accepted : 9 October 2023

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Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits

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Most cited references 52

A global reference for human genetic variation

ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data

Second-generation PLINK: rising to the challenge of larger and richer datasets

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Cited by 2

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