Your height affects your health: genetic determinants and health-related outcomes in Taiwan

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Abstract

Background

Height is an important anthropometric measurement and is associated with many health-related outcomes. Genome-wide association studies (GWASs) have identified hundreds of genetic loci associated with height, mainly in individuals of European ancestry.

Methods

We performed genome-wide association analyses and replicated previously reported GWAS-determined single nucleotide polymorphisms (SNPs) in the Taiwanese Han population (Taiwan Biobank; n = 67,452). A genetic instrument composed of 251 SNPs was selected from our GWAS, based on height and replication results as the best-fit polygenic risk score (PRS), in accordance with the clumping and p-value threshold method. We also examined the association between genetically determined height (PRS ₂₅₁) and measured height (phenotype). We performed observational (phenotype) and genetic PRS ₂₅₁ association analyses of height and health-related outcomes.

Results

GWAS identified 6843 SNPs in 89 genomic regions with genome-wide significance, including 18 novel loci. These were the most strongly associated genetic loci ( EFEMP1, DIS3L2, ZBTB38, LCORL, HMGA1, CS, and GDF5) previously reported to play a role in height. There was a positive association between PRS ₂₅₁ and measured height ( p < 0.001). Of the 14 traits and 49 diseases analyzed, we observed significant associations of measured and genetically determined height with only eight traits ( p < 0.05/[14 + 49]). Height was positively associated with body weight, waist circumference, and hip circumference but negatively associated with body mass index, waist-hip ratio, body fat, total cholesterol, and low-density lipoprotein cholesterol ( p < 0.05/[14 + 49]).

Conclusions

This study contributes to the understanding of the genetic features of height and health-related outcomes in individuals of Han Chinese ancestry in Taiwan.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12916-022-02450-w.

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Most cited references 81

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PLINK: a tool set for whole-genome association and population-based linkage analyses.

Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)

Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.

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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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An Atlas of Genetic Correlations across Human Diseases and Traits

Brendan Bulik-Sullivan, Hilary Finucane, Verneri Anttila … (2015)

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

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Author and article information

Contributors

Ying-Ju Lin:

ORCID: http://orcid.org/0000-0003-2267-7058

yjlin.kath@gmail.com

Fuu-Jen Tsai: d0704@mail.cmuh.org.tw

Journal

Journal ID (nlm-ta): BMC Med

Journal ID (iso-abbrev): BMC Med

Title: BMC Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1741-7015

Publication date (Electronic): 13 July 2022

Publication date PMC-release: 13 July 2022

Publication date Collection: 2022

Volume: 20

Electronic Location Identifier: 250

Affiliations

[1 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, PhD Program for Health Science and Industry, College of Health Care, , China Medical University, ; Taichung, Taiwan

[2 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, Department of Health Services Administration, , China Medical University, ; Taichung, Taiwan

[3 ]GRID grid.411508.9, ISNI 0000 0004 0572 9415, Big Data Center and Genetic Center, Department of Medical Research, , China Medical University Hospital, ; Taichung, Taiwan

[4 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, Department of Pediatrics, , China Medical University Children’s Hospital, ; Taichung, Taiwan

[5 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, School of Medicine, , China Medical University, ; Taichung, Taiwan

[6 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, School of Post Baccalaureate Chinese Medicine, , China Medical University, ; Taichung, Taiwan

[7 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, School of Chinese Medicine, , China Medical University, ; Taichung, Taiwan

[8 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, PhD Program for Cancer Biology and Drug Discovery, , China Medical University and Academia Sinica, ; Taichung, Taiwan

[9 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, Research Center for Cancer Biology, , China Medical University, ; Taichung, Taiwan

[10 ]GRID grid.254145.3, ISNI 0000 0001 0083 6092, Department of Medical Laboratory Science and Biotechnology, , China Medical University, ; Taichung, Taiwan

[11 ]GRID grid.252470.6, ISNI 0000 0000 9263 9645, Department of Biotechnology and Bioinformatics, , Asia University, ; Taichung, Taiwan

Author information

Ying-Ju Lin http://orcid.org/0000-0003-2267-7058

Article

Publisher ID: 2450

DOI: 10.1186/s12916-022-02450-w

PMC ID: 9281111

PubMed ID: 35831902

SO-VID: bde5ea9a-222f-4811-a85c-33d0a2c602de

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 4 February 2022

Date accepted : 22 June 2022

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100012544, China Medical University, Taiwan;

Award ID: CMU-110-S-17

Award ID: CMU-110-S-24

Award ID: CMU110-MF-115

Award ID: CMU110-MF-49

Award Recipient : Wen-Miin Liang Ying-Ju Lin

Funded by: FundRef http://dx.doi.org/10.13039/501100004391, China Medical University Hospital;

Award ID: DMR-111-062

Award ID: DMR-111-153

Award Recipient : Ying-Ju Lin Fuu-Jen Tsai

Funded by: FundRef http://dx.doi.org/10.13039/501100004663, Ministry of Science and Technology, Taiwan;

Award ID: MOST 108-2314-B-039-044-MY3

Award ID: MOST 109-2320-B-039-035-MY3

Award ID: MOST 109-2410-H-039-002

Award ID: MOST 110-2410-H-039-002

Award Recipient : Wen-Miin Liang Ying-Ju Lin Fuu-Jen Tsai

Custom metadata

ScienceOpen disciplines: Medicine

Keywords: height,genome-wide association studies,genetic single nucleotide polymorphisms,polygenic risk score,health-related outcomes

Data availability:

ScienceOpen disciplines: Medicine

Keywords: height, genome-wide association studies, genetic single nucleotide polymorphisms, polygenic risk score, health-related outcomes

Your height affects your health: genetic determinants and health-related outcomes in Taiwan

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

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IEEE: COVID-19 related research

Most cited references 81

PLINK: a tool set for whole-genome association and population-based linkage analyses.

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

An Atlas of Genetic Correlations across Human Diseases and Traits

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