Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort

  • Chung, Wonil (Department of Statistics and Actuarial Science, Soongsil University) ;
  • Hwang, Hyunji (Department of Statistics and Actuarial Science, Soongsil University) ;
  • Park, Taesung (Department of Statistics, Seoul National University)
  • Received : 2022.04.13
  • Accepted : 2022.05.22
  • Published : 2022.06.30
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Abstract

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2020R1C1C1A01012657) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A1A10044154). This work was supported by the Soongsil University Research Fund. GWAS dataset and epidemiological data for KoGES are third-party data and are available under the approval of the data access committee of the National Biobank of Korea (http://www.nih.go.kr/NIH/eng/contents/NihEngContentView.jsp?cid=65714&menuIds=HOME004-MNU2210-MNU2327-MNU2329-MNU2338).

References

  1. Couto Alves A, De Silva NM, Karhunen V, Sovio U, Das S, Taal HR, et al. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI. Sci Adv 2019;5:eaaw3095. https://doi.org/10.1126/sciadv.aaw3095
  2. Vicuna L, Barrientos E, Norambuena T, Alvares D, Gana JC, Leiva V, et al. New insights from GWAS on longitudinal and cross-sectional BMI and related phenotypes in admixed children with Native American and European ancestries Preprint at https://doi.org/10.1101/2021.09.24.21263664 (2021).
  3. Ning C, Wang D, Zhou L, Wei J, Liu Y, Kang H, et al. Efficient multivariate analysis algorithms for longitudinal genome-wide association studies. Bioinformatics 2019;35:4879-4885. https://doi.org/10.1093/bioinformatics/btz304
  4. Chung W. Statistical models and computational tools for predicting complex traits and diseases. Genomics Inform 2021;19:e36. https://doi.org/10.5808/gi.21053
  5. Chung W, Chen J, Turman C, Lindstrom S, Zhu Z, Loh PR, et al. Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes. Nat Commun 2019;10:569. https://doi.org/10.1038/s41467-019-08535-0
  6. Ning C, Wang D, Zheng X, Zhang Q, Zhang S, Mrode R, et al. Eigen decomposition expedites longitudinal genome-wide association studies for milk production traits in Chinese Holstein. Genet Sel Evol 2018;50:12. https://doi.org/10.1186/s12711-018-0383-0
  7. Gong Y, Zou F. Varying coefficient models for mapping quantitative trait loci using recombinant inbred intercrosses. Genetics 2012;190:475-486. https://doi.org/10.1534/genetics.111.132522
  8. Kwak IY, Moore CR, Spalding EP, Broman KW. A simple regression-based method to map quantitative trait loci underlying function-valued phenotypes. Genetics 2014;197:1409-1416. https://doi.org/10.1534/genetics.114.166306
  9. Chung W. Grid-based Gaussian process models for longitudinal genetic data. Commun Stat Appl Methods 2022;29:65-83.
  10. Chung W, Cho Y. Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies. Genomics Inform 2022;20:e8. https://doi.org/10.5808/gi.21080
  11. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Series B Stat Methodol 2002;64:583-639. https://doi.org/10.1111/1467-9868.00353
  12. Ando T. Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models. Biometrika 2007;94:443-458. https://doi.org/10.1093/biomet/asm017
  13. Gouveia MH, Bentley AR, Leonard H, Meeks KA, Ekoru K, Chen G, et al. Trans-ethnic meta-analysis identifies new loci associated with longitudinal blood pressure traits. Sci Rep 2021;11:4075. https://doi.org/10.1038/s41598-021-83450-3
  14. Lee CH, Eskin E, Han B. Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects. Bioinformatics 2017;33:i379-i388. https://doi.org/10.1093/bioinformatics/btx242
  15. Kim Y, Han BG; KoGES Group. Cohort Profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. Int J Epidemiol 2017;46:e20. https://doi.org/10.1093/ije/dyv316
  16. Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A largescale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534. https://doi.org/10.1038/ng.357
  17. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-575. https://doi.org/10.1086/519795
  18. Browning BL, Zhou Y, Browning SR. A one-penny imputed genome from next-generation reference panels. Am J Hum Genet 2018;103:338-348. https://doi.org/10.1016/j.ajhg.2018.07.015
  19. Loh PR, Tucker G, Bulik-Sullivan BK, Vilhjalmsson BJ, Finucane HK, Salem RM, et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 2015;47: 284-290. https://doi.org/10.1038/ng.3190
  20. Loh PR, Kichaev G, Gazal S, Schoech AP, Price AL. Mixed-model association for biobank-scale datasets. Nat Genet 2018;50:906-908. https://doi.org/10.1038/s41588-018-0144-6
  21. Bates D, Machler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4 Preprint at https://doi.org/10.48550/arXiv.1406.5823 (2014).
  22. Cochran WG. The combination of estimates from different experiments. Biometrics 1954;10:101-129. https://doi.org/10.2307/3001666
  23. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986;7:177-188. https://doi.org/10.1016/0197-2456(86)90046-2
  24. Lin DY, Sullivan PF. Meta-analysis of genome-wide association studies with overlapping subjects. Am J Hum Genet 2009;85: 862-872. https://doi.org/10.1016/j.ajhg.2009.11.001
  25. Chen Z, Dunson DB. Random effects selection in linear mixed models. Biometrics 2003;59:762-769. https://doi.org/10.1111/j.0006-341X.2003.00089.x
  26. Jeffreys H. Theory of Probability. 3rd ed. Oxford: Clarendon Press, 1961.
  27. Yandell BS, Mehta T, Banerjee S, Shriner D, Venkataraman R, Moon JY, et al. R/qtlbim: QTL with Bayesian interval mapping in experimental crosses. Bioinformatics 2007;23:641-643. https://doi.org/10.1093/bioinformatics/btm011