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MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS

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  • Paul F O’Reilly
  • Clive J Hoggart
  • Yotsawat Pomyen
  • Federico C F Calboli
  • Paul Elliott
  • Marjo-Riitta Jarvelin
  • Lachlan J M Coin

Abstract

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes.

Suggested Citation

  • Paul F O’Reilly & Clive J Hoggart & Yotsawat Pomyen & Federico C F Calboli & Paul Elliott & Marjo-Riitta Jarvelin & Lachlan J M Coin, 2012. "MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-1, May.
  • Handle: RePEc:plo:pone00:0034861
    DOI: 10.1371/journal.pone.0034861
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    1. Robert Sladek & Ghislain Rocheleau & Johan Rung & Christian Dina & Lishuang Shen & David Serre & Philippe Boutin & Daniel Vincent & Alexandre Belisle & Samy Hadjadj & Beverley Balkau & Barbara Heude &, 2007. "A genome-wide association study identifies novel risk loci for type 2 diabetes," Nature, Nature, vol. 445(7130), pages 881-885, February.
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    Cited by:

    1. Kodi Taraszka & Noah Zaitlen & Eleazar Eskin, 2022. "Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations," PLOS Genetics, Public Library of Science, vol. 18(11), pages 1-24, November.
    2. Young Lee & Suyeon Park & Sanghoon Moon & Juyoung Lee & Robert C. Elston & Woojoo Lee & Sungho Won, 2014. "On the Analysis of a Repeated Measure Design in Genome-Wide Association Analysis," IJERPH, MDPI, vol. 11(12), pages 1-21, November.
    3. Kai Wang, 2014. "Testing Genetic Association by Regressing Genotype over Multiple Phenotypes," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-9, September.
    4. Heejung Shim & Daniel I Chasman & Joshua D Smith & Samia Mora & Paul M Ridker & Deborah A Nickerson & Ronald M Krauss & Matthew Stephens, 2015. "A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    5. Nan Lin & Yun Zhu & Ruzong Fan & Momiao Xiong, 2017. "A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-33, October.
    6. Yang, Chiao-Yu & Lei, Lihua & Ho, Nhat & Fithian, William, 2022. "BONuS: Multiple Multivariate Testing with a Data-Adaptive Test Statistic," Research Papers 4031, Stanford University, Graduate School of Business.
    7. Xue Yuan & Wang Jinjuan & Ding Juan & Zhang Sanguo & Li Qizhai, 2019. "A powerful test for ordinal trait genetic association analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-9, April.
    8. Jose A Seoane & Colin Campbell & Ian N M Day & Juan P Casas & Tom R Gaunt, 2014. "Canonical Correlation Analysis for Gene-Based Pleiotropy Discovery," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-13, October.
    9. repec:plo:pgen00:1003235 is not listed on IDEAS
    10. Lin Zhang & Lei Sun, 2022. "A generalized robust allele‐based genetic association test," Biometrics, The International Biometric Society, vol. 78(2), pages 487-498, June.
    11. Zhenchuan Wang & Qiuying Sha & Shuanglin Zhang, 2016. "Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    12. repec:plo:pone00:0071345 is not listed on IDEAS
    13. Huanhuan Zhu & Shuanglin Zhang & Qiuying Sha, 2018. "A novel method to test associations between a weighted combination of phenotypes and genetic variants," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-17, January.
    14. Xiaoyu Liang & Xuewei Cao & Qiuying Sha & Shuanglin Zhang, 2022. "HCLC-FC: A novel statistical method for phenome-wide association studies," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-19, November.
    15. Zihuai He & Erin K Payne & Bhramar Mukherjee & Seunggeun Lee & Jennifer A Smith & Erin B Ware & Brisa N Sánchez & Teresa E Seeman & Sharon L R Kardia & Ana V Diez Roux, 2015. "Association between Stress Response Genes and Features of Diurnal Cortisol Curves in the Multi-Ethnic Study of Atherosclerosis: A New Multi-Phenotype Approach for Gene-Based Association Tests," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-15, May.

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