Startseite GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing
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GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing

  • Janaka S. S. Liyanage , Jeremie H. Estepp , Kumar Srivastava , Yun Li , Motomi Mori und Guolian Kang EMAIL logo
Veröffentlicht/Copyright: 2. Mai 2022
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Statistical Applications in Genetics and Molecular Biology
Aus der Zeitschrift Statistical Applications in Genetics and Molecular Biology Band 21 Heft 1

Abstract

Due to many advantages such as higher statistical power of detecting the association of genetic variants in human disorders and cost saving, extreme phenotype sequencing (EPS) is a rapidly emerging study design in epidemiological and clinical studies investigating how genetic variations associate with complex phenotypes. However, the investigation of the mediation effect of genetic variants on phenotypes is strictly restrictive under the EPS design because existing methods cannot well accommodate the non-random extreme tails sampling process incurred by the EPS design. In this paper, we propose a likelihood approach for testing the mediation effect of genetic variants through continuous and binary mediators on a continuous phenotype under the EPS design (GMEPS). Besides implementing in EPS design, it can also be utilized as a general mediation analysis procedure. Extensive simulations and two real data applications of a genome-wide association study of benign ethnic neutropenia under EPS design and a candidate-gene study of neurocognitive performance in patients with sickle cell disease under random sampling design demonstrate the superiority of GMEPS under the EPS design over widely used mediation analysis procedures, while demonstrating compatible capabilities under the general random sampling framework.

Keywords: extreme phenotype sequencing; genome-wide association studies; mediation analysis; mediation effect; next generation sequencing studies
Corresponding author: Guolian Kang, Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis 38105, TN, USA, E-mail:

Acknowledgments

We acknowledge dbGAP for approval of our use of benign ethnic neutropenia data. The data were obtained from Matthew Hsieh’s ancillary proposal to the Reasons of Geographic and Racial Differences in Stroke (REGARDS) study. Matthew Hsieh is supported by the intramural research program of NHLBI and NIDDK at NIH. Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is funded through a federal contract from the National Institutes of Health to The Johns Hopkins University, contract number HHSN268200782096C and HHSN268201100011I. This manuscript was also prepared using CSSCD Research Materials obtained from the NHLBI Biologic Specimen and Data Repository information Coordinating Center and does not necessarily reflect the opinions or views of the CSSCD or the NHLBI. We acknowledge the High Performance Computing Facility (HPCF) at SJCRH for providing shared HPC resources that have contributed to the research results reported within this article. We also thank reviewers whose suggestions helped improve and clarify this manuscript.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research is supported by the American Lebanese and Syrian Associated Charities (ALSAC).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/sagmb-2021-0071).

Received: 2021-09-14
Revised: 2022-02-14
Accepted: 2022-02-17
Published Online: 2022-05-02

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/sagmb-2021-0071
Schlagwörter für diesen Artikel
extreme phenotype sequencing; genome-wide association studies; mediation analysis; mediation effect; next generation sequencing studies

Artikel in diesem Heft

  1. Review Article
  2. Challenges for machine learning in RNA-protein interaction prediction
  3. Research Articles
  4. Distinct characteristics of correlation analysis at the single-cell and the population level
  5. pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
  6. Use of SVM-based ensemble feature selection method for gene expression data analysis
  7. A robust association test with multiple genetic variants and covariates
  8. Estimation of the covariance structure from SNP allele frequencies
  9. GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing
  10. Sparse latent factor regression models for genome-wide and epigenome-wide association studies
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