A functional U-statistic method for association analysis of sequencing data

Sneha Jadhav, Xiaoran Tong, Qing Lu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease-associated genes, the different types of phenotypes pose challenges for association analysis. To address these challenges, we propose a nonparametric method, functional U-statistic method (FU), for multivariate analysis of sequencing data. It first constructs smooth functions from individuals’ sequencing data, and then tests the association of these functions with multiple phenotypes by using a U-statistic. The method provides a general framework for analyzing various types of phenotypes (e.g., binary and continuous phenotypes) with unknown distributions. Fitting the genetic variants within a gene using a smoothing function also allows us to capture complexities of gene structure (e.g., linkage disequilibrium, LD), which could potentially increase the power of association analysis. Through simulations, we compared our method to the multivariate outcome score test (MOST), and found that our test attained better performance than MOST. In a real data application, we apply our method to the sequencing data from Minnesota Twin Study (MTS) and found potential associations of several nicotine receptor subunit (CHRN) genes, including CHRNB3, associated with nicotine dependence and/or alcohol dependence.

Original languageEnglish
Pages (from-to)636-643
Number of pages8
JournalGenetic Epidemiology
Volume41
Issue number7
DOIs
StatePublished - Nov 2017

Bibliographical note

Publisher Copyright:
© 2017 WILEY PERIODICALS, INC.

Keywords

  • Functional data analysis
  • multivariate method
  • nonparametric method
  • similarity measure

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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