Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models

Olga A. Vsevolozhskaya, Dmitri V. Zaykin, David A. Barondess, Xiaoren Tong, Sneha Jadhav, Qing Lu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology-driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample-specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.

Original languageEnglish
Pages (from-to)210-221
Number of pages12
JournalGenetic Epidemiology
Issue number3
StatePublished - Apr 1 2016

Bibliographical note

Funding Information:
This work was supported by a National Institute of Drug Abuse T32 research training program award (NIDA; T32DA021129) for OAV’s postdoctoral fellowship, DVZ’s Intramural Research Program of the National Institute of Environmental Health Sciences (NIEHS), DAB’s research award (NIDA;R01DA016558), and QL’sMentoredResearch ScientistDevelopment Award (NIDA; K01DA033346). We have no conflicts of interest to declare.

Publisher Copyright:
© 2016 Wiley Periodicals, Inc.


  • Functional analysis
  • Genome-wide association studies
  • Multivariate analysis
  • Pleiotropy
  • Qualitative traits
  • Quantitative traits
  • Sequencing studies

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)


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