Smoothed functional principal component analysis for testing association of the entire allelic spectrum of genetic variation. Academic Article uri icon

start page

  • 217

end page

  • 224

abstract

  • Fast and cheaper next-generation sequencing technologies will generate unprecedentedly massive and highly dimensional genetic variation data that allow nearly complete evaluation of genetic variation including both common and rare variants. There are two types of association tests: variant-by-variant test and group test. The variant-by-variant test is designed to test the association of common variants, while the group test is suitable to collectively test the association of multiple rare variants. We propose here a smoothed functional principal component analysis (SFPCA) statistic as a general approach for testing association of the entire allelic spectrum of genetic variation (both common and rare variants), which utilizes the merits of both variant-by-variant analysis and group tests. By intensive simulations, we demonstrate that the SFPCA statistic has the correct type 1 error rates and much higher power than the existing methods to detect association of (1) common variants, (2) rare variants, (3) both common and rare variants and (4) variants with opposite directions of effects. To further evaluate its performance, the SFPCA statistic is applied to ANGPTL4 sequence and six continuous phenotypes data from the Dallas Heart Study as an example for testing association of rare variants and a GWAS of schizophrenia data as an example for testing association of common variants. The results show that the SFPCA statistic has much smaller P-values than many existing statistics in both real data analysis examples.

date/time value

  • February 2013

Digital Object Identifier (DOI)

  • 10.1038/ejhg.2012.141

PubMed Identifier

  • 22781089

volume

  • 21

number

  • 2

keywords

  • Alleles
  • Angiopoietins
  • Data Interpretation, Statistical
  • Genetic Variation
  • Genome-Wide Association Study
  • Heart Diseases
  • Humans
  • Principal Component Analysis
  • Schizophrenia