title: Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.
abstract: BACKGROUND
  Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.
  
  
  RESULTS
  The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.
  
  
  CONCLUSIONS
  The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ .
authors.affiliation: Department of Public Health Science, Seoul National University, Seoul, Korea. won1@snu.ac.kr.|Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea. won1@snu.ac.kr.|Institute of Health and Environment, Seoul National University, Seoul, Korea. won1@snu.ac.kr.
authors.forename: Sungho
authors.initials: S
authors.lastname: Won
authors.affiliation: Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea. dnjswlzz11@gmail.com.
authors.forename: Wonji
authors.initials: W
authors.lastname: Kim
authors.affiliation: Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea. biznok@gmail.com.
authors.forename: Sungyoung
authors.initials: S
authors.lastname: Lee
authors.affiliation: The Center for Genome Science, Korea National Institute of Health, KCDC, Osong, Korea. lyou7688@gmail.com.
authors.forename: Young
authors.initials: Y
authors.lastname: Lee
authors.affiliation: Department of Public Health Science, Seoul National University, Seoul, Korea. jsung@snu.ac.kr.|Institute of Health and Environment, Seoul National University, Seoul, Korea. jsung@snu.ac.kr.
authors.forename: Joohon
authors.initials: J
authors.lastname: Sung
authors.affiliation: Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea. tspark@stats.snu.ac.kr.|Department of Statistics, Seoul National University, Seoul, Korea. tspark@stats.snu.ac.kr.
authors.forename: Taesung
authors.initials: T
authors.lastname: Park
mesh_terms: D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005190:Family; D005260:Female; D014644:Genetic Variation; D005828:Genetics, Population; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis; D010375:Pedigree; D010641:Phenotype; D018433:Twin Studies as Topic