title: Local genealogies in a linear mixed model for genome-wide association mapping in complex pedigreed populations.
abstract: BACKGROUND
  The state-of-the-art for dealing with multiple levels of relationship among the samples in genome-wide association studies (GWAS) is unified mixed model analysis (MMA). This approach is very flexible, can be applied to both family-based and population-based samples, and can be extended to incorporate other effects in a straightforward and rigorous fashion. Here, we present a complementary approach, called 'GENMIX (genealogy based mixed model)' which combines advantages from two powerful GWAS methods: genealogy-based haplotype grouping and MMA.
  
  
  METHODS
  We validated GENMIX using genotyping data of Danish Jersey cattle and simulated phenotype and compared to the MMA. We simulated scenarios for three levels of heritability (0.21, 0.34, and 0.64), seven levels of MAF (0.05, 0.10, 0.15, 0.20, 0.25, 0.35, and 0.45) and five levels of QTL effect (0.1, 0.2, 0.5, 0.7 and 1.0 in phenotypic standard deviation unit). Each of these 105 possible combinations (3 h(2) x 7 MAF x 5 effects) of scenarios was replicated 25 times.
  
  
  RESULTS
  GENMIX provides a better ranking of markers close to the causative locus' location. GENMIX outperformed MMA when the QTL effect was small and the MAF at the QTL was low. In scenarios where MAF was high or the QTL affecting the trait had a large effect both GENMIX and MMA performed similarly.
  
  
  CONCLUSIONS
  In discovery studies, where high-ranking markers are identified and later examined in validation studies, we therefore expect GENMIX to enrich candidates brought to follow-up studies with true positives over false positives more than the MMA would.
authors.affiliation: Department of Molecular Biology and Genetics, Faculty of Science and Technology, Aarhus University, Tjele, Denmark. Goutam.Sahana@agrsci.dk
authors.forename: Goutam
authors.initials: G
authors.lastname: Sahana
authors.forename: Thomas
authors.initials: T
authors.lastname: Mailund
authors.forename: Mogens Sandø
authors.initials: MS
authors.lastname: Lund
authors.forename: Bernt
authors.initials: B
authors.lastname: Guldbrandtsen
mesh_terms: D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D010375:Pedigree; D010641:Phenotype; D040641:Quantitative Trait Loci
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