title: Comparison of genome-wide association methods in analyses of admixed populations with complex familial relationships.
abstract: Population structure is known to cause false-positive detection in association studies. We compared the power, precision, and type-I error rates of various association models in analyses of a simulated dataset with structure at the population (admixture from two populations; P) and family (K) levels. We also compared type-I error rates among models in analyses of publicly available human and dog datasets. The models corrected for none, one, or both structure levels. Correction for K was performed with linear mixed models incorporating familial relationships estimated from pedigrees or genetic markers. Linear models that ignored K were also tested. Correction for P was performed using principal component or structured association analysis. In analyses of simulated and real data, linear mixed models that corrected for K were able to control for type-I error, regardless of whether they also corrected for P. In contrast, correction for P alone in linear models was insufficient. The power and precision of linear mixed models with and without correction for P were similar. Furthermore, power, precision, and type-I error rate were comparable in linear mixed models incorporating pedigree and genomic relationships. In summary, in association studies using samples with both P and K, ancestries estimated using principal components or structured assignment were not sufficient to correct type-I errors. In such cases type-I errors may be controlled by use of linear mixed models with relationships derived from either pedigree or from genetic markers.
authors.affiliation: Centre for Quantitative genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.
authors.forename: Naveen K
authors.initials: NK
authors.lastname: Kadri
authors.affiliation: Centre for Quantitative genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.
authors.forename: Bernt
authors.initials: B
authors.lastname: Guldbrandtsen
authors.affiliation: Centre for Quantitative genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.
authors.forename: Peter
authors.initials: P
authors.lastname: Sørensen
authors.affiliation: Centre for Quantitative genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark.
authors.forename: Goutam
authors.initials: G
authors.lastname: Sahana
mesh_terms: D000818:Animals; D004032:Diet; D004285:Dogs; D005189:False Positive Reactions; D005260:Female; D055106:Genome-Wide Association Study / Q000379:methods*; D006619:Hip Dysplasia, Canine / Q000235:genetics; D006801:Humans; D006824:Hybridization, Genetic; D000960:Hypolipidemic Agents / Q000494:pharmacology; D016014:Linear Models; D008297:Male; D008957:Models, Genetic; D010375:Pedigree; D010802:Phylogeny; D025341:Principal Component Analysis
chemical_list: D000960:Hypolipidemic Agents