Maqola

Robust relationship inference in genome-wide association studies

Ani Manichaikul1 Center for Public Health Genomics, 2Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, 3Department of Otolaryngology, University of Minnesota, Minneapolis, MN, 4Department of Medicine and 5Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USAJosyf C. Mychaleckyj1 Center for Public Health Genomics, 2Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, 3Department of Otolaryngology, University of Minnesota, Minneapolis, MN, 4Department of Medicine and 5Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USAStephen S. Rich1 Center for Public Health Genomics, 2Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, 3Department of Otolaryngology, University of Minnesota, Minneapolis, MN, 4Department of Medicine and 5Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USAKathy Daly1 Center for Public Health Genomics, 2Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, 3Department of Otolaryngology, University of Minnesota, Minneapolis, MN, 4Department of Medicine and 5Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USAMichèle M. Sale1 Center for Public Health Genomics, 2Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, 3Department of Otolaryngology, University of Minnesota, Minneapolis, MN, 4Department of Medicine and 5Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USAWei‐Min Chen1 Center for Public Health Genomics, 2Department of Public Health Sciences, Division of Biostatistics and Epidemiology, University of Virginia, Charlottesville, VA, 3Department of Otolaryngology, University of Minnesota, Minneapolis, MN, 4Department of Medicine and 5Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA

2010en

ABI

Annotatsiya

MOTIVATION: Genome-wide association studies (GWASs) have been widely used to map loci contributing to variation in complex traits and risk of diseases in humans. Accurate specification of familial relationships is crucial for family-based GWAS, as well as in population-based GWAS with unknown (or unrecognized) family structure. The family structure in a GWAS should be routinely investigated using the SNP data prior to the analysis of population structure or phenotype. Existing algorithms for relationship inference have a major weakness of estimating allele frequencies at each SNP from the entire sample, under a strong assumption of homogeneous population structure. This assumption is often untenable. RESULTS: Here, we present a rapid algorithm for relationship inference using high-throughput genotype data typical of GWAS that allows the presence of unknown population substructure. The relationship of any pair of individuals can be precisely inferred by robust estimation of their kinship coefficient, independent of sample composition or population structure (sample invariance). We present simulation experiments to demonstrate that the algorithm has sufficient power to provide reliable inference on millions of unrelated pairs and thousands of relative pairs (up to 3rd-degree relationships). Application of our robust algorithm to HapMap and GWAS datasets demonstrates that it performs properly even under extreme population stratification, while algorithms assuming a homogeneous population give systematically biased results. Our extremely efficient implementation performs relationship inference on millions of pairs of individuals in a matter of minutes, dozens of times faster than the most efficient existing algorithm known to us. AVAILABILITY: Our robust relationship inference algorithm is implemented in a freely available software package, KING, available for download at http://people.virginia.edu/∼wc9c/KING.

Hali tarjima qilinmagan

Identifikatorlar

DOI: 10.1093/bioinformatics/btq559

Iqtiboslar va manbalar

4 ta iqtibos0 ta foydalanilgan manba

Koʻrsatkichlar — AkademScholar