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Robust relationship inference in genome-wide association studies

Manichaikul, Ani ; Mychaleckyj, Josyf C. ; Rich, Stephen S. ; Daly, Kathy ; Sale, Michèle ; Chen, Wei-Min

Bioinformatics, 2010-11, Vol.26 (22), p.2867-2873 [Periódico revisado por pares]

Oxford: Oxford University Press

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  • Título:
    Robust relationship inference in genome-wide association studies
  • Autor: Manichaikul, Ani ; Mychaleckyj, Josyf C. ; Rich, Stephen S. ; Daly, Kathy ; Sale, Michèle ; Chen, Wei-Min
  • Assuntos: Algorithms ; Biological and medical sciences ; Fundamental and applied biological sciences. Psychology ; General aspects ; Genome, Human ; Genome-Wide Association Study ; Genotype ; Humans ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Original Papers ; Phenotype ; Polymorphism, Single Nucleotide ; Population Groups - genetics
  • É parte de: Bioinformatics, 2010-11, Vol.26 (22), p.2867-2873
  • Notas: ark:/67375/HXZ-K11K9M02-F
    ArticleID:btq559
    To whom correspondence should be addressed.
    istex:0460F037769672E413CE27B9EF6C31AFE78FCD2E
    Associate Editor: Jeffrey Barrett
    ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: 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. Contact: wmchen@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
  • Editor: Oxford: Oxford University Press
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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