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      Ancestral haplotype-based association mapping with generalized linear mixed models accounting for stratification.

      Bioinformatics
      Animals, Cattle, Chromosome Mapping, methods, Computational Biology, Computer Simulation, Haplotypes, Linear Models, Male, Markov Chains, Polymorphism, Single Nucleotide, Software

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          Abstract

          In many situations, genome-wide association studies are performed in populations presenting stratification. Mixed models including a kinship matrix accounting for genetic relatedness among individuals have been shown to correct for population and/or family structure. Here we extend this methodology to generalized linear mixed models which properly model data under various distributions. In addition we perform association with ancestral haplotypes inferred using a hidden Markov model. The method was shown to properly account for stratification under various simulated scenari presenting population and/or family structure. Use of ancestral haplotypes resulted in higher power than SNPs on simulated datasets. Application to real data demonstrates the usefulness of the developed model. Full analysis of a dataset with 4600 individuals and 500 000 SNPs was performed in 2 h 36 min and required 2.28 Gb of RAM. The software GLASCOW can be freely downloaded from www.giga.ulg.ac.be/jcms/prod_381171/software. francois.guillaume@jouy.inra.fr Supplementary data are available at Bioinformatics online.

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          Journal
          22711794
          10.1093/bioinformatics/bts348

          Chemistry
          Animals,Cattle,Chromosome Mapping,methods,Computational Biology,Computer Simulation,Haplotypes,Linear Models,Male,Markov Chains,Polymorphism, Single Nucleotide,Software

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