Mapping trait loci by use of inferred ancestral recombination graphs. Export

@article{Minichiello2006, abstract = {Large-scale association studies are being undertaken with the hope of uncovering the genetic determinants of complex disease. We describe a computationally efficient method for inferring genealogies from population genotype data and show how these genealogies can be used to fine map disease loci and interpret association signals. These genealogies take the form of the ancestral recombination graph (ARG). The ARG defines a genealogical tree for each locus, and, as one moves along the chromosome, the topologies of consecutive trees shift according to the impact of historical recombination events. There are two stages to our analysis. First, we infer plausible ARGs, using a heuristic algorithm, which can handle unphased and missing data and is fast enough to be applied to large-scale studies. Second, we test the genealogical tree at each locus for a clustering of the disease cases beneath a branch, suggesting that a causative mutation occurred on that branch. Since the true ARG is unknown, we average this analysis over an ensemble of inferred ARGs. We have characterized the performance of our method across a wide range of simulated disease models. Compared with simpler tests, our method gives increased accuracy in positioning untyped causative loci and can also be used to estimate the frequencies of untyped causative alleles. We have applied our method to Ueda et al.'s association study of CTLA4 and Graves disease, showing how it can be used to dissect the association signal, giving potentially interesting results of allelic heterogeneity and interaction. Similar approaches analyzing an ensemble of ARGs inferred using our method may be applicable to many other problems of inference from population genotype data.}, author = {Minichiello, Mark J. and Durbin, Richard}, citeulike-article-id = {2318680}, citeulike-linkout-0 = {http://dx.doi.org/10.1086/508901}, doi = {10.1086/508901}, journal = {Am J Hum Genet}, keywords = {algorithms, alleles, antigens, case-control, cd, chromosome, computer, data, databases, differentiation, disease, evolution, from\_jabref, genetic, genetics, graves, humans, interpretation, loci, mapping, models, mutation, population, predisposition, quantitative, recombination, simulation, statistical, studies, to, trait}, month = {Nov}, number = {5}, pages = {910--922}, pdf = {mark\_arg.pdf}, posted-at = {2008-02-01 09:55:08}, priority = {2}, title = {Mapping trait loci by use of inferred ancestral recombination graphs.}, url = {10.1086/508901}, volume = {79}, year = {2006} }

TY - JOUR ID - Minichiello2006 L3 - citeulike-article-id:2318680 N2 - Large-scale association studies are being undertaken with the hope of uncovering the genetic determinants of complex disease. We describe a computationally efficient method for inferring genealogies from population genotype data and show how these genealogies can be used to fine map disease loci and interpret association signals. These genealogies take the form of the ancestral recombination graph (ARG). The ARG defines a genealogical tree for each locus, and, as one moves along the chromosome, the topologies of consecutive trees shift according to the impact of historical recombination events. There are two stages to our analysis. First, we infer plausible ARGs, using a heuristic algorithm, which can handle unphased and missing data and is fast enough to be applied to large-scale studies. Second, we test the genealogical tree at each locus for a clustering of the disease cases beneath a branch, suggesting that a causative mutation occurred on that branch. Since the true ARG is unknown, we average this analysis over an ensemble of inferred ARGs. We have characterized the performance of our method across a wide range of simulated disease models. Compared with simpler tests, our method gives increased accuracy in positioning untyped causative loci and can also be used to estimate the frequencies of untyped causative alleles. We have applied our method to Ueda et al.'s association study of CTLA4 and Graves disease, showing how it can be used to dissect the association signal, giving potentially interesting results of allelic heterogeneity and interaction. Similar approaches analyzing an ensemble of ARGs inferred using our method may be applicable to many other problems of inference from population genotype data. IS - 5 JF - Am J Hum Genet EP - 922 TI - Mapping trait loci by use of inferred ancestral recombination graphs. VL - 79 SP - 910 KW - algorithms KW - alleles KW - antigens KW - case-control KW - cd KW - chromosome KW - computer KW - data KW - databases KW - differentiation KW - disease KW - evolution KW - from_jabref KW - genetic KW - genetics KW - graves KW - humans KW - interpretation KW - loci KW - mapping KW - models KW - mutation KW - population KW - predisposition KW - quantitative KW - recombination KW - simulation KW - statistical KW - studies KW - to KW - trait AU - Minichiello, Mark AU - Durbin, Richard PY - 2006/Nov// UR - http://dx.doi.org/10.1086/508901 ER -