The Experts below are selected from a list of 5421 Experts worldwide ranked by ideXlab platform
Peter Donnelly - One of the best experts on this subject based on the ideXlab platform.
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a new Multipoint Method for genome wide association studies by imputation of genotypes
Nature Genetics, 2007Co-Authors: Jonathan Marchini, Bryan Howie, Simon Myers, Gil Mcvean, Peter DonnellyAbstract:Genome-wide association studies are set to become the Method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful Multipoint Methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation Method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing Methods for Multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our Method will be to boost power by combining data from genome-wide scans that use different SNP sets. It has been known for over 10 years that genome-wide association studies may be a powerful alternative to more traditional familybased linkage studies for mapping the genetic variants that underlie common human diseases 1 . It has taken the Human Genome Project, comprehensive SNP databases, substantial catalogs of human haplotype variation 2 , extensive case series collections and technological advances in genotyping for these studies to become a reality.
Pete Donnelly - One of the best experts on this subject based on the ideXlab platform.
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a new Multipoint Method for genome wide association studies by imputation of genotypes
Nature Genetics, 2007Co-Authors: Jonatha Marchini, Ya Howie, Simo Myers, Gil Mcvea, Pete DonnellyAbstract:Genome-wide association studies are set to become the Method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful Multipoint Methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation Method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing Methods for Multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our Method will be to boost power by combining data from genome-wide scans that use different SNP sets.
Jonathan Marchini - One of the best experts on this subject based on the ideXlab platform.
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a new Multipoint Method for genome wide association studies by imputation of genotypes
Nature Genetics, 2007Co-Authors: Jonathan Marchini, Bryan Howie, Simon Myers, Gil Mcvean, Peter DonnellyAbstract:Genome-wide association studies are set to become the Method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful Multipoint Methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation Method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing Methods for Multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our Method will be to boost power by combining data from genome-wide scans that use different SNP sets. It has been known for over 10 years that genome-wide association studies may be a powerful alternative to more traditional familybased linkage studies for mapping the genetic variants that underlie common human diseases 1 . It has taken the Human Genome Project, comprehensive SNP databases, substantial catalogs of human haplotype variation 2 , extensive case series collections and technological advances in genotyping for these studies to become a reality.
Jonatha Marchini - One of the best experts on this subject based on the ideXlab platform.
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a new Multipoint Method for genome wide association studies by imputation of genotypes
Nature Genetics, 2007Co-Authors: Jonatha Marchini, Ya Howie, Simo Myers, Gil Mcvea, Pete DonnellyAbstract:Genome-wide association studies are set to become the Method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful Multipoint Methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation Method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing Methods for Multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our Method will be to boost power by combining data from genome-wide scans that use different SNP sets.
Joseph D Terwilliger - One of the best experts on this subject based on the ideXlab platform.
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linkage analysis in the presence of errors ii marker locus genotyping errors modeled with hypercomplex recombination fractions
American Journal of Human Genetics, 2000Co-Authors: Harald H H Goring, Joseph D TerwilligerAbstract:It is well known that genotyping errors lead to loss of power in gene-mapping studies and underestimation of the strength of correlations between trait- and marker-locus genotypes. In two-point linkage analysis, these errors can be absorbed in an inflated recombination-fraction estimate, leaving the test statistic quite robust. In Multipoint analysis, however, genotyping errors can easily result in false exclusion of the true location of a disease-predisposing gene. In a companion article, we described a "complex-valued" extension of the recombination fraction to accommodate errors in the assignment of trait-locus genotypes, leading to a Multipoint LOD score with the same robustness to errors in trait-locus genotypes that is seen with the conventional two-point LOD score. Here, a further extension of this model to "hypercomplex-valued" recombination fractions (hereafter referred to as "hypercomplex recombination fractions") is presented, to handle random and systematic sources of marker-locus genotyping errors. This leads to a Multipoint Method (either "model-based" or "model-free") with the same robustness to marker-locus genotyping errors that is seen with conventional two-point analysis but with the advantage that multiple marker loci can be used jointly to increase meiotic informativeness. The cost of this increased robustness is a decrease in fine-scale resolution of the estimated map location of the trait locus, in comparison with traditional Multipoint analysis. This probability model further leads to algorithms for the estimation of the lower bounds for the error rates for genomewide and locus-specific genotyping, based on the null-hypothesis distribution of the LOD-score statistic in the presence of such errors. It is argued that those genome scans in which the LOD score is 0 for >50% of the genome are likely to be characterized by high rates of genotyping errors in general.
