The Experts below are selected from a list of 36168 Experts worldwide ranked by ideXlab platform
George Davey Smith - One of the best experts on this subject based on the ideXlab platform.
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apparent latent structure within the uk biobank sample has implications for epidemiological analysis
Nature Communications, 2019Co-Authors: Simon Haworth, Laura J Corbin, Kaitlin H Wade, Tom Dudding, Ashley Buduaggrey, David Carslake, Gibran Hemani, Lavinia Paternoster, Ruth E Mitchell, George Davey SmithAbstract:Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when Making Inference from genotype data in large studies. Population structure can bias the results of genetic and epidemiological analysis. Here, Haworth et al. report that fine-scale structure is detectable in apparently homogeneous samples such as ALSPAC when measured very precisely, and remains detectable in UK Biobank despite conventional approaches to account for it.
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apparent latent structure within the uk biobank sample has implications for epidemiological analysis
Nature Communications, 2019Co-Authors: Simon Haworth, Ruth Mitchell, Laura J Corbin, Kaitlin H Wade, Tom Dudding, Ashley Buduaggrey, David Carslake, Gibran Hemani, Lavinia Paternoster, George Davey SmithAbstract:Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when Making Inference from genotype data in large studies.
Eleftheria Palkopoulou - One of the best experts on this subject based on the ideXlab platform.
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holarctic genetic structure and range dynamics in the woolly mammoth
Proceedings of The Royal Society B: Biological Sciences, 2013Co-Authors: Sergey Vartanyan, Veronica Nystrom Edmark, Andrei Sher, Mikhail V. Sablin, Eleftheria Palkopoulou, Love Dalen, Adrian M Lister, Mikael BrandstromAbstract:Ancient DNA analyses have provided enhanced resolution of population histories in many Pleistocene taxa. However, most studies are spatially restricted, Making Inference of species-level biogeographic histories difficult. Here, we analyse mitochondrial DNA (mtDNA) variation in the woolly mammoth from across its Holarctic range to reconstruct its history over the last 200 thousand years (kyr). We identify a previously undocumented major mtDNA lineage in Europe, which was replaced by another major mtDNA lineage 32–34 kyr before present (BP). Coalescent simulations provide support for demographic expansions at approximately 121 kyr BP, suggesting that the previous interglacial was an important driver for demography and intraspecific genetic divergence. Furthermore, our results suggest an expansion into Eurasia from America around 66 kyr BP, coinciding with the first exposure of the Bering Land Bridge during the Late Pleistocene. Bayesian Inference indicates Late Pleistocene demographic stability until 20–15 kyr BP, when a severe population size decline occurred.
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holarctic genetic structure and range dynamics in the woolly mammoth
Proceedings of The Royal Society B: Biological Sciences, 2013Co-Authors: Sergey Vartanyan, Veronica Nystrom Edmark, Andrei Sher, Mikhail V. Sablin, Eleftheria Palkopoulou, Love Dalen, Adrian M Lister, Mikael BrandstromAbstract:Ancient DNA analyses have provided enhanced resolution of population histories in many Pleistocene taxa. However, most studies are spatially restricted, Making Inference of species-level biogeographic histories difficult. Here, we analyse mitochondrial DNA (mtDNA) variation in the woolly mammoth from across its Holarctic range to reconstruct its history over the last 200 thousand years (kyr). We identify a previously undocumented major mtDNA lineage in Europe, which was replaced by another major mtDNA lineage 32–34 kyr before present (BP). Coalescent simulations provide support for demographic expansions at approximately 121 kyr BP, suggesting that the previous interglacial was an important driver for demography and intraspecific genetic divergence. Furthermore, our results suggest an expansion into Eurasia from America around 66 kyr BP, coinciding with the first exposure of the Bering Land Bridge during the Late Pleistocene. Bayesian Inference indicates Late Pleistocene demographic stability until 20–15 kyr BP, when a severe population size decline occurred.
Elvezio Ronchetti - One of the best experts on this subject based on the ideXlab platform.
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robust small sample accurate Inference in moment condition models
Computational Statistics & Data Analysis, 2012Co-Authors: Elvezio RonchettiAbstract:Procedures based on the Generalized Method of Moments (GMM) are basic tools in modern econometrics. In most cases, the theory available for Making Inference with these procedures is based on first order asymptotic theory. It is well-known that the (first order) asymptotic distribution does not provide accurate p-values and confidence intervals in moderate to small samples. Moreover, in the presence of small deviations from the assumed model, p-values and confidence intervals based on classical GMM procedures can be drastically affected (nonrobustness). Several alternative techniques have been proposed to improve the accuracy of GMM procedures. These alternatives address either the first order accuracy of the approximations (information and entropy econometrics (IEE)) or the nonrobustness (Robust GMM estimators and tests). A new procedure which combines robustness properties and accuracy in small samples is proposed. Specifically, IEE techniques are combined with robust methods obtained by bounding the original orthogonality function. This leads to new robust estimators and tests in moment condition models with excellent finite sample accuracy. Finally, the accuracy of the new statistic is illustrated with Monte Carlo simulations for three models on overidentifying moment conditions.
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robust small sample accurate Inference in moment condition models
Research Papers in Economics, 2006Co-Authors: Elvezio RonchettiAbstract:Procedures based on the Generalized Method of Moments (GMM) (Hansen, 1982) are basic tools in modern econometrics. In most cases, the theory available for Making Inference with these procedures is based on first order asymptotic theory. It is well-known that the (first order) asymptotic distribution does not provide accurate p-values and confidence intervals in moderate to small samples. Moreover, in the presence of small deviations from the assumed model, p-values and confidence intervals based on classical GMM procedures can be drastically affected (nonrobustness). Several alternative techniques have been proposed in the literature to improve the accuracy of GMM procedures. These alternatives address either the first order accuracy of the approximations (information and entropy econometrics (IEE)) or the nonrobustness (Robust GMM estimators and tests). In this paper, we propose a new alternative procedure which combines robustness properties and accuracy in small samples. Specifically, we combine IEE techniques as developed in Imbens, Spady, Johnson (1998) to obtain finite sample accuracy with robust methods obtained by bounding the original orthogonality function as proposed in Ronchetti and Trojani (2001). This leads to new robust estimators and tests in moment condition models with excellent finite sample accuracy. Finally, we illustrate the accuracy of the new statistic by means of some simulations for three models on overidentifying moment conditions.
Simon Haworth - One of the best experts on this subject based on the ideXlab platform.
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apparent latent structure within the uk biobank sample has implications for epidemiological analysis
Nature Communications, 2019Co-Authors: Simon Haworth, Laura J Corbin, Kaitlin H Wade, Tom Dudding, Ashley Buduaggrey, David Carslake, Gibran Hemani, Lavinia Paternoster, Ruth E Mitchell, George Davey SmithAbstract:Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when Making Inference from genotype data in large studies. Population structure can bias the results of genetic and epidemiological analysis. Here, Haworth et al. report that fine-scale structure is detectable in apparently homogeneous samples such as ALSPAC when measured very precisely, and remains detectable in UK Biobank despite conventional approaches to account for it.
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apparent latent structure within the uk biobank sample has implications for epidemiological analysis
Nature Communications, 2019Co-Authors: Simon Haworth, Ruth Mitchell, Laura J Corbin, Kaitlin H Wade, Tom Dudding, Ashley Buduaggrey, David Carslake, Gibran Hemani, Lavinia Paternoster, George Davey SmithAbstract:Large studies use genotype data to discover genetic contributions to complex traits and infer relationships between those traits. Co-incident geographical variation in genotypes and health traits can bias these analyses. Here we show that single genetic variants and genetic scores composed of multiple variants are associated with birth location within UK Biobank and that geographic structure in genotype data cannot be accounted for using routine adjustment for study centre and principal components derived from genotype data. We find that major health outcomes appear geographically structured and that coincident structure in health outcomes and genotype data can yield biased associations. Understanding and accounting for this phenomenon will be important when Making Inference from genotype data in large studies.
Mikael Brandstrom - One of the best experts on this subject based on the ideXlab platform.
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holarctic genetic structure and range dynamics in the woolly mammoth
Proceedings of The Royal Society B: Biological Sciences, 2013Co-Authors: Sergey Vartanyan, Veronica Nystrom Edmark, Andrei Sher, Mikhail V. Sablin, Eleftheria Palkopoulou, Love Dalen, Adrian M Lister, Mikael BrandstromAbstract:Ancient DNA analyses have provided enhanced resolution of population histories in many Pleistocene taxa. However, most studies are spatially restricted, Making Inference of species-level biogeographic histories difficult. Here, we analyse mitochondrial DNA (mtDNA) variation in the woolly mammoth from across its Holarctic range to reconstruct its history over the last 200 thousand years (kyr). We identify a previously undocumented major mtDNA lineage in Europe, which was replaced by another major mtDNA lineage 32–34 kyr before present (BP). Coalescent simulations provide support for demographic expansions at approximately 121 kyr BP, suggesting that the previous interglacial was an important driver for demography and intraspecific genetic divergence. Furthermore, our results suggest an expansion into Eurasia from America around 66 kyr BP, coinciding with the first exposure of the Bering Land Bridge during the Late Pleistocene. Bayesian Inference indicates Late Pleistocene demographic stability until 20–15 kyr BP, when a severe population size decline occurred.
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holarctic genetic structure and range dynamics in the woolly mammoth
Proceedings of The Royal Society B: Biological Sciences, 2013Co-Authors: Sergey Vartanyan, Veronica Nystrom Edmark, Andrei Sher, Mikhail V. Sablin, Eleftheria Palkopoulou, Love Dalen, Adrian M Lister, Mikael BrandstromAbstract:Ancient DNA analyses have provided enhanced resolution of population histories in many Pleistocene taxa. However, most studies are spatially restricted, Making Inference of species-level biogeographic histories difficult. Here, we analyse mitochondrial DNA (mtDNA) variation in the woolly mammoth from across its Holarctic range to reconstruct its history over the last 200 thousand years (kyr). We identify a previously undocumented major mtDNA lineage in Europe, which was replaced by another major mtDNA lineage 32–34 kyr before present (BP). Coalescent simulations provide support for demographic expansions at approximately 121 kyr BP, suggesting that the previous interglacial was an important driver for demography and intraspecific genetic divergence. Furthermore, our results suggest an expansion into Eurasia from America around 66 kyr BP, coinciding with the first exposure of the Bering Land Bridge during the Late Pleistocene. Bayesian Inference indicates Late Pleistocene demographic stability until 20–15 kyr BP, when a severe population size decline occurred.
