The Experts below are selected from a list of 1208529 Experts worldwide ranked by ideXlab platform
François Van Lishout - One of the best experts on this subject based on the ideXlab platform.
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Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data
European Journal of Human Genetics, 2011Co-Authors: Kristel Van Steen, Jestinah M Mahachie John, François Van LishoutAbstract:Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the Power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis Models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the Power is lowest in the presence of phenotypic mixture or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical Power estimates reduce even further with main effects corrections, but at the same time, false positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis
Lars Østergaard - One of the best experts on this subject based on the ideXlab platform.
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The Power of Model-to-crop translation illustrated by reducing seed loss from pod shatter in oilseed rape
Plant Reproduction, 2019Co-Authors: Pauline Stephenson, Nicola Stacey, Marie Brüser, Nick Pullen, Muhammad Ilyas, Carmel O’neill, Rachel Wells, Lars ØstergaardAbstract:Key message Elucidation of key regulators in Arabidopsis fruit patterning has facilitated knowledge-translation into crop species to address yield loss caused by premature seed dispersal (pod shatter). Abstract In the 1980s, plant scientists descended on a small weed Arabidopsis thaliana (thale cress) and developed it into a Powerful Model system to study plant biology. The massive advances in genetics and genomics since then have allowed us to obtain incredibly detailed knowledge on specific biological processes of Arabidopsis growth and development, its genome sequence and the function of many of the individual genes. This wealth of information provides immense potential for translation into crops to improve their performance and address issues of global importance such as food security. Here, we describe how fundamental insight into the genetic mechanism by which seed dispersal occurs in members of the Brassicaceae family can be exploited to reduce seed loss in oilseed rape ( Brassica napus ). We demonstrate that by exploiting data on gene function in Model species, it is possible to adjust the pod-opening process in oilseed rape, thereby significantly increasing yield. Specifically, we identified mutations in multiple paralogues of the INDEHISCENT and GA4 genes in B. napus and have overcome genetic redundancy by combining mutant alleles. Finally, we present novel software for the analysis of pod shatter data that is applicable to any crop for which seed dispersal is a serious problem. These findings highlight the tremendous potential of fundamental research in guiding strategies for crop improvement.
Kristel Van Steen - One of the best experts on this subject based on the ideXlab platform.
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Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data
European Journal of Human Genetics, 2011Co-Authors: Kristel Van Steen, Jestinah M Mahachie John, François Van LishoutAbstract:Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the Power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis Models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the Power is lowest in the presence of phenotypic mixture or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical Power estimates reduce even further with main effects corrections, but at the same time, false positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis
Pauline Stephenson - One of the best experts on this subject based on the ideXlab platform.
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The Power of Model-to-crop translation illustrated by reducing seed loss from pod shatter in oilseed rape
Plant Reproduction, 2019Co-Authors: Pauline Stephenson, Nicola Stacey, Marie Brüser, Nick Pullen, Muhammad Ilyas, Carmel O’neill, Rachel Wells, Lars ØstergaardAbstract:Key message Elucidation of key regulators in Arabidopsis fruit patterning has facilitated knowledge-translation into crop species to address yield loss caused by premature seed dispersal (pod shatter). Abstract In the 1980s, plant scientists descended on a small weed Arabidopsis thaliana (thale cress) and developed it into a Powerful Model system to study plant biology. The massive advances in genetics and genomics since then have allowed us to obtain incredibly detailed knowledge on specific biological processes of Arabidopsis growth and development, its genome sequence and the function of many of the individual genes. This wealth of information provides immense potential for translation into crops to improve their performance and address issues of global importance such as food security. Here, we describe how fundamental insight into the genetic mechanism by which seed dispersal occurs in members of the Brassicaceae family can be exploited to reduce seed loss in oilseed rape ( Brassica napus ). We demonstrate that by exploiting data on gene function in Model species, it is possible to adjust the pod-opening process in oilseed rape, thereby significantly increasing yield. Specifically, we identified mutations in multiple paralogues of the INDEHISCENT and GA4 genes in B. napus and have overcome genetic redundancy by combining mutant alleles. Finally, we present novel software for the analysis of pod shatter data that is applicable to any crop for which seed dispersal is a serious problem. These findings highlight the tremendous potential of fundamental research in guiding strategies for crop improvement.
Jestinah M Mahachie John - One of the best experts on this subject based on the ideXlab platform.
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Model-Based Multifactor Dimensionality Reduction to detect epistasis for quantitative traits in the presence of error-free and noisy data
European Journal of Human Genetics, 2011Co-Authors: Kristel Van Steen, Jestinah M Mahachie John, François Van LishoutAbstract:Detecting gene-gene interactions or epistasis in studies of human complex diseases is a big challenge in the area of epidemiology. To address this problem, several methods have been developed, mainly in the context of data dimensionality reduction. One of these methods, Model-Based Multifactor Dimensionality Reduction, has so far mainly been applied to case-control studies. In this study, we evaluate the Power of Model-Based Multifactor Dimensionality Reduction for quantitative traits to detect gene-gene interactions (epistasis) in the presence of error-free and noisy data. Considered sources of error are genotyping errors, missing genotypes, phenotypic mixtures and genetic heterogeneity. Our simulation study encompasses a variety of settings with varying minor allele frequencies and genetic variance for different epistasis Models. On each simulated data, we have performed Model-Based Multifactor Dimensionality Reduction in two ways: with and without adjustment for main effects of (known) functional SNPs. In line with binary trait counterparts, our simulations show that the Power is lowest in the presence of phenotypic mixture or genetic heterogeneity compared to scenarios with missing genotypes or genotyping errors. In addition, empirical Power estimates reduce even further with main effects corrections, but at the same time, false positive percentages are reduced as well. In conclusion, phenotypic mixtures and genetic heterogeneity remain challenging for epistasis detection, and careful thought must be given to the way important lower-order effects are accounted for in the analysis