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Vincent Segura

Researcher at Institut national de la recherche agronomique

Publications -  61
Citations -  3368

Vincent Segura is an academic researcher from Institut national de la recherche agronomique. The author has contributed to research in topics: Quantitative trait locus & Population. The author has an hindex of 21, co-authored 54 publications receiving 2747 citations. Previous affiliations of Vincent Segura include Austrian Academy of Sciences & SupAgro.

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An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations.

TL;DR: Simulations suggest that the proposed multi-locus mixed model as a general method for mapping complex traits in structured populations outperforms existing methods in terms of power as well as false discovery rate.
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A mixed-model approach for genome-wide association studies of correlated traits in structured populations

TL;DR: This work extends this linear mixed-model approach to carry out GWAS of correlated phenotypes, deriving a fully parameterized multi-Trait mixed model (MTMM) that considers both the within-trait and between-traits variance components simultaneously for multiple traits.
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The Genetic Map of Artemisia annua L. Identifies Loci Affecting Yield of the Antimalarial Drug Artemisinin

TL;DR: A genetic map of the plant Artemisia annua from which artemisinin is derived is developed, laying the foundation for improving agricultural productivity of this natural product, which is becoming increasingly important in the fight against malaria.
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LAMINA: a tool for rapid quantification of leaf size and shape parameters

TL;DR: LAMINA (Leaf shApe deterMINAtion), a new tool for the automated analysis of images of leaves, is developed and it is shown that the software provides an efficient and accurate means of analysing leaf area in large datasets in an automated or semi-automated work flow.
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Genome Wide Association in tomato reveals 44 candidate loci for fruit metabolic traits

TL;DR: The results not only provide a list of candidate loci to be functionally validated but also a powerful analytical approach for finding genetic variants that can be directly used for crop improvement and deciphering the genetic architecture of complex traits.