@article{fdi:010061961,
  title = {{A}ssociation studies including genotype by environment interactions : prospects and limits},
  author = {{S}aidou, {A}. {A}. and {T}huillet, {A}nne-{C}{\'e}line and {C}ouderc, {M}arie and {M}ariac, {C}{\'e}dric and {V}igouroux, {Y}ves},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {A}ssociation mapping studies offer great promise to identify polymorphisms associated with phenotypes and for understanding the genetic basis of quantitative trait variation. {T}o date, almost all association mapping studies based on structured plant populations examined the main effects of genetic factors on the trait but did not deal with interactions between genetic factors and environment. {I}n this paper, we propose a methodological prospect of mixed linear models to analyze genotype by environment interaction effects using association mapping designs. {F}irst, we simulated datasets to assess the power of linear mixed models to detect interaction effects. {T}his simulation was based on two association panels composed of 90 inbreds (pearl millet) and 277 inbreds (maize). {R}esults: {B}ased on the simulation approach, we reported the impact of effect size, environmental variation, allele frequency, trait heritability, and sample size on the power to detect the main effects of genetic loci and diverse effect of interactions implying these loci. {I}nteraction effects specified in the model included {SNP} by environment interaction, ancestry by environment interaction, {SNP} by ancestry interaction and three way interactions. {T}he method was finally used on real datasets from field experiments conducted on the two considered panels. {W}e showed two types of interactions effects contributing to genotype by environment interactions in maize: {SNP} by environment interaction and ancestry by environment interaction. {T}his last interaction suggests differential response at the population level in function of the environment. {C}onclusions: {O}ur results suggested the suitability of mixed models for the detection of diverse interaction effects. {T}he need of samples larger than that commonly used in current plant association studies is strongly emphasized to ensure rigorous model selection and powerful interaction assessment. {T}he use of ancestry interaction component brought valuable information complementary to other available approaches.},
  keywords = {{A}ssociation study ; {G} x {E} ; {P}ower simulation ; {M}odel selection ; {REML} ; {PHYC} ; {V}gt1},
  booktitle = {},
  journal = {{B}mc {G}enetics},
  volume = {15},
  numero = {},
  pages = {art. 3},
  ISSN = {1471-2156},
  year = {2014},
  DOI = {10.1186/1471-2156-15-3},
  URL = {https://www.documentation.ird.fr/hor/fdi:010061961},
}