genetic structure of the selected accessions (see Note 47). Such
SNP datasets can be extracted from the 1001 genomes database (see Table 1).
51. Worldwide analyses of SNPs with strong geographic structure
might limit the detection of significant associations due to the
restriction of the minor allele within a specific genetic group.
Testing statistical associations in the geographic area shared by
the two alleles of a SNP might provide a better and complementary scenario to find environmental associations with adaptive relevance [75].
52. Results from previous GWA analyses carried out for multiple
phenotypes and environmental variables are available at the
AraGWAS and CLIMtools websites, respectively (see Table 1).
53. Phenotypic data related with traits associated with your gene
can also be compared statistically with environmental variables
using software packages like SAM, which take the spatial autocorrelation of variables into account (see Note 48 and Table 1;
[72]). Phenotypic data can be collected from the AraPheno
database (see Table 1) or can be generated in field experiments
such as those described in Subheading 3.1 of this chapter.
Finding significant phenotype-environment correlations
might support the gene-environment and gene-phenotype
relationships detected, thus integrating the three sources of
variation required for adaptive evolution [75].
Acknowledgements
C.A.-B. and F.X.P. laboratories have been funded by grants
BIO2016-75754-P and CGL2016-77720-P (AEI/FEDER, UE),
respectively.
References
1. Somerville C, Koornneef M (2002) A fortunate choice: the history of Arabidopsis as a
model plant. Nat Rev Genet 3:883–889
2. Bergelson J, Roux F (2010) Towards identifying genes underlying ecologically relevant traits
in Arabidopsis thaliana. Nat Rev Genet
11:867–879
3. Weigel D, Nordborg M (2015) Population
genomics for understanding adaptation in
wild plant species. Annu Rev Genet
49:315–338
4. Kramer U (2015) Planting molecular functions
in an ecological context with Arabidopsis thaliana. elife 4:e06100
5. Takou M, Wieters B, Kopriva S et al (2019)
Linking genes with ecological strategies in
Arabidopsis thaliana. J Exp Bot 70:1141–1151
6. Hoffman MH (2002) Biogeography of Arabidopsis thaliana (L.) Heynh. (Brassicaceae). J
Biogeogr 29:125–134
7. Brennan AC, Mendez-Vigo B, Haddioui A
et al (2014) The genetic structure of Arabidopsis thaliana in the South-Western Mediterranean range reveals a shared history between
North Africa and southern Europe. BMC
Plant Biol 14:17
8. Kasulin L, Rowan BA, Leon RJC et al (2017) A
single haplotype hyposensitive to light and
requiring strong vernalization dominates
Analyses of Natural Variation
109
SNP datasets can be extracted from the 1001 genomes database (see Table 1).
51. Worldwide analyses of SNPs with strong geographic structure
might limit the detection of significant associations due to the
restriction of the minor allele within a specific genetic group.
Testing statistical associations in the geographic area shared by
the two alleles of a SNP might provide a better and complementary scenario to find environmental associations with adaptive relevance [75].
52. Results from previous GWA analyses carried out for multiple
phenotypes and environmental variables are available at the
AraGWAS and CLIMtools websites, respectively (see Table 1).
53. Phenotypic data related with traits associated with your gene
can also be compared statistically with environmental variables
using software packages like SAM, which take the spatial autocorrelation of variables into account (see Note 48 and Table 1;
[72]). Phenotypic data can be collected from the AraPheno
database (see Table 1) or can be generated in field experiments
such as those described in Subheading 3.1 of this chapter.
Finding significant phenotype-environment correlations
might support the gene-environment and gene-phenotype
relationships detected, thus integrating the three sources of
variation required for adaptive evolution [75].
Acknowledgements
C.A.-B. and F.X.P. laboratories have been funded by grants
BIO2016-75754-P and CGL2016-77720-P (AEI/FEDER, UE),
respectively.
References
1. Somerville C, Koornneef M (2002) A fortunate choice: the history of Arabidopsis as a
model plant. Nat Rev Genet 3:883–889
2. Bergelson J, Roux F (2010) Towards identifying genes underlying ecologically relevant traits
in Arabidopsis thaliana. Nat Rev Genet
11:867–879
3. Weigel D, Nordborg M (2015) Population
genomics for understanding adaptation in
wild plant species. Annu Rev Genet
49:315–338
4. Kramer U (2015) Planting molecular functions
in an ecological context with Arabidopsis thaliana. elife 4:e06100
5. Takou M, Wieters B, Kopriva S et al (2019)
Linking genes with ecological strategies in
Arabidopsis thaliana. J Exp Bot 70:1141–1151
6. Hoffman MH (2002) Biogeography of Arabidopsis thaliana (L.) Heynh. (Brassicaceae). J
Biogeogr 29:125–134
7. Brennan AC, Mendez-Vigo B, Haddioui A
et al (2014) The genetic structure of Arabidopsis thaliana in the South-Western Mediterranean range reveals a shared history between
North Africa and southern Europe. BMC
Plant Biol 14:17
8. Kasulin L, Rowan BA, Leon RJC et al (2017) A
single haplotype hyposensitive to light and
requiring strong vernalization dominates
Analyses of Natural Variation
109