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"Missing" G x E Variation Controls Flowering Time in Arabidopsis thaliana.

Sasaki E, Zhang P, Atwell S, Meng D, Nordborg M - PLoS Genet. (2015)

Bottom Line: The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both.Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%).Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.

View Article: PubMed Central - PubMed

Affiliation: Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria.

ABSTRACT
Understanding how genetic variation interacts with the environment is essential for understanding adaptation. In particular, the life cycle of plants is tightly coordinated with local environmental signals through complex interactions with the genetic variation (G x E). The mechanistic basis for G x E is almost completely unknown. We collected flowering time data for 173 natural inbred lines of Arabidopsis thaliana from Sweden under two growth temperatures (10°C and 16°C), and observed massive G x E variation. To identify the genetic polymorphisms underlying this variation, we conducted genome-wide scans using both SNPs and local variance components. The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both. Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%). Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.

No MeSH data available.


Enrichment for a priori flowering time candidates in MTMM.Top row: enrichment and FDR (upper bound among a priori candidates. The horizontal dashed lines at 1 corresponds to no enrichment. Bottom row: Quantile-quantile plots comparing the distribution of p-values in windows containing a priori candidates with windows that do not. The different curves show results after removing windows significant using more stringent thresholds (see text). The shaded region corresponds to a 95% confidence interval. See Methods for details.
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pgen.1005597.g003: Enrichment for a priori flowering time candidates in MTMM.Top row: enrichment and FDR (upper bound among a priori candidates. The horizontal dashed lines at 1 corresponds to no enrichment. Bottom row: Quantile-quantile plots comparing the distribution of p-values in windows containing a priori candidates with windows that do not. The different curves show results after removing windows significant using more stringent thresholds (see text). The shaded region corresponds to a 95% confidence interval. See Methods for details.

Mentions: For the full SNP test, a significant enrichment of a priori candidates persists as we increase the significance threshold (i.e., lower the stringency) to 10−5 (Fig 3). Although associations at this level are far from significant in the genome-wide sense, the enrichment of a priori candidates implies that the false-discovery rate (FDR) among these candidates is less than 20% [12]. Three a priori candidates were identified using this approach (Table 3): FLC (which also reaches genome-wide significance); SHORT VEGETATIVE PHASE (SVP), which mediates ambient temperature signaling by regulating FLOWERING LOCUS T (FT) [22], and has been shown to be involved in natural variation in other samples [23]; and VERNALIZATION INSENSITIVE 3 (VIN3), which is involved in the epigenetic silencing of FLC during vernalization, but has hitherto not been identified in natural populations [20, 24]. Some of the associated SNPs were found in promoter regions (common SNP effects of FLC, VIN3). These SNPs are excellent candidates for being causal, and it seems likely that we simply lack the power to pick them up in a genome-wide scan. What the FDR is among the approximately 10 peaks that do not correspond to a priori candidates but are significant using the same threshold is not known (S4 Table).


"Missing" G x E Variation Controls Flowering Time in Arabidopsis thaliana.

Sasaki E, Zhang P, Atwell S, Meng D, Nordborg M - PLoS Genet. (2015)

Enrichment for a priori flowering time candidates in MTMM.Top row: enrichment and FDR (upper bound among a priori candidates. The horizontal dashed lines at 1 corresponds to no enrichment. Bottom row: Quantile-quantile plots comparing the distribution of p-values in windows containing a priori candidates with windows that do not. The different curves show results after removing windows significant using more stringent thresholds (see text). The shaded region corresponds to a 95% confidence interval. See Methods for details.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4608753&req=5

pgen.1005597.g003: Enrichment for a priori flowering time candidates in MTMM.Top row: enrichment and FDR (upper bound among a priori candidates. The horizontal dashed lines at 1 corresponds to no enrichment. Bottom row: Quantile-quantile plots comparing the distribution of p-values in windows containing a priori candidates with windows that do not. The different curves show results after removing windows significant using more stringent thresholds (see text). The shaded region corresponds to a 95% confidence interval. See Methods for details.
Mentions: For the full SNP test, a significant enrichment of a priori candidates persists as we increase the significance threshold (i.e., lower the stringency) to 10−5 (Fig 3). Although associations at this level are far from significant in the genome-wide sense, the enrichment of a priori candidates implies that the false-discovery rate (FDR) among these candidates is less than 20% [12]. Three a priori candidates were identified using this approach (Table 3): FLC (which also reaches genome-wide significance); SHORT VEGETATIVE PHASE (SVP), which mediates ambient temperature signaling by regulating FLOWERING LOCUS T (FT) [22], and has been shown to be involved in natural variation in other samples [23]; and VERNALIZATION INSENSITIVE 3 (VIN3), which is involved in the epigenetic silencing of FLC during vernalization, but has hitherto not been identified in natural populations [20, 24]. Some of the associated SNPs were found in promoter regions (common SNP effects of FLC, VIN3). These SNPs are excellent candidates for being causal, and it seems likely that we simply lack the power to pick them up in a genome-wide scan. What the FDR is among the approximately 10 peaks that do not correspond to a priori candidates but are significant using the same threshold is not known (S4 Table).

Bottom Line: The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both.Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%).Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.

View Article: PubMed Central - PubMed

Affiliation: Gregor Mendel Institute, Austrian Academy of Sciences, Vienna Biocenter (VBC), Vienna, Austria.

ABSTRACT
Understanding how genetic variation interacts with the environment is essential for understanding adaptation. In particular, the life cycle of plants is tightly coordinated with local environmental signals through complex interactions with the genetic variation (G x E). The mechanistic basis for G x E is almost completely unknown. We collected flowering time data for 173 natural inbred lines of Arabidopsis thaliana from Sweden under two growth temperatures (10°C and 16°C), and observed massive G x E variation. To identify the genetic polymorphisms underlying this variation, we conducted genome-wide scans using both SNPs and local variance components. The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both. Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%). Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.

No MeSH data available.