The degree to which genomic architecture varies across space and time is central to the evolution of genomes in response to natural selection. Bulked-segregant mapping combined with pooled sequencing provides an efficient means to estimate the effect of genetic variants on quantitative traits. We develop a novel likelihood framework to identify segregating variation within multiple populations and generations while accommodating estimation error on a sample-and SNP-specific basis. We use this method to map loci for flowering time within natural populations of Mimulus guttatus, collecting the early-and late-flowering plants from each of three neighboring populations and two consecutive generations. Structural variants, such as inversions, and genes from multiple flowering-time pathways exhibit the strongest associations with flowering time. We find appreciable variation in genetic effects on flowering time across both time and space; the greatest differences evident between populations, where numerous factors (environmental variation, genomic background, and private polymorphisms) likely contribute to heterogeneity. However, the changes across years within populations clearly identify genotype-by-environment interactions as an important influence on flowering time variation.
Bibliographical noteFunding Information:
We thank the following individuals for the helpful comments on the manuscript: S. J. MacDonald, M. E. Orive, and L. Hileman. We acknowledge funding from the University of Kansas Botany Endowment, the University of Kansas Graduate Research Fund, and the National Institutes of Health (R01 GM-073990-02).
© 2017 by the Genetics Society of America.
- Flowering time
- Genomic mapping
- Natural variation