Genome-Wide Association Study in Arabidopsis thaliana of Natural Variation in Seed Oil Melting Point: A Widespread Adaptive Trait in Plants.

Seed oil melting point is an adaptive, quantitative trait determined by the relative proportions of the fatty acids that compose the oil. Micro- and macro-evolutionary evidence suggests selection has changed the melting point of seed oils to covary with germination temperatures because of a trade-off between total energy stores and the rate of energy acquisition during germination under competition. The seed oil compositions of 391 natural accessions of Arabidopsis thaliana, grown under common-garden conditions, were used to assess whether seed oil melting point within a species varied with germination temperature. In support of the adaptive explanation, long-term monthly spring and fall field temperatures of the accession collection sites significantly predicted their seed oil melting points. In addition, a genome-wide association study (GWAS) was performed to determine which genes were most likely responsible for the natural variation in seed oil melting point. The GWAS found a single highly significant association within the coding region of FAD2, which encodes a fatty acid desaturase central to the oil biosynthesis pathway. In a separate analysis of 15 a priori oil synthesis candidate genes, 2 (FAD2 and FATB) were located near significant SNPs associated with seed oil melting point. These results comport with others' molecular work showing that lines with alterations in these genes affect seed oil melting point as expected. Our results suggest natural selection has acted on a small number of loci to alter a quantitative trait in response to local environmental conditions.

Genome-Wide Association Study of Arabidopsis thaliana Identifies Determinants of Natural Variation in Seed Oil Composition.

The renewable source of highly reduced carbon provided by plant triacylglycerols (TAGs) fills an ever increasing demand for food, biodiesel, and industrial chemicals. Each of these uses requires different compositions of fatty acid proportions in seed oils. Identifying the genes responsible for variation in seed oil composition in nature provides targets for bioengineering fatty acid proportions optimized for various industrial and nutrition goals. Here, we characterized the seed oil composition of 391 world-wide, wild accessions of Arabidopsis thaliana, and performed a genome-wide association study (GWAS) of the 9 major fatty acids in the seed oil and 4 composite measures of the fatty acids. Four to 19 regions of interest were associated with the seed oil composition traits. Thirty-four of the genes in these regions are involved in lipid metabolism or transport, with 14 specific to fatty acid synthesis or breakdown. Eight of the genes encode transcription factors. We have identified genes significantly associated with variation in fatty acid proportions that can be used as a resource across the Brassicaceae. Two-thirds of the regions identified contain candidate genes that have never been implicated in lipid metabolism and represent potential new targets for bioengineering.

Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq.

BACKGROUND: Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events. RESULTS: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for ~25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold). CONCLUSIONS: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.

Computational tests of a thermal cycling strategy to isolate more complex functional nucleic acid motifs from random sequence pools by in vitro selection.

The dual information-function nature of nucleic acids has been exploited in the laboratory to isolate novel receptors and catalysts from random DNA and RNA sequences by cycles of in vitro selection and amplification. This strategy is particularly effective because, unlike polypeptides with random amino acid sequences, nucleic acids with random base sequences are often capable of stably folding into defined three-dimensional structures. However, the pervasive base-pairing potential of nucleic acids is also known to lead to kinetic traps in their folding landscapes. That is, the same DNA or RNA sequence can often adopt alternative base-paired structures that are local energy minima, and these folds may interconvert very slowly. We have used simulations with nucleic acid folding algorithms to evaluate the effect of misfolding on in vitro selection experiments. We demonstrate that kinetic traps can prevent the recovery of novel families of complex functional motifs by two mechanisms. First, misfolding can lead to the stochastic loss of unique sequences in the first round of selection. Second, frequent misfolding can reduce the average activity of multiple copies of a sequence to such an extent that it will be outcompeted after multiple rounds of selection. In these simulations, adding thermal cycling to sample multiple folds of one sequence during a selection for a self-modifying catalytic activity can improve the recovery of rare examples of more complex structures. Although newly isolated sequences may fold poorly, they can represent footholds in sequence space that can be improved to reliably fold after a few mutations. Thus, it is plausible that thermal cycling by day-night cycles or other mechanisms on the primordial earth may have been important for the evolution of the first RNA catalysts, and a fold sampling strategy might be used to search for more effective nucleic acid catalysts in the laboratory today.