Richard M. Clark

The Arabidopsis lyrata genome sequence and the basis of rapid genome size change

We report the 207-Mb genome sequence of the North American Arabidopsis lyrata strain MN47 based on 8.3× dideoxy sequence coverage. We predict 32,670 genes in this outcrossing species compared to the 27,025 genes in the selfing species Arabidopsis thaliana. The much smaller 125-Mb genome of A. thaliana, which diverged from A. lyrata 10 million years ago, likely constitutes the derived state for the family. We found evidence for DNA loss from large-scale rearrangements, but most of the difference in genome size can be attributed to hundreds of thousands of small deletions, mostly in noncoding DNA and transposons. Analysis of deletions and insertions still segregating in A. thaliana indicates that the process of DNA loss is ongoing, suggesting pervasive selection for a smaller genome. The high-quality reference genome sequence for A. lyrata will be an important resource for functional, evolutionary and ecological studies in the genus Arabidopsis.

Genomewide SNP variation reveals relationships among landraces and modern varieties of rice.

Rice, the primary source of dietary calories for half of humanity, is the first crop plant for which a high-quality reference genome sequence from a single variety was produced. We used resequencing microarrays to interrogate 100 Mb of the unique fraction of the reference genome for 20 diverse varieties and landraces that capture the impressive genotypic and phenotypic diversity of domesticated rice. Here, we report the distribution of 160,000 nonredundant SNPs. Introgression patterns of shared SNPs revealed the breeding history and relationships among the 20 varieties; some introgressed regions are associated with agronomic traits that mark major milestones in rice improvement. These comprehensive SNP data provide a foundation for deep exploration of rice diversity and gene–trait relationships and their use for future rice improvement.

A distant upstream enhancer at the maize domestication gene tb1 has pleiotropic effects on plant and inflorescent architecture

Although quantitative trait locus (QTL) mapping has been successful in describing the genetic architecture of complex traits, the molecular basis of quantitative variation is less well understood, especially in plants such as maize that have large genome sizes. Regulatory changes at the teosinte branched1 (tb1) gene have been proposed to underlie QTLs of large effect for morphological differences that distinguish maize (Zea mays ssp. mays) from its wild ancestors, the teosintes (Z. mays ssp. parviglumis and mexicana). We used a fine mapping approach to show that intergenic sequences ∼58–69 kb 5′ to the tb1 cDNA confer pleiotropic effects on Z. mays morphology. Moreover, using an allele-specific expression assay, we found that sequences >41 kb upstream of tb1 act in cis to alter tb1 transcription. Our findings show that the large stretches of noncoding DNA that comprise the majority of many plant genomes can be a source of variation affecting gene expression and quantitative phenotypes.

The PHYTOCHROME C photoreceptor gene mediates natural variation in flowering and growth responses of Arabidopsis thaliana.

Light has an important role in modulating seedling growth and flowering time. We show that allelic variation at the PHYTOCHROME C (PHYC) photoreceptor locus affects both traits in natural populations of A. thaliana. Two functionally distinct PHYC haplotype groups are distributed in a latitudinal cline dependent on FRIGIDA, a locus that together with FLOWERING LOCUS C explains a large portion of the variation in A. thaliana flowering time. In a genome-wide scan for association of 65 loci with latitude, there was an excess of significant P values, indicative of population structure. Nevertheless, PHYC was the most strongly associated locus across 163 strains, suggesting that PHYC alleles are under diversifying selection in A. thaliana. Our work, together with previous findings, suggests that photoreceptor genes are major agents of natural variation in plant flowering and growth response.

The Evolution of Selfing in Arabidopsis thaliana

Unlike most of its close relatives, Arabidopsis thaliana is capable of self-pollination. In other members of the mustard family, outcrossing is ensured by the complex self-incompatibility (S) locus,which harbors multiple diverged specificity haplotypes that effectively prevent selfing. We investigated the role of the S locus in the evolution of and transition to selfing in A. thaliana. We found that the S locus of A. thaliana harbored considerable diversity, which is an apparent remnant of polymorphism in the outcrossing ancestor. Thus, the fixation of a single inactivated S-locus allele cannot have been a key step in the transition to selfing. An analysis of the genome-wide pattern of linkage disequilibrium suggests that selfing most likely evolved roughly a million years ago or more.

Major Regulatory Genes in Maize Contribute to Standing Variation in Teosinte ( Zea mays ssp. parviglumis )

In plants, many major regulatory genes that control plant growth and development have been identified and characterized. Despite a detailed knowledge of the function of these genes little is known about how they contribute to the natural variation for complex traits. To determine whether major regulatory genes of maize contribute to standing variation in Balsas teosinte we conducted association mapping in 584 Balsas teosinte individuals. We tested 48 markers from nine candidate regulatory genes against 13 traits for plant and inflorescence architecture. We identified significant associations using a mixed linear model that controls for multiple levels of relatedness. Ten associations involving five candidate genes were significant after correction for multiple testing, and two survive the conservative Bonferroni correction. zfl2, the maize homolog of FLORICAULA of Antirrhinum, was associated with plant height. zap1, the maize homolog of APETALA1 of Arabidopsis, was associated with inflorescence branching. Five SNPs in the maize domestication gene, teosinte branched1, were significantly associated with either plant or inflorescence architecture. Our data suggest that major regulatory genes in maize do play a role in the natural variation for complex traits in teosinte and that some of the minor variants we identified may have been targets of selection during domestication.

Detecting polymorphic regions in Arabidopsis thaliana with resequencing microarrays

Whole-genome oligonucleotide resequencing arrays have allowed the comprehensive discovery of single nucleotide polymorphisms (SNPs) in eukaryotic genomes of moderate to large size. With this technology, the detection rate for isolated SNPs is typically high. However, it is greatly reduced when other polymorphisms are located near a SNP as multiple mismatches inhibit hybridization to arrayed oligonucleotides. Contiguous tracts of suppressed hybridization therefore typify polymorphic regions (PRs) such as clusters of SNPs or deletions. We developed a machine learning method, designated margin-based prediction of polymorphic regions (mPPR), to predict PRs from resequencing array data. Conceptually similar to hidden Markov models, the method is trained with discriminative learning techniques related to support vector machines, and accurately identifies even very short polymorphic tracts (<10 bp). We applied this method to resequencing array data previously generated for the euchromatic genomes of 20 strains (accessions) of the best-characterized plant, Arabidopsis thaliana. Nonredundantly, 27% of the genome was included within the boundaries of PRs predicted at high specificity ( approximately 97%). The resulting data set provides a fine-scale view of polymorphic sequences in A. thaliana; patterns of polymorphism not apparent in SNP data were readily detected, especially for noncoding regions. Our predictions provide a valuable resource for evolutionary genetic and functional studies in A. thaliana, and our method is applicable to similar data sets in other species. More broadly, our computational approach can be applied to other segmentation tasks related to the analysis of genomic variation.