Project description:The data set submitted here contains the raw SNP genotyping data obtained from the analysis of 24 biparental segregating maize (Zea mays L.) populations and their respective parents. The processed and filtered data were used to construct genetic linkage maps which we used in our study of variation of recombination rate in maize. In sexually reproducing organisms, meiotic crossovers ensure the proper segregation of chromosomes and contribute to genetic diversity by shuffling allelic combinations. Such genetic reassortment is exploited in breeding to combine favorable alleles, and in genetic research to identify genetic factors underlying traits of interest via linkage or association-based approaches. Crossover numbers and distributions along chromosomes vary between species, but little is known about their intraspecies variation. In our study, we report on the variation of recombination rates between 22 European maize inbred lines that belong to the Dent and Flint gene pools. We genotyped 23 doubled-haploid populations derived from crosses between these lines with a 50k-SNP array and constructed high-density genetic maps, showing good correspondence with the maize B73 genome sequence assembly. By aligning each genetic map to the B73 sequence, we obtained the recombination rates along chromosomes specific to each population. We identified significant differences in recombination rates at the genome-wide, chromosome, and intrachromosomal levels between populations, as well as significant variation for genome-wide recombination rates among maize lines. Crossover interference analysis using a two-pathway modeling framework revealed a negative association between recombination rate and interference strength. To our knowledge, the present work provides the most comprehensive study on intraspecific variation of recombination rates and crossover interference strength in eukaryotes. Differences found in recombination rates will allow for selection of high or low recombining lines in crossing programs. Our methodology should pave the way for precise identification of genes controlling recombination rates in maize and other organisms. Related publication: Bauer E, Falque M, Walter H, Bauland C, Camisan C, Campo L, Meyer N, Ranc N, Rincent R, Schipprack W, Altmann T, Flament P, Melchinger AE, Menz M, Moreno-González J, Ouzunova M, Revilla P, Charcosset A, Martin OC, Schön C-C (2013) Intraspecific variation of recombination rate in maize. Genome Biology (submitted) We genotyped 2233 maize DH lines from 24 biparental populations, and the 23 parents of these populations using the Illumina MaizeSNP50 BeadChip. We created two large half-sib panels, one each for the Dent and the Flint germplasm. The Dent populations have the prefix CFD, the Flint populations have the prefix CFF. In each panel, a common central parent was crossed to diverse founder lines, and doubled haploids were generated from the respective F1 plants. For a detailed description of the material, see Bauer et al. (2013) Genome Biology (submitted). We submit here three datasets: 1) Dataset Parents comprises all 23 parental lines. 2) Dataset CFD comprises all 1005 DH lines from Dent crosses, 3) Dataset CFF comprises all 1262 DH lines from Flint crosses.

Project description:The data set submitted here contains the raw SNP genotyping data obtained from the analysis of 24 biparental segregating maize (Zea mays L.) populations and their respective parents. The processed and filtered data were used to construct genetic linkage maps which we used in our study of variation of recombination rate in maize. In sexually reproducing organisms, meiotic crossovers ensure the proper segregation of chromosomes and contribute to genetic diversity by shuffling allelic combinations. Such genetic reassortment is exploited in breeding to combine favorable alleles, and in genetic research to identify genetic factors underlying traits of interest via linkage or association-based approaches. Crossover numbers and distributions along chromosomes vary between species, but little is known about their intraspecies variation. In our study, we report on the variation of recombination rates between 22 European maize inbred lines that belong to the Dent and Flint gene pools. We genotyped 23 doubled-haploid populations derived from crosses between these lines with a 50k-SNP array and constructed high-density genetic maps, showing good correspondence with the maize B73 genome sequence assembly. By aligning each genetic map to the B73 sequence, we obtained the recombination rates along chromosomes specific to each population. We identified significant differences in recombination rates at the genome-wide, chromosome, and intrachromosomal levels between populations, as well as significant variation for genome-wide recombination rates among maize lines. Crossover interference analysis using a two-pathway modeling framework revealed a negative association between recombination rate and interference strength. To our knowledge, the present work provides the most comprehensive study on intraspecific variation of recombination rates and crossover interference strength in eukaryotes. Differences found in recombination rates will allow for selection of high or low recombining lines in crossing programs. Our methodology should pave the way for precise identification of genes controlling recombination rates in maize and other organisms.

Project description:The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing.In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues, including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7-4.8 Å C(?)-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.

Dataset Information

Rice 3D chromatin structure correlates with sequence variation and meiotic recombination rate.

Publications

Rice 3D chromatin structure correlates with sequence variation and meiotic recombination rate.

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