Insights into the Evolution of Cotton Diploids and Polyploids from Whole-Genome Re-sequencing

Understanding the composition, evolution, and function of the Gossypium hirsutum (cotton) genome is complicated by the joint presence of two genomes in its nucleus (AT and DT genomes). These two genomes were derived from progenitor A-genome and D-genome diploids involved in ancestral allopolyploidization. To better understand the allopolyploid genome, we re-sequenced the genomes of extant diploid relatives that contain the A1 (Gossypium herbaceum), A2 (Gossypium arboreum), or D5 (Gossypium raimondii) genomes. We conducted a comparative analysis using deep re-sequencing of multiple accessions of each diploid species and identified 24 million SNPs between the A-diploid and D-diploid genomes. These analyses facilitated the construction of a robust index of conserved SNPs between the A-genomes and D-genomes at all detected polymorphic loci. This index is widely applicable for read mapping efforts of other diploid and allopolyploid Gossypium accessions. Further analysis also revealed locations of putative duplications and deletions in the A-genome relative to the D-genome reference sequence. The approximately 25,400 deleted regions included more than 50% deletion of 978 genes, including many involved with starch synthesis. In the polyploid genome, we also detected 1,472 conversion events between homoeologous chromosomes, including events that overlapped 113 genes. Continued characterization of the Gossypium genomes will further enhance our ability to manipulate fiber and agronomic production of cotton

Conversion events in gene clusters

<p>Abstract</p> <p>Background</p> <p>Gene clusters containing multiple similar genomic regions in close proximity are of great interest for biomedical studies because of their associations with inherited diseases. However, such regions are difficult to analyze due to their structural complexity and their complicated evolutionary histories, reflecting a variety of large-scale mutational events. In particular, conversion events can mislead inferences about the relationships among these regions, as traced by traditional methods such as construction of phylogenetic trees or multi-species alignments.</p> <p>Results</p> <p>To correct the distorted information generated by such methods, we have developed an automated pipeline called CHAP (Cluster History Analysis Package) for detecting conversion events. We used this pipeline to analyze the conversion events that affected two well-studied gene clusters (α-globin and β-globin) and three gene clusters for which comparative sequence data were generated from seven primate species: CCL (chemokine ligand), IFN (interferon), and CYP2abf (part of cytochrome P450 family 2). CHAP is freely available at <url>http://www.bx.psu.edu/miller_lab</url>.</p> <p>Conclusions</p> <p>These studies reveal the value of characterizing conversion events in the context of studying gene clusters in complex genomes.</p

Gene tree species tree reconciliation with gene conversion

International audienc

An Effective Method for Detecting Gene Conversion Events in Whole Genomes

Gene conversion events are often overlooked in analyses of genome evolution. In a conversion event, an interval of DNA sequence (not necessarily containing a gene) overwrites a highly similar sequence. The event creates relationships among genomic intervals that can confound attempts to identify orthologs and to transfer functional annotation between genomes. Here we examine 1,616,329 paralogous pairs of mouse genomic intervals, and detect conversion events in about 7.5% of them. Properties of the putative gene conversions are analyzed, such as the lengths of the paralogous pairs and the spacing between their sources and targets. Our approach is illustrated using conversion events in primate CCL gene clusters. Source code for our program is included in the 3SEQ_2D package, which is freely available at www.bx.psu.edu/miller_lab/