Genome research promises to promote continued and enhanced plant genetic improvement. As a world's leading crop and a model system for studies of many biological processes, genomics research of cottons has advanced rapidly in the past few years. This article presents a comprehensive review on the recent advances of cotton genomics research. The reviewed areas include DNA markers, genetic maps, mapped genes and QTLs, ESTs, microarrays, gene expression profiling, BAC and BIBAC libraries, physical mapping, genome sequencing, and applications of genomic tools in cotton breeding. Analysis of the current status of each of the genome research areas suggests that the areas of physical mapping, QTL fine mapping, genome sequencing, nonfiber and nonovule EST development, gene expression profiling, and association studies between gene expression and fiber trait performance should be emphasized currently and in near future to accelerate utilization of the genomics research achievements for enhancing cotton genetic improvement.

A genome-wide physical map constructed with bacterial artificial chromosomes (BACs) is an essential component in linking phenotypic traits to the responsible genetic variation in the genomes of plants and animals. We have constructed a physical map of the chicken genome from 57,091 BACs (7.9-fold haploid genome coverage) by restriction fingerprint analysis using high-resolution polyacrylamide gel electrophoresis. The physical map consists of 2331 overlapping BAC contigs and is estimated to span 1510 Mb in physical length. BAC contigs were verified manually and by screening the BACs with 367 DNA markers. A total of 361 of the contigs have been anchored to the existing chicken genetic map. This map represents the first genome-wide, BAC-based physical map of the chicken genome. It provides a powerful platform for many areas of chicken genomics, including targeted marker development, fine mapping of genes and QTL alleles, positional cloning, analysis of avian genome organization and evolution, chicken-mammalian comparative genomics, and large-scale genome sequencing.

Many genes exist in the form of families; however, little is known about their size variation, evolution and biology. Here, we present the size variation and evolution of the nucleotide-binding site (NBS)-encoding gene family and receptor-like kinase (RLK) gene family in Oryza , Glycine and Gossypium . The sizes of both families vary by numeral fold, not only among species, surprisingly, also within a species. The size variations of the gene families are shown to correlate with each other, indicating their interactions, and driven by natural selection, artificial selection and genome size variation, but likely not by polyploidization. The numbers of genes in the families in a polyploid species are similar to those of one of its diploid donors, suggesting that polyploidization plays little roles in the expansion of the gene families and that organisms tend not to maintain their ‘surplus’ genes in the course of evolution. Furthermore, it is found that the size variations of both gene families are associated with organisms’ phylogeny, suggesting their roles in speciation and evolution. Since both selection and speciation act on organism’s morphological, physiological and biological variation, our results indicate that the variation of gene family size provides a source of genetic variation and evolution.

Most traits of agricultural importance are quantitative traits controlled by numerous genes. However, it remains unclear about the molecular mechanisms underpinning quantitative traits. Here, we report the molecular characteristics of the genes controlling three quantitative traits randomly selected from three diverse plant species, including ginsenoside biosynthesis in ginseng (Panax ginseng C.A. Meyer), fiber length in cotton (Gossypium hirsutum L. and G. barbadense L.) and grain yield in maize (Zea mays L.). We found that a vast majority of the genes controlling a quantitative trait were significantly more likely spliced into multiple transcripts while they expressed. Nevertheless, only one to four, but not all, of the transcripts spliced from each of the genes were significantly correlated with the phenotype of the trait. The genes controlling a quantitative trait were multiple times more likely to form a co-expression network than other genes expressed in an organ. The network varied substantially among genotypes of a species and was associated with their phenotypes. These findings indicate that the genes controlling a quantitative trait are more likely pleiotropic and functionally correlated, thus providing new insights into the molecular basis underpinning quantitative traits and knowledge necessary to develop technologies for efficient manipulation of quantitative traits.