Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis.

In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the 'wrong' genotypes retained, and how often are the 'correct' genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.

Distribution and evolution of cotton fiber development genes in the fibreless Gossypium raimondii genome.

Cotton fiber represents the largest single cell in plants and they serve as models to study cell development. This study investigated the distribution and evolution of fiber Unigenes anchored to recombination hotspots between tetraploid cotton (Gossypium hirsutum) At and Dt subgenomes, and within a parental diploid cotton (Gossypium raimondii) D genome. Comparative analysis of At vs D and Dt vs D showed that 1) the D genome provides many fiber genes after its merger with another parental diploid cotton (Gossypium arboreum) A genome although the D genome itself does not produce any spinnable fiber; 2) similarity of fiber genes is higher between At vs D than between Dt vs D genomic hotspots. This is the first report that fiber genes have higher similarity between At and D than between Dt and D. The finding provides new insights into cotton genomic regions that would facilitate genetic improvement of natural fiber properties.

BAC-pool sequencing and analysis of large segments of A12 and D12 homoeologous chromosomes in upland cotton.

Although new and emerging next-generation sequencing (NGS) technologies have reduced sequencing costs significantly, much work remains to implement them for de novo sequencing of complex and highly repetitive genomes such as the tetraploid genome of Upland cotton (Gossypium hirsutum L.). Herein we report the results from implementing a novel, hybrid Sanger/454-based BAC-pool sequencing strategy using minimum tiling path (MTP) BACs from Ctg-3301 and Ctg-465, two large genomic segments in A12 and D12 homoeologous chromosomes (Ctg). To enable generation of longer contig sequences in assembly, we implemented a hybrid assembly method to process ~35x data from 454 technology and 2.8-3x data from Sanger method. Hybrid assemblies offered higher sequence coverage and better sequence assemblies. Homology studies revealed the presence of retrotransposon regions like Copia and Gypsy elements in these contigs and also helped in identifying new genomic SSRs. Unigenes were anchored to the sequences in Ctg-3301 and Ctg-465 to support the physical map. Gene density, gene structure and protein sequence information derived from protein prediction programs were used to obtain the functional annotation of these genes. Comparative analysis of both contigs with Arabidopsis genome exhibited synteny and microcollinearity with a conserved gene order in both genomes. This study provides insight about use of MTP-based BAC-pool sequencing approach for sequencing complex polyploid genomes with limited constraints in generating better sequence assemblies to build reference scaffold sequences. Combining the utilities of MTP-based BAC-pool sequencing with current longer and short read NGS technologies in multiplexed format would provide a new direction to cost-effectively and precisely sequence complex plant genomes.

Polyploidization altered gene functions in cotton (Gossypium spp.).

Cotton (Gossypium spp.) is an important crop plant that is widely grown to produce both natural textile fibers and cottonseed oil. Cotton fibers, the economically more important product of the cotton plant, are seed trichomes derived from individual cells of the epidermal layer of the seed coat. It has been known for a long time that large numbers of genes determine the development of cotton fiber, and more recently it has been determined that these genes are distributed across At and Dt subgenomes of tetraploid AD cottons. In the present study, the organization and evolution of the fiber development genes were investigated through the construction of an integrated genetic and physical map of fiber development genes whose functions have been verified and confirmed. A total of 535 cotton fiber development genes, including 103 fiber transcription factors, 259 fiber development genes, and 173 SSR-contained fiber ESTs, were analyzed at the subgenome level. A total of 499 fiber related contigs were selected and assembled. Together these contigs covered about 151 Mb in physical length, or about 6.7% of the tetraploid cotton genome. Among the 499 contigs, 397 were anchored onto individual chromosomes. Results from our studies on the distribution patterns of the fiber development genes and transcription factors between the At and Dt subgenomes showed that more transcription factors were from Dt subgenome than At, whereas more fiber development genes were from At subgenome than Dt. Combining our mapping results with previous reports that more fiber QTLs were mapped in Dt subgenome than At subgenome, the results suggested a new functional hypothesis for tetraploid cotton. After the merging of the two diploid Gossypium genomes, the At subgenome has provided most of the genes for fiber development, because it continues to function similar to its fiber producing diploid A genome ancestor. On the other hand, the Dt subgenome, with its non-fiber producing D genome ancestor, provides more transcription factors that regulate the expression of the fiber genes in the At subgenome. This hypothesis would explain previously published mapping results. At the same time, this integrated map of fiber development genes would provide a framework to clone individual full-length fiber genes, to elucidate the physiological mechanisms of the fiber differentiation, elongation, and maturation, and to systematically study the functional network of these genes that interact during the process of fiber development in the tetraploid cottons.

An integrated genetic and physical map of homoeologous chromosomes 12 and 26 in Upland cotton (G. hirsutum L.).

BACKGROUND: Upland cotton (G. hirsutum L.) is the leading fiber crop worldwide. Genetic improvement of fiber quality and yield is facilitated by a variety of genomics tools. An integrated genetic and physical map is needed to better characterize quantitative trait loci and to allow for the positional cloning of valuable genes. However, developing integrated genomic tools for complex allotetraploid genomes, like that of cotton, is highly experimental. In this report, we describe an effective approach for developing an integrated physical framework that allows for the distinguishing between subgenomes in cotton. RESULTS: A physical map has been developed with 220 and 115 BAC contigs for homeologous chromosomes 12 and 26, respectively, covering 73.49 Mb and 34.23 Mb in physical length. Approximately one half of the 220 contigs were anchored to the At subgenome only, while 48 of the 115 contigs were allocated to the Dt subgenome only. Between the two chromosomes, 67 contigs were shared with an estimated overall physical similarity between the two chromosomal homeologs at 40.0 %. A total of 401 fiber unigenes plus 214 non-fiber unigenes were located to chromosome 12 while 207 fiber unigenes plus 183 non-fiber unigenes were allocated to chromosome 26. Anchoring was done through an overgo hybridization approach and all anchored ESTs were functionally annotated via blast analysis. CONCLUSION: This integrated genomic map describes the first pair of homoeologous chromosomes of an allotetraploid genome in which BAC contigs were identified and partially separated through the use of chromosome-specific probes and locus-specific genetic markers. The approach used in this study should prove useful in the construction of genome-wide physical maps for polyploid plant genomes including Upland cotton. The identification of Gene-rich islands in the integrated map provides a platform for positional cloning of important genes and the targeted sequencing of specific genomic regions.

Cloning, characterization, and evolution of the NBS-LRR-encoding resistance gene analogue family in polyploid cotton (Gossypium hirsutum L.).

The nucleotide-binding site-leucine-rich repeat (NBS-LRR)-encoding gene family has attracted much research interest because approximately 75% of the plant disease resistance genes that have been cloned to date are from this gene family. We cloned the NBS-LRR-encoding genes from polyploid cotton by a polymerase chain reaction-based approach. A sample of 150 clones was selected from the NBS-LRR gene sequence library and was sequenced, and 61 resistance gene analogs (RGA) were identified. Sequence analysis revealed that RGA are abundant and highly diverged in the cotton genome and could be categorized into 10 distinct subfamilies based on the similarities of their nucleotide sequences. The numbers of members vary many fold among different subfamilies, and gene index analysis showed that each of the subfamilies is at a different stage of RGA family evolution. Genetic mapping of a selection of RGA indicates that the RGA reside on a limited number of the cotton chromosomes, with those from a single subfamily tending to cluster and two of the RGA loci being colocalized with the cotton bacterial blight resistance genes. The distribution of RGA between the two subgenomes A and D of cotton is uneven, with RGA being more abundant in the A subgenome than in the D subgenome. The data provide new insights into the organization and evolution of the NBS-LRR-encoding RGA family in polyploid plants.