Saturation of an Intra-Gene Pool Linkage Map: Towards a Unified Consensus Linkage Map for Fine Mapping and Synteny Analysis in Common Bean

Map-based cloning and fine mapping to find genes of interest and marker assisted selection (MAS) requires good genetic maps with reproducible markers. In this study, we saturated the linkage map of the intra-gene pool population of common bean DOR364×BAT477 (DB) by evaluating 2,706 molecular markers including SSR, SNP, and gene-based markers. On average the polymorphism rate was 7.7% due to the narrow genetic base between the parents. The DB linkage map consisted of 291 markers with a total map length of 1,788 cM. A consensus map was built using the core mapping populations derived from inter-gene pool crosses: DOR364×G19833 (DG) and BAT93×JALO EEP558 (BJ). The consensus map consisted of a total of 1,010 markers mapped, with a total map length of 2,041 cM across 11 linkage groups. On average, each linkage group on the consensus map contained 91 markers of which 83% were single copy markers. Finally, a synteny analysis was carried out using our highly saturated consensus maps compared with the soybean pseudo-chromosome assembly. A total of 772 marker sequences were compared with the soybean genome. A total of 44 syntenic blocks were identified. The linkage group Pv6 presented the most diverse pattern of synteny with seven syntenic blocks, and Pv9 showed the most consistent relations with soybean with just two syntenic blocks. Additionally, a co-linear analysis using common bean transcript map information against soybean coding sequences (CDS) revealed the relationship with 787 soybean genes. The common bean consensus map has allowed us to map a larger number of markers, to obtain a more complete coverage of the common bean genome. Our results, combined with synteny relationships provide tools to increase marker density in selected genomic regions to identify closely linked polymorphic markers for indirect selection, fine mapping or for positional cloning

Diversity analysis of cotton (Gossypium hirsutum L.) germplasm using the CottonSNP63K Array

Cotton germplasm resources contain beneficial alleles that can be exploited to develop germplasm adapted to emerging environmental and climate conditions. Accessions and lines have traditionally been characterized based on phenotypes, but phenotypic profiles are limited by the cost, time, and space required to make visual observations and measurements. With advances in molecular genetic methods, genotypic profiles are increasingly able to identify differences among accessions due to the larger number of genetic markers that can be measured. A combination of both methods would greatly enhance our ability to characterize germplasm resources. Recent efforts have culminated in the identification of sufficient SNP markers to establish high-throughput genotyping systems, such as the CottonSNP63K array, which enables a researcher to efficiently analyze large numbers of SNP markers and obtain highly repeatable results. In the current investigation, we have utilized the SNP array for analyzing genetic diversity primarily among cotton cultivars, making comparisons to SSR-based phylogenetic analyses, and identifying loci associated with seed nutritional traits. (Résumé d'auteur

Incorporating pleiotropic quantitative trait loci in dissection of complex traits: seed yield in rapeseed as an example

© The Author(s) 2017 This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Most agronomic traits of interest for crop improvement (including seed yield) are highly complex quantitative traits controlled by numerous genetic loci, which brings challenges for comprehensively capturing associated markers/ genes. We propose that multiple trait interactions underlie complex traits such as seed yield, and that considering these component traits and their interactions can dissect individual quantitative trait loci (QTL) effects more effectively and improve yield predictions. Using a segregating rapeseed (Brassica napus) population, we analyzed a large set of trait data generated in 19 independent experiments to investigate correlations between seed yield and other complex traits, and further identified QTL in this population with a SNP-based genetic bin map. A total of 1904 consensus QTL accounting for 22 traits, including 80 QTL directly affecting seed yield, were anchored to the B. napus reference sequence. Through trait association analysis and QTL meta-analysis, we identified a total of 525 indivisible QTL that either directly or indirectly contributed to seed yield, of which 295 QTL were detected across multiple environments. A majority (81.5%) of the 525 QTL were pleiotropic. By considering associations between traits, we identified 25 yield-related QTL previously ignored due to contrasting genetic effects, as well as 31 QTL with minor complementary effects. Implementation of the 525 QTL in genomic prediction models improved seed yield prediction accuracy. Dissecting the genetic and phenotypic interrelationships underlying complex quantitative traits using this method will provide valuable insights for genomics-based crop improvement.Peer reviewedFinal Published versio

Yield and Economic Performance of Organic and Conventional Cotton-Based Farming Systems – Results from a Field Trial in India

The debate on the relative benefits of conventional and organic farming systems has in recent time gained significant interest. So far, global agricultural development has focused on increased productivity rather than on a holistic natural resource management for food security. Thus, developing more sustainable farming practices on a large scale is of utmost importance. However, information concerning the performance of farming systems under organic and conventional management in tropical and subtropical regions is scarce. This study presents agronomic and economic data from the conversion phase (2007–2010) of a farming systems comparison trial on a Vertisol soil in Madhya Pradesh, central India. A cotton-soybean-wheat crop rotation under biodynamic, organic and conventional (with and without Bt cotton) management was investigated. We observed a significant yield gap between organic and conventional farming systems in the 1st crop cycle (cycle 1: 2007–2008) for cotton (229%) and wheat (227%), whereas in the 2nd crop cycle (cycle 2: 2009–2010) cotton and wheat yields were similar in all farming systems due to lower yields in the conventional systems. In contrast, organic soybean (a nitrogen fixing leguminous plant) yields were marginally lower than conventional yields (21% in cycle 1, 211% in cycle 2). Averaged across all crops, conventional farming systems achieved significantly higher gross margins in cycle 1 (+29%), whereas in cycle 2 gross margins in organic farming systems were significantly higher (+25%) due to lower variable production costs but similar yields. Soybean gross margin was significantly higher in the organic system (+11%) across the four harvest years compared to the conventional systems. Our results suggest that organic soybean production is a viable option for smallholder farmers under the prevailing semi-arid conditions in India. Future research needs to elucidate the long-term productivity and profitability, particularly of cotton and wheat, and the ecological impact of the different farming systems

Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC1 population between gossypium hirsutum and G. barbadense

<p>Abstract</p> <p>Background</p> <p>Cotton, with a large genome, is an important crop throughout the world. A high-density genetic linkage map is the prerequisite for cotton genetics and breeding. A genetic map based on simple polymerase chain reaction markers will be efficient for marker-assisted breeding in cotton, and markers from transcribed sequences have more chance to target genes related to traits. To construct a genome-wide, functional marker-based genetic linkage map in cotton, we isolated and mapped expressed sequence tag-simple sequence repeats (EST-SSRs) from cotton ESTs derived from the A₁, D₅, (AD)₁, and (AD)₂genome.</p> <p>Results</p> <p>A total of 3177 new EST-SSRs developed in our laboratory and other newly released SSRs were used to enrich our interspecific BC₁genetic linkage map. A total of 547 loci and 911 loci were obtained from our EST-SSRs and the newly released SSRs, respectively. The 1458 loci together with our previously published data were used to construct an updated genetic linkage map. The final map included 2316 loci on the 26 cotton chromosomes, 4418.9 cM in total length and 1.91 cM in average distance between adjacent markers. To our knowledge, this map is one of the three most dense linkage maps in cotton. Twenty-one segregation distortion regions (SDRs) were found in this map; three segregation distorted chromosomes, Chr02, Chr16, and Chr18, were identified with 99.9% of distorted markers segregating toward the heterozygous allele. Functional analysis of SSR sequences showed that 1633 loci of this map (70.6%) were transcribed loci and 1332 loci (57.5%) were translated loci.</p> <p>Conclusions</p> <p>This map lays groundwork for further genetic analyses of important quantitative traits, marker-assisted selection, and genome organization architecture in cotton as well as for comparative genomics between cotton and other species. The segregation distorted chromosomes can be a guide to identify segregation distortion loci in cotton. The annotation of SSR sequences identified frequent and rare gene ontology items on each chromosome, which is helpful to discover functions of cotton chromosomes.</p

A combined functional and structural genomics approach identified an EST-SSR marker with complete linkage to the Ligon lintless-2 genetic locus in cotton (Gossypium hirsutum L.)

<p>Abstract</p> <p>Background</p> <p>Cotton fiber length is an important quality attribute to the textile industry and longer fibers can be more efficiently spun into yarns to produce superior fabrics. There is typically a negative correlation between yield and fiber quality traits such as length. An understanding of the regulatory mechanisms controlling fiber length can potentially provide a valuable tool for cotton breeders to improve fiber length while maintaining high yields. The cotton (<it>Gossypium hirsutum </it>L.) fiber mutation Ligon lintless-2 is controlled by a single dominant gene (<it>Li₂</it>) that results in significantly shorter fibers than a wild-type. In a near-isogenic state with a wild-type cotton line, <it>Li₂</it>is a model system with which to study fiber elongation.</p> <p>Results</p> <p>Two near-isogenic lines of Ligon lintless-2 (<it>Li₂</it>) cotton, one mutant and one wild-type, were developed through five generations of backcrosses (BC₅). An F₂population was developed from a cross between the two <it>Li₂</it>near-isogenic lines and used to develop a linkage map of the <it>Li₂</it>locus on chromosome 18. Five simple sequence repeat (SSR) markers were closely mapped around the <it>Li₂</it>locus region with two of the markers flanking the <it>Li₂</it>locus at 0.87 and 0.52 centimorgan. No apparent differences in fiber initiation and early fiber elongation were observed between the mutant ovules and the wild-type ones. Gene expression profiling using microarrays suggested roles of reactive oxygen species (ROS) homeostasis and cytokinin regulation in the <it>Li₂</it>mutant phenotype. Microarray gene expression data led to successful identification of an EST-SSR marker (NAU3991) that displayed complete linkage to the <it>Li₂</it>locus.</p> <p>Conclusions</p> <p>In the field of cotton genomics, we report the first successful conversion of gene expression data into an SSR marker that is associated with a genomic region harboring a gene responsible for a fiber trait. The EST-derived SSR marker NAU3991 displayed complete linkage to the <it>Li₂</it>locus on chromosome 18 and resided in a gene with similarity to a putative plectin-related protein. The complete linkage suggests that this expressed sequence may be the <it>Li₂</it>gene.</p