Abstract:Various population sizes and number of markers have been used to obtain genetic maps. However, the precise number of individuals and markers needed for obtaining reliable maps is not known. We used data simulation to determine the influence of population size, the effect of the degree of marker saturation of the genome, and the number of individuals required for mapping of recombinant inbred lines (RILs). Three genomes with 11 linkage groups were generated with saturation levels of 5, 10 and 20 cM. For each sa… Show more
“…Conflict between linkage and physical maps was also reported in a GBS study in apple with some SNP positions in the linkage map conflicting with their physical map locations (Gardner et al 2014). This might be due to misassembly of pseudochromosomes or a small population size (Silva et al 2007). The F 2 population used for linkage map construction had 93 individuals and this might not be sufficient for accurate ordering of large numbers of SNPs (Silva et al 2007).…”
Section: Linkage Map Constructionmentioning
confidence: 99%
“…This might be due to misassembly of pseudochromosomes or a small population size (Silva et al 2007). The F 2 population used for linkage map construction had 93 individuals and this might not be sufficient for accurate ordering of large numbers of SNPs (Silva et al 2007). …”
Recently developed plant genomics approaches (LD mapping and genome-wide selection) require many molecular markers distributed throughout the plant genome. As a result, the availability of an increasing number of markers is essential for maintaining highly efficient and accurate plant breeding programs. In this study, we identified SNP loci in sunflower using a genotyping by sequencing (GBS) approach in an intraspecific F 2 mapping population. A total of 271,445,770 reads were generated by the Genome Analyzer II next-generation sequencing platform and 29.2 % of the reads were aligned to unique locations in the genome. A total of 46,278 SNP loci were identified and 7646 SNP loci were validated in an F 2 population. In addition, a SNP-based linkage map was constructed. This is the first report of SNP discovery in sunflower by GBS. The SNP markers and SNP-based linkage map will be valuable molecular genetics tools for sunflower breeding.
“…Conflict between linkage and physical maps was also reported in a GBS study in apple with some SNP positions in the linkage map conflicting with their physical map locations (Gardner et al 2014). This might be due to misassembly of pseudochromosomes or a small population size (Silva et al 2007). The F 2 population used for linkage map construction had 93 individuals and this might not be sufficient for accurate ordering of large numbers of SNPs (Silva et al 2007).…”
Section: Linkage Map Constructionmentioning
confidence: 99%
“…This might be due to misassembly of pseudochromosomes or a small population size (Silva et al 2007). The F 2 population used for linkage map construction had 93 individuals and this might not be sufficient for accurate ordering of large numbers of SNPs (Silva et al 2007). …”
Recently developed plant genomics approaches (LD mapping and genome-wide selection) require many molecular markers distributed throughout the plant genome. As a result, the availability of an increasing number of markers is essential for maintaining highly efficient and accurate plant breeding programs. In this study, we identified SNP loci in sunflower using a genotyping by sequencing (GBS) approach in an intraspecific F 2 mapping population. A total of 271,445,770 reads were generated by the Genome Analyzer II next-generation sequencing platform and 29.2 % of the reads were aligned to unique locations in the genome. A total of 46,278 SNP loci were identified and 7646 SNP loci were validated in an F 2 population. In addition, a SNP-based linkage map was constructed. This is the first report of SNP discovery in sunflower by GBS. The SNP markers and SNP-based linkage map will be valuable molecular genetics tools for sunflower breeding.
“…Several simulation studies have predicted that a population of 200 RILs is required for a statistically accurate analysis (Kim et al, 2005; Silva et al, 2007). However, most of the time, such populations only allow the detection of phenotypic QTL with major effects, a severe limitation when partial resistance alleles are the target for discovery (de Koning and Haley, 2005).…”
Section: Challenges To Designing Executing and Analyzing An Eqtl Exmentioning
Rusts are one of the most severe threats to cereal crops because new pathogen races emerge regularly, resulting in infestations that lead to large yield losses. In 1999, a new race of stem rust, Puccinia graminis f. sp. tritici (Pgt TTKSK or Ug99), was discovered in Uganda. Most of the wheat and barley cultivars grown currently worldwide are susceptible to this new race. Pgt TTKSK has already spread northward into Iran and will likely spread eastward throughout the Indian subcontinent in the near future. This scenario is not unique to stem rust; new races of leaf rust (Puccinia triticina) and stripe rust (Puccinia striiformis) have also emerged recently. One strategy for countering the persistent adaptability of these pathogens is to stack complete- and partial-resistance genes, which requires significant breeding efforts in order to reduce deleterious effects of linkage drag. These varied resistance combinations are typically more difficult for the pathogen to defeat, since they would be predicted to apply lower selection pressure. Genetical genomics or expression Quantitative Trait Locus (eQTL) analysis enables the identification of regulatory loci that control the expression of many to hundreds of genes. Integrated deployment of these technologies coupled with efficient phenotyping offers significant potential to elucidate the regulatory nodes in genetic networks that orchestrate host defense responses. The focus of this review will be to present advances in genetical genomic experimental designs and analysis, particularly as they apply to the prospects for discovering partial disease resistance alleles in cereals.
“…Based on a simulation, Silva et al (2007) estimated that populations of 200, 300, and 500 RILs are required for obtaining reliable maps with high, medium and low saturation levels, respectively. These saturation levels would correspond to 5, 10, and 20 cM between markers, respectively.…”
ABSTRACT. Recombinant inbred lines (RILs) are a valuable resource for building genetic linkage maps. The presence of genetic variability in the RILs is essential for detecting associations between molecular markers and loci controlling agronomic traits of interest. The main goal of this study was to quantify the genetic diversity of a common bean 2 L.C. Silva et al. Genetics and Molecular Research 15 (3): gmr.15038112 RIL population derived from a cross between Rudá (Mesoamerican gene pool) and AND 277 (Andean gene pool). This population was developed by the single seed descent method from 500 F 2 plants until the F 10 generation. Seven quantitative traits were evaluated in the field in 393 RILs, the parental lines, and five control cultivars. The plants were grown using a randomized block design with additional controls and three replicates. Significant differences were observed among the RILs for all evaluated traits (P < 0.01). A comparison of the RILs and parental lines showed significant differences (P < 0.01) for the number of days to flowering (DFL) and to harvest (DH), productivity (PROD) and mass of 100 beans (M100); however, there were no significant differences for plant architecture, degree of seed flatness, or seed shape. These results indicate the occurrence of additive x additive epistatic interactions for DFL, DH, PROD, and M100. The 393 RILs were shown to fall into 10 clusters using Tocher's method. This RIL population clearly contained genetic variability for the evaluated traits, and this variability will be crucial for future studies involving genetic mapping and quantitative trait locus identification and analysis.
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