Effect of small mapping population sizes on reliability of quantitative trait locus (QTL) mapping

A limitation of quantitative trait loci (QTL) mapping is that accuracy of determining QTL position and effects  are largely determined by population size. Despite the importance of this concept, known as the "Beavis effect  there has generally been a lack of understanding by molecular geneticists and breeders. One possible  explanation for this may be that this concept has been explored by using computer simulations and that these  findings may not be clearly understood. In this study, we demonstrated the effect of population size on the  accuracy of determining QTL positions and effects in a simple and concise manner. Simulated data was  generated for extremely large mapping populations (n = 1000) and smaller mapping populations (n = 94 or n = 190) were obtained by random sampling. Populations were defined to segregate for either three or five QTLs with heritabilities of h2 = 0.75 or h2 = 0.50. When small populations were used, errors were detected in determining QTL positions, and in some cases, QTLs were not detected (that is, false negatives) especially when h2 = 0.50. More importantly, R2 values were overestimated or underestimated. Composite interval  mapping was more reliable for detecting QTLs compared to simple interval mapping. These findings have  important implications for QTLs which are selected in breeding programs via marker-assisted selection.Key words: Quantitative trait loci (QTL) mapping, Beavis effect, population size, confidence intervals, marker-assisted selection

High resolution mapping of a novel late blight resistance gene Rpi-avll, from the wild Bolivian species Solanum avilesii

Both Mexico and South America are rich in Solanum species that might be valuable sources of resistance (R) genes to late blight (Phytophthora infestans). Here, we focus on an R gene present in the diploid Bolivian species S. avilesii. The genotype carrying the R gene was resistant to eight out of 10 Phytophthora isolates of various provenances. The identification of a resistant phenotype and the generation of a segregating population allowed the mapping of a single dominant R gene, Rpi-avl1, which is located in an R gene cluster on chromosome 11. This R gene cluster is considered as an R gene “hot spot”, containing R genes to at least five different pathogens. High resolution mapping of the Rpi-avl1 gene revealed a marker co-segregating in 3890 F1 individuals, which may be used for marker assisted selection in breeding programs and for further cloning of Rpi-avl

Development of a RAD-Seq Based DNA Polymorphism Identification Software, AgroMarker Finder, and Its Application in Rice Marker-Assisted Breeding

Abstract Rapid and accurate genome-wide marker detection is essential to the marker-assisted breeding and functional genomics studies. In this work, we developed an integrated software, AgroMarker Finder (AMF: http://erp.novelbio.com/AMF), for providing graphical user interface (GUI) to facilitate the recently developed restriction-site associated DNA (RAD) sequencing data analysis in rice. By application of AMF, a total of 90,743 high-quality markers (82,878 SNPs and 7,865 InDels) were detected between rice varieties JP69 and Jiaoyuan5A. The density of the identified markers is 0.2 per Kb for SNP markers, and 0.02 per Kb for InDel markers. Sequencing validation revealed that the accuracy of genome-wide marker detection by AMF is 93%. In addition, a validated subset of 82 SNPs and 31 InDels were found to be closely linked to 117 important agronomic trait genes, providing a basis for subsequent marker-assisted selection (MAS) and variety identification. Furthermore, we selected 12 markers from 31 validated InDel markers to identify seed authenticity of variety Jiaoyuanyou69, and we also identified 10 markers closely linked to the fragrant gene BADH2 to minimize linkage drag for Wuxiang075 (BADH2 donor)/Jiachang1 recombinants selection. Therefore, this software provides an efficient approach for marker identification from RAD-seq data, and it would be a valuable tool for plant MAS and variety protection

Identifying Molecular Markers Suitable For Frl Selection in Tomato Breeding

Modern plant breeding heavily relies on the use of molecular markers. In recent years, next generation sequencing (NGS) emerged as a powerful technology to discover DNA sequence polymorphisms and generate molecular markers very rapidly and cost effectively, accelerating the plant breeding programmes. A single dominant locus, Frl, in tomato provides resistance to the fungal pathogen Fusarium oxysporum f. sp. radicis-lycopersici (FORL), causative agent of Fusarium crown and root rot. In this study, we describe the generation of molecular markers associated with the Frl locus. An F2 mapping population between an FORL resistant and a susceptible cultivar was generated. NGS technology was then used to sequence the genomes of a susceptible and a resistant parent as well the genomes of bulked resistant and susceptible F2 lines. We zoomed into the Frl locus and mapped the locus to a 900 kb interval on chromosome 9. Polymorphic single-nucleotide polymorphisms (SNPs) within the interval were identified and markers co-segregating with the resistant phenotype were generated. Some of these markers were tested successfully with commercial tomato varieties indicating that they can be used for marker-assisted selection in large-scale breeding programmes

A genome scan for quantitative trait loci affecting cyanogenic potential of cassava root in an outbred population

<p>Abstract</p> <p>Background</p> <p>Cassava (<it>Manihot esculenta </it>Crantz) can produce cyanide, a toxic compound, without self-injury. That ability was called the cyanogenic potential (CN). This project aimed to identify quantitative trait loci (QTL) associated with the CN in an outbred population derived from 'Hanatee' × 'Huay Bong 60', two contrasting cultivars. CN was evaluated in 2008 and in 2009 at Rayong province, and in 2009 at Lop Buri province, Thailand. CN was measured using a picrate paper kit. QTL analysis affecting CN was performed with 303 SSR markers.</p> <p>Results</p> <p>The phenotypic values showed continuous variation with transgressive segregation events with more (115 ppm) and less CN (15 ppm) than either parent ('Hanatee' had 33 ppm and 'Huay Bong 60' had 95 ppm). The linkage map consisted of 303 SSR markers, on 27 linkage groups with a map that encompassed 1,328 cM. The average marker interval was 5.8 cM. Five QTL underlying CN were detected. <it>CN08R1</it>from 2008 at Rayong, <it>CN09R1</it>and <it>CN09R2 </it>from 2009 at Rayong, and <it>CN09L1 </it>and <it>CN09L2 </it>from 2009 at Lop Buri were mapped on linkage group 2, 5, 10 and 11, respectively. Among all the identified QTL, <it>CN09R1 </it>was the most significantly associated with the CN trait with LOD score 5.75 and explained the greatest percentage of phenotypic variation (%Expl.) of 26%.</p> <p>Conclusions</p> <p>Five new QTL affecting CN were successfully identified from 4 linkage groups. Discovery of these QTL can provide useful markers to assist in cassava breeding and studying genes affecting the trait.</p

Introgression and pyramiding into common bean market class fabada of genes conferring resistance to anthracnose and potyvirus

Anthracnose and bean common mosaic (BCM) are considered major diseases in common bean crop causing severe yield losses worldwide. This work describes the introgression and pyramiding of genes conferring genetic resistance to BCM and anthracnose local races into line A25, a bean genotype classified as market class fabada. Resistant plants were selected using resistance tests or combining resistance tests and marker-assisted selection. Lines A252, A321, A493, Sanilac BC6-Are, and BRB130 were used as resistance sources. Resistance genes to anthracnose (Co-2 ( C ), Co-2 ( A252 ) and Co-3/9) and/or BCM (I and bc-3) were introgressed in line A25 through six parallel backcrossing programs, and six breeding lines showing a fabada seed phenotype were obtained after six backcross generations: line A1258 from A252; A1231 from A321; A1220 from A493; A1183 and A1878 from Sanilac BC6-Are; and line A2418 from BRB130. Pyramiding of different genes were developed using the pedigree method from a single cross between lines obtained in the introgression step: line A1699 (derived from cross A1258 × A1220), A2438 (A1220 × A1183), A2806 (A1878 × A2418), and A3308 (A1699 × A2806). A characterization based on eight morpho-agronomic traits revealed a limited differentiation among the obtained breeding lines and the recurrent line A25. However, using a set of seven molecular markers linked to the loci used in the breeding programs it was possible to differentiate the 11 fabada lines. Considering the genetic control of the resistance in resistant donor lines, the observed segregations in the last backcrossing generation, the reaction against the pathogens, and the expression of the molecular markers it was also possible to infer the genotype conferring resistance in the ten fabada breeding lines obtained. As a result of these breeding programs, genetic resistance to three anthracnose races controlled by genes included in clusters Co-2 and Co-3/9, and genetic resistance to BCM controlled by genotype I + bc-3 was combined in the fabada line A3308

A pipeline for high throughput detection and mapping of SNPs from EST databases

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic variation that can be used as molecular markers. The SNPs that are hidden in sequence databases can be unlocked using bioinformatic tools. For efficient application of these SNPs, the sequence set should be error-free as much as possible, targeting single loci and suitable for the SNP scoring platform of choice. We have developed a pipeline to effectively mine SNPs from public EST databases with or without quality information using QualitySNP software, select reliable SNP and prepare the loci for analysis on the Illumina GoldenGate genotyping platform. The applicability of the pipeline was demonstrated using publicly available potato EST data, genotyping individuals from two diploid mapping populations and subsequently mapping the SNP markers (putative genes) in both populations. Over 7000 reliable SNPs were identified that met the criteria for genotyping on the GoldenGate platform. Of the 384 SNPs on the SNP array approximately 12% dropped out. For the two potato mapping populations 165 and 185 SNPs segregating SNP loci could be mapped on the respective genetic maps, illustrating the effectiveness of our pipeline for SNP selection and validation

Restriction site polymorphism-based candidate gene mapping for seedling drought tolerance in cowpea [Vigna unguiculata (L.) Walp.]

Quantitative trait loci (QTL) studies provide insight into the complexity of drought tolerance mechanisms. Molecular markers used in these studies also allow for marker-assisted selection (MAS) in breeding programs, enabling transfer of genetic factors between breeding lines without complete knowledge of their exact nature. However, potential for recombination between markers and target genes limit the utility of MAS-based strategies. Candidate gene mapping offers an alternative solution to identify trait determinants underlying QTL of interest. Here, we used restriction site polymorphisms to investigate co-location of candidate genes with QTL for seedling drought stress-induced premature senescence identified previously in cowpea. Genomic DNA isolated from 113 F2:8 RILs of drought-tolerant IT93K503-1 and drought susceptible CB46 genotypes was digested with combinations of EcoR1 and HpaII, Mse1, or Msp1 restriction enzymes and amplified with primers designed from 13 drought-responsive cDNAs. JoinMap 3.0 and MapQTL 4.0 software were used to incorporate polymorphic markers onto the AFLP map and to analyze their association with the drought response QTL. Seven markers co-located with peaks of previously identified QTL. Isolation, sequencing, and blast analysis of these markers confirmed their significant homology with drought or other abiotic stress-induced expressed sequence tags (EST) from cowpea and other plant systems. Further, homology with coding sequences for a multidrug resistance protein 3 and a photosystem I assembly protein ycf3 was revealed in two of these candidates. These results provide a platform for the identification and characterization of genetic trait determinants underlying seedling drought tolerance in cowpea

QTLs for oil yield components in an elite oil palm (Elaeis guineensis) cross

Increased modern farming of superior types of the oil palm, Elaeis guineensis Jacq., which has naturally efficient oil biosynthesis, has made it the world’s foremost edible oil crop. Breeding improvement is, however, circumscribed by time and costs associated with the tree’s long reproductive cycle, large size and 10–15 years of field testing. Marker-assisted breeding has considerable potential for improving this crop. Towards this, quantitative trait loci (QTL) linked to oil yield component traits were mapped in a high-yield population. In total, 164 QTLs associated with 21 oil yield component traits were discovered, with cumulative QTL effects increasing in tandem with the number of QTL markers and matching the QT+ alleles for each trait. The QTLs confirmed all traits to be polygenic, with many genes of individual small effects on independent loci, but epistatic interactions are not ruled out. Furthermore, several QTLs maybe pleiotropic as suggested by QTL clustering of inter-related traits on almost all linkage groups. Certain regions of the chromosomes seem richer in the genes affecting a particular yield component trait and likely encompass pleiotropic, epistatic and heterotic effects. A large proportion of the identified additive effects from QTLs may actually arise from genic interactions between loci. Comparisons with previous mapping studies show that most of the QTLs were for similar traits and shared similar marker intervals on the same linkage groups. Practical applications for such QTLs in marker-assisted breeding will require seeking them out in different genetic backgrounds and environments