Multiparent Advanced Generation Intercross Research Articles

Multiple quantitative trait analysis using bayesian networks.

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.

Characterizing Uncertainty in High-Density Maps from Multiparental Populations

Multiparental populations are of considerable interest in high-density genetic mapping due to their increased levels of polymorphism and recombination relative to biparental populations. However, errors in map construction can have significant impact on QTL discovery in later stages of analysis, and few methods have been developed to quantify the uncertainty attached to the reported order of markers or intermarker distances. Current methods are computationally intensive or limited to assessing uncertainty only for order or distance, but not both simultaneously. We derive the asymptotic joint distribution of maximum composite likelihood estimators for intermarker distances. This approach allows us to construct hypothesis tests and confidence intervals for simultaneously assessing marker-order instability and distance uncertainty. We investigate the effects of marker density, population size, and founder distribution patterns on map confidence in multiparental populations through simulations. Using these data, we provide guidelines on sample sizes necessary to map markers at sub-centimorgan densities with high certainty. We apply these approaches to data from a bread wheat Multiparent Advanced Generation Inter-Cross (MAGIC) population genotyped using the Illumina 9K SNP chip to assess regions of uncertainty and validate them against the recently released pseudomolecule for the wheat chromosome 3B.

Whole-genome QTL analysis for MAGIC.

An efficient whole genome method of QTL analysis is presented for Multi-parent advanced generation integrated crosses. Multi-parent advanced generation inter-cross (MAGIC) populations have been developed for mice and several plant species and are useful for the genetic dissection of complex traits. The analysis of quantitative trait loci (QTL) in these populations presents some additional challenges compared with traditional mapping approaches. In particular, pedigree and marker information need to be integrated and founder genetic data needs to be incorporated into the analysis. Here, we present a method for QTL analysis that utilizes the probability of inheriting founder alleles across the whole genome simultaneously, either for intervals or markers. The probabilities can be found using three-point or Hidden Markov Model (HMM) methods. This whole-genome approach is evaluated in a simulation study and it is shown to be a powerful method of analysis. The HMM probabilities lead to low rates of false positives and low bias of estimated QTL effect sizes. An implementation of the approach is available as an R package. In addition, we illustrate the approach using a bread wheat MAGIC population.

Efficient Imputation of Missing Markers in Low-Coverage Genotyping-by-Sequencing Data from Multiparental Crosses

We consider genomic imputation for low-coverage genotyping-by-sequencing data with high levels of missing data. We compensate for this loss of information by utilizing family relationships in multiparental experimental crosses. This nearly quadruples the number of usable markers when applied to a large rice Multiparent Advanced Generation InterCross (MAGIC) study.

Genetic characterization of a multiparent recombinant inbred line of rice population

The field experiment was conducted at test base in Department of Agronomy, Yangtze University and Rice Research Institute of Guangdong, Academy of Agricultural Sciences, during 2010–11 to study the genetic characterization of a multiparent recombinant inbred line rice population. Multi-parent advanced generation inter-cross (MAGIC) population can be used to dissect complex agronomic traits by both linkage and association mapping. In this study, we report the development of the first rice MAGIC population with 12 founder lines. A subset of 206 lines of the MAGIC population was analyzed with SSR markers. The results indicated that plentiful recombination types were detected in the MAGIC population. Cluster analysis showed that the 12 founder and 206 lines were divided into 11 and 64 groups, respectively, when the genetic similarity coefficient was 0.685. Single-marker analysis detected 13 and 25 SSR markers associated with plant height and heading date, respectively. Step-wise regression further confirmed that 5 and 11 of the markers were associated with plant height and heading date, respectively. Seven and 11 of the identified markers had been reported previously to be linked to plant height and heading date QTLs, respectively. The results showed that MAGIC populations had abundant genetic diversity and could be efficiently used to study multi-alleles and association analysis.

Genomics-assisted breeding for drought tolerance in chickpea.

Terminal drought is one of the major constraints in chickpea (Cicer arietinum L.), causing more than 50% production losses. With the objective of accelerating genetic understanding and crop improvement through genomics-assisted breeding, a draft genome sequence has been assembled for the CDC Frontier variety. In this context, 544.73Mb of sequence data were assembled, capturing of 73.8% of the genome in scaffolds. In addition, large-scale genomic resources including several thousand simple sequence repeats and several million single nucleotide polymorphisms, high-density diversity array technology (15360 clones) and Illumina GoldenGate assay genotyping platforms, high-density genetic maps and transcriptome assemblies have been developed. In parallel, by using linkage mapping approach, one genomic region harbouring quantitative trait loci for several drought tolerance traits has been identified and successfully introgressed in three leading chickpea varieties (e.g. JG 11, Chefe, KAK 2) by using a marker-assisted backcrossing approach. A multilocation evaluation of these marker-assisted backcrossing lines provided several lines with 10-24% higher yield than the respective recurrent parents.Modern breeding approaches like marker-assisted recurrent selection and genomic selection are being deployed for enhancing drought tolerance in chickpea. Some novel mapping populations such as multiparent advanced generation intercross and nested association mapping populations are also being developed for trait mapping at higher resolution, as well as for enhancing the genetic base of chickpea. Such advances in genomics and genomics-assisted breeding will accelerate precision and efficiency in breeding for stress tolerance in chickpea.

Use of a large multiparent wheat mapping population in genomic dissection of coleoptile and seedling growth

Identification of alleles towards the selection for improved seedling vigour is a key objective of many wheat breeding programmes. A multiparent advanced generation intercross (MAGIC) population developed from four commercial spring wheat cultivars (cvv. Baxter, Chara, Westonia and Yitpi) and containing ca. 1000 F(2) -derived, F(6:7) RILs was assessed at two contrasting soil temperatures (12 and 20 °C) for shoot length and coleoptile characteristics length and thickness. Narrow-sense heritabilities were high for coleoptile and shoot length (h(2) = 0.68-0.70), indicating a strong genetic basis for the differences among progeny. Genotypic variation was large, and distributions of genotype means were approximately Gaussian with evidence for transgressive segregation for all traits. A number of significant QTL were identified for all early growth traits, and these were commonly repeatable across the different soil temperatures. The largest negative effects on coleoptile lengths were associated with Rht-B1b (-8.2%) and Rht-D1b (-10.9%) dwarfing genes varying in the population. Reduction in coleoptile length with either gene was particularly large at the warmer soil temperature. Other large QTL for coleoptile length were identified on chromosomes 1A, 2B, 4A, 5A and 6B, but these were relatively smaller than allelic effects at the Rht-B1 and Rht-D1 loci. A large coleoptile length effect allele (a = 5.3 mm at 12 °C) was identified on chromosome 1AS despite the relatively shorter coleoptile length of the donor Yitpi. Strong, positive genetic correlations for coleoptile and shoot lengths (r(g) = 0.85-0.90) support the co-location of QTL for these traits and suggest a common physiological basis for both. The multiparent population has enabled the identification of promising shoot and coleoptile QTL despite the potential for the confounding of large effect dwarfing gene alleles present in the commercial parents. The incidence of these alleles in commercial wheat breeding programmes should facilitate their ready implementation in selection of varieties with improved establishment and early growth.

Multi-parent advanced generation inter-cross (MAGIC) populations in rice: progress and potential for genetics research and breeding

BackgroundThis article describes the development of Multi-parent Advanced Generation Inter-Cross populations (MAGIC) in rice and discusses potential applications for mapping quantitative trait loci (QTLs) and for rice varietal development. We have developed 4 multi-parent populations: indica MAGIC (8 indica parents); MAGIC plus (8 indica parents with two additional rounds of 8-way F1 inter-crossing); japonica MAGIC (8 japonica parents); and Global MAGIC (16 parents – 8 indica and 8 japonica). The parents used in creating these populations are improved varieties with desirable traits for biotic and abiotic stress tolerance, yield, and grain quality. The purpose is to fine map QTLs for multiple traits and to directly and indirectly use the highly recombined lines in breeding programs. These MAGIC populations provide a useful germplasm resource with diverse allelic combinations to be exploited by the rice community.ResultsThe indica MAGIC population is the most advanced of the MAGIC populations developed thus far and comprises 1328 lines produced by single seed descent (SSD). At the S4 stage of SSD a subset (200 lines) of this population was genotyped using a genotyping-by-sequencing (GBS) approach and was phenotyped for multiple traits, including: blast and bacterial blight resistance, salinity and submergence tolerance, and grain quality. Genome-wide association mapping identified several known major genes and QTLs including Sub1 associated with submergence tolerance and Xa4 and xa5 associated with resistance to bacterial blight. Moreover, the genome-wide association study (GWAS) results also identified potentially novel loci associated with essential traits for rice improvement.ConclusionThe MAGIC populations serve a dual purpose: permanent mapping populations for precise QTL mapping and for direct and indirect use in variety development. Unlike a set of naturally diverse germplasm, this population is tailor-made for breeders with a combination of useful traits derived from multiple elite breeding lines. The MAGIC populations also present opportunities for studying the interactions of genome introgressions and chromosomal recombination.Electronic supplementary materialThe online version of this article (doi:10.1186/1939-8433-6-11) contains supplementary material, which is available to authorized users.

Standardizing resistance screening to Pseudomonas fuscovaginae and evaluation of rice germplasm at seedling and adult plant growth stages

Sheath lesions, grain sterility and grain discolouration of rice caused by Pseudomonas fuscovaginae can cause yield losses of up to 100 %. The most sustainable method of managing this disease is the use of host plant resistance. To identify sources of resistance an inoculation method that is practical, rapid and reliable is needed. We compare three different inoculation methods. Results showed that the pin-prick method is appropriate for identifying sources of resistance to P. fuscovaginae, while the spray method could be useful for mass screening of rice genotypes. The seed-soaking method was also evaluated and has showed potential in detection of early disease resistance. A total of 16 Multiparent Advanced Generation Inter-Crosses and 20 OryzaSNP set varieties from the International Rice Research Institute were evaluated using the pin-prick and seed-soaking methods. All growth stages were susceptible to the pathogen and the 107 cfu mL−1 inoculum concentration was optimal for discriminating between resistant and susceptible genotypes. For the pin-prick method, a single point assessment of disease severity at 14 days post-inoculation could be used instead of the AUDPC values to classify genotypes. An index of reduction in seedling height 10 days after seed soaking was established for the classification of the genotypes reaction to the disease. Resistant varieties identified using both the pin-prick and seed-soaking methods could be verified for use in disease resistance breeding programs. Of the 36 genotypes evaluated 22 were found to be resistant at the late booting or early panicle exsertion stage by pin-prick method, while 25 were resistant at the seed to germination stage. No correlation was found between the resistance classification of varieties between the two inoculation methods, indicating that there could be different mechanisms of resistance to P. fuscovaginae in rice.

Selecting subsets of genotyped experimental populations for phenotyping to maximize genetic diversity

Selective phenotyping is a way of capturing the benefits of large population sizes without the need to carry out large-scale phenotyping and hence is a cost-effective means of capturing information about gene-trait relationships within a population. The diversity within the sample gives an indication of the efficiency of this information capture; less diversity implies greater redundancy of the genetic information. Here, we propose a method to maximize genetic diversity within the selected samples. Our method is applicable to general experimental designs and robust to common problems such as missing data and dominant markers. In particular, we discuss its application to multi-parent advanced generation intercross (MAGIC) populations, where, although thousands of lines may be genotyped as a large population resource, only hundreds may need to be phenotyped for individual studies. Through simulation, we compare our method to simple random sampling and the minimum moment aberration method. While the gain in power over simple random sampling for all tested methods is not large, our method results in a much more diverse sample of genotypes. This diversity can be applied to improve fine mapping resolution once a QTL region has been detected. Further, when applied to two wheat datasets from doubled haploid and MAGIC progeny, our method detects known QTL for small sample sizes where other methods fail.

A multiparent advanced generation inter‐cross population for genetic analysis in wheat

We present the first results from a novel multiparent advanced generation inter-cross (MAGIC) population derived from four elite wheat cultivars. The large size of this MAGIC population (1579 progeny), its diverse genetic composition and high levels of recombination all contribute to its value as a genetic resource. Applications of this resource include interrogation of the wheat genome and the analysis of gene-trait association in agronomically important wheat phenotypes. Here, we report the utilization of a MAGIC population for the first time for linkage map construction. We have constructed a linkage map with 1162 DArT, single nucleotide polymorphism and simple sequence repeat markers distributed across all 21 chromosomes. We benchmark this map against a high-density DArT consensus map created by integrating more than 100 biparental populations. The linkage map forms the basis for further exploration of the genetic architecture within the population, including characterization of linkage disequilibrium, founder contribution and inclusion of an alien introgression into the genetic map. Finally, we demonstrate the application of the resource for quantitative trait loci mapping using the complex traits plant height and hectolitre weight as a proof of principle.

A Multiparent Advanced Generation Inter-Cross to Fine-Map Quantitative Traits in Arabidopsis thaliana

Identifying natural allelic variation that underlies quantitative trait variation remains a fundamental problem in genetics. Most studies have employed either simple synthetic populations with restricted allelic variation or performed association mapping on a sample of naturally occurring haplotypes. Both of these approaches have some limitations, therefore alternative resources for the genetic dissection of complex traits continue to be sought. Here we describe one such alternative, the Multiparent Advanced Generation Inter-Cross (MAGIC). This approach is expected to improve the precision with which QTL can be mapped, improving the outlook for QTL cloning. Here, we present the first panel of MAGIC lines developed: a set of 527 recombinant inbred lines (RILs) descended from a heterogeneous stock of 19 intermated accessions of the plant Arabidopsis thaliana. These lines and the 19 founders were genotyped with 1,260 single nucleotide polymorphisms and phenotyped for development-related traits. Analytical methods were developed to fine-map quantitative trait loci (QTL) in the MAGIC lines by reconstructing the genome of each line as a mosaic of the founders. We show by simulation that QTL explaining 10% of the phenotypic variance will be detected in most situations with an average mapping error of about 300 kb, and that if the number of lines were doubled the mapping error would be under 200 kb. We also show how the power to detect a QTL and the mapping accuracy vary, depending on QTL location. We demonstrate the utility of this new mapping population by mapping several known QTL with high precision and by finding novel QTL for germination data and bolting time. Our results provide strong support for similar ongoing efforts to produce MAGIC lines in other organisms.

Novel Genomic Tools and Modern Genetic and Breeding Approaches for Crop Improvement

In recent past, genomic tools especially molecular markers have been extensively used for understanding genome dynamics as well for applied aspects in crop breeding. Several new genomics technologies such as next generation sequencing (NGS), high-throughput marker genotyping, -omics technologies have emerged as powerful tools for understanding genome variation in crop species at DNA, RNA as well as protein level. These technologies promise to provide an insight into the way gene(s) are expressed and regulated in cell and to unveil metabolic pathways involved in trait(s) of interest for breeders not only in model-/major- but even for under-resourced crop species which were once considered orphan crops. In parallel, genetic variation for a species present not only in cultivated genepool but even in landraces and wild species can be harnessed by using new genetic approaches such as advanced-backcross QTL (AB-QTL) analysis, introgression libraries (ILs), multi-parent advanced generation intercross (MAGIC) population and association genetics. The gene(s) or genomic regions, responsible for trait(s) of interest, identified either through conventional linkage mapping or above mentioned approaches can be introgressed or pyramided to develop superior genotypes through molecular breeding approaches such as marker-assisted back crossing (MABC), marker assisted recurrent selection (MARS) and genome wide selection (GWS). This article provides an overview on some recent genomic tools and novel genetic and breeding approaches as mentioned above with a final aim of crop improvement.

From mutations to MAGIC: resources for gene discovery, validation and delivery in crop plants

The dissection of gene-trait associations and its translation into practice through plant breeding is a central aspect of modern plant biology. The identification of genes underlying simply inherited traits has been very successful. However, the identification of gene-trait associations for complex (multi-genic) traits in crop plants with large, often polyploid genomes has been limited by the absence of appropriate genetic resources that allow quantitative trait loci (QTL) and causal genes to be identified and localised. There has also been a tendency for genetic resources to be developed in germplasm not directly relevant to the breeding community limiting effective implementation. In this review, we discuss approaches to mapping genes and the development of Multi-parent Advanced Generation Inter-cross (MAGIC) populations derived from breeder-relevant germplasm as a platform for a new generation of gene-trait analysis in crop species.

2104 Expression of serotonin N-acetyltransferase mRNA in the rat retina

Genetics and genomics will have the potential to enhance sustainable peanut production. The major contribution of these technologies for peanut will likely be improved disease resistance, oil quality, and enhanced productivity. Those attributes may be achieved more effectively through genomic biotechnology to utilize the genetic resources preserved in germplasm collections for maximizing the genetic potential in breeding. Superior cultivars will maximize desirable genetic traits and provide growers with cultivars that are locally adapted and highly productive. Genomics involves the study of the complete genetic makeup, through mapping, sequencing, and functional studies to identify genes that regulate, control or modify trait expression. With the development in sequencing technologies, next-generation sequencing (NGS) has opened the gateway to sequence the genomes in a fast, reliable, and cost-effective manner. The international peanut genome sequencing project was discussed in 2010 and launched in 2012 after much deliberation and strategic planning. The exciting news is that the Peanut Genome Consortium, representing a coalition of international scientists and stakeholders engaged in the International Peanut Genome Initiative, announced the public release of the first chromosomal-scale draft sequences of two peanut species (Arachis duranensis and Arachis ipaënsis) on April 2, 2014 (http://www.peanutbioscience.com/images/4-2-14_News_Release_Notice.pdf). In this chapter, we update the information on the molecular markers, genetic linkage maps, and quantitative trait loci (QTLs) of traits mapped to date. Information of the preview of genome sequences, which would be available in peanut populations for high resolution genetic and trait mapping such as multiparent advanced generation intercross and nested association mapping, is also discussed. Furthermore, an innovative method to identify a causal mutant single nucleotide polymorphism or major allele for QTLs through NGS techniques such as MutMap and QTLseq is also discussed in the section on emerging trait mapping strategies. A special emphasis on Africa and China is given in different sections to update on the activities in peanut research and emerging issues. The goal of this chapter is to update the new progress in the development of genetic and genomic resources and technologies in peanuts.

2103 Light-induced FOS and junb proteins in the suprachiasmatic nucleus of CS mice during rhythm splitting

Genetics and genomics will have the potential to enhance sustainable peanut production. The major contribution of these technologies for peanut will likely be improved disease resistance, oil quality, and enhanced productivity. Those attributes may be achieved more effectively through genomic biotechnology to utilize the genetic resources preserved in germplasm collections for maximizing the genetic potential in breeding. Superior cultivars will maximize desirable genetic traits and provide growers with cultivars that are locally adapted and highly productive. Genomics involves the study of the complete genetic makeup, through mapping, sequencing, and functional studies to identify genes that regulate, control or modify trait expression. With the development in sequencing technologies, next-generation sequencing (NGS) has opened the gateway to sequence the genomes in a fast, reliable, and cost-effective manner. The international peanut genome sequencing project was discussed in 2010 and launched in 2012 after much deliberation and strategic planning. The exciting news is that the Peanut Genome Consortium, representing a coalition of international scientists and stakeholders engaged in the International Peanut Genome Initiative, announced the public release of the first chromosomal-scale draft sequences of two peanut species (Arachis duranensis and Arachis ipaënsis) on April 2, 2014 (http://www.peanutbioscience.com/images/4-2-14_News_Release_Notice.pdf). In this chapter, we update the information on the molecular markers, genetic linkage maps, and quantitative trait loci (QTLs) of traits mapped to date. Information of the preview of genome sequences, which would be available in peanut populations for high resolution genetic and trait mapping such as multiparent advanced generation intercross and nested association mapping, is also discussed. Furthermore, an innovative method to identify a causal mutant single nucleotide polymorphism or major allele for QTLs through NGS techniques such as MutMap and QTLseq is also discussed in the section on emerging trait mapping strategies. A special emphasis on Africa and China is given in different sections to update on the activities in peanut research and emerging issues. The goal of this chapter is to update the new progress in the development of genetic and genomic resources and technologies in peanuts.

New sources and stability of resistance to aphids in cowpea germplasm across locations in Uganda

The cowpea aphid (Aphis craccivora Koch) is an economically important pest, whose feeding effects cause stunting, delayed flower initiation and yield reduction in cowpea (Vigna unguiculata L. Walp). Host plant resistance offers an alternative for controlling aphids; while simultaneously reducing reliance on chemical pesticides. The objective of this study was to evaluate a multi-parent advanced generation inter-cross (MAGIC) population of cowpea against aphids, across cowpea growing regions in Uganda. The study was arranged in alpha lattice design, with two replicates in three locations over two seasons (2018B and 2019A). Results revealed significant effects (P<0.001) for the main treatment effects, genotype x location and location x season interaction for both infestation and damage. The genotype x season interaction was significant (P<0.01) for both aphid infestation and damage; while the three-way interaction was only significant (P<0.001) for aphid infestation, but not for damage. The study identified five new resistant and stable genotypes from the MAGIC panel, including MAGIC131, MAGIC-132, MAGIC149, MAGIC170 and MAGIC280; and one resistant parent, SUVITA-2. The study further revealed MAGIC-125, MAGIC-171, MAGIC153, MAGIC-333, MAGIC177, MAGIC-292, MAGIC282, MAGIC249, MAGIC162, SEC 4W * SEC 5T, NAROCOWPEA 4, MAGIC-204, MAGIC-039, MAGIC060, MAGIC-097, NAROCOWPEA 3, MAGIC-233, MAGIC090 and MU 9 to be moderately resistant and high yielding genotypes. The above genotypes are recommended for use in the cowpea breeding programme, to develop improved resistant lines against aphids in Uganda.