Genome-wide association mapping of agronomic traits in sugar beet

Recent results indicate that association mapping in populations from applied plant breeding is a powerful tool to detect QTL which are of direct relevance for breeding. The focus of this study was to unravel the genetic architecture of six agronomic traits in sugar beet. To this end, we employed an association mapping approach, based on a very large population of 924 elite sugar beet lines from applied plant breeding, fingerprinted with 677 single nucleotide polymorphism (SNP) markers covering the entire genome. We show that in this population linkage disequilibrium decays within a short genetic distance and is sufficient for the detection of QTL with a large effect size. To increase the QTL detection power and the mapping resolution a much higher number of SNPs is required. We found that for QTL detection, the mixed model including only the kinship matrix performed best, even in the presence of a considerable population structure. In genome-wide scans, main effect QTL and epistatic QTL were detected for all six traits. Our full two-dimensional epistasis scan revealed that for complex traits there appear to be epistatic master regulators, loci which are involved in a large number of epistatic interactions throughout the genome.     

Evaluation of multi-locus models for genome-wide association studies: a case study in sugar beet

Association mapping has become a widely applied genomic approach to dissect the genetic architecture of complex traits. A major issue for association mapping is the need to control for the confounding effects of population structure, which is commonly done by mixed models incorporating kinship information. In this case study, we employed experimental data from a large sugar beet population to evaluate multi-locus models for association mapping. As in linkage mapping, markers are selected as cofactors to control for population structure and genetic background variation. We compared different biometric models with regard to important quantitative trait locus (QTL) mapping parameters like the false-positive rate, the QTL detection power and the predictive power for the proportion of explained genotypic variance. Employing different approaches we show that the multi-locus model, that is, incorporating cofactors, outperforms the other models, including the mixed model used as a reference model. Thus, multi-locus models are an attractive alternative for association mapping to efficiently detect QTL for knowledge-based breeding.     

Multiple-line cross quantitative trait locus mapping in sugar beet (Beta vulgaris L.)

Linkage mapping based on multiple-line crosses is a promising strategy for mapping quantitative trait loci (QTL) underlying important agronomic traits. The main goal of this survey was to study the advantages of QTL mapping across versus within biparental populations using experimental data from three connected sugar beet (Beta vulgaris L.) populations evaluated for beet yield and potassium and sodium content. For the combined analysis across populations, we used two approaches for cofactor selection. In Model A, we assumed identical cofactors for every segregating population. In contrast, in Model B we selected cofactors specific for every segregating population. Model A performed better than Model B with respect to the number of QTL detected and the total proportion of phenotypic variance explained. The QTL analyses across populations revealed a substantially higher number of QTL compared to the analyses of single biparental populations. This clearly emphasizes the potential to increase QTL detection power with a joint analysis across biparental populations.     

Localization of QTLs for tolerance to Cercospora beticola on sugar beet linkage groups

We present a new linkage map for sugar beet (Beta vulgaris) which has been developed using a population segregating for genetic factors that confer tolerance to the leaf spot fungus (Cercospora beticola), the causal factor of leaf spot disease in sugar beet). In the F₂ population studied, a subset of 36 RFLP probes, mapping on eight out of the nine linkage groups of sugar beet, provided the anchor markers to assign chromosomes. A total of 224 markers, including RFLPs, AFLPs, SCARs and microsatellites, were mapped. Estimates of leaf damage in F₂ and test-cross families were repeated at different stages of plant development. Each set of data was analysed as such. An average estimate was also considered. QTLs with highly significant LOD scores revealed both by the F₂ and test-cross analyses were localized on linkage groups 2, 6 and 9. Linkage groups 4 and 5 gave a clear indication of the presence of a QTL only when F₂ data were considered. One highly significant QTL with a LOD of 16.0 was revealed only by the data obtained under conditions of artificial inoculation. This QTL maps at position 90 on chromosome 3. Received: 3 February 1999 / Accepted: 20 February 1999     

Genome-based prediction of test cross performance in two subsequent breeding cycles

Genome-based prediction of genetic values is expected to overcome shortcomings that limit the application of QTL mapping and marker-assisted selection in plant breeding. Our goal was to study the genome-based prediction of test cross performance with genetic effects that were estimated using genotypes from the preceding breeding cycle. In particular, our objectives were to employ a ridge regression approach that approximates best linear unbiased prediction of genetic effects, compare cross validation with validation using genetic material of the subsequent breeding cycle, and investigate the prospects of genome-based prediction in sugar beet breeding. We focused on the traits sugar content and standard molasses loss (ML) and used a set of 310 sugar beet lines to estimate genetic effects at 384 SNP markers. In cross validation, correlations >0.8 between observed and predicted test cross performance were observed for both traits. However, in validation with 56 lines from the next breeding cycle, a correlation of 0.8 could only be observed for sugar content, for standard ML the correlation reduced to 0.4. We found that ridge regression based on preliminary estimates of the heritability provided a very good approximation of best linear unbiased prediction and was not accompanied with a loss in prediction accuracy. We conclude that prediction accuracy assessed with cross validation within one cycle of a breeding program can not be used as an indicator for the accuracy of predicting lines of the next cycle. Prediction of lines of the next cycle seems promising for traits with high heritabilities.     

Association mapping in an elite maize breeding population

Association mapping (AM) is a powerful approach to dissect the genetic architecture of quantitative traits. The main goal of our study was to empirically compare several statistical methods of AM using data of an elite maize breeding program with respect to QTL detection power and possibility to correct for population stratification. These models were based on the inclusion of cofactors (Model A), cofactors and population effect (Model B), and SNP effects nested within populations (Model C). A total of 930 testcross progenies of an elite maize breeding population were field-evaluated for grain yield and grain moisture in multi-location trials and fingerprinted with 425 SNP markers. For grain yield, population stratification was effectively controlled by Model A. For grain moisture with a high ratio of variance among versus within populations, Model B should be applied in order to avoid potential false positives. Model C revealed large differences among allele substitution effects for trait-associated SNPs across multiple plant breeding populations. This heterogeneous SNP allele substitution effects have a severe impact for genomic selection studies, where SNP effects are often assumed to be independent of the genetic background.     

QTL modulating ear size and erectness in pigs

Ear size and erectness are important conformation measurements in pigs. An F(2) population established by crossing European Large White (small, erect ears) with Chinese Meishan (large, flop ears) was used to study the genetic influence of the two ear traits for the first time. A linkage map incorporating 152 markers on 18 autosomal chromosomes was utilised in a genome scan for QTL. Significant QTL were found on SSC1, 5, 7, 9 and 12 for the two traits. The QTL on SSC5 and SSC7 had major effects and were significant at the genome-wide level (P < 0.01). The QTL on SSC1 for ear erectness also had a major effect and was genome-wide significant (P < 0.01). The 95% confidence interval (CI) of the ear size QTL on SSC5 spanned only 4 cM. The QTL on SSC7 for the two ear traits each had a CI of <20 cM, and their positions overlapped with those of the major QTL affecting subcutaneous fat depths on the same chromosome. This study provides insights on the complex genetic influences underlying pig ear traits and will facilitate positional candidate gene analysis to identify causative DNA variants.     

QTL for body weight,morphometric traits and stress response in European sea bass Dicentrarchus labrax

Natural mating and mass spawning in the European sea bass (Dicentrarchus labrax L., Moronidae, Teleostei) complicate genetic studies and the implementation of selective breeding schemes. We utilized a two‐step experimental design for detecting QTL in mass‐spawning species: 2122 offspring from natural mating between 57 parents (22 males, 34 females and one missing) phenotyped for body weight, eight morphometric traits and cortisol levels, had been previously assigned to parents based on genotypes of 31 DNA microsatellite markers. Five large full‐sib families (five sires and two dams) were selected from the offspring (570 animals), which were genotyped with 67 additional markers. A new genetic map was compiled, specific to our population, but based on the previously published map. QTL mapping was performed with two methods: half‐sib regression analysis (paternal and maternal) and variance component analysis accounting for all family relationships. Two significant QTL were found for body weight on linkage group 4 and 6, six significant QTL for morphometric traits on linkage groups 1B, 4, 6, 7, 15 and 23 and three suggestive QTL for stress response on linkage groups 3, 14 and 23. The QTL explained between 8% and 38% of phenotypic variance. The results are the first step towards identifying genes involved in economically important traits like body weight and stress response in European sea bass.     

Comparative genetics in sugarcane enables structured map enhancement and validation of marker-trait associations

As sugarcane is a complex polyaneuploid with many chromosomes, large numbers of markers are required to generate genetic maps with reasonable levels of genome coverage. Comparative mapping was investigated as an approach for both quantitative trait loci (QTL) validation and genetic map enhancement in sugarcane. More than 1000 SSR and AFLP markers were scored in a bi-parental Australian sugarcane population (Q3) that was segregating widely for sugar content-related traits. Two maps were constructed, one for each parent. The Q117 (female) and MQ77-340 (male) maps each contained almost 400 markers distributed onto approximately 100 linkage groups (LGs), of which nearly half could be assigned to homology groups (HGs) on the basis of SSRs. Then, using common SSR and AFLP markers, the two Q3 parental maps were aligned with the maps of the French cultivar, R570, and of the Australian cultivar, Q165^A (A denotes variety covered by Australian plant breeding rights). As a result of comparative mapping, all ten HGs in the Q117 map, and all eleven HGs in the MQ77-340 map could be re-assigned to seven of the expected eight sugarcane HGs, revealing that one sugarcane HG was not covered at all in either Q3 parental map, and that other HGs were poorly represented. QTL analysis in the Q3 population identified approximately 75 marker-trait associations (MTAs) from approximately 18 chromosomal regions or putative QTL in each map for three sugar content-related traits. QTL location appeared to be consistent between the 4 maps; two of the eight HGs were observed to contain MTAs for brix in two or three maps, strongly suggesting the location of sugar content-related trait loci in these HGs. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Association mapping in multiple segregating populations of sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.)

Association mapping in multiple segregating populations (AMMSP) combines high power to detect QTL in genome-wide approaches of linkage mapping with high mapping resolution of association mapping. The main objectives of this study were to (1) examine the applicability of AMMSP in a plant breeding context based on segregating populations of various size of sugar beet (Beta vulgaris L.), (2) compare different biometric approaches for AMMSP, and (3) detect markers with significant main effect across locations for nine traits in sugar beet. We used 768 F _n (n = 2, 3, 4) sugar beet genotypes which were randomly derived from 19 crosses among diploid elite sugar beet clones. For all nine traits, the genotypic and genotype × location interaction variances were highly significant (P < 0.01). Using a one-step AMMSP approach, the total number of significant (P < 0.05) marker-phenotype associations was 44. The identification of genome regions associated with the traits under consideration indicated that not only segregating populations derived from crosses of parental genotypes in a systematic manner could be used for AMMSP but also populations routinely derived in plant breeding programs from multiple, related crosses. Furthermore, our results suggest that data sets, whose size does not permit analysis by the one-step AMMSP approach, might be analyzed using the two-step approach based on adjusted entry means for each location without losing too much power for detection of marker-phenotype associations.     

Genome-wide association mapping of fruit-quality traits using genotyping-by-sequencing approach in citrus landraces,modern cultivars,and breeding lines in Japan

Association mapping is an attractive method to identify QTLs in perennial horticultural crops such as citrus, as it does not need a designed cross between parental genotypes and can save time and labor to construct a segregating population. It usually requires more genetic markers than linkage-based QTL mapping owing to a lower degree of linkage disequilibrium (LD). However, recent advances in next-generation sequencing offer high-throughput, cost-effective methods, including genotyping-by-sequencing (GBS), for genotyping massive amounts of single nucleotide polymorphisms (SNPs). In this study, we performed a genome-wide association study (GWAS) of fruit-quality traits in citrus using SNPs obtained by GBS. We evaluated 110 citrus accessions, including landraces, modern cultivars, and breeding lines, for eight fruit-quality traits (fruit weight, fruit skin color, fruit surface texture, peelability, pulp firmness, segment firmness, sugar content, and acid content) during 2005 to 2012 (except 2007). GBS found 2309 SNPs, which we anchored to the clementine reference genome. We evaluated LD in the 110 accessions and confirmed that GBS gave enough SNPs to conduct GWAS. We identified seven QTLs, including four novel ones, comprising four significant QTLs for fruit weight and one QTL each for fruit skin color, pulp firmness, and segment firmness. These QTLs offer promise for use in citrus crossbreeding.     

Quantitative trait locus responsible for resistance to Aphanomyces root rot (black root) caused by Aphanomyces cochlioides Drechs. in sugar beet

Aphanomyces root rot, caused by Aphanomyces cochlioides Drechs., is one of the most serious diseases of sugar beet (Beta vulgaris L.). Identification and characterization of resistance genes is a major task in sugar beet breeding. To ensure the effectiveness of marker-assisted screening for Aphanomyces root rot resistance, genetic analysis of mature plants’ phenotypic and molecular markers’ segregation was carried out. At a highly infested field site, some 187 F₂ and 66 F₃ individuals, derived from a cross between lines ‘NK-310mm-O’ (highly resistant) and ‘NK-184mm-O’ (susceptible), were tested, over two seasons, for their level of resistance to Aphanomyces root rot. This resistance was classified into six categories according to the extent and intensity of whole plant symptoms. Simultaneously, two selected RAPD and 159 ‘NK-310mm-O’-coupled AFLP were used in the construction of a linkage map of 695.7 cM. Each of nine resultant linkage groups was successfully anchored to one of nine sugar beet chromosomes by incorporating 16 STS markers. Combining data for phenotype and molecular marker segregation, a single QTL was identified on chromosome III. This QTL explained 20% of the variance in F₂ population (in the year 2002) and 65% in F₃ lines (2003), indicating that this QTL plays a major role in the Aphanomyces root rot resistance. This is the first report of the genetic mapping of resistance to Aphanomyces-caused diseases in sugar beet.     

Genome-wide association mapping of yield and yield components of spring wheat under contrasting moisture regimes

Key message

A stable QTL that may be used in marker-assisted selection in wheat breeding programs was detected for yield, yield components and drought tolerance-related traits in spring wheat association mapping panel.

Abstract

Genome-wide association mapping has become a widespread method of quantitative trait locus (QTL) identification for many crop plants including wheat (Triticum aestivum L.). Its benefit over traditional bi-parental mapping approaches depends on the extent of linkage disequilibrium in the mapping population. The objectives of this study were to determine linkage disequilibrium decay rate and population structure in a spring wheat association mapping panel (n = 285–294) and to identify markers associated with yield and yield components, morphological, phenological, and drought tolerance-related traits. The study was conducted under fully irrigated and rain-fed conditions at Greeley, CO, USA and Melkassa, Ethiopia in 2010 and 2011 (five total environments). Genotypic data were generated using diversity array technology markers. Linkage disequilibrium decay rate extended over a longer genetic distance for the D genome (6.8 cM) than for the A and B genomes (1.7 and 2.0 cM, respectively). Seven subpopulations were identified with population structure analysis. A stable QTL was detected for grain yield on chromosome 2DS both under irrigated and rain-fed conditions. A multi-trait region significant for yield and yield components was found on chromosome 5B. Grain yield QTL on chromosome 1BS co-localized with harvest index QTL. Vegetation indices shared QTL with harvest index on chromosome 1AL and 5A. After validation in relevant genetic backgrounds and environments, QTL detected in this study for yield, yield components and drought tolerance-related traits may be used in marker-assisted selection in wheat breeding programs.     

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture,Training Population Composition,Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite,Tropical Rice Breeding Lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute''s (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.     

Mapping of yield influencing QTL in <Emphasis Type="Italic">Brassica juncea</Emphasis>: implications for breeding of a major oilseed crop of dryland areas

Quantitative trait loci (QTL) analysis of yield influencing traits was carried out in Brassica juncea (AABB) using a doubled haploid (DH) mapping population of 123 lines derived from a cross between Varuna (a line representing the Indian gene pool) and Heera (representing the east European gene pool) to identify potentially useful alleles from both the parents. The existing AFLP based map of B. juncea was further saturated with RFLP and SSR markers which led to the identification of the linkage groups belonging to the A (B. rapa) and B (B. nigra) genome components of B. juncea. For QTL dissection, the DH lines were evaluated at three different environments and phenotyped for 12 quantitative traits. A total of 65 QTL spread over 13 linkage groups (LG) were identified from the three environments. QTL analysis showed that the A genome has contributed more than the B genome to productivity (68% of the total QTL detected) suggesting a more prominent role of the A genome towards domestication of this crop. The east European line, Heera, carried favorable alleles for 42% of the detected QTL and the remaining 58% were in the Indian gene pool line, Varuna. We observed clustering of major QTL in a few linkage groups, particularly in J7 and J10 of the A genome, with QTL of different traits having agronomically antagonistic allelic effects co-mapping to the same genetic interval. QTL analysis also identified some well-separated QTL which could be readily transferred between the two pools. Based on the QTL analysis, we propose that improvement in yield could be achieved more readily by heterosis breeding rather than by pure line breeding. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Genome-wide association and genomic selection in animal breeding

Results from genome-wide association studies in livestock, and humans, has lead to the conclusion that the effect of individual quantitative trait loci (QTL) on complex traits, such as yield, are likely to be small; therefore, a large number of QTL are necessary to explain genetic variation in these traits. Given this genetic architecture, gains from marker-assisted selection (MAS) programs using only a small number of DNA markers to trace a limited number of QTL is likely to be small. This has lead to the development of alternative technology for using the available dense single nucleotide polymorphism (SNP) information, called genomic selection. Genomic selection uses a genome-wide panel of dense markers so that all QTL are likely to be in linkage disequilibrium with at least one SNP. The genomic breeding values are predicted to be the sum of the effect of these SNPs across the entire genome. In dairy cattle breeding, the accuracy of genomic estimated breeding values (GEBV) that can be achieved and the fact that these are available early in life have lead to rapid adoption of the technology. Here, we discuss the design of experiments necessary to achieve accurate prediction of GEBV in future generations in terms of the number of markers necessary and the size of the reference population where marker effects are estimated. We also present a simple method for implementing genomic selection using a genomic relationship matrix. Future challenges discussed include using whole genome sequence data to improve the accuracy of genomic selection and management of inbreeding through genomic relationships.     

Mapping QTL for grain yield and other agronomic traits in post-rainy sorghum [Sorghum bicolor (L.) Moench]

Sorghum, a cereal of economic importance ensures food and fodder security for millions of rural families in the semi-arid tropics. The objective of the present study was to identify and validate quantitative trait loci (QTL) for grain yield and other agronomic traits using replicated phenotypic data sets from three post-rainy dry sorghum crop seasons involving a mapping population with 245 F₉ recombinant inbred lines derived from a cross of M35-1 × B35. A genetic linkage map was constructed with 237 markers consisting of 174 genomic, 60 genic and 3 morphological markers. The QTL analysis for 11 traits following composite interval mapping identified 91 QTL with 5–12 QTL for each trait. QTL detected in the population individually explained phenotypic variation between 2.5 and 30.3 % for a given trait and six major genomic regions with QTL effect on multiple traits were identified. Stable QTL across seasons were identified. Of the 60 genic markers mapped, 21 were found at QTL peak or tightly linked with QTL. A gene-based marker XnhsbSFCILP67 (Sb03g028240) on SBI-03, encoding indole-3-acetic acid-amido synthetase GH3.5, was found to be involved in QTL for seven traits. The QTL-linked markers identified for 11 agronomic traits may assist in fine mapping, map-based gene isolation and also for improving post-rainy sorghum through marker-assisted breeding.     

QTL Mapping for Frond Length and Width in Laminaria japonica Aresch (Laminarales, Phaeophyta) Using AFLP and SSR Markers

In Laminaria japonica Aresch breeding practice, two quantitative traits, frond length (FL) and frond width (FW), are the most important phenotypic selection index. In order to increase the breeding efficiency by integrating phenotypic selection and marker-assisted selection, the first set of QTL controlling the two traits were determined in F₂ family using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers. Two prominent L. japonicas inbred lines, one with “broad and thin blade” characteristics and another with “long and narrow blade” characteristics, were applied in the hybridization to yield the F₂ mapping population with 92 individuals. A total of 287 AFLP markers and 11 SSR markers were used to construct a L. japonica genetic map. The yielded map was consisted of 28 linkage groups (LG) named LG1 to LG28, spanning 1,811.1 cM with an average interval of 6.7 cM and covering the 82.8% of the estimated genome 2,186.7 cM. While three genome-wide significant QTL were detected on LG1 (two QTL) and LG4 for “FL,” explaining in total 42.36% of the phenotypic variance, two QTL were identified on LG3 and LG5 for the trait “FW,” accounting for the total of 36.39% of the phenotypic variance. The gene action of these QTL was additive and partially dominant. The yielded linkage map and the detected QTL can provide a tool for further genetic analysis of two traits and be potential for maker-assisted selection in L. japonica breeding.