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.     

Quantitative trait loci for yield components in oil palm (Elaeis guineensis Jacq.)

The development of an oil palm RFLP marker map has enabled marker-based QTL mapping studies to be undertaken. Information from 153 RFLP markers was used in combination with phenotypic data from an F₂ population to estimate the position and effects of quantitative trait loci (QTLs) for traits including yield of fruit and its components and measures of vegetative growth. The mapping population consisted of 84 palms segregating for the major gene influencing shell thickness. Marker data were analysed to produce a linkage map consisting of 22 linkage groups. The QTL mapping analysis was carried out by interval mapping and single-marker analysis for the unlinked markers; significance thresholds were generated by permutation. Using both single-marker and interval-mapping analysis significant marker associated QTL effects were found for 11 of the 13 traits analysed. The results of interval-mapping analysis of fruit weight, petiole cross section and rachis length, and ratios of shell:fruit, mesocarp:fruit and kernel:fruit indicated significant (P<0.05) QTLs at the genome-wide threshold. The putative QTLs were associated with between 8.2% and 44.0% of the phenotypic variation, with an average of 27% for the single-marker analysis and 19% for the interval-mapping analysis. The higher percentage of phenotypic variation explained in the single-marker analysis, when compared to the interval-mapping analysis, is likely to be due to the lower stringency associated with the single-marker analysis. Large dominance deviations were associated with a sizeable proportion of the putative QTLs. The ultimate objective of mapping QTLs in commercial populations is to utilise novel breeding strategies such as marker-assisted selection (MAS). The potential impact of MAS in oil palm breeding programmes is discussed. Received: 26 June 2000 / Accepted: 24 October 2000     

银川番茄斑萎病毒的分子鉴定

为明确银川番茄(Lycopersicon esculentum)是否遭受了番茄斑萎病毒(TSWV)的危害, 采用国家标准TSWV RT- PCR检测技术对银川番茄上采集的14份疑似感染TSWV病叶样本进行分子鉴定, 对克隆得到的核衣壳蛋白基因N (Nucleocapsid)序列进行多序列比对和系统进化树分析, 随后对PCR阳性样本进行蛋白检测。结果表明, 14份病叶样本中有8份扩增出长度为394 bp的TSWV N基因序列, 且8条序列完全一致; 获得的银川番茄TSWV分离物与云南番茄、中国莴苣(Lactuca sativa)、中国鸢尾(Iris tectorum)和重庆辣椒(Capsicum annuum) TSWV分离物相对近缘, 与山东、黑龙江和北京等地及国外TSWV分离物相对远缘; 利用TSWV的抗体通过Western blot对8个PCR阳性样本进一步检测, 结果也证实8个阳性样本中存在TSWV感染。该研究首次通过分子鉴定及蛋白检测证明银川番茄上存在TSWV感染, 需要加快抗TSWV番茄品种的选育工作。     

生物信息学分析方法I: 全基因组关联分析概述

全基因组关联分析(GWAS)是动植物复杂性状相关基因定位的常用手段。高通量基因分型技术的应用极大地推动了GWAS的发展。在植物中, 利用GWAS不仅能够以较高的分辨率在全基因组水平鉴定出各种自然群体特定性状相关的基因或区间, 而且可揭示表型变异的遗传架构全景图。目前, 人们利用GWAS分析方法已在拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、小麦(Triticum aestivum)、玉米(Zea mays)和大豆(Glycine max)等模式植物和重要农作物品系中发掘出与各种性状显著相关的数量性状座位(QTL)及其候选基因位点, 阐明了这些性状的遗传基础, 并为揭示这些性状背后的分子机理提供候选基因, 也为作物高产优质品种的选育提供了理论依据。该文对GWAS的方法、影响因素及数据分析流程进行了详细描述, 以期为相关研究提供参考。     

Genome-Wide Prediction Methods in Highly Diverse and Heterozygous Species: Proof-of-Concept through Simulation in Grapevine

Nowadays, genome-wide association studies (GWAS) and genomic selection (GS) methods which use genome-wide marker data for phenotype prediction are of much potential interest in plant breeding. However, to our knowledge, no studies have been performed yet on the predictive ability of these methods for structured traits when using training populations with high levels of genetic diversity. Such an example of a highly heterozygous, perennial species is grapevine. The present study compares the accuracy of models based on GWAS or GS alone, or in combination, for predicting simple or complex traits, linked or not with population structure. In order to explore the relevance of these methods in this context, we performed simulations using approx 90,000 SNPs on a population of 3,000 individuals structured into three groups and corresponding to published diversity grapevine data. To estimate the parameters of the prediction models, we defined four training populations of 1,000 individuals, corresponding to these three groups and a core collection. Finally, to estimate the accuracy of the models, we also simulated four breeding populations of 200 individuals. Although prediction accuracy was low when breeding populations were too distant from the training populations, high accuracy levels were obtained using the sole core-collection as training population. The highest prediction accuracy was obtained (up to 0.9) using the combined GWAS-GS model. We thus recommend using the combined prediction model and a core-collection as training population for grapevine breeding or for other important economic crops with the same characteristics.     

Oil palm (Elaeis guineensis Jacq.) linkage map,and quantitative trait locus analysis for sex ratio and related traits

Sex ratio and shell-thickness type are among the main components determining yield in oil palm. An integrated linkage map of oil palm was constructed based on 208 offspring derived from a cross between two tenera palms differing in inherited sex ratio. The map consisted of 210 genomic simple sequence repeats (SSRs), 28 expressed sequence tag SSRs, 185 amplified fragment length polymorphism markers, and the Sh locus, which controls shell-thickness phenotype, distributed across 16 linkage groups covering 1,931 cM, with an average marker distance of 4.6 cM. Quantitative trait locus (QTL) analysis identified eight QTLs across six linkage groups associated with sex ratio and related traits. These QTLs explained 8.1–13.1 % of the total phenotypic variance. The QTL for sex ratio on linkage group 8 overlapped with a QTL for number of male inflorescences. In most cases a specific QTL allele combination was responsible for genotype class mean differences, suggesting that most QTLs in heterozygous oil palm are likely to be segregating for multiple alleles with different degrees of dominance. In addition, two new SSRs were shown to flank the major Sh locus controlling the fruit variety type in oil palm.     

Mapping of quantitative trait loci (QTLs) for oil yield using SSRs and gene-based markers in African oil palm (<Emphasis Type="Italic">Elaeis guineensis</Emphasis> Jacq.)

The identification of quantitative trait loci (QTLs) based on anchor markers, especially candidate genes that control a trait of interest, has been noted to increase the power of QTL detection. Since these markers can be scored as co-dominant data, they are also valuable for comparing and integrating the QTL linkage maps from diverse mapping populations. To estimate the position and effects of QTLs linked to oil yield traits in African oil palm, co-dominant microsatellites (SSR) and candidate gene-based sequence polymorphisms were applied to construct a linkage map for a progeny showing large differences in oil yield components. The progeny was genotyped for 97 SSR markers, 93 gene-linked markers, and 12 non-gene-linked SNP markers. From these, 190 segregating loci could be arranged into 31 linkage groups while 12 markers remained unmapped. Using the single marker linkage, interval mapping and multiple QTL methods, 16 putative QTLs on seven linkage groups affecting important oil yield related traits such as fresh fruit bunch yield (FFB), ratio of oil per fruit (OF), oil per bunch (OB), fruit per bunch (FB) and wet mesocarp per fruit (WMF) could be identified in the segregating population with estimated values for explained variance ranging from 12.4 % to 54.5 %. Markers designed from some candidate genes involved in lipid biosynthesis were found to be mapped near significant QTLs for various economic yield traits. Associations between QTLs and potential candidate genes are discussed.     

Identification of loci governing eight agronomic traits using a GBS‐GWAS approach and validation by QTL mapping in soya bean

Soya bean is a major source of edible oil and protein for human consumption as well as animal feed. Understanding the genetic basis of different traits in soya bean will provide important insights for improving breeding strategies for this crop. A genome‐wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of agronomic traits in soya bean. A genotyping‐by‐sequencing (GBS) approach was used to provide dense genome‐wide marker coverage (>47 000 SNPs) for a panel of 304 short‐season soya bean lines. A subset of 139 lines, representative of the diversity among these, was characterized phenotypically for eight traits under six environments (3 sites × 2 years). Marker coverage proved sufficient to ensure highly significant associations between the genes known to control simple traits (flower, hilum and pubescence colour) and flanking SNPs. Between one and eight genomic loci associated with more complex traits (maturity, plant height, seed weight, seed oil and protein) were also identified. Importantly, most of these GWAS loci were located within genomic regions identified by previously reported quantitative trait locus (QTL) for these traits. In some cases, the reported QTLs were also successfully validated by additional QTL mapping in a biparental population. This study demonstrates that integrating GBS and GWAS can be used as a powerful complementary approach to classical biparental mapping for dissecting complex traits in soya bean.     

基因组选择技术在农业动物育种中的应用

基因组选择(genomic selection, GS)是畜禽经济性状遗传改良的重要方法。随着高密度SNP芯片和二代测序价格的下降,GS技术越来越多被应用于奶牛、猪、鸡等农业动物育种中。然而,降低全基因组SNP分型成本、提高基因组育种值(genomic estimated breeding value,GEBV)估计准确性仍然是GS研究的主要难题。本文从全基因组SNP分型策略和GEBV估计模型两个方面进行了综述,并对目前GS技术在主要畜禽品种中的应用现状进行了介绍,以期为GS在农业动物育种中的深入开展提供借鉴和参考。     

Population genetics of genomics-based crop improvement methods

Many genome-wide association studies (GWAS) in humans are concluding that, even with very large sample sizes and high marker densities, most of the genetic basis of complex traits may remain unexplained. At the same time, recent research in plant GWAS is showing much greater success with fewer resources. Both GWAS and genomic selection (GS), a method for predicting phenotypes by the use of genome-wide marker data, are receiving considerable attention among plant breeders. In this review we explore how differences in population genetic histories, as well as past selection for traits of interest, have produced trait architectures and patterns of linkage disequilibrium (LD) that frequently differ dramatically between domesticated plants and humans, making detection of quantitative trait loci (QTL) effects in crops more rewarding and less costly than in humans.     

Genome-Wide Association Mapping for Yield and Other Agronomic Traits in an Elite Breeding Population of Tropical Rice (Oryza sativa)

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.     

Long-term genomic selection for heterosis without dominance in multiplicative traits: case study of bunch production in oil palm

Background

To study the potential of genomic selection for heterosis resulting from multiplicative interactions between additive and antagonistic components, we focused on oil palm, where bunch production is the product of bunch weight and bunch number. We simulated two realistic breeding populations and compared current reciprocal recurrent selection (RRS) with reciprocal recurrent genomic selection (RRGS) over four generations. All breeding strategies aimed at selecting the best individuals in parental populations to increase bunch production in hybrids. For RRGS, we obtained the parental genomic estimated breeding values using GBLUP with hybrid phenotypes as data records and population specific allele models. We studied the effects of four RRGS parameters on selection response and genetic parameters: (1) the molecular data used to calibrate the GS model: in RRGS_PAR, we used parental genotypes and in RRGS_HYB we also used hybrid genotypes; (2) frequency of progeny tests (model calibration); (3) number of candidates and (4) number of genotyped hybrids in RRGS_HYB.

Results

We concluded that RRGS could increase the annual selection response compared to RRS by decreasing the generation interval and by increasing the selection intensity. With 1700 genotyped hybrids, calibration every four generations and 300 candidates per generation and population, selection response of RRGS_HYB was 71.8 % higher than RRS. RRGS_PAR with calibration every two generations and 300 candidates was a relevant alternative, as a good compromise between the annual response, risk around the expected response, increased inbreeding and cost. RRGS required inbreeding management because of a higher annual increase in inbreeding than RRS.

Conclusions

RRGS appeared as a valuable method to achieve a long-term increase in the performance for a trait showing heterosis due to the multiplicative interaction between additive and negatively correlated components, such as oil palm bunch production.     

Training genomic selection models across several breeding cycles increases genetic gain in oil palm in silico study

Genomic selection (GS) is expected to increase the rate of genetic gain in oil palm. In a GS scheme, breeding cycles with progeny tests (phenotypic selection, PS) used to calibrate the GS predictive model and for selection alternate with GS cycles, making it possible to train the GS model with aggregated data from several cycles. To evaluate this possibility, we simulated four cycles of hybrid breeding for bunch production and compared two methods of calibrating the GS model, one using aggregated data from the two most recent cycles (Tr2Gen), the other using data from the last cycle (Tr1Gen). We also compared a GS scheme with two PS cycles and two GS cycles (2PT-2noPT), and a scheme with PS every other cycle and GS otherwise (PT-noPT). We showed that Tr2Gen had a 10.7% higher genetic gain per cycle than Tr1Gen, mostly due to increased selection accuracy, particularly in across-cycle selection, despite the decreased relationship between training individuals and selection candidates. After four cycles, Tr2Gen had a 5% higher cumulative genetic gain than Tr1Gen, with a lower coefficient of variation. PT-noPT benefited more from the advantages offered by Tr2Gen than 2PT-2noPT. Over four breeding cycles, combining PT-noPT and Tr2Gen largely outperformed conventional reciprocal recurrent selection (RRS), with an increase in annual genetic gain ranging from 37.6 to 57.5%, depending on the number of GS candidates. This study confirms the advantages of GS over RRS and indicated that oil palm breeders should train GS models using all data from past breeding cycles.     

Genome-wide association study-based identification genes influencing agronomic traits in rice (Oryza sativa L.)

Rice is one of the most important cereal crops, providing the daily dietary intake for approximately 50% of the global human population. Here, we re-sequenced 259 rice accessions, generating 1371.65 Gb of raw data. Furthermore, we performed genome-wide association studies (GWAS) on 13 agronomic traits using 2.8 million single nucleotide polymorphisms (SNPs) characterized in 259 rice accessions. Phenotypic data and best linear unbiased prediction (BLUP) values of each of the 13 traits over two years of each trait were used for the GWAS. The results showed that 816 SNP signals were significantly associated with the 13 agronomic traits. Then we detected candidate genes related to target traits within 200 kb upstream and downstream of the associated SNP loci, based on linkage disequilibrium (LD) blocks in the whole rice genome. These candidate genes were further identified through haplotype block constructions. This comprehensive study provides a timely and important genomic resource for breeding high yielding rice cultivars.     

QTL mapping of seed-quality traits in sunflower recombinant inbred lines under different water regimes.

The objectives of the present research were to determine the effects of water stress on seed-quality traits and to map QTLs controlling the studied traits under two different water treatments in a population of sunflower recombinant inbred lines (RILs). Two experiments were conducted in greenhouse and field conditions, each with well-watered and water-stressed treatments. The experiments consisted of a split-plot design (water treatment and RIL) with three blocks. Analyses of variance showed significant variation among genotypes, and a water treatment x genotype interaction was also observed for most of the traits. Two to 15 QTLs were found, depending on trait and growth conditions, and the percentage of phenotypic variance explained by the QTLs ranged from 5% to 31%. Several QTLs for oil content overlapped with QTLs for palmitic and stearic acid contents in all four conditions. An overlapping region on linkage group 3 (QTLs 2.OC.3.1 and 4.SA.3.1) was linked to an SSR marker (ORS657). A principal component analysis was performed on four fatty acid traits. Two principal components, P1 and P2, were used for QTL analysis. This method improved the ability to identify chromosomal regions affecting the fatty acids. We also detected the principal-component QTLs that did not overlap with the fatty acid QTLs. The results highlight genomic regions of interest in marker-based breeding programmes for increasing oil content in sunflower.     

Genome-wide association studies for agronomical traits in winter rice accessions of Assam

The 297 winter rice accessions of Assam, North East India were genotyped by sequencing (GBS). The 50,985 high-quality SNPs were filtered and assigned to 12 rice chromosomes. The population structure analysis revealed three major subgroups SG1, SG2, and SG3 consisting of 30, 8, and 143 accessions respectively. The remaining 116 accessions were grouped as admixture population. Phenotypic data were recorded on13 agronomical traits for genome-wide association studies (GWAS). The 60 significant marker-trait associations (MTAs) were identified for 11 agronomical traits, which explained 0 to 15% of phenotypic variance (PV). A QTL ‘hot spot’ was detected near the centromeric region on chromosome 6. The identified QTLs may be validated and utilized in ‘genomics assisted breeding’ for improvement of existing rice cultivars of Assam and North East India.     

Relevance of genetic relationship in GWAS and genomic prediction

The objective of this study was to analyze the relevance of relationship information on the identification of low heritability quantitative trait loci (QTLs) from a genome-wide association study (GWAS) and on the genomic prediction of complex traits in human, animal and cross-pollinating populations. The simulation-based data sets included 50 samples of 1000 individuals of seven populations derived from a common population with linkage disequilibrium. The populations had non-inbred and inbred progeny structure (50 to 200) with varying number of members (5 to 20). The individuals were genotyped for 10,000 single nucleotide polymorphisms (SNPs) and phenotyped for a quantitative trait controlled by 10 QTLs and 90 minor genes showing dominance. The SNP density was 0.1 cM and the narrow sense heritability was 25%. The QTL heritabilities ranged from 1.1 to 2.9%. We applied mixed model approaches for both GWAS and genomic prediction using pedigree-based and genomic relationship matrices. For GWAS, the observed false discovery rate was kept below the significance level of 5%, the power of detection for the low heritability QTLs ranged from 14 to 50%, and the average bias between significant SNPs and a QTL ranged from less than 0.01 to 0.23 cM. The QTL detection power was consistently higher using genomic relationship matrix. Regardless of population and training set size, genomic prediction provided higher prediction accuracy of complex trait when compared to pedigree-based prediction. The accuracy of genomic prediction when there is relatedness between individuals in the training set and the reference population is much higher than the value for unrelated individuals.     

Using a system of differential equations that models cattle growth to uncover the genetic basis of complex traits

The interplay between dynamic models of biological systems and genomics is based on the assumption that genetic variation of the complex trait (i.e., outcome of model behavior) arises from component traits (i.e., model parameters) in lower hierarchical levels. In order to provide a proof of concept of this statement for a cattle growth model, we ask whether model parameters map genomic regions that harbor quantitative trait loci (QTLs) already described for the complex trait. We conducted a genome-wide association study (GWAS) with a Bayesian hierarchical LASSO method in two parameters of the Davis Growth Model, a system of three ordinary differential equations describing DNA accretion, protein synthesis and degradation, and fat synthesis. Phenotypic and genotypic data were available for 893 Nellore (Bos indicus) cattle. Computed values for parameter k₁ (DNA accretion rate) ranged from 0.005 ± 0.003 and for α (constant for energy for maintenance requirement) 0.134 ± 0.024. The expected biological interpretation of the parameters is confirmed by QTLs mapped for k₁ and α. QTLs within genomic regions mapped for k₁ are expected to be correlated with the DNA pool: body size and weight. Single nucleotide polymorphisms (SNPs) which were significant for α mapped QTLs that had already been associated with residual feed intake, feed conversion ratio, average daily gain (ADG), body weight, and also dry matter intake. SNPs identified for k₁ were able to additionally explain 2.2% of the phenotypic variability of the complex ADG, even when SNPs for k₁ did not match the genomic regions associated with ADG. Although improvements are needed, our findings suggest that genomic analysis on component traits may help to uncover the genetic basis of more complex traits, particularly when lower biological hierarchies are mechanistically described by mathematical simulation models.     

Morpho-agronomic and molecular characterisation of oil palm Elaeis guineensis Jacq. material from Angola

The characterisation of phytogenetic resources is used to improve conservation strategies, promote new sources of plant material, and design breeding strategies. In this study, we evaluated oil palm material with nine morpho-agronomic traits and 30 microsatellite markers (Simple Sequence Repeats; SSRs) that had been previously collected in five geographical regions of Angola. The analysis of variance for components of bunch production and oil yield showed highly significant (p?<?0.001) statistical differences between geographical regions and among families for all traits evaluated. The SSRs were highly informative, suggesting high genetic diversity (H _T ?=?0.666) among the accessions evaluated. However, the clustering pattern at both morpho-agronomic and molecular levels did not match the geographical distribution of accessions, showing a low genetic differentiation (G _ST ?=?0.039) between regions. On the other hand, genotypic (G _ST ?=?0.150) and phenotypic differences were found among families, which could offer the potential for future genetic gains in the oil palm. The information generated indicates that the evaluated accessions have desirable characteristics that should be included in breeding programs, which could expand the genetic basis of the crop.