Genomic consequences of selection and genome-wide association mapping in soybean

Background

Crop improvement always involves selection of specific alleles at genes controlling traits of agronomic importance, likely resulting in detectable signatures of selection within the genome of modern soybean (Glycine max L. Merr.). The identification of these signatures of selection is meaningful from the perspective of evolutionary biology and for uncovering the genetic architecture of agronomic traits.

Results

To this end, two populations of soybean, consisting of 342 landraces and 1062 improved lines, were genotyped with the SoySNP50K Illumina BeadChip containing 52,041 single nucleotide polymorphisms (SNPs), and systematically phenotyped for 9 agronomic traits. A cross-population composite likelihood ratio (XP-CLR) method was used to screen the signals of selective sweeps. A total of 125 candidate selection regions were identified, many of which harbored genes potentially involved in crop improvement. To further investigate whether these candidate regions were in fact enriched for genes affected by selection, genome-wide association studies (GWAS) were conducted on 7 selection traits targeted in soybean breeding (grain yield, plant height, lodging, maturity date, seed coat color, seed protein and oil content) and 2 non-selection traits (pubescence and flower color). Major genomic regions associated with selection traits overlapped with candidate selection regions, whereas no overlap of this kind occurred for the non-selection traits, suggesting that the selection sweeps identified are associated with traits of agronomic importance. Multiple novel loci and refined map locations of known loci related to these traits were also identified.

Conclusions

These findings illustrate that comparative genomic analyses, especially when combined with GWAS, are a promising approach to dissect the genetic architecture of complex traits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1872-y) contains supplementary material, which is available to authorized users.     

Genomic selection: prediction of accuracy and maximisation of long term response

Genomic selection refers to the use of dense markers covering the whole genome to estimate the breeding value of selection candidates for a quantitative trait. This paper considers prediction of breeding value based on a linear combination of the markers. In this case the best estimate of each marker’s effect is the expectation of the effect conditional on the data. To calculate this requires a prior distribution of marker effects. If the marker effects are normally distributed with constant variance, BLUP can be used to calculate the estimated effects of the markers and hence the estimated breeding value (EBV). In this case the model is equivalent to a conventional animal model in which the relationship matrix among the animals is estimated from the markers instead of the pedigree. The accuracy of the EBV can approach 1.0 but a very large amount of data is required. An alternative model was investigated in which only some markers have non-zero effects and these effects follow a reflected exponential distribution. In this case the expected effect of a marker is a non-linear function of the data such that apparently small effects are regressed back almost to zero and consequently these markers can be deleted from the model. The accuracy in this case is considerably higher than when marker effects are normally distributed. If genomic selection is practiced for several generations the response declines in a manner that can be predicted from the marker allele frequencies. Genomic selection is likely to lead to a more rapid decline in the selection response than phenotypic selection unless new markers are continually added to the prediction of breeding value. A method to find the optimum index to maximise long term selection response is derived. This index varies the weight given to a marker according to its frequency such that markers where the favourable allele has low frequency receive more weight in the index.     

Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations

Oil palm (Elaeis guineensis Jacq.) requires 19 years per cycle of phenotypic selection. The use of molecular markers may reduce the generation interval and the cost of oil-palm breeding. Our objectives were to compare, by simulation, the response to phenotypic selection, marker-assisted recurrent selection (MARS), and genomewide selection with small population sizes in oil palm, and assess the efficiency of each method in terms of years and cost per unit gain. Markers significantly associated with the trait were used to calculate the marker scores in MARS, whereas all markers were used (without significance tests) to calculate the marker scores in genomewide selection. Responses to phenotypic selection and genomewide selection were consistently greater than the response to MARS. With population sizes of N = 50 or 70, responses to genomewide selection were 4–25% larger than the corresponding responses to phenotypic selection, depending on the heritability and number of quantitative trait loci. Cost per unit gain was 26–57% lower with genomewide selection than with phenotypic selection when markers cost US $1.50 per data point, and 35–65% lower when markers cost $0.15 per data point. With population sizes of N = 50 or 70, time per unit gain was 11–23 years with genomewide selection and 14–25 years with phenotypic selection. We conclude that for a realistic yet relatively small population size of N = 50 in oil palm, genomewide selection is superior to MARS and phenotypic selection in terms of gain per unit cost and time. Our results should be generally applicable to other tree species that are characterized by long generation intervals, high costs of maintaining breeding plantations, and small population sizes in selection programs.     

It's about time: the temporal dynamics of phenotypic selection in the wild

Selection is a central process in nature. Although our understanding of the strength and form of selection has increased, a general understanding of the temporal dynamics of selection in nature is lacking. Here, we assembled a database of temporal replicates of selection from studies of wild populations to synthesize what we do (and do not) know about the temporal dynamics of selection. Our database contains 5519 estimates of selection from 89 studies, including estimates of both direct and indirect selection as well as linear and nonlinear selection. Morphological traits and studies focused on vertebrates were well-represented, with other traits and taxonomic groups less well-represented. Overall, three major features characterize the temporal dynamics of selection. First, the strength of selection often varies considerably from year to year, although random sampling error of selection coefficients may impose bias in estimates of the magnitude of such variation. Second, changes in the direction of selection are frequent. Third, changes in the form of selection are likely common, but harder to quantify. Although few studies have identified causal mechanisms underlying temporal variation in the strength, direction and form of selection, variation in environmental conditions driven by climatic fluctuations appear to be common and important.     

Genomic assisted selection for enhancing line breeding: merging genomic and phenotypic selection in winter wheat breeding programs with preliminary yield trials

Key message

Early generation genomic selection is superior to conventional phenotypic selection in line breeding and can be strongly improved by including additional information from preliminary yield trials.

Abstract

The selection of lines that enter resource-demanding multi-environment trials is a crucial decision in every line breeding program as a large amount of resources are allocated for thoroughly testing these potential varietal candidates. We compared conventional phenotypic selection with various genomic selection approaches across multiple years as well as the merit of integrating phenotypic information from preliminary yield trials into the genomic selection framework. The prediction accuracy using only phenotypic data was rather low (r = 0.21) for grain yield but could be improved by modeling genetic relationships in unreplicated preliminary yield trials (r = 0.33). Genomic selection models were nevertheless found to be superior to conventional phenotypic selection for predicting grain yield performance of lines across years (r = 0.39). We subsequently simplified the problem of predicting untested lines in untested years to predicting tested lines in untested years by combining breeding values from preliminary yield trials and predictions from genomic selection models by a heritability index. This genomic assisted selection led to a 20% increase in prediction accuracy, which could be further enhanced by an appropriate marker selection for both grain yield (r = 0.48) and protein content (r = 0.63). The easy to implement and robust genomic assisted selection gave thus a higher prediction accuracy than either conventional phenotypic or genomic selection alone. The proposed method took the complex inheritance of both low and high heritable traits into account and appears capable to support breeders in their selection decisions to develop enhanced varieties more efficiently.

     

A general model of the relation between phenotypic selection and genetic response

The phenotypic view of selection assumes that genetic responses can be predicted from selective forces and heritability — or in the classical quantitative genetic equation: R = h²S. However, data on selection in bird populations show that often no selection responses is found, despite consistent selective forces on phenotypes and significant heritable variation. Such discrepancies may arise due to the assumption that selection only acts on observed phenotypes. We derive a general selection equation that takes into account the possibility that some relevant (internal or external) traits are not measured. This equation shows that the classic equation applies if selection directly acts on the measured, phenotypic traits. This is not the case when, for instance, there are unknown internal genetic trade-offs, or unknown common environmental factors affecting both trait and fitness. In such cases, any relationship between phenotypic selection and genetic response is possible. Fortunately, the classical model can be tested by comparing phenotypic and genetic covariances between traits and fitness; an indication that important internal or external traits are missing can thus be obtained. Such an analysis was indeed found in the literature; for selection on fledging weight in Great Tits it yielded valuable extra information.     

Variation in phenotypic plasticity and selection patterns in blue tit breeding time: between- and within-population comparisons

1.?Phenotypic plasticity, the response of individual phenotypes to their environment, can allow organisms to cope with spatio-temporal variation in environmental conditions. Recent studies have shown that variation exists among individuals in their capacity to adjust their traits to environmental changes and that this individual plasticity can be under strong selection. Yet, little is known on the extent and ultimate causes of variation between populations and individuals in plasticity patterns. 2.?In passerines, timing of breeding is a key life-history trait strongly related to fitness and is known to vary with the environment, but few studies have investigated the within-species variation in individual plasticity. 3.?Here, we studied between- and within-population variation in breeding time, phenotypic plasticity and selection patterns for this trait in four Mediterranean populations of blue tits (Cyanistes caeruleus) breeding in habitats varying in structure and quality. 4.?Although there was no significant warming over the course of the study, we found evidence for earlier onset of breeding in warmer years in all populations, with reduced plasticity in the less predictable environment. In two of four populations, there was significant inter-individual variation in plasticity for laying date. Interestingly, selection for earlier laying date was significant only in populations where there was no inter-individual differences in plasticity. 5.?Our results show that generalization of plasticity patterns among populations of the same species might be challenging even at a small spatial scale and that the amount of within-individual variation in phenotypic plasticity may be linked to selective pressures acting on these phenotypic traits.     

An integrated analysis of phenotypic selection on insect body size and development time

Most studies of phenotypic selection do not estimate selection or fitness surfaces for multiple components of fitness within a unified statistical framework. This makes it difficult or impossible to assess how selection operates on traits through variation in multiple components of fitness. We describe a new generation of aster models that can evaluate phenotypic selection by accounting for timing of life‐history transitions and their effect on population growth rate, in addition to survival and reproductive output. We use this approach to estimate selection on body size and development time for a field population of the herbivorous insect, Manduca sexta (Lepidoptera: Sphingidae). Estimated fitness surfaces revealed strong and significant directional selection favoring both larger adult size (via effects on egg counts) and more rapid rates of early larval development (via effects on larval survival). Incorporating the timing of reproduction and its influence on population growth rate into the analysis resulted in larger values for size in early larval development at which fitness is maximized, and weaker selection on size in early larval development. These results illustrate how the interplay of different components of fitness can influence selection on size and development time. This integrated modeling framework can be readily applied to studies of phenotypic selection via multiple fitness components in other systems.     

基因组选择及其应用

品种选育在农业生产中占有十分重要的地位,育种值估计是品种选育的核心。随着遗传标记的发展,尤其是高通量的基因分型技术,使得从基因组水平估计育种值成为可能,即基因组选择。文章将基因组选择的方法分为两类:一是基于估计等位基因效应来预测基因组估计育种值(GEBV),如最小二乘法,随机回归-最佳线性无偏预测(RR-BLUP)、Bayes、主成分分析等方法;二是基于遗传关系矩阵来预测GEBV,通过采用高通量标记构建个体间的遗传关系矩阵,然后用线性混合模型来预测育种值,即GBLUP法,并以这两种分类简要介绍了基因组选择各种方法的大致原理。影响基因组选择准确性的因素主要有标记类型和密度、单倍型长度、参考群体大小和标记-数量性状基因座(QTL)连锁不平衡(LD)大小等;在基因组选择的各种方法中,一般说来Bayes方法和GBLUP方法具有较高的准确性,最小二乘法最差;GBLUP计算速度快,能够将标记和系谱结合起来,因而比其他方法更具优势。尽管基因组选择取得了很大进展,但在理论方面还面临着一些挑战,如联合育种、长期选择的遗传进展及如何解析与性状有关和无关的标记等。基因组选择在一些动植物育种上已经开始应用,在人类遗传倾向预测和进化动力学研究中也有潜在的应用前景。基因组选择在个体间亲缘关系的量化上有了突破,比传统方法更加精确,因此,基因组选择将会是动植物育种史上革命性的事件。     

Genomic organization of glycinin genes in soybean

Glycinin is the predominant seed storage protein in most soybean varieties. Previously, five major genes (designated Gy1 to Gy5) encoding glycinin subunits have been described. In this report two new genes are identified and mapped: a glycinin pseudogene, gy6, and a functional gene, Gy7. Messenger RNA for the gy6 pseudogene is not detected in developing seeds. While Gy7 mRNA was present at the midmaturation stage of seed development in the soybean variety Resnik, the steady state amount of this message was at least an order of magnitude less-prevalent than the mRNA encoding each of the other five glycinin subunits. Even though the amino-acid sequence of the glycinin subunit G7 is related to the other five soybean 11S subunits, it does not fit into either the Group-1 (G1, G2, G3) or the Group-2 (G4, G5) glycinin subunit families. The Gy7 gene is tandemly linked 3' to Gy3 on Linkage Group L (chromosome 19) of the public molecular linkage map. By contrast, the gy6 gene occupies a locus downstream from Gy2 on Linkage Group N (chromosome 3) in a region that is related to the position where Gy7 is located on chromosome 19.     

Estimation of phenotypic selection differentials for predicting genetic responses to ratio-based selection

Ratios of the phenotypic values of two traits may be used as selection criteria in animal and plant breeding to improve the ratio traits themselves or to effect changes in their two component (numerator and denominator) traits. Prediction of genetic responses to ratio-based selection would facilitate quantitative analysis and evaluation of selection based on ratios. Methods for predicting such responses are derived and presented here. They employ expressions for the truncation value of a ratio and for the phenotypic selection differentials of the numerator and denominator traits. The derivation of these expressions is based upon the assumption that the phenotypic values of each of these traits are normally distributed. Worked examples relating to livestock and crop improvement are included to demonstrate how responses to selection for ratios may be predicted.     

Genomic creativity and natural selection: a modern synthesis

In the early 1930s, the synthesis of Darwinian natural selection, mutation, and Mendelian genetics gave rise to the paradigm of 'modern Darwinism', also known as 'neo-Darwinism'. This has contributed greatly to our understanding. But increasing knowledge of other mechanisms, including endosymbiosis, genetic and genomic duplication, polyploidy, hybridization, epigenetics, horizontal gene transfer in prokaryotes, and the modern synthesis of embryonic development and evolution, has widened our horizons to a diversity of possibilities for change. All of these can be gathered under the umbrella concept of 'genomic creativity', which, in partnership with natural selection, affords a more comprehensive modern explanation of evolution.  © 2006 The Linnean Society of London, Biological Journal of the Linnean Society , 2006, 88 , 655–672.     

Genomic selection in forest tree breeding

Genomic selection (GS) involves selection decisions based on genomic breeding values estimated as the sum of the effects of genome-wide markers capturing most quantitative trait loci (QTL) for the target trait(s). GS is revolutionizing breeding practice in domestic animals. The same approach and concepts can be readily applied to forest tree breeding where long generation times and late expressing complex traits are also a challenge. GS in forest trees would have additional advantages: large training populations can be easily assembled and accurately phenotyped for several traits, and the extent of linkage disequilibrium (LD) can be high in elite populations with small effective population size (N _e) frequently used in advanced forest tree breeding programs. Deterministic equations were used to assess the impact of LD (modeled by N _e and intermarker distance), the size of the training set, trait heritability, and the number of QTL on the predicted accuracy of GS. Results indicate that GS has the potential to radically improve the efficiency of tree breeding. The benchmark accuracy of conventional BLUP selection is reached by GS even at a marker density ~2 markers/cM when N _e ≤ 30, while up to 20 markers/cM are necessary for larger N _e. Shortening the breeding cycle by 50% with GS provides an increase ≥100% in selection efficiency. With the rapid technological advances and declining costs of genotyping, our cautiously optimistic outlook is that GS has great potential to accelerate tree breeding. However, further simulation studies and proof-of-concept experiments of GS are needed before recommending it for operational implementation.     

Ethanol and acetaldehyde in imbibing soybean seeds in relation to deterioration

Deterioration as evidenced by decline in germination or seedling growth of soybean (cv. Essex) seeds during accelerated aging treatments at 41 C and 100% relative humidity is accompanied by increased levels of acetaldehyde and ethanol in imbibing embryonic axes and seeds. These increases become more pronounced with duration of the aging treatment. A similar inverse relationship between levels of acetaldehyde and ethanol and deterioration was observed when seeds were “naturally” aged for several years. During imbibition of low-vigor, accelerated-aged seeds at 25 C, acetaldehyde and ethanol increased from near trace amounts in dry tissues to maximum levels at 4 hours. Increases in acetaldehyde and ethanol during imbibition were less in high- than in low-vigor seeds. Increases were also less pronounced in low-vigor seeds when water uptake injury was avoided by osmotically decreasing water uptake rate with 30% polyethylene glycol. Embryonic axes from deteriorated seeds were characterized by low rates of O₂ uptake and high respiratory quotients relative to the unaged controls. Anaerobic conditions and respiratory inhibitors, such as sodium azide, increased acetaldehyde and ethanol in unaged seeds to levels similar to those in accelerated-aged seeds after 2 hours imbibition. It is suggested that, during aging, an imbalance between tricarboxylic and glycolytic activities, present during early imbibition to some degree even in vigorous unaged seeds, becomes more pronounced and leads to accumulation of ethanol and acetaldehyde.