Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 1. Identification of genomic regions from winter germplasm

The introgression of winter germplasm into spring canola (Brassica napus L.) represents a novel approach to improve seed yield of hybrid spring canola. In this study, quantitative trait loci (QTL) for seed yield and other traits were genetically mapped to determine the effects of genomic regions introgressed from winter germplasm into spring canola. Plant materials used comprised of two populations of doubled haploid (DH) lines having winter germplasm introgression from two related French winter cultivars and their testcrosses with a spring line used in commercial hybrids. These populations were evaluated for 2 years at two locations (Wisconsin, USA and Saskatchewan, Canada). Genetic linkage maps based on RFLP loci were constructed for each DH population. Six QTL were detected in the testcross populations for which the winter alleles increased seed yield. One of these QTL explained 11 and 19% of the phenotypic variation in the two Canadian environments. The winter allele for another QTL that increased seed yield was linked in coupling to a QTL allele for high glucosinolate content, suggesting that the transition of rapeseed into canola could have resulted in the loss of favorable seed yield alleles. Most QTL for which the introgressed allele decreased seed yield of hybrids mapped to genomic regions having homoeologous non-reciprocal transpositions. This suggests that allelic configurations created by these rearrangements might make an important contribution to genetic variation for complex traits in oilseed B. napus and could account for a portion of the heterotic effects in hybrids. Electronic Supplementary Material Supplementary material is available to authorised users in the online version of this article at .     

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821–0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825–0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.     

Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 2. Identification of alleles from unadapted germplasm

Unadapted germplasm may contain alleles that could improve hybrid cultivars of spring oilseed Brassica napus. Quantitative trait loci (QTL) mapping was used to identify potentially useful alleles from two unadapted germplasm sources, a Chinese winter cultivar and a re-synthesized B. napus, that increase seed yield when introgressed into a B. napus spring hybrid combination. Two populations of 160 doubled haploid (DH) lines were created from crosses between the unadapted germplasm source and a genetically engineered male-fertility restorer line (P1804). A genetically engineered male-sterile tester line was used to create hybrids with each DH line (testcrosses). The two DH line populations were evaluated in two environments and the two testcross populations were evaluated in three or four environments for seed yield and other agronomic traits. Several genomic regions were found in the two testcross populations which contained QTL for seed yield. The map positions of QTL for days to flowering and resistance to a bacterial leaf blight disease coincided with QTL for seed yield and other agronomic traits, suggesting the occurrence of pleiotropic or linked effects. For two hybrid seed yield QTL, the favorable alleles increasing seed yield originated from the unadapted parents, and one of these QTL was detected in multiple environments and in both populations. In this QTL region, a chromosome rearrangement was identified in P1804, which may have affected seed yield.Electronic Supplementary Material Supplementary material is available to authorised users in the online version of this article at .     

Dissecting the genetic architecture of agronomic traits in multiple segregating populations in rapeseed (<Emphasis Type="Italic">Brassica napus</Emphasis> L.)

Detection of QTL in multiple segregating populations is of high interest as it includes more alleles than mapping in a single biparental population. In addition, such populations are routinely generated in applied plant breeding programs and can thus be used to identify QTL which are of direct relevance for a marker-assisted improvement of elite germplasm. Multiple-line cross QTL mapping and joint linkage association mapping were used for QTL detection. We empirically compared these two different biometrical approaches with regard to QTL detection for important agronomic traits in nine segregating populations of elite rapeseed lines. The plants were intensively phenotyped in multi-location field trials and genotyped with 253 SNP markers. Both approaches detected several additive QTL for diverse traits, including flowering time, plant height, protein content, oil content, glucosinolate content, and grain yield. In addition, we identified one epistatic QTL for flowering time. Consequently, both approaches appear suited for QTL detection in multiple segregating populations.     

Genomic regions underlying agronomic traits in linseed （Linum usitatissimum L.） as revealed by association mapping

The extreme climate of the Canadian Prairies poses a major chal enge to improve yield. Although it is possible to breed for yield per se, focusing on yield-related traits could be advantageous because of their simpler genetic architecture. The Canadian flax core col ection of 390 accessions was genotyped with 464 simple sequence repeat markers, and phenotypic data for nine agronomic traits including yield, bol s per area, 1,000 seed weight, seeds per bol , start of flowering, end of flowering, plant height, plant branching, and lodging col ected from up to eight environments was used for association mapping. Based on a mixed model （principal component analysis （PCA） t kinship matrix （K））, 12 significant marker-trait associations for six agronomic traits were identi-fied. Most of the associations were stable across environments as revealed by multivariate analyses. Statistical simulation for five markers associated with 1000 seed weight indicated that the favorable al eles have additive effects. None of the modern cultivars carried the five favorable al eles and the maximum number of four observed in any accessions was mostly in＆amp;nbsp;breeding lines. Our results confirmed the complex genetic architecture of yield-related traits and the inherent difficulties associated with their identification while il ustrating the potential for improvement through marker-assisted selection.     

Combining ability of tropical maize lines for seed quality and agronomic traits

Studies of genetic effects of early selection of maize based on seed quality traits are rare, especially those that use materials from different heterotic groups. These studies are also useful in tropical environments and for the advancement of sustainable agriculture with cropping during seasons not commonly used for cultivation. We estimated, through diallel crosses, the predominant genetic effects on the expression of agronomic traits and seed quality and on the general combining ability of nine maize lines from commercial hybrids and the specific combining ability of hybrid combinations among them. In the evaluation of seed quality, seven tests were used: first count and final count of seed germination, seedling vigor classification, cold tolerance, seedling emergence rate in a sand seedbed, speed of emergence in a sand seedbed, and speed of emergence index. Plant height, first ear height and grain yield were the estimated agronomic traits. In the diallel analysis, method 3 (model I) proposed by Griffing was used. There was a greater significance of non-additive genetic effects in the genetic control of seed quality of the various lines. The Flash, Dekalb 350 and P 30F80 lines combined high seed quality and high grain yield. For growth during the normal planting season, the combinations CD 3121-1 x P 30F80, Speed x CD 3121-2, Dow 8330 x AG 8080 and Dekalb 350 x CD 3121-2 were the most promising for both seed quality and agronomic traits.     

Genomic regions underlying agronomic traits in linseed (Linum usitatissimum L.) as revealed by association mapping OA简

The extreme climate of the Canadian Prairies poses a major challenge to improve yield. Although it is possible to breed for yield per se, focusing on yield‐related traits could be advantageous because of their simpler genetic architecture. The Canadian flax core collection of 390 accessions was genotyped with 464 simple sequence repeat markers, and phenotypic data for nine agronomic traits including yield, bolls per area, 1,000 seed weight, seeds per boll, start of flowering, end of flowering, plant height, plant branching, and lodging collected from up to eight environments was used for association mapping. Based on a mixed model (principal component analysis (PCA) + kinship matrix (K)), 12 significant marker‐trait associations for six agronomic traits were identified. Most of the associations were stable across environments as revealed by multivariate analyses. Statistical simulation for five markers associated with 1000 seed weight indicated that the favorable alleles have additive effects. None of the modern cultivars carried the five favorable alleles and the maximum number of four observed in any accessions was mostly in breeding lines. Our results confirmed the complex genetic architecture of yield‐related traits and the inherent difficulties associated with their identification while illustrating the potential for improvement through marker‐assisted selection.     

Sulphur application improves the growth, seed yield and oil quality of canola

Sulphur (S) nutrition is very important for harvesting potential seed and oil yield of rapeseed. This study evaluated response of foliage applied thiourea on the performance of two canola cultivars Shiralee and Dunkeld. Sulphur was applied to soil (40 kg ha^?1) or foliage (500 and 1,000 mg L^?1) at rosette, bud initiation and flowering stages using elemental S or thiourea as source, respectively; no S application was taken as control. Among all the treatments, soil application of S improved the crop growth, yield and oil quality in both cultivars and was followed by foliar application of thiourea at 1,000 mg L^?1 compared with no application. Soil applied S and foliar thiourea (1,000 mg L^?1) delayed the flowering and maturity. Soil and foliar applied S significantly improved leaf area index, crop growth rate, net assimilation rate and chlorophyll contents. Plant height, number of branches, siliqua per plant, seed number per siliqua, 1,000-seed weight, biological and seed yield were also increased by soil applied S and foliage applied thiourea (1,000 mg L^?1). Nonetheless, improvement in harvest index, seed oil, protein and glucosinolate contents was only observed from foliage applied thiourea (1,000 mg L^?1). Response of cv. Shiralee to sulphur application was better than cv. Dunkeld. In conclusion, foliar applied thiourea (1,000 mg L^?1) can have potential to improve growth, yield and oil quality in canola and can be economically viable and attractive alternative source.     

The effects of lodging and a paclobutrazol — chlormequat chloride mixture on the yield and quality of oilseed rape

The effects of lodging and a plant growth regulator mixture on oilseed rape cv. Ariana were studied in three field experiments. Natural and artificially induced lodging treatments varying in time of imposition and severity were compared to a supported control. A mixture of paclobutrazol and chlormequat chloride was applied either as a spray or as paclobutrazol granules followed immediately by a chlormequat chloride spray.
In 1987, severe lodging treatments reduced yield by up to 52%. Yield penalties varied with the time at which lodging was imposed. Yield was inversely correlated with the ground cover of volunteers growing from shed seed under lodged crops.
In 1988, two experiments showed increased incidence of disease and decreases in seed yield and quality in lodged crops. Yield reductions were related to the severity of lodging. Lodging decreased oil contents and increased glucosinolate levels. PGR treatments reduced lodging and maintained yield at a level not significantly different to a supported control treatment. Oil contents were also similar in seed from PGR treated and control plots. Glucosinolate levels in PGR treated seed were similar to control levels in one experiment and intermediate to those from control and artificially lodged plots in another experiment.
The results are discussed in relation to the use of PGRs to prevent lodging in 'double zero' varieties of oilseed rape, and the potential losses from using ground vehicles to apply pesticides after flowering.     

Accuracy of genomic selection in European maize elite breeding populations

Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3–4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.     

Genetic diversity among oat varieties of worldwide origin and associations of AFLP markers with quantitative traits

One hundred and fourteen oat (Avena sativa L.) varieties of worldwide origin were evaluated for genetic diversity based on 77 molecular polymorphisms produced by eight selective AFLP primer combinations. Genetic similarity, calculated using the DICE coefficient, was used for cluster analysis and principal component analysis was applied. In addition population structure was explored to identify discrete subpopulations based on allele frequency. Although clustering and population structure showed relationships with region and country of origin, there was no obvious relationship to hull presence or hull colour. Oat varieties originating from European breeding programs showed less diversity than varieties originating from North and South America. Associations between AFLP markers and agronomic traits (grain yield, groat yield, panicle emergence, plant height, and lodging) as well as kernel quality traits (kernel weight, test weight, screening percent and groat percent) were also investigated. Marker-trait associations were tested using a naïve simple regression model and five additional models that account for population structure. Significant associations were found for 23 AFLP markers, with many of these affecting multiple traits. This study demonstrates that diversity can be significantly enhanced using a global collection, and provides evidence for marker-trait associations that can be validated in segregating populations and exploited through marker-assisted selection.     

Validation of mega-environment universal and specific QTL associated with seed yield and agronomic traits in soybeans

The value of quantitative trait loci (QTL) is dependant on the strength of association with the traits of interest, allelic diversity at the QTL and the effect of the genetic background on the expression of the QTL. A number of recent studies have identified QTL associated with traits of interest that appear to be independent of the environment but dependant on the genetic background in which they are found. Therefore, the objective of this study was to validate universal and/or mega-environment-specific seed yield QTL that have been previously reported in an independent recombinant inbred line (RIL) population derived from the cross between an elite Chinese and Canadian parent. The population was evaluated at two field environments in China and in five environments in Canada in 2005 and 2006. Of the seven markers linked to seed yield QTL reported by our group in a previous study, four were polymorphic between the two parents. No association between seed yield and QTL was observed. The result could imply that seed yield QTL were either not stable in this particular genetic background or harboured different alleles than the ones in the original mapping population. QTL_U Satt162 was associated with several agronomic traits of which lodging was validated. Both the non-adapted and adapted parent contributed favourable alleles to the progeny. Therefore, plant introductions have been validated as a source of favourable alleles that could increase the genetic variability of the soybean germplasm pool and lead to further improvements in seed yield and other agronomic traits.     

Genome-based prediction of testcross values in maize

This is the first large-scale experimental study on genome-based prediction of testcross values in an advanced cycle breeding population of maize. The study comprised testcross progenies of 1,380 doubled haploid lines of maize derived from 36 crosses and phenotyped for grain yield and grain dry matter content in seven locations. The lines were genotyped with 1,152 single nucleotide polymorphism markers. Pedigree data were available for three generations. We used best linear unbiased prediction and stratified cross-validation to evaluate the performance of prediction models differing in the modeling of relatedness between inbred lines and in the calculation of genome-based coefficients of similarity. The choice of similarity coefficient did not affect prediction accuracies. Models including genomic information yielded significantly higher prediction accuracies than the model based on pedigree information alone. Average prediction accuracies based on genomic data were high even for a complex trait like grain yield (0.72–0.74) when the cross-validation scheme allowed for a high degree of relatedness between the estimation and the test set. When predictions were performed across distantly related families, prediction accuracies decreased significantly (0.47–0.48). Prediction accuracies decreased with decreasing sample size but were still high when the population size was halved (0.67–0.69). The results from this study are encouraging with respect to genome-based prediction of the genetic value of untested lines in advanced cycle breeding populations and the implementation of genomic selection in the breeding process.     

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.     

Genetic analysis of agronomic and quality traits in mustard (Brassica juncea)

To develop an efficient mustard (Brassica juncea) breeding programme, a better knowledge of the genetic control and relationships of the main selected characters is needed. Thus, doubled haploid (DH) lines were evaluated over 2 years in the field. Days to flowering, plant height, thousand-seed weight, fatty acid composition, seed oil content, sinigrin, gluconapin and total glucosinolate contents were determined in the DH population. The influence of seed coat colour was estimated. Results showed significant differences between yellow and brown seeds only for oil and fatty acid contents. Molecular analysis revealed that seed coat colour is associated with two Mendelian trait loci: Bjc1 [on linkage group (LG) 3] and Bjc2 (on LG6). The quantitative trait loci associated with characters—detected by composite interval mapping—were not co-localised and revealed a genetic independence. The results obtained in this study show that the most important agronomic and quality traits of brown mustard could be bred independently. Correlation between the studied traits is also discussed.     

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.     

Genomewide predictions from maize single-cross data

Maize (Zea mays L.) breeders evaluate many single-cross hybrids each year in multiple environments. Our objective was to determine the usefulness of genomewide predictions, based on marker effects from maize single-cross data, for identifying the best untested single crosses and the best inbreds within a biparental cross. We considered 479 experimental maize single crosses between 59 Iowa Stiff Stalk Synthetic (BSSS) inbreds and 44 non-BSSS inbreds. The single crosses were evaluated in multilocation experiments from 2001 to 2009 and the BSSS and non-BSSS inbreds had genotypic data for 669 single nucleotide polymorphism (SNP) markers. Single-cross performance was predicted by a previous best linear unbiased prediction (BLUP) approach that utilized marker-based relatedness and information on relatives, and from genomewide marker effects calculated by ridge-regression BLUP (RR-BLUP). With BLUP, the mean prediction accuracy (r _MG) of single-cross performance was 0.87 for grain yield, 0.90 for grain moisture, 0.69 for stalk lodging, and 0.84 for root lodging. The BLUP and RR-BLUP models did not lead to r _MG values that differed significantly. We then used the RR-BLUP model, developed from single-cross data, to predict the performance of testcrosses within 14 biparental populations. The r _MG values within each testcross population were generally low and were often negative. These results were obtained despite the above-average level of linkage disequilibrium, i.e., r ² between adjacent markers of 0.35 in the BSSS inbreds and 0.26 in the non-BSSS inbreds. Overall, our results suggested that genomewide marker effects estimated from maize single crosses are not advantageous (compared with BLUP) for predicting single-cross performance and have erratic usefulness for predicting testcross performance within a biparental cross.     

Genetic control of soybean seed oil: II. QTL and genes that increase oil concentration without decreasing protein or with increased seed yield

Soybean [Glycine max (L.) Merrill] seed oil is the primary global source of edible oil and a major renewable and sustainable feedstock for biodiesel production. Therefore, increasing the relative oil concentration in soybean is desirable; however, that goal is complex due to the quantitative nature of the oil concentration trait and possible effects on major agronomic traits such as seed yield or protein concentration. The objectives of the present study were to study the relationship between seed oil concentration and important agronomic and seed quality traits, including seed yield, 100-seed weight, protein concentration, plant height, and days to maturity, and to identify oil quantitative trait loci (QTL) that are co-localized with the traits evaluated. A population of 203 F_4:6 recombinant inbred lines, derived from a cross between moderately high oil soybean genotypes OAC Wallace and OAC Glencoe, was developed and grown across multiple environments in Ontario, Canada, in 2009 and 2010. Among the 11 QTL associated with seed oil concentration in the population, which were detected using either single-factor ANOVA or multiple QTL mapping methods, the number of QTL that were co-localized with other important traits QTL were six for protein concentration, four for seed yield, two for 100-seed weight, one for days to maturity, and one for plant height. The oil-beneficial allele of the QTL tagged by marker Sat_020 was positively associated with seed protein concentration. The oil favorable alleles of markers Satt001 and GmDGAT2B were positively correlated with seed yield. In addition, significant two-way epistatic interactions, where one of the interacting markers was solely associated with seed oil concentration, were identified for the selected traits in this study. The number of significant epistatic interactions was seven for yield, four for days to maturity, two for 100-seed weight, one for protein concentration, and one for plant height. The identified molecular markers associated with oil-related QTL in this study, which also have positive effects on other important traits such as seed yield and protein concentration, could be used in the soybean marker breeding programs aimed at developing either higher seed yield and oil concentration or higher seed protein and oil concentration per hectare. Alternatively, selecting complementary parents with greater breeding values due to positive epistatic interactions could lead to the development of higher oil soybean cultivars.     

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.     

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。