scholarly journals Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 585 ◽  
Author(s):  
Seema Yadav ◽  
Phillip Jackson ◽  
Xianming Wei ◽  
Elizabeth M. Ross ◽  
Karen Aitken ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Cycle Length ◽  
Primary Source ◽  
Genetic Effects ◽  
Breeding Cycle ◽  
Narrow Sense Heritability ◽  
Breeding Values ◽  
Sugarcane Breeding ◽  
Additive Genetic Effects

Sugarcane is a major industrial crop cultivated in tropical and subtropical regions of the world. It is the primary source of sugar worldwide, accounting for more than 70% of world sugar consumption. Additionally, sugarcane is emerging as a source of sustainable bioenergy. However, the increase in productivity from sugarcane has been small compared to other major crops, and the rate of genetic gains from current breeding programs tends to be plateauing. In this review, some of the main contributors for the relatively slow rates of genetic gain are discussed, including (i) breeding cycle length and (ii) low narrow-sense heritability for major commercial traits, possibly reflecting strong non-additive genetic effects involved in quantitative trait expression. A general overview of genomic selection (GS), a modern breeding tool that has been very successfully applied in animal and plant breeding, is given. This review discusses key elements of GS and its potential to significantly increase the rate of genetic gain in sugarcane, mainly by (i) reducing the breeding cycle length, (ii) increasing the prediction accuracy for clonal performance, and (iii) increasing the accuracy of breeding values for parent selection. GS approaches that can accurately capture non-additive genetic effects and potentially improve the accuracy of genomic estimated breeding values are particularly promising for the adoption of GS in sugarcane breeding. Finally, different strategies for the efficient incorporation of GS in a practical sugarcane breeding context are presented. These proposed strategies hold the potential to substantially increase the rate of genetic gain in future sugarcane breeding.

scholarly journals SNP-based mate allocation strategies to maximize total genetic value in pigs

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
David González-Diéguez ◽  
Llibertat Tusell ◽  
Céline Carillier-Jacquin ◽  
Alban Bouquet ◽  
Zulma G. Vitezica
Keyword(s):  
Inbreeding Depression ◽  
Genetic Gain ◽  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Effects ◽  
Breeding Values ◽  
Genetic Merit ◽  
Additive Genetic Effects ◽  
Allocation Strategies

Abstract Background Mate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored. Results Genetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE. Conclusions Genomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain.

Combining Ability of Plant Height and Yield components in Indian Mustard (Brassica junceaL.Czern & Coss.) under Salt affected Soil using Line×Tester Analysis

2016 ◽  
Vol 3 (2) ◽  
Author(s):  
SHAILESH CHAND GAUTAM ◽  
MP Chauhan
Keyword(s):  
Plant Height ◽  
Seed Yield ◽  
Combining Ability ◽  
Yield Components ◽  
Indian Mustard ◽  
Heritability Estimate ◽  
Genetic Effects ◽  
Narrow Sense ◽  
Narrow Sense Heritability ◽  
Additive Genetic Effects

Line × tester analysis of twenty lines and three testers of Indian mustard (Brassica juncea L. Czern & Coss.) cultivars were used to estimate general combining ability (GCA), specific combining ability (SCA) effects, high parent heterosis and narrow-sense heritability estimate for plant height, yield components and seed yield. Significant variance of line x tester for the traits like pods per plant and seed yield indicating non additive genetic effects have important role for controlling these traits. Significant mean squares of parents v/s crosses which are indicating significant average heterosis were also significant for all the traits except seeds per pod. High narrow-sense heritability estimates for all the traits except seeds per pod exhibited the prime importance of additive genetic effects for these traits except seeds per pod. Most of the crosses with negative SCA effect for plant height had at least one parent with significant negative or negative GCA effect for this trait. For most of the traits except pods per plant, the efficiency of high parent heterosis effect was more than SCA effect for determining superior cross combinations.

scholarly journals Designing dairy cattle breeding schemes under genomic selection: a review of international research

2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler
Keyword(s):  
Dairy Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Reproductive Technologies ◽  
Progeny Testing ◽  
Genomic Breeding ◽  
Breeding Values ◽  
Scheme Design ◽  
Dairy Cattle Breeding ◽  
Breeding Schemes

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

scholarly journals Identification of an Elite Core Panel as a Key Breeding Resource to Accelerate the Rate of Genetic Improvement for Irrigated Rice

2021 ◽  
Author(s):  
Roselyne U. Juma ◽  
Jérôme Bartholomé ◽  
Parthiban Thathapalli Prakash ◽  
Waseem Hussain ◽  
John Damien Platten ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genetic Gain ◽  
Genetic Improvement ◽  
Critical Role ◽  
The Philippines ◽  
Breeding Program ◽  
Breeding Cycle ◽  
Relationship Matrix ◽  
Irrigated Rice ◽  
Breeding Values

Abstract Rice genetic improvement is a key component of achieving and maintaining food security in Asia and Africa in the face of growing populations and climate change. In this effort, the International Rice Research Institute (IRRI) continues to play a critical role in creating and disseminating rice varieties with higher productivity. Due to increasing demand for rice, especially in Africa, there is a strong need to accelerate the rate of genetic improvement for grain yield.In an effort to identify and characterize the elite breeding pool of IRRI’s irrigated rice breeding program, we analyzed 102 historical yield trials conducted in the Philippines during the period 2012-2016 and representing 15,286 breeding lines (including released varieties). A mixed model approach based on the pedigree relationship matrix was used to estimate breeding values for grain yield, which ranged from 2.12 to 6.27 t·ha-1. The rate of genetic gain for grain yield was estimated at 8.75 kg·ha-1·year-1 (0.23%) for crosses made in the period from 1964 to 2014. Reducing the data to only IRRI released varieties, the rate doubled to 17.36 kg·ha-1·year-1 (0.46%). Regressed against breeding cycle the rate of gain for grain yield was 185 kg·ha-1·cycle-1 (4.95%). We selected 72 top performing lines based on breeding values for grain yield to create an elite core panel (ECP) representing the genetic diversity in the breeding program with the highest heritable yield values from which new products can be derived. The ECP closely aligns with the indica 1B sub-group of Oryza sativa that includes most modern varieties for irrigated systems. Agronomic performance of the ECP under multiple environments in Asia and Africa confirmed its high yield potential.We found that the rate of genetic gain for grain yield found in this study was limited primarily by long cycle times and the direct introduction of non-improved material into the elite pool. Consequently, the current breeding scheme for irrigated rice at IRRI is based on rapid recurrent selection among highly elite lines. In this context, the ECP constitutes an important resource for IRRI and NAREs breeders to carefully characterize and manage that elite diversity.

scholarly journals Heterobeltiosis and interrelationship of some of important quantitative traits in oilseed rape genotypes

2019 ◽  
Vol 4 (1) ◽  
pp. 591-598
Author(s):  
Valiollah Rameeh
Keyword(s):  
Plant Height ◽  
Oilseed Rape ◽  
Seed Yield ◽  
Seed Weight ◽  
Selection Criterion ◽  
Yield Component ◽  
Genetic Effects ◽  
Narrow Sense Heritability ◽  
Additive Genetic Effects ◽  
Half Diallel

AbstractHalf diallel crosses of eight spring genotypes of oilseed rape (Brassica napus L.) were considered to evaluate heterobeltiosis effects of plant height, yield component characters, seed yield and harvest index. Significant mean squares of general and specific combining abilities (GCA and SCA) were determined for all the traits except 1000-seed weight demonstrating prominence of additive and non additive genetic effects for the mentioned traits. Narrow-sense heritability estimates were high for siliquae on main raceme and 1000-seed weight representing the major importance of additive genetic effects for the characters. Most of the crosses with significant positive high parent heterosis for seed yield had also significant heterotic effects for siliquae per plant; therefore, this trait can be considered as indirect selection criterion for enhancing seed yield. Seed yield was significantly correlated with the traits including plant height, siliquae on main raceme and siliquae per plant based on mean performances of the traits and this result was confirmed with correlations based on heterobeltiosis. The crosses including L41×LF2 and L31×L401 with highly significant heterobeltiosis estimates of grain yield were superior combinations for breeding this trait. which proved good specific combiners for most of the traits.

scholarly journals Line × tester analysis in rapeseed: Identification of superior parents and combinations for seed yield and its components

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Valiollah Rameeh
Keyword(s):  
Seed Yield ◽  
Combining Ability ◽  
Selection Criterion ◽  
Genetic Effects ◽  
Narrow Sense Heritability ◽  
Mean Values ◽  
Early Maturing ◽  
Additive Genetic Effects ◽  
Pod Length ◽  
Additive Genes

AbstractThe objectives of this research were to investigate the genetic structure of the 20 F1s rapeseed hybrids established from five female moderate maturing lines and four early maturing male testers, determine parents showing general combining ability (GCA) and assess crosses demonstrating specific combining ability (SCA). Significant mean squares of lines and testers determined GCA and confirmed the presence of additive genes that were influencing the traits, while the significance of line×tester interactions indicated the importance of SCA of parents and demonstrated the importance of dominance or non-additive genetic effects. Significant variance of parents vs. crosses revealed significant average heterosis for all the traits except first pod height and seeds per pod. High narrow-sense heritability estimates for number of branches and pod length indicated the importance of additive genetic effects for these traits. Significantly positive correlation was exhibited between GCA effects on pods on main raceme and seed yield and, therefore, the GCA effect on pods on main raceme can be used as indirect selection criterion for improvement of seed yield. The crosses L41×Foma2, Zafar×R42 and L22B×R38 recorded significant positive SCA effects and high mean values of seed yield of 3400, 3311.3 and 2904.2 kg ha-1, respectively.

Genetic evaluation of ewe performance in Outaouais Arcott and Suffolk sheep and their crosses

1996 ◽  
Vol 76 (1) ◽  
pp. 7-14 ◽  
Author(s):  
A. M. Shafto ◽  
G. H. Crow ◽  
R. J. Parker ◽  
W. M. Palmer ◽  
J. N. B. Shrestha ◽  
...  
Keyword(s):  
Key Words ◽  
Reciprocal Cross ◽  
Genetic Evaluation ◽  
Genetic Effects ◽  
Sheep Breed ◽  
Breeding Cycle ◽  
Litter Weight ◽  
Additive Genetic Effects ◽  
Suffolk Sheep

Ewes from a newly developed sheep breed in Canada, the Outaouais Arcott, consistently out-performed Suffolk ewes in prolificacy at birth and at 42 d of age by 0.37 lambs at first parity and by 0.55 and 0.41 lambs, respectively, over all parities. In contrast, mean litter weights of Suffolk and Outaouais ewes did not differ at these times. Additive genetic effects in prolificacy and litter weight tended to favour the Outaouais breed over the Suffolk breed at first parity. Corresponding values over all panties were significant, favouring the Outaouais, for both prolificacy and litter weight at 42 d of age. Suffolk ewes excelled in maternal genetic effects for litter weight for all parities at birth and 42 d of age. Reciprocal cross ewes of the Outaouais and Suffolk breeds were similar (P > 0.05) in prolifacacy and litter weight. In general, crossbred ewes consistently exceeded Suffolk ewes in prolificacy but were not significantly different from Outaouais ewes. Though not always significant, crossbred ewes exceeded the average of their purebred parents m prolifacacy and litter weight. Furthermore, estimates of heterosis were always positive, and the 19% value for litter weight at 42 d of age at first parity was significant. This study, conducted over 6 yr, demonstrates the superiority of the Outaouais breed for production of commercial crossbred ewes to perform in a moderately intensive, semi-confinement operation. This superiority exists in addition to the previously-established ability of the Outaouais ewe to produce large litters in an 8-mo breeding cycle. Key words: Ewes, prolifacacy, litter weight, genetic effects, heterosis

scholarly journals Sequencing depth and genotype quality: Accuracy and breeding operation considerations for genomic selection applications in autopolyploid crops

2020 ◽  
Author(s):  
Dorcus C Gemenet ◽  
Hannele Lindqvist-Kreuze ◽  
Bode A Olukolu ◽  
Bert De Boeck ◽  
Guilherme da Silva Pereira ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Service Providers ◽  
Predictive Ability ◽  
Genetic Effects ◽  
Additive Models ◽  
Sequencing Depth ◽  
Nucleotide Polymorphisms ◽  
Breeding Populations ◽  
Wide Range ◽  
Additive Genetic Effects

AbstractThe autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: i) do polyploid crop breeders need to invest more for additional sequencing depth? ii) how many markers are required to make selection decisions? iii) does considering non-additive genetic effects improve predictive ability (PA)? iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms (SNPs; ≤ 1000) are adequate for prediction, hence it is possible to get this number at the current sequencing depth from most service providers. We also show that considering dosage information and additive-effects only models had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.Key messagePolypoid crop breeders do not need more investment for sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture.

scholarly journals A review of how dairy farmers can use and profit from genomic technologies

2012 ◽  
Vol 52 (3) ◽  
pp. 180 ◽  
Author(s):  
Jennie Pryce ◽  
Ben Hayes
Keyword(s):  
Energy Balance ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Diseases ◽  
Breeding Value ◽  
Feed Conversion ◽  
Breeding Values ◽  
Genomic Technologies ◽  
Net Profit ◽  
Estimated Breeding Value

New genomic technologies can help farmers to (1) achieve higher annual rates of genetic gain through using genomically tested bulls in their herds, (2) select for ‘difficult’ to measure traits, such as feed conversion efficiency, methane emissions and energy balance, (3) select the best heifers to become herd replacements, (4) sell pedigree heifers at a premium, (5) use mating plans to optimise rates of genetic gain while controlling inbreeding, (6) achieve certainty in parentage of individual cows and (7) avoid genetic defects that could arise from mating cows to bulls that are known carriers of genetic diseases that are the result of a single lethal mutation. The first use does not require genotyping females and could approximately double the net income per cow that arises due to genetic improvement, mainly through a reduction in generation interval. On the basis of current rates of genetic gain, the net profit from using genotyped bulls could be worth AU$20/cow per year and is permanent and cumulative. One of the most powerful uses of genomic selection is to select for economically important, yet difficult- or expensive-to-measure traits, such as residual feed intake or energy balance. Provided the accuracy of genomic breeding values is high enough (i.e. correlation between the true and estimated breeding values), these traits lend themselves well to genomic selection. For selecting replacement heifers, if genotyping costs are AU$50/cow, the net profit of genotyping 40 heifers to select the top 20 as replacements (per 100 cows) would be worth approximately AU$41 per cow. However, using parent average estimated breeding-value information is free and can already be used to select replacement heifers. So, genotyping costs would need to be very low to be more profitable than selecting on parent average estimated breeding value. However, extra value from genotyping can also be captured by using other strategies. For example, mating plans that use genomic relationships rather than pedigree relationships to capture inbreeding are superior in terms of reducing progeny inbreeding at a desired level of genetic gain, although pedigree does an adequate job. So, again, the benefits of genotyping are small (<AU$10). Ascertainment of pedigree is an additional use of genotyping and is potentially worth ~AU$30 per cow. Avoidance of genetic diseases and selling of pedigree heifers have a value that should be estimated case-by-case. Because genotyping costs continue to fall, it may become increasingly popular to capture the extra value from genotyping females.

scholarly journals Simulation studies to optimize genomic selection in honey bees

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Richard Bernstein ◽  
Manuel Du ◽  
Andreas Hoppe ◽  
Kaspar Bienefeld
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Honey Bees ◽  
Reference Population ◽  
Breeding Program ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Genomic Breeding ◽  
Breeding Values

Abstract Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

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