scholarly journals Genomic heritability estimates in sweet cherry reveal non-additive genetic variance is relevant for industry-prioritized traits

2017 ◽  
Author(s):  
J. Piaskowski ◽  
Craig Hardner ◽  
Lichun Cai ◽  
Yunyang Zhao ◽  
Amy Iezzoni ◽  
...  
Keyword(s):  
Genetic Variance ◽  
Sweet Cherry ◽  
Additive Genetic Variance ◽  
Economic Benefits ◽  
Fruit Weight ◽  
Phenotypic Data ◽  
Data Set ◽  
Breeding Values ◽  
A Genome ◽  
Epistatic Variance

ABSTRACTBackgroundSweet cherry is consumed widely across the world and provides substantial economic benefits in regions where it is grown. While cherry breeding has been conducted in the Pacific Northwest for over half a century, little is known about the genetic architecture of important traits. We used a genome-enabled mixed model to predict the genetic performance of 505 individuals for 32 phenological, disease response and fruit quality traits evaluated in the RosBREED sweet cherry crop data set. Genome-wide predictions were estimated using a repeated measures model for phenotypic data across 3 years, incorporating additive, dominance and epistatic variance components. Genomic relationship matrices were constructed with high-density SNP data and were used to estimate relatedness and account for incomplete replication across years.ResultsHigh broad-sense heritabilities of 0.83, 0.77, and 0.75 were observed for days to maturity, firmness, and fruit weight, respectively. Epistatic variance exceeded 40% of the total genetic variance for maturing timing, firmness and powdery mildew response. Dominance variance was the largest for fruit weight and fruit size at 34% and 27%, respectively. Omission of non-additive sources of genetic variance from the genetic mode resulted in inflation of narrow-sense heritability but minimally influenced prediction accuracy of genetic values in validation. Predicted genetic rankings of individuals from single-year models were inconsistent across years, likely due to incomplete sampling of the population genetic variance.ConclusionsPredicted breeding values and genetic values a measure revealed many high-performing individuals for use as parents and the most promising selections to advance for cultivar release consideration, respectively. This study highlights the importance of using the appropriate genetic model for calculating breeding values to avoid inflation of expected parental contribution to genetic gain. The genomic predictions obtained will enable breeders to efficiently leverage the genetic potential of North American sweet cherry germplasm by identifying high quality individuals more rapidly than with phenotypic data alone.

Potential biases of incomplete linear models in heritability estimation and breeding value prediction

1999 ◽  
Vol 29 (6) ◽  
pp. 724-736 ◽  
Author(s):  
P X Lu ◽  
D A Huber ◽  
T L White
Keyword(s):  
Genetic Variance ◽  
Linear Models ◽  
Additive Genetic Variance ◽  
Genetic Effect ◽  
Breeding Value ◽  
Additive Genetic Effect ◽  
Unbalanced Data ◽  
Breeding Values ◽  
Incomplete Model ◽  
Heritability Estimation

Potential biases associated with incomplete linear models in the estimation of heritability and the prediction of breeding values have been investigated. Results indicate that estimates of additive genetic variance and heritability as well as predicted parental breeding values from incomplete models will inevitably be biased as long as the true variance components of ignored effects are not zero. While models ignoring the interaction effect of males and females (SCA) × environment (E) interaction downwardly biased the estimates of additive genetic variance and heritability, models ignoring SCA and (or) the additive genetic effect (GCA) × E interaction yielded upward biases. The magnitudes of biases are functions of population genetic architecture, mating design, and field experimental design and can be precisely assessed with formulae derived for balanced data. Numerical simulations using unbalanced data of different mating and field experimental designs suggest that the formulae from balanced data can be used to approximate the minimum biases associated with unbalanced data. Because of the magnitudes of biases for some typical forest genetic scenarios, it is suggested that models ignoring SCA and (or) GCA × E should be avoided when the numbers of test sites and crosses per parent are small. However, incomplete model ignoring SCA × E interaction may be used to reduce computational demand with only negligible consequences.

scholarly journals Combining ability analysis in near homozygous lines of okra [Abelmoschus esculentus (L.) Moench] for yield and yield attributing parameters

2017 ◽  
Vol 9 (1) ◽  
pp. 324-331
Author(s):  
Y. A. Lyngdoh ◽  
R. Mulge ◽  
A. Shadap ◽  
Jogendra Singh ◽  
Seema Sangwan
Keyword(s):  
Combining Ability ◽  
Genetic Variance ◽  
Gene Action ◽  
Additive Genetic Variance ◽  
Total Yield ◽  
Fruit Weight ◽  
Additive Gene Action ◽  
Combining Ability Analysis ◽  
Additive Gene ◽  
Number Of Branches

Line × tester analysis was carried out with the objective of identifying the good combiners and to decide the breeding strategies for developing potential and productive genotypes or cultivars. Parents and hybrids differed significantly for GCA and SCA effects for all the characters respectively. Specific combining ability (SCA) variance was higher than the general combining ability (GCA) variance which shows the predominance of non-additive gene action for the improvement of all the characters studied. The parents and crosses having highest and significant GCA and SCA effects viz., KO-18 (13.69), KO-6 (9.54) and KO-2 × Parbhani Kranti (19.28) for plant height; KO-12 (0.34), KO-14 (0.19) and KO-5 × V5 (0.60) for number of branches per plant; KO-14 (-0.66) and KO-15 × Arka Anamika(-1.66) for days to first flowering; KO-1(1.10), Arka Anamika (0.46) and KO-9 × VRO-5 (3.28) for fruit length; KO-7 (7.91), VRO-5(1.68) and KO-18 × VRO-6 (8.64) for average fruit weight; KO-2 (1.18) and KO-17 × Arka Anamika (2.80) for number of fruits per plant; KO-9(0.05), VRO-6 (0.01) and KO-11 × VRO-6 (0.10) for total yield per plant were identified as good general and specific combiners. The results establish the worth of heterosis breeding for effective usage of non-additive genetic variance in okra.

The struggle to exploit non-additive variation

2005 ◽  
Vol 56 (9) ◽  
pp. 873 ◽  
Author(s):  
Bruce Walsh
Keyword(s):  
Genetic Variation ◽  
Asexual Reproduction ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Epistatic Variance

Whereas animal breeders largely focus on improvement using additive genetic variance, inbreeding and asexual reproduction allow plant breeders to at least partially exploit non-additive genetic variance as well. We briefly review various approaches used by breeders to exploit dominance and epistatic variance, discuss their constraints and limitations, and examine what (if anything) can be done to improve our ability to further use often untapped genetic variation.

Asymptotic rates of response from index selection

1989 ◽  
Vol 49 (2) ◽  
pp. 217-227 ◽  
Author(s):  
Naomi R. Wray ◽  
W. G. Hill
Keyword(s):  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Index Selection ◽  
Selection Intensity ◽  
Response To Selection ◽  
Multiple Trait ◽  
Breeding Values ◽  
Breeding Programmes ◽  
Breeding Schemes ◽  
Rate Of Response

ABSTRACTThe reduction in additive genetic variance due to selection is investigated when index selection using family records is practised. A population of infinite size with no accumulation of inbreeding, an infinitesimal model and discrete generations are assumed. After several generations of selection, the additive genetic variance and the rate of response to selection reach an asymptote. A prediction of the asymptotic rate of response is considered to be more appropriate for comparing response from alternative breeding programmes and for comparing predicted and realized response than the response following the first generation of selection that is classically used. Algorithms to calculate asymptotic response rate are presented for selection based on indices which include some or all of the records of an individual, its full- and half-sibs and its parental estimated breeding values. An index using all this information is used to predict response when selection is based on breeding values estimated by using a Best Linear Unbiased Prediction (BLUP) animal model, and predictions agree well with simulation results. The predictions are extended to multiple trait selection.Asymptotic responses are compared with one-generation responses for a variety of alternative breeding schemes differing in population structure, selection intensity and heritability of the trait. Asymptotic responses can be up to one-quarter less than one-generation responses, the difference increasing with selection intensity and accuracy of the index. Between family variance is reduced considerably by selection, perhaps to less than half its original value, so selection indices which do not account for this tend to place too much emphasis on family information. Asymptotic rates of response to selection, using indices including family information for traits not measurable on the individuals available for selection, such as sex limited or post-slaughter traits, are found to be as much as two-fifths less than their expected one-generation responses. Despite this, the ranking of the breeding schemes is not greatly altered when compared by one-generation rather than asymptotic responses, so the one-generation prediction is usually likely to be adequate for determining optimum breeding structure.

Selection on purebred and crossbred Performance for litter size in pigs*

2000 ◽  
Vol 43 (3) ◽  
pp. 249-262 ◽  
Author(s):  
M. Bösch ◽  
R. Röhe ◽  
H. Looft ◽  
E. Kalm
Keyword(s):  
Genetic Correlation ◽  
Litter Size ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Genetic Parameter ◽  
Selection Method ◽  
Data Sets ◽  
Phenotypic Variance ◽  
Breeding Values ◽  
Estimated Breeding Values

Abstract. The present study deals with estimation of genetic parameter for purebred and crossbred Performance of live born piglets, in order to choose the optimal selection method. Data sets of two pure breeds, line L03 and L04, with 5,422 sows, a two line crossbred, L303, with 3,553 sows and a three line crossbred, L350, with 3,609 sows of a North-German breeding Company were recorded. Estimated genetic correlation between purebred and crossbred Performance were rg = 0.59 and 0.40 for reciprocal crosses L03xL04 and L04xL03, respectively. Further investigations showed that the genetic correlation is influenced by genotype-environment interactions between a nucleus farm and a farm on production level. Full-sib effects showed a proportion of FS = 0.06 on the phenotypic variance of litter size. They were confounded with additive genetic variance and permanent environment variance, when full-sib effects were neglected. The percentage of equal selected purebred sires of line L03 were 80% when 30% of the sires selected on purebred or crossbred breeding values. Accuracy of estimated breeding values of purebred sires increased when crossbred Information were considered additionally from 0.32 to 0.38 for line L03 and 0.46 to 0.47 for line L04. Genetic correlation between purebred and crossbred Performance, the genetic connectedness between nucleus and production and the presence of genotype-environment interactions were analysed to have high influence on the value of additionally considered crossbred Performance.

scholarly journals Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

2020 ◽  
Author(s):  
Valentin Hivert ◽  
Julia Sidorenko ◽  
Florian Rohart ◽  
Michael E Goddard ◽  
Jian Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Sampling Variance ◽  
Analysis Model ◽  
Additive Variance ◽  
Snp Data ◽  
Causal Variants ◽  
The Uk ◽  
Epistatic Variance

AbstractNon-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive , dominance and additive-by-additive genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide new theory to predict standard errors estimated using either least squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits . In contrast, the average estimate of across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance across the traits was 0.058, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive, and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.

scholarly journals Standing genetic variation as a major contributor to adaptation in the Virginia chicken lines selection experiment

2015 ◽  
Author(s):  
Zheya Sheng ◽  
Mats E Pettersson ◽  
Christa F Honaker ◽  
Paul B Siegel ◽  
Örjan Carlborg
Keyword(s):  
Body Weight ◽  
Genetic Variation ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Selection Response ◽  
Selective Sweeps ◽  
Base Population ◽  
A Genome ◽  
Wide Scale ◽  
Genomic Regions

Artificial selection has, for decades, provided a powerful approach to study the genetics of adaptation. Using selective-sweep mapping, it is possible to identify genomic regions in populations where the allele-frequencies have diverged during selection. To avoid misleading signatures of selection, it is necessary to show that a sweep has an effect on the selected trait before it can be considered adaptive. Here, we confirm candidate selective-sweeps on a genome-wide scale in one of the longest, on-going bi-directional selection experiments in vertebrates, the Virginia high and low body-weight selected chicken lines. The candidate selective-sweeps represent standing genetic variants originating from the common base-population. Using a deep-intercross between the selected lines, 16 of 99 evaluated regions were confirmed to contain adaptive selective-sweeps based on their association with the selected trait, 56-day body-weight. Although individual additive effects were small, the fixation for alternative alleles in the high and low body-weight lines across these loci contributed at least 40% of the divergence between them and about half of the additive genetic variance present within and between the lines after 40 generations of selection. The genetic variance contributed by the sweeps corresponds to about 85% of the additive genetic variance of the base-population, illustrating that these loci were major contributors to the realised selection-response. Thus, the gradual, continued, long- term selection response in the Virginia lines was likely due to a considerable standing genetic variation in a highly polygenic genetic architecture in the base-population with contributions from a steady release of selectable genetic variation from new mutations and epistasis throughout the course of selection.

scholarly journals Genetic Variance Components and Heritabilities of Several Avocado Traits

1993 ◽  
Vol 118 (3) ◽  
pp. 400-404 ◽  
Author(s):  
Uri Lavi ◽  
Emanuel Lahav ◽  
Chemda Degani ◽  
Shmuel Gazit ◽  
Jossi Hillel
Keyword(s):  
Variance Components ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Fruit Weight ◽  
Persea Americana ◽  
Broad Sense Heritability ◽  
Low Performance ◽  
Breeding Efficiency ◽  
Flowering Intensity ◽  
Inflorescence Length

Genetic variance components for avocado (Persea americana Mill.) traits were estimated to improve avocado breeding efficiency. The additive and nonadditive genetic variance components were calculated from the variances between and within crosses. In all nine traits examined, i.e.-anise scent, fruit density, flowering intensity, fruit weight, harvest duration, inflorescence length, seed size, softening time, and tree size-a significant nonadditive genetic variance was detected. Additive genetic variance in all traits was lower and nonsignificant. The existence of major nonadditive variance was indicated also by narrow-sense and broad-sense heritability values estimated for each trait. Therefore, parental selection should not be based solely on cultivar performance. Crosses between parents of medium and perhaps even low performance should also be included in the breeding program.

scholarly journals Genome-wide identification of loci associated with growth in rainbow trout

2020 ◽  
Author(s):  
Ali Ali ◽  
Rafet Al-Tobasei ◽  
Daniela Lourenco ◽  
Tim Leeds ◽  
Brett Kenney ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Additive Genetic Variance ◽  
Chromatin Modification ◽  
Bodyweight Gain ◽  
Single Step ◽  
Production Traits ◽  
Phenotypic Data ◽  
Developmental Processes ◽  
Genome Wide ◽  
A Genome

Abstract Background Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout. Results A previously developed 50K gene-transcribed SNP chip, containing ~21K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n = 50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 33 SNPs were identified in association with bodyweight gain. The highest SNP explaining variation in bodyweight gain was identified in a gene coding for thrombospondin-1 (THBS1) (R 2 = 0.09). Conclusion The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.

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