scholarly journals Genetic architecture and genomic selection of female reproduction traits in rainbow trout

2020 ◽  
Author(s):  
Jonathan D’Ambrosio ◽  
Romain Morvezen ◽  
Sophie Brard-Fudulea ◽  
Anastasia Bestin ◽  
Charles Poncet ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Genomic Selection ◽  
Genetic Architecture ◽  
Egg Size ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Female Reproduction ◽  
Egg Number ◽  
Reproduction Traits ◽  
Selection For

Abstract Background Rainbow trout is a significant fish farming industry under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits. Results A pedigreed population of 1,343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits. Conclusions Our genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection.

scholarly journals Genetic architecture and genomic selection of female reproduction traits in rainbow trout

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
J. D’Ambrosio ◽  
R. Morvezen ◽  
S. Brard-Fudulea ◽  
A. Bestin ◽  
A. Acin Perez ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
Genomic Selection ◽  
Genetic Architecture ◽  
Female Reproduction ◽  
Reproduction Traits ◽  
Selection Of

Genetic parameters for egg weight v. age curve, and other egg production and egg weight traits, in synthetic lines of chickens

1983 ◽  
Vol 34 (1) ◽  
pp. 85 ◽  
Author(s):  
BH Yoo ◽  
BL Sheldon ◽  
RN Podger
Keyword(s):  
Standard Deviation ◽  
Genetic Correlation ◽  
Egg Production ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Egg Mass ◽  
Control Line ◽  
Egg Weight ◽  
Egg Number ◽  
Selection For

An exponential curve, W = P-Qexp(- Rt), where W is egg weight at age t, was fitted to egg weights of individual pullets, and genetic parameters were estimated for P, Q and R, the residual standard deviation and other egg weight and egg production characters. The data consisted of records collected over six generations on more than 4000 pullets in two selection lines and a control line which originated from a synthetic gene pool of White Leghorn x Australorp crosses. The half-sib and offspring-on-parent regression estimates of heritability pooled over the lines were 0.23 and 0.33 for P, 0.14 and 0.20 for Q, and 0.14 and 0.25 for R. Genetic correlations were estimated to be -0.10 between P and Q, -0.46 between P and R, and 0.90 between Q and R. These estimates suggest that the egg weight v. age curve may be modified to increase the proportion of eggs in desirable weight grades and reduce the incidence of oversized eggs later in the production year. The genetic correlation between mean weight of first 10 eggs and egg weight at 62 weeks of age was estimated to be 0.68, further suggesting that early egg weight may be improved partly independently of late egg weight. The heritability estimates of egg mass output were not higher than those of egg number in spite of the highly heritable average egg weight being an important component of egg mass, probably because of the negative genetic correlation (r = -0.49) between egg number and average egg weight. The standard deviation of individual pullet's egg weights was moderately heritable and genetically correlated positively with egg weight characters and negatively with egg production; these estimates were consistent with the responses to selection for reduced egg weight variability observed elsewhere

Genomic selection for female reproduction in Australian tropically adapted beef cattle

2014 ◽  
Vol 54 (1) ◽  
pp. 16 ◽  
Author(s):  
Y. D. Zhang ◽  
D. J. Johnston ◽  
S. Bolormaa ◽  
R. J. Hawken ◽  
B. Tier
Keyword(s):  
Genomic Selection ◽  
Cross Validation ◽  
Population Data ◽  
Northern Australia ◽  
Female Reproduction ◽  
Genetic Complexity ◽  
Reproduction Traits ◽  
Experimental Populations ◽  
Snp Panels ◽  
Genomic Predictions

The usefulness of genomic selection was assessed for female reproduction in tropically adapted breeds in northern Australia. Records from experimental populations of Brahman (996) and Tropical Composite (1097) cattle that had had six calving opportunities were used to derive genomic predictions for several measures of female fertility. These measures included age at first corpus luteum (AGECL), at first calving and subsequent postpartum anoestrous interval and measures of early and lifetime numbers of calves born or weaned. In a second population, data on pregnancy and following status (anoestrous or pregnancy) were collected from 27 commercial herds from northern Australia to validate genomic predictions. Cows were genotyped with a variety of single nucleotide polymorphism (SNP) panels and, where necessary, genotypes imputed to the highest density (729 068 SNPs). Genetic parameters of subsets of the complete data were estimated. These subsets were used to validate genomic predictions using genomic best linear unbiased prediction using both univariate cross-validation and bivariate analyses. Estimated heritability ranged from 0.56 for AGECL to 0.03 for lifetime average calving rate in the experimental cows, and from 0.09 to 0.25 for early life reproduction traits in the commercial cows. Accuracies of predictions were generally low, reflecting the limited number of data in the experimental populations. For AGECL and postpartum anoestrous interval, the highest accuracy was 0.35 for experimental Brahman cows using five-fold univariate cross-validation. Greater genetic complexity in the Tropical Composite cows resulted in the corresponding accuracy of 0.23 for AGECL. Similar level of accuracies (from univariate and bivariate analyses) were found for some of the early measures of female reproduction in commercial cows, indicating that there is potential for genomic selection but it is limited by the number of animals with phenotypes.

scholarly journals VisEgg: A robust phenotyping tool to assess rainbow trout egg features and viability

2020 ◽  
Author(s):  
Emilie Cardona ◽  
Jérôme Bugeon ◽  
Emilien Segret ◽  
Julien Bobe
Keyword(s):  
Rainbow Trout ◽  
Embryonic Development ◽  
Egg Size ◽  
Vitelline Membrane ◽  
Hatching Rate ◽  
Egg Viability ◽  
Image Processing Algorithm ◽  
Indirect Measure ◽  
Developmental Potential ◽  
Egg Number

AbstractAssessing female fish reproductive success requires a thorough evaluation of egg characteristics, including egg number, size and variability as well as egg developmental potential through the monitoring of embryo survival after fertilization. While embryonic success relies, at least in part, on paternal contribution, some parameters are strictly related to egg characteristics, one of the main ones being the viability of the egg when released into the water at spawning. It is however not necessarily possible, at least in salmonid fish that lay non-transparent eggs, to separate the different causes of egg/embryo failure.In this context, our aim was i) to develop a simple and rapid system to capture images of rainbow trout eggs combined with computerized processing of these images to perform a fully automatic individual characterization of egg features including number and size ii) to estimate unfertilized egg viability through the monitoring of the percentage of eggs that will not survive to water hydration.To evaluate the VisEgg system, unfertilized eggs (approximatively 400 eggs per batch) originating from 105 different females were hydrated in water. After 24h, a picture of the eggs was obtained using a dedicated shooting system consisting of a light source and a digital single-lens reflex (SLR) camera. An image processing algorithm was developed to allow the automatic detection and separation of the eggs and to perform automatic measurements of egg number and individual egg size. The presence of white egg was used as an indirect measure of egg integrity, the “whitening” being the result of water entry into the egg through the vitelline membrane. These white eggs were therefore considered as non-viable, as a result of their lack of physical integrity.Fertilization assays were performed in parallel using a subsample of the same egg batch. Embryonic development was monitored and hatching rate was calculated. A significant correlation between white egg percentage after hydration and hatching rate was observed (Spearman coefficient =-0.557, p<0.001), in consistency with the fact that non-viable egg will not allow successful embryonic development. In contrast, the percentage of eggs that do not successfully hatched includes egg/embryo failures of different nature including egg viability, their capacity to be fertilized and to develop into an embryo. Using VisEgg, we were able to quantify the lack of viability of the eggs separately from the different other events that may occur during fertilization and incubation. VisEgg is a convenient and reliable tool to obtain individuals measures on trout eggs. It can be used to assess not only egg size and egg number but also unfertilized egg viability before fertilization.

Corrigendum - Response to selection in Australian Merino sheep. II. Estimates of phenotypic and genetic parameters for some production traits in Merino ewes and an analysis of the possible effects of selection on them

1968 ◽  
Vol 19 (2) ◽  
pp. 303
Author(s):  
GH Brown ◽  
HN Turner
Keyword(s):  
Genetic Parameters ◽  
Genetic Variance ◽  
Statistical Significance ◽  
Additive Genetic Variance ◽  
Genetic Correlations ◽  
Production Traits ◽  
Average Value ◽  
Selection History ◽  
Order Of Magnitude ◽  
Selection For

Estimates of heritabilities and of phenotypic and genetic correlations are given, based on extensive measurements on medium Peppin Merino ewes at 15–16 months of age. In general these substantiate results obtained by other workers and, in particular, confirm the high heritabilities of the traits measured. An effort has been made to try to detect possible changes in additive genetic variance for the trait under selection (clean wool weight). Estimates are obtained for data from animals at different stages of selection: (A) either unselected, or with little selection history, and (B and C) with varying amounts of selection. For stage A data the average estimated additive genetic variance was 0.31. There are problems involved in estimating from stage (B+C) data but an upper limit average value of 0.22 was obtained. Thus, although a decrease in additive genetic variance has occurred, its statistical significance is unknown and conclusions about the decrease must necessarily be tentative. In practically all cases the estimates of phenotypic and genetic correlations are of the same order of magnitude, and for the genetic correlations may be summarized as: See PDFAll other combinations of traits have negligible genetic correlations (in the range –0.20 to + 0.2).

48 Genomic Studies of Feed Efficiency and Component Traits in American Mink

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 24-25
Author(s):  
Pourya Davoudi ◽  
Duy Ngoc Do ◽  
Guoyu Hu ◽  
Siavash Salek Ardestani ◽  
Younes Miar
Keyword(s):  
Genomic Selection ◽  
Feed Intake ◽  
Feed Efficiency ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Relationships ◽  
American Mink ◽  
Final Body Weight ◽  
Component Traits ◽  
Selection For

Abstract Feed costs are the largest expense in mink production systems. Therefore, improvement of feed efficiency (FE) is the best way to use limited resources efficiently and increase the mink industry’s sustainability. The objectives of this project are to 1) identify the genetic relationships among different FE measures and their component traits, and 2) discover the genetic architecture of FE and implement genomic selection for FE traits to increase the genetic gain in American mink. Final body weight (FBW), final body length (FBL), daily feed intake (DFI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), residual gain (RG), residual intake and gain (RIG) and Kleiber ratio (KR) traits were measured based on the phenotypic records on 1,088 American mink from the Canadian Center for Fur Animal Research (Nova Scotia, Canada). Univariate models were applied to test the significance of sex, color type, age, and nested Row(Year) as fixed effects and random maternal effect. Genetic parameters were estimated via bivariate models using ASReml-R 4. Estimated heritabilities (±SE) were 0.38±0.10, 0.36±0.10, 0.25±0.10, 0.34±0.09, 0.38±0.08, 0.37±0.07, 0.29±0.10, 0.32±0.10 and 0.34±0.10 for FBW, FBL, DFI, ADG, FCR, RFI, RG, RIG and KR, respectively. RFI showed non-significant (P &gt;0.05) genetic correlations with component traits such as FBW (0.00±0.17) and FBL (0.30±0.16) but significant (P &lt; 0.05) high genetic correlation with DFI (0.74±0.09), indicating that selection based on RFI will reduce the feed intake without any negative effects on the size and growth. The estimated genetic parameters for FE traits suggested the possibility to implement genetic/genomic selection to improve the FE in American mink. Consequently, the ongoing project on genetic mapping and genomic selection will enhance the knowledge of FE and improve the efficacy of selection for more feed-efficient mink.

Genetic parameters for a heavy female turkey line

2005 ◽  
Vol 2005 ◽  
pp. 9-9
Author(s):  
A. D. Kranis ◽  
J. A. Woolliams ◽  
W. G. Hill ◽  
P. M. Hocking
Keyword(s):  
Body Weight ◽  
Egg Production ◽  
Genetic Parameters ◽  
Selection Criterion ◽  
Breast Muscle ◽  
Commercial Company ◽  
Egg Number ◽  
Research Population ◽  
Simultaneous Selection ◽  
Selection For

The major selection criterion in the turkey breeding industry is increased breast muscle and body weight in order to adapt to market demands. In female lines a dual selection for both body weight and egg production is performed. However, most published estimates indicate a variable correlation between growth and egg number (Nestor et al., 1996) and so the challenge posed is how to best to select for those opposing goals. The objective of this study was to investigate the effects of simultaneous selection for body weight and egg number by estimating the genetic parameters for a research population held by a commercial company in two different locations.

scholarly journals Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing among full-sibs

2021 ◽  
Author(s):  
Antoine Fraimout ◽  
Zitong Li ◽  
Mikko J. Sillanpää ◽  
Pasi Rastas ◽  
Juha Merilä
Keyword(s):  
Natural Selection ◽  
Variance Components ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Identity By Descent ◽  
Quantitative Genetic ◽  
Genome Wide ◽  
Quantitative Genetic Parameters

ABSTRACTAdditive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most wild animal populations. Using estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci, we estimated quantitative genetic parameters for traits known to be under directional natural selection in nine-spined sticklebacks (Pungitius pungitius) and compared these to traditional pedigree-based estimators. Using four different datasets, with varying sample sizes and pedigree complexity, we further assessed the performance of different Genomic Relationship Matrices (GRM) to estimate additive and dominance variance components. Large variance in IBD relationships allowed accurate estimation of genetic variance components, and revealed significant heritability for all measured traits, with negligible dominance contributions. Genome-partitioning analyses revealed that all traits have a polygenic basis and are controlled by genes at multiple chromosomes. The results demonstrate how large full-sib families of highly fecund vertebrates can be used to obtain accurate estimates quantitative genetic parameters to provide insights on genetic architecture of quantitative traits in non-model organisms from the wild. This approach should be particularly useful for studies requiring estimates of genetic variance components from multiple populations as for instance when aiming to infer the role of natural selection as a cause for population differentiation in quantitative traits.

scholarly journals 303 ASAS-EAAP Talk: On the estimation of genetic parameters from molecular data

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 33-33
Author(s):  
Miguel Toro Ibáñez
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Correlation ◽  
Genomic Selection ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Molecular Data ◽  
Snp Markers ◽  
Directional Selection ◽  
Recombination Rates

Abstract We deal with several problems that arise when inferring genetic parameters at the level of quantitative trait loci (QTL) from molecular data such as SNP markers. Linkage Disequilibrium (LD) is recognized as a factor creating ambiguity in the partition of genetic variance. Here, using a simple model with three loci (one QTL and two markers), it is shown that the markers generate apparent AxA and DxD epistasis, even if only third order disequilibrium exists and the QTL is dominant. The problem of “phantom epistasis” is not alleviated by larger sample sizes (de los Campos et al., 2019). We also show that markers can give a distorted picture of the genetic correlation between traits: The genomic correlation could be greater, lower or even of opposite sign than the true genetic correlation. Therefore, speculating about genetic correlations and even about their causes (e.g., pleiotropy) using genomic data is often conjectural. Thirdly, we examine the problem of directional selection generating negative linkage disequilibrium (“Bulmer effect”) in the short term from a genomic selection perspective. It seems that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP. However, the reduction in response with genomic selection is greater than when selection is based directly on phenotypes only (Van Grevenhof et al. 2012). It is also expected that directional selection for a polygenic trait should increase recombination rate, provided there is genetic variance for recombination. It is then of relevance to ask whether recombination rates could be manipulated in order to increase selection response (Battagin et al., 2016). Finally, we consider that recombination and epistasis are closely intertwined: Epistasis generate LD and recombination break them up. Then, we should expect genomes to be modular: regions with low recombination containing functionally related genes loosely linked to other regions.

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