scholarly journals Comparison of breeding strategies for the creation of a synthetic pig line

2021 ◽  
Author(s):  
Audrey Ganteil ◽  
Torsten Pook ◽  
Silvia T. Rodriguez-Ramilo ◽  
Bruno Ligonesche ◽  
Catherine Larzul
Keyword(s):  
Management Strategy ◽  
First Generation ◽  
Genomic Data ◽  
Diversity Management ◽  
Moderate Degree ◽  
Pedigree Data ◽  
Genetic Progress ◽  
Phenotypic Data ◽  
Inbreeding Coefficients ◽  
Diversity Control

Creating a new synthetic line by crossbreeding means complementary traits from pure breeds can be combined in the new population. Although diversity is generated during the crossbreeding stage, in this study, we analyze diversity management before selection starts. Using genomic and phenotypic data from animals belonging to the first generation (G0) of a new line, different simulations were run to evaluate diversity management during the first generations of a new line and to test the effects of starting selection at two alternative times, G3 and G4. Genetic diversity was characterized by allele frequency, inbreeding coefficients based on genomic and pedigree data, and expected heterozygosity. Breeding values were extracted at each generation to evaluate differences in starting selection at G3 or G4. All simulations were run for ten generations. A scenario with genomic data to manage diversity during the first generations of a new line was compared with a random and a selection scenario. As expected, loss of diversity was higher in the selection scenario, while the scenario with diversity control preserved diversity. We also combined the diversity management strategy with different selection scenarios involving different degrees of diversity control. Our simulation results show that a diversity management strategy combining genomic data with selection starting at G4 and a moderate degree of diversity control generates genetic progress and preserves diversity.

scholarly journals Genetic Variability in Polish Lowland Sheepdogs Assessed by Pedigree and Genomic Data

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1520
Author(s):  
Paula Wiebke Michels ◽  
Ottmar Distl
Keyword(s):  
Genetic Variability ◽  
Population Size ◽  
Effective Population Size ◽  
Ductus Arteriosus ◽  
Genomic Data ◽  
Reference Population ◽  
Pedigree Data ◽  
Effective Population ◽  
Inbreeding Coefficients ◽  
Candidate Loci

Genetic variability of Polish Lowland Sheepdog (PON) population was evaluated using both pedigree and genomic data. The analyzed pedigree encompassed 8628 PONs, including 153 individuals genotyped on the Illumina CanineHD BeadChip. Runs of homozygosity (ROH) were defined for homozygous stretches extending over 60 to 4300 kb. The inbreeding coefficients FPed based on pedigree data and FROH50 based on ROHs were at 0.18 and 0.31. The correlation between both was 0.41 but 0.52 when excluding animals with less than seven complete generations. The realized effective population size (Ne¯) was 22.2 with an increasing trend over years. Five PONs explained 79% of the genetic diversity of the reference population. The effective population size derived from linkage disequilibrium measured by r² was 36. PANTHER analysis of genes in ROHs shared by ≥50% of the PONs revealed four highly over- or underrepresented biological processes. One among those is the 7.35 fold enriched “forelimb morphogenesis”. Candidate loci for hip dysplasia and patent ductus arteriosus were discovered in frequently shared ROHs. In conclusion, the inbreeding measures of the PONs were high and the genetic variability small compared to various dog breeds. Regarding Ne¯, PON population was minimally endangered according to the European Association for Animal Production.

scholarly journals Genetic Parameters of Workability Traits in the Population of Polish Holstein-Friesian Cows Based on Conventional and Genomic Data

Animals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 2443
Author(s):  
Bartosz Szymik ◽  
Piotr Topolski ◽  
Wojciech Jagusiak
Keyword(s):  
Fixed Effects ◽  
Genetic Parameters ◽  
Genetic Parameter ◽  
Correlation Coefficients ◽  
Genomic Data ◽  
Phenotypic Correlation ◽  
Pedigree Data ◽  
Effective Population ◽  
Phenotypic Data ◽  
Holstein Friesian

Heritabilities of workability (WT) traits—milking speed (MS) and temperament (MT)—as well as genetic and phenotypic correlations between these traits in the population of Polish Holstein-Friesian (PHF) cows were estimated. The estimation of genetic parameters was performed twice: first with the use of pedigree data; and second with the use of pedigree and genomic data. Phenotypic data from routinely conducted MS and MT evaluations for 1,045,511 cows born from 2004 to 2013 were available; the cows were evaluated from 2011 to 2015. The main dataset was reduced based on imposed restrictions (e.g., on age of calving, stage of lactation and day of first trial milking). The dataset prepared in this manner comprised 391,615 cows. It was then reduced to daughters of 10% randomly selected sires for computational reasons. Finally, for genetic parameter estimation, 13,280 records of cows were used. The linear observation model included additive random effects of animal, fixed effects of herd-year-season of calving subclass (HYS) and lactation phase, fixed regressions on cow age at calving and the percent of HF breed genes in the cow genotype. Heritabilities estimated based on pedigree data were 0.12 (±0.0067) for MS and 0.08 (±0.0063) for MT, the genetic correlation between MS and MT was estimated at 0.05 (±0.0002) and the phenotypic correlation coefficient was estimated at 0.14 (±0.0004). The inclusion of genomic information of sire bulls had no clear effect on the size of the estimated WT genetic parameters. The heritabilities of MS and MT were 0.11 (±0.0065) and 0.09 (±0.0012), respectively. The genetic and phenotypic correlation coefficients were 0.07 (±0.0003) and 0.12 (±0.0005), respectively. The sizes of the obtained heritabilities of WT and of the genetic and phenotypic correlation between these traits indicate the possibility of effective population improvement for both WT traits.

scholarly journals The theory on and software simulating large-scale genomic data for genotype-by-environment interactions

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiujin Li ◽  
Hailiang Song ◽  
Zhe Zhang ◽  
Yunmao Huang ◽  
Qin Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Simulated Data ◽  
Genomic Data ◽  
Efficient Tool ◽  
Phenotypic Data ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Threshold Trait ◽  
Genome Wide ◽  
Increasing Demand

Abstract Background With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. Results We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. Conclusions This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

scholarly journals Beef cattle breeding in Australia with genomics: opportunities and needs

2012 ◽  
Vol 52 (3) ◽  
pp. 100 ◽  
Author(s):  
D. J. Johnston ◽  
B. Tier ◽  
H.-U. Graser
Keyword(s):  
Beef Cattle ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Information Needs ◽  
Genomic Information ◽  
Female Reproduction ◽  
Genetic Progress ◽  
Cattle Breeding ◽  
Phenotypic Data ◽  
Genomic Predictions

Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.

Large-Scale Analysis of Genetic and Clinical Patient Data

2018 ◽  
Vol 1 (1) ◽  
pp. 263-274 ◽  
Author(s):  
Marylyn D. Ritchie
Keyword(s):  
Clinical Data ◽  
Large Scale ◽  
Data Science ◽  
Genomic Analysis ◽  
Genomic Data ◽  
Data Sets ◽  
Biomedical Data ◽  
Data Types ◽  
Phenotypic Data ◽  
Clinical Patient

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

scholarly journals Homology Models of Wuhan Coronavirus 3CLpro Protease

2020 ◽  
Author(s):  
Martin Stoermer
Keyword(s):  
Cysteine Protease ◽  
Viral Genome ◽  
First Generation ◽  
Genomic Data ◽  
Hubei Province ◽  
Rapid Release ◽  
Main Protease ◽  
Novel Coronavirus ◽  
Homology Models

A regional outbreak of pneumonia in Wuhan, Hubei Province of China in late 2019 was associated with a novel coronavirus. Rapid release of genomic data for the isolated virus enabled the construction of first-generation homology models of the new CoV 3CL<sup>pro</sup> cysteine protease. Whilst the overall viral genome was most closely associated with bat coronaviruses, the main protease is most closely related (96% identity) to SARS CoV protease.

scholarly journals Estimation of Pool Construction and Technical Error

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1091
Author(s):  
John Keele ◽  
Tara McDaneld ◽  
Ty Lawrence ◽  
Jenny Jennings ◽  
Larry Kuehn
Keyword(s):  
Low Cost ◽  
Genetic Evaluation ◽  
Technical Error ◽  
Pedigree Data ◽  
Phenotypic Data ◽  
Tissue Mass ◽  
Novel Traits ◽  
Extreme Phenotypes ◽  
Processing Plants ◽  
Average Contribution

Pooling animals with extreme phenotypes can improve the accuracy of genetic evaluation or provide genetic evaluation for novel traits at relatively low cost by exploiting large amounts of low-cost phenotypic data from animals in the commercial sector without pedigree (data from commercial ranches, feedlots, stocker grazing or processing plants). The average contribution of each animal to a pool is inversely proportional to the number of animals in the pool or pool size. We constructed pools with variable planned contributions from each animal to approximate errors with different numbers of animals per pool. We estimate pool construction error based on combining liver tissue, from pulverized frozen tissue mass from multiple animals, into eight sub-pools containing four animals with planned proportionality (1:2:3:4) by mass. Sub-pools were then extracted for DNA and genotyped using a commercial array. The extracted DNA from the sub-pools was used to form super pools based on DNA concentration as measured by spectrophotometry with planned contribution of sub-pools of 1:2:3:4. We estimate technical error by comparing estimated animal contribution using sub-samples of single nucleotide polymorphism (SNP). Overall, pool construction error increased with planned contribution of individual animals. Technical error in estimating animal contributions decreased with the number of SNP used.

scholarly journals The Crisis of Multiculturalism in Modern Germany

2021 ◽  
Vol 9 (SPE2) ◽  
Author(s):  
Marat Zufarovich Galiullin ◽  
Viktor Evgenievich Tumanin ◽  
Ildar Kimovich Kalimonov ◽  
Ildar Kimovich Kalimonov ◽  
Elvira Imbelevna Kamaletdinova ◽  
...  
Keyword(s):  
Management Strategy ◽  
Major Part ◽  
Diversity Management ◽  
European Politics ◽  
Far Right ◽  
Modern Germany

Criticism of far-right diversity management strategy has become a major part of European politics. In addition, from Scandinavian countries to Greece, anti-immigration parties and the number of votes has increased among the population concerned about the effects of growing diversity.

scholarly journals Evaluating genomic data for management of local adaptation in a changing climate: A lodgepole pine case study

2019 ◽  
Author(s):  
Colin R. Mahony ◽  
Ian R. MacLachlan ◽  
Brandon M. Lind ◽  
Jeremy B. Yoder ◽  
Tongli Wang ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Local Adaptation ◽  
Lodgepole Pine ◽  
Genomic Data ◽  
Genomic Variation ◽  
Similar Species ◽  
Adaptive Variation ◽  
Phenotypic Data ◽  
Standing Variation ◽  
Climatic Drivers

AbstractThe need for tools to cost-effectively identify adaptive variation within ecologically and economically important plant species is mounting as the detrimental effects of climate change become increasingly apparent. For crop and wild populations alike, mismatches between adaptive variation and climatic optima will reduce health, growth, survival, reproduction, and continued establishment. The ease with which land managers can quantify the relative importance of different climate factors or the spatial scale of local adaptation to climate will have direct implications for the potential of mitigating or resolving such risks. Using seed collected from 281 provenances of lodgepole pine (Pinus contorta) from across western Canada, we compare genomic data to phenotypic and climatic data to assess their effectiveness in characterizing the climatic drivers and spatial scale of local adaptation in this species. We find that genomic and climate data are nearly equivalent for describing local adaptation in seedling traits. We also find strong agreement between the climate variables associated with genomic variation and with 20-year heights from a long-term provenance trial, suggesting that genomic data may be a viable option for identifying climatic drivers of local adaptation where phenotypic data are unavailable. Genetic clines associated with cold injury occur at broad spatial scales, suggesting that standing variation of adaptive alleles for this and similar species does not require management at scales finer than are indicated by phenotypic data. This study demonstrates that genomic data are most useful when paired with phenotypic data, but can also fill some of the traditional roles of phenotypic data in management of species for which phenotypic trials are not feasible.

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