Example of Genetics Selection Evolution format
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Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format
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Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format Example of Genetics Selection Evolution format
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open access Open Access
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Genetics Selection Evolution — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Animal Science and Zoology #9 of 416 up up by 2 ranks
Ecology, Evolution, Behavior and Systematics #50 of 647 up up by 21 ranks
Genetics #67 of 325 up up by 31 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 303 Published Papers | 2131 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 07/07/2020
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Related Journals

open access Open Access
recommended Recommended

Wiley

Quality:  
High
CiteRatio: 5.3
SJR: 1.204
SNIP: 1.714
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Springer

Quality:  
Medium
CiteRatio: 1.0
SJR: 0.271
SNIP: 0.686
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Hindawi

Quality:  
High
CiteRatio: 3.1
SJR: 0.429
SNIP: 1.331
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PLOS

Quality:  
High
CiteRatio: 7.3
SJR: 2.628
SNIP: 1.713

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

3.95

28% from 2018

Impact factor for Genetics Selection Evolution from 2016 - 2019
Year Value
2019 3.95
2018 3.094
2017 3.743
2016 2.964
graph view Graph view
table view Table view

7.0

13% from 2019

CiteRatio for Genetics Selection Evolution from 2016 - 2020
Year Value
2020 7.0
2019 6.2
2018 6.2
2017 5.4
2016 4.4
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 28% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 13% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.356

10% from 2019

SJR for Genetics Selection Evolution from 2016 - 2020
Year Value
2020 1.356
2019 1.5
2018 1.583
2017 1.745
2016 1.535
graph view Graph view
table view Table view

1.69

17% from 2019

SNIP for Genetics Selection Evolution from 2016 - 2020
Year Value
2020 1.69
2019 1.445
2018 1.235
2017 1.314
2016 1.334
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 10% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 17% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Genetics Selection Evolution

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Springer

Genetics Selection Evolution

Approved by publishing and review experts on SciSpace, this template is built as per for Genetics Selection Evolution formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 542 authors to write and format their manuscripts to this journal.

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Last updated on
06 Jul 2020
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ISSN
1606-8610
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Open Access
Yes
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Sherpa RoMEO Archiving Policy
White faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder, G.E., Tinkham, M., Klapwijk, T.M.: Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys. Rev. B 25(7), 4515–4532 (1982)

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/1297-9686-34-3-275
A review on SNP and other types of molecular markers and their use in animal genetics
Alain Vignal1, Denis Milan1, Magali SanCristobal1, André Eggen1

Abstract:

During the last ten years, the use of molecular markers, revealing polymorphism at the DNA level, has been playing an increasing part in animal genetics studies. Amongst others, the microsatellite DNA marker has been the most widely used, due to its easy use by simple PCR, followed by a denaturing gel electrophoresis for alle... During the last ten years, the use of molecular markers, revealing polymorphism at the DNA level, has been playing an increasing part in animal genetics studies. Amongst others, the microsatellite DNA marker has been the most widely used, due to its easy use by simple PCR, followed by a denaturing gel electrophoresis for allele size determination, and to the high degree of information provided by its large number of alleles per locus. Despite this, a new marker type, named SNP, for Single Nucleotide Polymorphism, is now on the scene and has gained high popularity, even though it is only a bi-allelic type of marker. In this review, we will discuss the reasons for this apparent step backwards, and the pertinence of the use of SNPs in animal genetics, in comparison with other marker types. read more read less

Topics:

SNP genotyping (61%)61% related to the paper, Molecular marker (58%)58% related to the paper, Microsatellite (54%)54% related to the paper, Animal Genetics (54%)54% related to the paper, Genetic marker (53%)53% related to the paper
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1,010 Citations
open accessOpen access Journal Article DOI: 10.1186/1297-9686-42-2
Genomic prediction when some animals are not genotyped
Ole F. Christensen1, Mogens Sandø Lund1

Abstract:

The use of genomic selection in breeding programs may increase the rate of genetic improvement, reduce the generation time, and provide higher accuracy of estimated breeding values (EBVs). A number of different methods have been developed for genomic prediction of breeding values, but many of them assume that all animals have... The use of genomic selection in breeding programs may increase the rate of genetic improvement, reduce the generation time, and provide higher accuracy of estimated breeding values (EBVs). A number of different methods have been developed for genomic prediction of breeding values, but many of them assume that all animals have been genotyped. In practice, not all animals are genotyped, and the methods have to be adapted to this situation. In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals. The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect, where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix. The extension of the model to non-genotyped animals is made by using the pedigree to derive an extension of the genomic relationship matrix to non-genotyped animals. As a result, in the extended model the estimated breeding values are obtained by blending the information used to compute traditional EBVs and the information used to compute purely genomic EBVs. Parameters in the model are estimated using average information REML and estimated breeding values are best linear unbiased predictions (BLUPs). The method is illustrated using a simulated data set. The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic, pedigree and phenotype information into a one-step procedure for genomic prediction. Such a one-step procedure results in more accurate estimated breeding values and has the potential to become the standard tool for genomic prediction of breeding values in future practical evaluations in pig and cattle breeding. read more read less
View PDF
681 Citations
open accessOpen access Journal Article DOI: 10.1186/1297-9686-15-2-201
Sire evaluation for ordered categorical data with a threshold model

Abstract:

A method of evaluation of ordered categorical responses is presented. The probability of response in a given category follows a normal integral with an argument dependent on fixed thresholds and random variables sampled from a conceptual distribution with known first and second moments, a priori. The prior distribution and th... A method of evaluation of ordered categorical responses is presented. The probability of response in a given category follows a normal integral with an argument dependent on fixed thresholds and random variables sampled from a conceptual distribution with known first and second moments, a priori. The prior distribution and the likelihood function are combined to yield the posterior density from which inferences are made. The mode of the posterior distribution is taken as an estimator of location. Finding this mode entails solving a non-linear system ; estimation equations are presented. Relationships of the procedure to \"generalized linear models\" and \"normal scores are discussed. A numerical example involving sire evaluation for calving ease is used to illustrate the method. read more read less

Topics:

Categorical variable (67%)67% related to the paper, Threshold model (53%)53% related to the paper, Sire (53%)53% related to the paper
View PDF
605 Citations
open accessOpen access Journal Article DOI: 10.1186/1297-9686-41-55
Deregressing estimated breeding values and weighting information for genomic regression analyses
Dorian J. Garrick1, Dorian J. Garrick2, Jeremy F. Taylor3, Rohan L. Fernando1

Abstract:

Background Genomic prediction of breeding values involves a so-called training analysis that predicts the influence of small genomic regions by regression of observed information on marker genotypes for a given population of individuals. Available observations may take the form of individual phenotypes, repeated observations... Background Genomic prediction of breeding values involves a so-called training analysis that predicts the influence of small genomic regions by regression of observed information on marker genotypes for a given population of individuals. Available observations may take the form of individual phenotypes, repeated observations, records on close family members such as progeny, estimated breeding values (EBV) or their deregressed counterparts from genetic evaluations. The literature indicates that researchers are inconsistent in their approach to using EBV or deregressed data, and as to using the appropriate methods for weighting some data sources to account for heterogeneous variance. read more read less

Topics:

Population (52%)52% related to the paper
View PDF
553 Citations
open accessOpen access Journal Article DOI: 10.1186/1297-9686-24-4-305
Computing inbreeding coefficients in large populations

Abstract:

An algorithm for computing inbreeding coefficients in large populations is presented. It is especially useful in large populations because of the small size of the memory required, which is linear with population size, and its speed, if the number of generations involved is not too large, ie not larger than about 12. The meth... An algorithm for computing inbreeding coefficients in large populations is presented. It is especially useful in large populations because of the small size of the memory required, which is linear with population size, and its speed, if the number of generations involved is not too large, ie not larger than about 12. The method is compared with 2 other methods for computational speed and memory requirement. The presented algorithm is suited for situations where the inbreeding coefficients for a few new animals are to be computed given that their ancestor’s inbreeding coefficients were calculated previously. read more read less

Topics:

Selection (genetic algorithm) (62%)62% related to the paper, Population genetics (60%)60% related to the paper, Inbreeding (53%)53% related to the paper
View PDF
507 Citations
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Frequently asked questions

1. Can I write Genetics Selection Evolution in LaTeX?

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2. Do you follow the Genetics Selection Evolution guidelines?

Yes, the template is compliant with the Genetics Selection Evolution guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Genetics Selection Evolution?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Genetics Selection Evolution citation style.

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Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Genetics Selection Evolution.

5. Can I use a manuscript in Genetics Selection Evolution that I have written in MS Word?

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After writing your paper autoformatting in Genetics Selection Evolution, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Genetics Selection Evolution's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Genetics Selection Evolution?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Genetics Selection Evolution. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Genetics Selection Evolution?

The 5 most common citation types in order of usage for Genetics Selection Evolution are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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16. Can I download Genetics Selection Evolution in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Genetics Selection Evolution Endnote style according to Elsevier guidelines.

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