Example of Genetic Epidemiology format
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Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format
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Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format Example of Genetic Epidemiology format
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This content is only for preview purposes. The original open access content can be found here.
open access Open Access

Genetic Epidemiology — Template for authors

Publisher: Wiley
Categories Rank Trend in last 3 yrs
Epidemiology #59 of 99 down down by 14 ranks
Genetics (clinical) #61 of 87 down down by 12 ranks
journal-quality-icon Journal quality:
Medium
calendar-icon Last 4 years overview: 268 Published Papers | 877 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 06/07/2020
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Related Journals

open access Open Access

Wiley

Quality:  
High
CiteRatio: 5.9
SJR: 1.0
SNIP: 1.132
open access Open Access

Springer

Quality:  
Good
CiteRatio: 3.5
SJR: 0.774
SNIP: 1.015
open access Open Access
recommended Recommended

PLOS

Quality:  
High
CiteRatio: 9.0
SJR: 3.587
SNIP: 1.457
open access Open Access
recommended Recommended

Cambridge University Press

Quality:  
High
CiteRatio: 8.4
SJR: 1.718
SNIP: 1.845

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.

1.954

22% from 2018

Impact factor for Genetic Epidemiology from 2016 - 2019
Year Value
2019 1.954
2018 2.5
2017 2.544
2016 1.884
graph view Graph view
table view Table view

3.3

31% from 2019

CiteRatio for Genetic Epidemiology from 2016 - 2020
Year Value
2020 3.3
2019 4.8
2018 3.9
2017 4.0
2016 5.6
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has decreased by 22% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has decreased by 31% 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.

1.301

38% from 2019

SJR for Genetic Epidemiology from 2016 - 2020
Year Value
2020 1.301
2019 2.094
2018 1.322
2017 1.819
2016 2.44
graph view Graph view
table view Table view

0.659

34% from 2019

SNIP for Genetic Epidemiology from 2016 - 2020
Year Value
2020 0.659
2019 0.995
2018 0.706
2017 0.718
2016 1.106
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 38% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 34% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Genetic Epidemiology

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Wiley

Genetic Epidemiology

Genetic Epidemiology is the official journal of the International Genetic Epidemiology Society Genetic Epidemiology is a peer-reviewed journal for discussion of research on the genetic causes of the distribution of human traits in families and populations. Emphasis is placed o...... Read More

Epidemiology

Genetics(clinical)

Medicine

i
Last updated on
06 Jul 2020
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ISSN
0741-0395
i
Impact Factor
High - 1.129
i
Open Access
Yes
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Sherpa RoMEO Archiving Policy
Yellow faq
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Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Bibliography Name
apa
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Citation Type
Numbered
[25]
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Bibliography Example
Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1002/GEPI.21965
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator.
Jack Bowden1, George Davey Smith1, Philip C Haycock1, Stephen Burgess2
07 Apr 2016 - Genetic Epidemiology

Abstract:

Developments in genome-wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse-vari... Developments in genome-wide association studies and the increasing availability of summary genetic association data have made application of Mendelian randomization relatively straightforward. However, obtaining reliable results from a Mendelian randomization investigation remains problematic, as the conventional inverse-variance weighted method only gives consistent estimates if all of the genetic variants in the analysis are valid instrumental variables. We present a novel weighted median estimator for combining data on multiple genetic variants into a single causal estimate. This estimator is consistent even when up to 50% of the information comes from invalid instrumental variables. In a simulation analysis, it is shown to have better finite-sample Type 1 error rates than the inverse-variance weighted method, and is complementary to the recently proposed MR-Egger (Mendelian randomization-Egger) regression method. In analyses of the causal effects of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol on coronary artery disease risk, the inverse-variance weighted method suggests a causal effect of both lipid fractions, whereas the weighted median and MR-Egger regression methods suggest a null effect of high-density lipoprotein cholesterol that corresponds with the experimental evidence. Both median-based and MR-Egger regression methods should be considered as sensitivity analyses for Mendelian randomization investigations with multiple genetic variants. read more read less

Topics:

Mendelian Randomization Analysis (71%)71% related to the paper, Mendelian randomization (67%)67% related to the paper, Weighted median (57%)57% related to the paper, Regression analysis (51%)51% related to the paper
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2,959 Citations
open accessOpen access Journal Article DOI: 10.1002/GEPI.20533
MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes
Yun Li1, Cristen J. Willer2, Jun Ding2, Paul Scheet3, Gonçalo R. Abecasis2
01 Dec 2010 - Genetic Epidemiology

Abstract:

Genome-wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously impleme... Genome-wide association studies (GWAS) can identify common alleles that contribute to complex disease susceptibility. Despite the large number of SNPs assessed in each study, the effects of most common SNPs must be evaluated indirectly using either genotyped markers or haplotypes thereof as proxies. We have previously implemented a computationally efficient Markov Chain framework for genotype imputation and haplotyping in the freely available MaCH software package. The approach describes sampled chromosomes as mosaics of each other and uses available genotype and shotgun sequence data to estimate unobserved genotypes and haplotypes, together with useful measures of the quality of these estimates. Our approach is already widely used to facilitate comparison of results across studies as well as meta-analyses of GWAS. Here, we use simulations and experimental genotypes to evaluate its accuracy and utility, considering choices of genotyping panels, reference panel configurations, and designs where genotyping is replaced with shotgun sequencing. Importantly, we show that genotype imputation not only facilitates cross study analyses but also increases power of genetic association studies. We show that genotype imputation of common variants using HapMap haplotypes as a reference is very accurate using either genome-wide SNP data or smaller amounts of data typical in fine-mapping studies. Furthermore, we show the approach is applicable in a variety of populations. Finally, we illustrate how association analyses of unobserved variants will benefit from ongoing advances such as larger HapMap reference panels and whole genome shotgun sequencing technologies. read more read less

Topics:

Imputation (genetics) (70%)70% related to the paper, International HapMap Project (59%)59% related to the paper, Shotgun sequencing (53%)53% related to the paper, Genome-wide association study (52%)52% related to the paper, Haplotype (51%)51% related to the paper
View PDF
2,015 Citations
open accessOpen access Journal Article DOI: 10.1002/GEPI.21758
Mendelian randomization analysis with multiple genetic variants using summarized data.
Stephen Burgess1, Adam S. Butterworth1, Simon G. Thompson1
01 Nov 2013 - Genetic Epidemiology

Abstract:

Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mende... Genome-wide association studies, which typically report regression coefficients summarizing the associations of many genetic variants with various traits, are potentially a powerful source of data for Mendelian randomization investigations. We demonstrate how such coefficients from multiple variants can be combined in a Mendelian randomization analysis to estimate the causal effect of a risk factor on an outcome. The bias and efficiency of estimates based on summarized data are compared to those based on individual-level data in simulation studies. We investigate the impact of gene–gene interactions, linkage disequilibrium, and ‘weak instruments’ on these estimates. Both an inverse-variance weighted average of variant-specific associations and a likelihood-based approach for summarized data give similar estimates and precision to the two-stage least squares method for individual-level data, even when there are gene–gene interactions. However, these summarized data methods overstate precision when variants are in linkage disequilibrium. If the P-value in a linear regression of the risk factor for each variant is less than , then weak instrument bias will be small. We use these methods to estimate the causal association of low-density lipoprotein cholesterol (LDL-C) on coronary artery disease using published data on five genetic variants. A 30% reduction in LDL-C is estimated to reduce coronary artery disease risk by 67% (95% CI: 54% to 76%). We conclude that Mendelian randomization investigations using summarized data from uncorrelated variants are similarly efficient to those using individual-level data, although the necessary assumptions cannot be so fully assessed. read more read less

Topics:

Mendelian Randomization Analysis (68%)68% related to the paper, Mendelian randomization (65%)65% related to the paper, Genome-wide association study (51%)51% related to the paper, Linkage disequilibrium (51%)51% related to the paper
View PDF
2,003 Citations
Journal Article DOI: 10.1002/GEPI.10252
Pedigree disequilibrium tests for multilocus haplotypes.
01 Sep 2003 - Genetic Epidemiology

Abstract:

Association tests of multilocus haplotypes are of interest both in linkage disequilibrium mapping and in candidate gene studies. For case-parent trios, I discuss the extension of existing multilocus methods to include ambiguous haplotypes in tests of models which distinguish between the cis and trans phase. A likelihood-ratio... Association tests of multilocus haplotypes are of interest both in linkage disequilibrium mapping and in candidate gene studies. For case-parent trios, I discuss the extension of existing multilocus methods to include ambiguous haplotypes in tests of models which distinguish between the cis and trans phase. A likelihood-ratio test is proposed, using the expectation-maximization (E-M) algorithm to account for haplotype ambiguities. Assumptions about the population structure are required, but realistic situations, including population stratification, which violate the assumptions lead to conservative tests. I describe a permutation procedure for the null hypothesis of interest, which controls for violation of the assumptions. For general pedigrees, I describe extensions of the pedigree disequilibrium test to include uncertain haplotypes. The summary statistics are replaced by their expected values over prior distributions of haplotype frequencies. If prior distributions are not available, a valid test is possible by using the E-M algorithm to estimate the null distribution of haplotype frequencies. Similar methods are available for quantitative traits. Exact permutation tests are difficult to construct in small samples, but an approximate procedure is appropriate in large samples, and can be used to account for dependencies between tests of multiple haplotypes and loci. read more read less

Topics:

Linkage Disequilibrium Mapping (60%)60% related to the paper, Null distribution (52%)52% related to the paper, Population stratification (52%)52% related to the paper
View PDF
1,182 Citations
Implementing a unified approach to family-based tests of association.
Nan M. Laird1, Steve Horvath2, Steve Horvath1, Xin Xu1
01 Jan 2000 - Genetic Epidemiology

Abstract:

We describe a broad class of family-based association tests that are adjusted for admixture; use either dichotomous or measured phenotypes; accommodate phenotype-unknown subjects; use nuclear families, sibships or a combination of the two, permit multiple nuclear families from a single pedigree; incorporate di- or multi-allel... We describe a broad class of family-based association tests that are adjusted for admixture; use either dichotomous or measured phenotypes; accommodate phenotype-unknown subjects; use nuclear families, sibships or a combination of the two, permit multiple nuclear families from a single pedigree; incorporate di- or multi-allelic marker data; allow additive, dominant or recessive models; and permit adjustment for covariates and gene-by-environment interactions. The test statistic is the covariance between a user-specified function of the genotype and a user-specified function of the trait. The distribution of the statistic is computed using the appropriate conditional distribution of offspring genotypes that adjusts for admixture. Genet. Epidemiol. 19(Suppl 1):S36–S42, 2000. © 2000 Wiley-Liss, Inc. read more read less

Topics:

Test statistic (55%)55% related to the paper, Statistic (52%)52% related to the paper, Nuclear family (51%)51% related to the paper
896 Citations
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Genetic Epidemiology format uses apa citation style.

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Frequently asked questions

1. Can I write Genetic Epidemiology in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Genetic Epidemiology guidelines and auto format it.

2. Do you follow the Genetic Epidemiology guidelines?

Yes, the template is compliant with the Genetic Epidemiology 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 Genetic Epidemiology?

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 Genetic Epidemiology citation style.

4. Can I use the Genetic Epidemiology templates for free?

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 Genetic Epidemiology.

5. Can I use a manuscript in Genetic Epidemiology that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Genetic Epidemiology that you can download at the end.

6. How long does it usually take you to format my papers in Genetic Epidemiology?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in Genetic Epidemiology.

7. Where can I find the template for the Genetic Epidemiology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Genetic Epidemiology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the Genetic Epidemiology's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. Genetic Epidemiology an online tool or is there a desktop version?

SciSpace's Genetic Epidemiology is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

10. I cannot find my template in your gallery. Can you create it for me like Genetic Epidemiology?

Sure. You can request any template and we'll have it setup within a few days. You can find the request box in Journal Gallery on the right side bar under the heading, "Couldn't find the format you were looking for like Genetic Epidemiology?”

11. What is the output that I would get after using Genetic Epidemiology?

After writing your paper autoformatting in Genetic Epidemiology, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Genetic Epidemiology'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 Genetic Epidemiology?

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 Genetic Epidemiology. 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 Genetic Epidemiology?

The 5 most common citation types in order of usage for Genetic Epidemiology are:.

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

15. How do I submit my article to the Genetic Epidemiology?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per Genetic Epidemiology's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download Genetic Epidemiology 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 Genetic Epidemiology Endnote style according to Elsevier guidelines.

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