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Research article
Dörte Wittenburg
Leibniz Institute for Farm Animal Biology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3639-2574
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background : Linkage and linkage disequilibrium (LD) between genome regions cause dependencies among genomic markers. Due to family stratification in populations with non-random mating in livestock or crop, the standard measures of population LD such as r 2 may be biased. Grouping of markers according to their interdependence needs to account for the actual population structure in order to allow proper inference in genome-based evaluations.
Results : Given a matrix reflecting the strength of association between markers, groups are built successively using a greedy algorithm; largest groups are built at first. As an option, a representative marker is selected for each group. We provide an implementation of the grouping approach as a new function to the R package hscovar. This package enables the calculation of the theoretical covariance between biallelic markers for half- or full-sib families and the calculation of representative markers. In case studies, we have shown that the number of groups comprising dependent markers was smaller and representative SNPs were spread more uniformly over the investigated chromosome region when the family stratification was respected compared to a population-LD approach. In a simulation study, we observed that sensitivity and specificity of a genome-based association study improved if selection of representative markers took family structure into account.
Conclusions : Chromosome segments which frequently recombine in the underlying population can be identified from the matrix of pairwise dependence between markers. Representative markers can be exploited, for instance, for dimension reduction prior to a genome-based association study or the grouping structure itself can be employed in a grouped penalization approach.
Keywords
Single nucleotide polymorphism, Covariance matrix, Clustering, TagSNP, Group lasso, SNP-BLUP
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
- additionalfile1covariance.pdf
Additional file 1 – PDF file summarizing the derivation of family-based correlation matrices Given the population structure, half-sib families or full-sib families, the covariance matrix is analytically retrieved. Its computation using the R package hscovar is shown.
- additionalfile2preparemousedata.R.txt
Additional file 2 — TXT file containing R code for mouse data Raw mouse data are processed and the matrix of correlation between markers is derived.
- additionalfile3preparecattledata.R.txt
Additional file 3 — TXT file containing R code for cattle data Cattle data are processed and the matrix of correlation between markers is derived
- additionalfile4preparemaizedata.R.txt
Additional file 4 — TXT file containing R code for maize data Raw maize data are processed and the matrix of correlation between markers is derived.
- additionalfile5simstudygwas.R.txt
Additional file 5 — TXT file containing R code for simulation study With this script, genotype and phenotype data of half-sib families are simulated and a genome-based association analysis is carried out.
- additionalfile6plotallexamples.R.txt
Additional file 6 — TXT file containing R code for creating plots Plots of correlation matrices and population-LD matrices are produced based on results with additional files 2–5.
- additionalfile7tables.pdf
Additional file 7 — PDF file with additional tables Number of groups, number of groups with at least three SNPs and Calinski-Harabasz index for different simulation scenarios and varying threshold.
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CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
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Posted 06 Jan, 2021
No community comments so far
Review #1 received
Received 28 Dec, 2020
Reviewer #2 agreed
On 27 Dec, 2020
Reviewers invited
Invitations sent on 22 Dec, 2020
Reviewer #1 agreed
On 22 Dec, 2020
Editor assigned
On 21 Dec, 2020
Submission checks complete
On 21 Dec, 2020
Editor invited
On 21 Dec, 2020
Community comments: 1
Editorial decision: Minor revision
On 11 Dec, 2020
Review #2 received
Received 08 Dec, 2020
Review #1 received
Received 23 Nov, 2020
Reviewer #2 agreed
On 15 Nov, 2020
Reviewer #1 agreed
On 02 Nov, 2020
Reviewers invited
Invitations sent on 30 Oct, 2020
Editor assigned
On 29 Oct, 2020
Submission checks complete
On 29 Oct, 2020
Editor invited
On 29 Oct, 2020
No comments provided
Editorial decision: Major revision
On 02 Oct, 2020
Review #2 received
Received 24 Sep, 2020
Review #1 received
Received 11 Sep, 2020
Reviewer #3 agreed
On 10 Sep, 2020
Reviewer #2 agreed
On 03 Sep, 2020
Reviewer #1 agreed
On 31 Aug, 2020
Reviewers invited
Invitations sent on 22 Aug, 2020
Editor assigned
On 20 Aug, 2020
Submission checks complete
On 14 Aug, 2020
Editor invited
On 14 Aug, 2020
First submitted
On 05 Aug, 2020
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Subject Areas
Bioinformatics
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at BMC Bioinformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Dörte Wittenburg
Leibniz Institute for Farm Animal Biology
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3639-2574
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Bioinformatics.
Version 3
Posted 06 Jan, 2021
No community comments so far
Review #1 received
Received 28 Dec, 2020
Reviewer #2 agreed
On 27 Dec, 2020
Reviewers invited
Invitations sent on 22 Dec, 2020
Reviewer #1 agreed
On 22 Dec, 2020
Editor assigned
On 21 Dec, 2020
Submission checks complete
On 21 Dec, 2020
Editor invited
On 21 Dec, 2020
Community comments: 1
Editorial decision: Minor revision
On 11 Dec, 2020
Review #2 received
Received 08 Dec, 2020
Review #1 received
Received 23 Nov, 2020
Reviewer #2 agreed
On 15 Nov, 2020
Reviewer #1 agreed
On 02 Nov, 2020
Reviewers invited
Invitations sent on 30 Oct, 2020
Editor assigned
On 29 Oct, 2020
Submission checks complete
On 29 Oct, 2020
Editor invited
On 29 Oct, 2020
No comments provided
Editorial decision: Major revision
On 02 Oct, 2020
Review #2 received
Received 24 Sep, 2020
Review #1 received
Received 11 Sep, 2020
Reviewer #3 agreed
On 10 Sep, 2020
Reviewer #2 agreed
On 03 Sep, 2020
Reviewer #1 agreed
On 31 Aug, 2020
Reviewers invited
Invitations sent on 22 Aug, 2020
Editor assigned
On 20 Aug, 2020
Submission checks complete
On 14 Aug, 2020
Editor invited
On 14 Aug, 2020
First submitted
On 05 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background : Linkage and linkage disequilibrium (LD) between genome regions cause dependencies among genomic markers. Due to family stratification in populations with non-random mating in livestock or crop, the standard measures of population LD such as r 2 may be biased. Grouping of markers according to their interdependence needs to account for the actual population structure in order to allow proper inference in genome-based evaluations.
Results : Given a matrix reflecting the strength of association between markers, groups are built successively using a greedy algorithm; largest groups are built at first. As an option, a representative marker is selected for each group. We provide an implementation of the grouping approach as a new function to the R package hscovar. This package enables the calculation of the theoretical covariance between biallelic markers for half- or full-sib families and the calculation of representative markers. In case studies, we have shown that the number of groups comprising dependent markers was smaller and representative SNPs were spread more uniformly over the investigated chromosome region when the family stratification was respected compared to a population-LD approach. In a simulation study, we observed that sensitivity and specificity of a genome-based association study improved if selection of representative markers took family structure into account.
Conclusions : Chromosome segments which frequently recombine in the underlying population can be identified from the matrix of pairwise dependence between markers. Representative markers can be exploited, for instance, for dimension reduction prior to a genome-based association study or the grouping structure itself can be employed in a grouped penalization approach.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
- additionalfile1covariance.pdf
Additional file 1 – PDF file summarizing the derivation of family-based correlation matrices Given the population structure, half-sib families or full-sib families, the covariance matrix is analytically retrieved. Its computation using the R package hscovar is shown.
- additionalfile2preparemousedata.R.txt
Additional file 2 — TXT file containing R code for mouse data Raw mouse data are processed and the matrix of correlation between markers is derived.
- additionalfile3preparecattledata.R.txt
Additional file 3 — TXT file containing R code for cattle data Cattle data are processed and the matrix of correlation between markers is derived
- additionalfile4preparemaizedata.R.txt
Additional file 4 — TXT file containing R code for maize data Raw maize data are processed and the matrix of correlation between markers is derived.
- additionalfile5simstudygwas.R.txt
Additional file 5 — TXT file containing R code for simulation study With this script, genotype and phenotype data of half-sib families are simulated and a genome-based association analysis is carried out.
- additionalfile6plotallexamples.R.txt
Additional file 6 — TXT file containing R code for creating plots Plots of correlation matrices and population-LD matrices are produced based on results with additional files 2–5.
- additionalfile7tables.pdf
Additional file 7 — PDF file with additional tables Number of groups, number of groups with at least three SNPs and Calinski-Harabasz index for different simulation scenarios and varying threshold.
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