Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms
Abstract Background It becomes clear that the increase in the density of marker panels and even the use of sequence data didn’t result in any meaningful increase in the accuracy of genomic selection (GS) using either regression (RM) or variance component (VC) approaches. This is in part due to the l...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-02-01
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| Series: | BMC Genetics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12863-019-0720-5 |
| _version_ | 1811292057524764672 |
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| author | Ling-Yun Chang Sajjad Toghiani Samuel E. Aggrey Romdhane Rekaya |
| author_facet | Ling-Yun Chang Sajjad Toghiani Samuel E. Aggrey Romdhane Rekaya |
| author_sort | Ling-Yun Chang |
| collection | DOAJ |
| description | Abstract Background It becomes clear that the increase in the density of marker panels and even the use of sequence data didn’t result in any meaningful increase in the accuracy of genomic selection (GS) using either regression (RM) or variance component (VC) approaches. This is in part due to the limitations of current methods. Association model are well over-parameterized and suffer from severe co-linearity and lack of statistical power. Even when the variant effects are not directly estimated using VC based approaches, the genomic relationships didn’t improve after the marker density exceeded a certain threshold. SNP prioritization-based fixation index (FST) scores were used to track the majority of significant QTL and to reduce the dimensionality of the association model. Results Two populations with average LD between adjacent markers of 0.3 (P1) and 0.7 (P2) were simulated. In both populations, the genomic data consisted of 400 K SNP markers distributed on 10 chromosomes. The density of simulated genomic data mimics roughly 1.2 million SNP markers in the bovine genome. The genomic relationship matrix (G) was calculated for each set of selected SNPs based on their FST score and similar numbers of SNPs were selected randomly for comparison. Using all 400 K SNPs, 46% of the off-diagonal elements (OD) were between − 0.01 and 0.01. The same portion was 31, 23 and 16% when 80 K, 40 K and 20 K SNPs were selected based on FST scores. For randomly selected 20 K SNP subsets, around 33% of the OD fell within the same range. Genomic similarity computed using SNPs selected based on FST scores was always higher than using the same number of SNPs selected randomly. Maximum accuracies of 0.741 and 0.828 were achieved when 20 and 10 K SNPs were selected based on FST scores in P1 and P2, respectively. Conclusions Genomic similarity could be maximized by the decrease in the number of selected SNPs, but it also leads to a decrease in the percentage of genetic variation explained by the selected markers. Finding the balance between these two parameters could optimize the accuracy of GS in the presence of high density marker panels. |
| first_indexed | 2024-04-13T04:39:43Z |
| format | Article |
| id | doaj.art-8292f3cc348548999f087c1e68f5eb66 |
| institution | Directory Open Access Journal |
| issn | 1471-2156 |
| language | English |
| last_indexed | 2024-04-13T04:39:43Z |
| publishDate | 2019-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genetics |
| spelling | doaj.art-8292f3cc348548999f087c1e68f5eb662022-12-22T03:02:03ZengBMCBMC Genetics1471-21562019-02-0120111010.1186/s12863-019-0720-5Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphismsLing-Yun Chang0Sajjad Toghiani1Samuel E. Aggrey2Romdhane Rekaya3Department of Animal and Dairy Science, University of GeorgiaDepartment of Animal and Dairy Science, University of GeorgiaDepartment of Poultry Science, University of GeorgiaDepartment of Animal and Dairy Science, University of GeorgiaAbstract Background It becomes clear that the increase in the density of marker panels and even the use of sequence data didn’t result in any meaningful increase in the accuracy of genomic selection (GS) using either regression (RM) or variance component (VC) approaches. This is in part due to the limitations of current methods. Association model are well over-parameterized and suffer from severe co-linearity and lack of statistical power. Even when the variant effects are not directly estimated using VC based approaches, the genomic relationships didn’t improve after the marker density exceeded a certain threshold. SNP prioritization-based fixation index (FST) scores were used to track the majority of significant QTL and to reduce the dimensionality of the association model. Results Two populations with average LD between adjacent markers of 0.3 (P1) and 0.7 (P2) were simulated. In both populations, the genomic data consisted of 400 K SNP markers distributed on 10 chromosomes. The density of simulated genomic data mimics roughly 1.2 million SNP markers in the bovine genome. The genomic relationship matrix (G) was calculated for each set of selected SNPs based on their FST score and similar numbers of SNPs were selected randomly for comparison. Using all 400 K SNPs, 46% of the off-diagonal elements (OD) were between − 0.01 and 0.01. The same portion was 31, 23 and 16% when 80 K, 40 K and 20 K SNPs were selected based on FST scores. For randomly selected 20 K SNP subsets, around 33% of the OD fell within the same range. Genomic similarity computed using SNPs selected based on FST scores was always higher than using the same number of SNPs selected randomly. Maximum accuracies of 0.741 and 0.828 were achieved when 20 and 10 K SNPs were selected based on FST scores in P1 and P2, respectively. Conclusions Genomic similarity could be maximized by the decrease in the number of selected SNPs, but it also leads to a decrease in the percentage of genetic variation explained by the selected markers. Finding the balance between these two parameters could optimize the accuracy of GS in the presence of high density marker panels.http://link.springer.com/article/10.1186/s12863-019-0720-5Genomic selectionHigh density panelSNP prioritization |
| spellingShingle | Ling-Yun Chang Sajjad Toghiani Samuel E. Aggrey Romdhane Rekaya Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms BMC Genetics Genomic selection High density panel SNP prioritization |
| title | Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms |
| title_full | Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms |
| title_fullStr | Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms |
| title_full_unstemmed | Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms |
| title_short | Increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms |
| title_sort | increasing accuracy of genomic selection in presence of high density marker panels through the prioritization of relevant polymorphisms |
| topic | Genomic selection High density panel SNP prioritization |
| url | http://link.springer.com/article/10.1186/s12863-019-0720-5 |
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