High density marker panels, SNPs prioritizing and accuracy of genomic selection
Abstract Background The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-01-01
|
| Series: | BMC Genetics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12863-017-0595-2 |
| _version_ | 1818040040767356928 |
|---|---|
| author | Ling-Yun Chang Sajjad Toghiani Ashley Ling Sammy E. Aggrey Romdhane Rekaya |
| author_facet | Ling-Yun Chang Sajjad Toghiani Ashley Ling Sammy E. Aggrey Romdhane Rekaya |
| author_sort | Ling-Yun Chang |
| collection | DOAJ |
| description | Abstract Background The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance. Results In this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers. Conclusions These results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications. |
| first_indexed | 2024-12-10T08:08:13Z |
| format | Article |
| id | doaj.art-e20d18b2480b46d38b6c489166dbecb8 |
| institution | Directory Open Access Journal |
| issn | 1471-2156 |
| language | English |
| last_indexed | 2024-12-10T08:08:13Z |
| publishDate | 2018-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genetics |
| spelling | doaj.art-e20d18b2480b46d38b6c489166dbecb82022-12-22T01:56:37ZengBMCBMC Genetics1471-21562018-01-0119111010.1186/s12863-017-0595-2High density marker panels, SNPs prioritizing and accuracy of genomic selectionLing-Yun Chang0Sajjad Toghiani1Ashley Ling2Sammy E. Aggrey3Romdhane Rekaya4Department of Animal and Dairy Science, University of GeorgiaDepartment 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 The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance. Results In this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers. Conclusions These results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications.http://link.springer.com/article/10.1186/s12863-017-0595-2SNP prioritizingGenomic selectionHigh density |
| spellingShingle | Ling-Yun Chang Sajjad Toghiani Ashley Ling Sammy E. Aggrey Romdhane Rekaya High density marker panels, SNPs prioritizing and accuracy of genomic selection BMC Genetics SNP prioritizing Genomic selection High density |
| title | High density marker panels, SNPs prioritizing and accuracy of genomic selection |
| title_full | High density marker panels, SNPs prioritizing and accuracy of genomic selection |
| title_fullStr | High density marker panels, SNPs prioritizing and accuracy of genomic selection |
| title_full_unstemmed | High density marker panels, SNPs prioritizing and accuracy of genomic selection |
| title_short | High density marker panels, SNPs prioritizing and accuracy of genomic selection |
| title_sort | high density marker panels snps prioritizing and accuracy of genomic selection |
| topic | SNP prioritizing Genomic selection High density |
| url | http://link.springer.com/article/10.1186/s12863-017-0595-2 |
| work_keys_str_mv | AT lingyunchang highdensitymarkerpanelssnpsprioritizingandaccuracyofgenomicselection AT sajjadtoghiani highdensitymarkerpanelssnpsprioritizingandaccuracyofgenomicselection AT ashleyling highdensitymarkerpanelssnpsprioritizingandaccuracyofgenomicselection AT sammyeaggrey highdensitymarkerpanelssnpsprioritizingandaccuracyofgenomicselection AT romdhanerekaya highdensitymarkerpanelssnpsprioritizingandaccuracyofgenomicselection |