A gene frequency model for QTL mapping using Bayesian inference
<p>Abstract</p> <p>Background</p> <p>Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | deu |
| Published: |
BMC
2010-06-01
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| Series: | Genetics Selection Evolution |
| Online Access: | http://www.gsejournal.org/content/42/1/21 |
| _version_ | 1818824837548736512 |
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| author | Dekkers Jack CM Fernando Rohan L He Wei Gilbert Helene |
| author_facet | Dekkers Jack CM Fernando Rohan L He Wei Gilbert Helene |
| author_sort | Dekkers Jack CM |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances. Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL. The parameters in this model include these gene frequencies, additive effect of the QTL, its location, and the residual variance. Bayesian methodology was used to estimate these parameters. The priors used were: logit-normal for gene frequencies, normal for the additive effect, uniform for location, and inverse chi-square for the residual variance. Computer simulation was used to compare the power to detect and accuracy to map QTL by this method with those from least squares analysis using a regression model (LSR).</p> <p>Results</p> <p>To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM.</p> <p>Conclusions</p> <p>In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.</p> |
| first_indexed | 2024-12-19T00:02:13Z |
| format | Article |
| id | doaj.art-6ed1f7ad43d14698a24510fff8c065f1 |
| institution | Directory Open Access Journal |
| issn | 0999-193X 1297-9686 |
| language | deu |
| last_indexed | 2024-12-19T00:02:13Z |
| publishDate | 2010-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Genetics Selection Evolution |
| spelling | doaj.art-6ed1f7ad43d14698a24510fff8c065f12022-12-21T20:46:26ZdeuBMCGenetics Selection Evolution0999-193X1297-96862010-06-014212110.1186/1297-9686-42-21A gene frequency model for QTL mapping using Bayesian inferenceDekkers Jack CMFernando Rohan LHe WeiGilbert Helene<p>Abstract</p> <p>Background</p> <p>Information for mapping of quantitative trait loci (QTL) comes from two sources: linkage disequilibrium (non-random association of allele states) and cosegregation (non-random association of allele origin). Information from LD can be captured by modeling conditional means and variances at the QTL given marker information. Similarly, information from cosegregation can be captured by modeling conditional covariances. Here, we consider a Bayesian model based on gene frequency (BGF) where both conditional means and variances are modeled as a function of the conditional gene frequencies at the QTL. The parameters in this model include these gene frequencies, additive effect of the QTL, its location, and the residual variance. Bayesian methodology was used to estimate these parameters. The priors used were: logit-normal for gene frequencies, normal for the additive effect, uniform for location, and inverse chi-square for the residual variance. Computer simulation was used to compare the power to detect and accuracy to map QTL by this method with those from least squares analysis using a regression model (LSR).</p> <p>Results</p> <p>To simplify the analysis, data from unrelated individuals in a purebred population were simulated, where only LD information contributes to map the QTL. LD was simulated in a chromosomal segment of 1 cM with one QTL by random mating in a population of size 500 for 1000 generations and in a population of size 100 for 50 generations. The comparison was studied under a range of conditions, which included SNP density of 0.1, 0.05 or 0.02 cM, sample size of 500 or 1000, and phenotypic variance explained by QTL of 2 or 5%. Both 1 and 2-SNP models were considered. Power to detect the QTL for the BGF, ranged from 0.4 to 0.99, and close or equal to the power of the regression using least squares (LSR). Precision to map QTL position of BGF, quantified by the mean absolute error, ranged from 0.11 to 0.21 cM for BGF, and was better than the precision of LSR, which ranged from 0.12 to 0.25 cM.</p> <p>Conclusions</p> <p>In conclusion given a high SNP density, the gene frequency model can be used to map QTL with considerable accuracy even within a 1 cM region.</p>http://www.gsejournal.org/content/42/1/21 |
| spellingShingle | Dekkers Jack CM Fernando Rohan L He Wei Gilbert Helene A gene frequency model for QTL mapping using Bayesian inference Genetics Selection Evolution |
| title | A gene frequency model for QTL mapping using Bayesian inference |
| title_full | A gene frequency model for QTL mapping using Bayesian inference |
| title_fullStr | A gene frequency model for QTL mapping using Bayesian inference |
| title_full_unstemmed | A gene frequency model for QTL mapping using Bayesian inference |
| title_short | A gene frequency model for QTL mapping using Bayesian inference |
| title_sort | gene frequency model for qtl mapping using bayesian inference |
| url | http://www.gsejournal.org/content/42/1/21 |
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