Limits...
A strategy for QTL fine-mapping using a dense SNP map.

Tarres J, Guillaume F, Fritz S - BMC Proc (2009)

Bottom Line: Two QTL explaining 9.4 and 3.3% of the genetic variance were found with high significance on chromosome 1 at positions 19.5 and 76.6 cM.The QTL detected on chromosome 3 at position 11.9 cM (5% of variance) was less important.The QTL with the highest effect (37% of variance) was detected on chromosome 4 at position 3.1 cM and another QTL (13.6% of variance) was detected on chromosome 5 at position 93.9 cM.

View Article: PubMed Central - HTML - PubMed

Affiliation: UR337 Station de Génétique Quantitative et Appliquée, INRA, Jouy-en-Josas F-78350, France. joaquim.tarres@dga.jouy.inra.fr

ABSTRACT

Background: Dense marker maps require efficient statistical methods for QTL fine mapping that work fast and efficiently with a large number of markers. In this study, the simulated dataset for the XIIth QTLMAS workshop was analyzed using a QTL fine mapping set of tools.

Methods: The QTL fine-mapping strategy was based on the use of statistical methods combining linkage and linkage disequilibrium analysis. Variance component based linkage analysis provided confidence intervals for the QTL. Within these regions, two additional analyses combining both linkage analysis and linkage disequilibrium information were applied. The first method estimated identity-by-descent probabilities among base haplotypes that were used to group them in different clusters. The second method constructed haplotype groups based on identity-by-state probabilities.

Results: Two QTL explaining 9.4 and 3.3% of the genetic variance were found with high significance on chromosome 1 at positions 19.5 and 76.6 cM. On chromosome 2, two QTL were also detected at positions 26.0 and 53.2 explaining respectively 9.0 and 7.8 of total genetic variance. The QTL detected on chromosome 3 at position 11.9 cM (5% of variance) was less important. The QTL with the highest effect (37% of variance) was detected on chromosome 4 at position 3.1 cM and another QTL (13.6% of variance) was detected on chromosome 5 at position 93.9 cM.

Conclusion: The proposed strategy for fine-mapping of QTL combining linkage and linkage disequilibrium analysis allowed detecting the most important QTL with an additive effect in a short period but it should be extended in the future in order to fine-map linked and epistatic QTL.

No MeSH data available.


Related in: MedlinePlus

LA and LDLA curves obtained on chromosome 1. LA curve (black), LDLA curve with model HAP3 (red) and LDLA curve with model IBD10 (blue).
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Figure 1: LA and LDLA curves obtained on chromosome 1. LA curve (black), LDLA curve with model HAP3 (red) and LDLA curve with model IBD10 (blue).

Mentions: The estimated total genetic variance of the trait was 1.32 and the heritability was 0.30. The locations of inferred QTL using the LA, IBD10 and HAP3 methods are shown in Table 1. IBD10 and HAP3 methods give several peaks with LRT higher than for linkage analysis. The use of the haplotypes of heterozygous sires at the QTL offered the possibility to give confidence to some of them. A QTL explaining 9.4% of the genetic variance was found with high significance in chromosome 1 at position 19.5 cM (Figure 1). In chromosome 2, the main QTL was detected at position 26.0 cM which explained 9.0% of total genetic variance (Figure 2). The QTL detected in chromosome 3 at position 11.9 cM was less important (5% of variance) (Figure 3). The QTL with the highest effect (37% of variance) was detected in chromosome 4 at position 3.1 cM (Figure 4) and another QTL (13.6% of variance) was detected in chromosome 5 at position 93.9 cM (Figure 5). No QTL was detected in chromosome 6.


A strategy for QTL fine-mapping using a dense SNP map.

Tarres J, Guillaume F, Fritz S - BMC Proc (2009)

LA and LDLA curves obtained on chromosome 1. LA curve (black), LDLA curve with model HAP3 (red) and LDLA curve with model IBD10 (blue).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2654497&req=5

Figure 1: LA and LDLA curves obtained on chromosome 1. LA curve (black), LDLA curve with model HAP3 (red) and LDLA curve with model IBD10 (blue).
Mentions: The estimated total genetic variance of the trait was 1.32 and the heritability was 0.30. The locations of inferred QTL using the LA, IBD10 and HAP3 methods are shown in Table 1. IBD10 and HAP3 methods give several peaks with LRT higher than for linkage analysis. The use of the haplotypes of heterozygous sires at the QTL offered the possibility to give confidence to some of them. A QTL explaining 9.4% of the genetic variance was found with high significance in chromosome 1 at position 19.5 cM (Figure 1). In chromosome 2, the main QTL was detected at position 26.0 cM which explained 9.0% of total genetic variance (Figure 2). The QTL detected in chromosome 3 at position 11.9 cM was less important (5% of variance) (Figure 3). The QTL with the highest effect (37% of variance) was detected in chromosome 4 at position 3.1 cM (Figure 4) and another QTL (13.6% of variance) was detected in chromosome 5 at position 93.9 cM (Figure 5). No QTL was detected in chromosome 6.

Bottom Line: Two QTL explaining 9.4 and 3.3% of the genetic variance were found with high significance on chromosome 1 at positions 19.5 and 76.6 cM.The QTL detected on chromosome 3 at position 11.9 cM (5% of variance) was less important.The QTL with the highest effect (37% of variance) was detected on chromosome 4 at position 3.1 cM and another QTL (13.6% of variance) was detected on chromosome 5 at position 93.9 cM.

View Article: PubMed Central - HTML - PubMed

Affiliation: UR337 Station de Génétique Quantitative et Appliquée, INRA, Jouy-en-Josas F-78350, France. joaquim.tarres@dga.jouy.inra.fr

ABSTRACT

Background: Dense marker maps require efficient statistical methods for QTL fine mapping that work fast and efficiently with a large number of markers. In this study, the simulated dataset for the XIIth QTLMAS workshop was analyzed using a QTL fine mapping set of tools.

Methods: The QTL fine-mapping strategy was based on the use of statistical methods combining linkage and linkage disequilibrium analysis. Variance component based linkage analysis provided confidence intervals for the QTL. Within these regions, two additional analyses combining both linkage analysis and linkage disequilibrium information were applied. The first method estimated identity-by-descent probabilities among base haplotypes that were used to group them in different clusters. The second method constructed haplotype groups based on identity-by-state probabilities.

Results: Two QTL explaining 9.4 and 3.3% of the genetic variance were found with high significance on chromosome 1 at positions 19.5 and 76.6 cM. On chromosome 2, two QTL were also detected at positions 26.0 and 53.2 explaining respectively 9.0 and 7.8 of total genetic variance. The QTL detected on chromosome 3 at position 11.9 cM (5% of variance) was less important. The QTL with the highest effect (37% of variance) was detected on chromosome 4 at position 3.1 cM and another QTL (13.6% of variance) was detected on chromosome 5 at position 93.9 cM.

Conclusion: The proposed strategy for fine-mapping of QTL combining linkage and linkage disequilibrium analysis allowed detecting the most important QTL with an additive effect in a short period but it should be extended in the future in order to fine-map linked and epistatic QTL.

No MeSH data available.


Related in: MedlinePlus