Limits...
Combining QTL data for HDL cholesterol levels from two different species leads to smaller confidence intervals.

Cox A, Sheehan SM, Klöting I, Paigen B, Korstanje R - Heredity (Edinb) (2010)

Bottom Line: The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL.The combinations and analyses resulted in QTL with smaller confidence intervals and increased logarithm of the odds ratio scores.This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals.

View Article: PubMed Central - PubMed

Affiliation: The Jackson Laboratory, Bar Harbor, ME 04609, USA.

ABSTRACT
Quantitative trait locus (QTL) analysis detects regions of a genome that are linked to a complex trait. Once a QTL is detected, the region is narrowed by positional cloning in the hope of determining the underlying candidate gene-methods used include creating congenic strains, comparative genomics and gene expression analysis. Combined cross analysis may also be used for species such as the mouse, if the QTL is detected in multiple crosses. This process involves the recoding of QTL data on a per-chromosome basis, with the genotype recoded on the basis of high- and low-allele status. The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL. Using parallel methods, we show that it is possible to narrow a QTL by combining data from two different species, the rat and the mouse. We combined standardized high-density lipoprotein phenotype values and genotype data for the rat and mouse using information from one rat cross and two mouse crosses. We successfully combined data within homologous regions from rat Chr 6 onto mouse Chr 12, and from rat Chr 10 onto mouse Chr 11. The combinations and analyses resulted in QTL with smaller confidence intervals and increased logarithm of the odds ratio scores. The numbers of candidate genes encompassed by the QTL on mouse Chr 11 and 12 were reduced from 1343 to 761 genes and from 613 to 304 genes, respectively. This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals.

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Combining rat chromosome 6 data with mouse chromosome 12 data. All cM positions are with respect to mouse chromosome 12. Confidence intervals for the original and species-additive QTL are depicted by the black boxes; the box closest to the bottom represents the original 95% confidence interval in the mouse. The interactive and additive plots are indistinguishable, suggesting the QTL is caused by the same gene in both the rat and the mouse.
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Figure 3: Combining rat chromosome 6 data with mouse chromosome 12 data. All cM positions are with respect to mouse chromosome 12. Confidence intervals for the original and species-additive QTL are depicted by the black boxes; the box closest to the bottom represents the original 95% confidence interval in the mouse. The interactive and additive plots are indistinguishable, suggesting the QTL is caused by the same gene in both the rat and the mouse.

Mentions: The phenotypes were standardized by Z-score in the individual QTL datasets and combined by identically coding the high- and low-allele strains. The results of the QTL analyses for the combinations are shown in Figure 3 and Figure 4. Table 1 lists the peaks, confidence intervals and LOD scores for the original mouse QTL, and for the additive and interactive QTL resulting from the combination of data from different species. The numbers of genes within the original and species-additive confidence intervals are shown in Table 2.


Combining QTL data for HDL cholesterol levels from two different species leads to smaller confidence intervals.

Cox A, Sheehan SM, Klöting I, Paigen B, Korstanje R - Heredity (Edinb) (2010)

Combining rat chromosome 6 data with mouse chromosome 12 data. All cM positions are with respect to mouse chromosome 12. Confidence intervals for the original and species-additive QTL are depicted by the black boxes; the box closest to the bottom represents the original 95% confidence interval in the mouse. The interactive and additive plots are indistinguishable, suggesting the QTL is caused by the same gene in both the rat and the mouse.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2958246&req=5

Figure 3: Combining rat chromosome 6 data with mouse chromosome 12 data. All cM positions are with respect to mouse chromosome 12. Confidence intervals for the original and species-additive QTL are depicted by the black boxes; the box closest to the bottom represents the original 95% confidence interval in the mouse. The interactive and additive plots are indistinguishable, suggesting the QTL is caused by the same gene in both the rat and the mouse.
Mentions: The phenotypes were standardized by Z-score in the individual QTL datasets and combined by identically coding the high- and low-allele strains. The results of the QTL analyses for the combinations are shown in Figure 3 and Figure 4. Table 1 lists the peaks, confidence intervals and LOD scores for the original mouse QTL, and for the additive and interactive QTL resulting from the combination of data from different species. The numbers of genes within the original and species-additive confidence intervals are shown in Table 2.

Bottom Line: The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL.The combinations and analyses resulted in QTL with smaller confidence intervals and increased logarithm of the odds ratio scores.This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals.

View Article: PubMed Central - PubMed

Affiliation: The Jackson Laboratory, Bar Harbor, ME 04609, USA.

ABSTRACT
Quantitative trait locus (QTL) analysis detects regions of a genome that are linked to a complex trait. Once a QTL is detected, the region is narrowed by positional cloning in the hope of determining the underlying candidate gene-methods used include creating congenic strains, comparative genomics and gene expression analysis. Combined cross analysis may also be used for species such as the mouse, if the QTL is detected in multiple crosses. This process involves the recoding of QTL data on a per-chromosome basis, with the genotype recoded on the basis of high- and low-allele status. The data are then combined and analyzed; a successful analysis results in a narrowed and more significant QTL. Using parallel methods, we show that it is possible to narrow a QTL by combining data from two different species, the rat and the mouse. We combined standardized high-density lipoprotein phenotype values and genotype data for the rat and mouse using information from one rat cross and two mouse crosses. We successfully combined data within homologous regions from rat Chr 6 onto mouse Chr 12, and from rat Chr 10 onto mouse Chr 11. The combinations and analyses resulted in QTL with smaller confidence intervals and increased logarithm of the odds ratio scores. The numbers of candidate genes encompassed by the QTL on mouse Chr 11 and 12 were reduced from 1343 to 761 genes and from 613 to 304 genes, respectively. This is the first time that QTL data from different species were successfully combined; this method promises to be a useful tool for narrowing QTL intervals.

Show MeSH