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Identification of causal genes for complex traits.

Hormozdiari F, Kichaev G, Yang WY, Pasaniuc B, Eskin E - Bioinformatics (2015)

Bottom Line: As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD.Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches.Software is freely available for download at genetics.cs.ucla.edu/caviar.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.

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Related in: MedlinePlus

CAVIAR-Gene adjusts for population structure. Panel a illustrates the case where the data have population structure and the statistics is not corrected for the population structure. Panels b and c illustrate the cases where we have corrected the statistics for the population structure. However, in Panel b, we compute the correlation between the original genotypes and in Panel c the correlation is computed from the transformed genotypes. Then, we calculate the difference between the correlation computed from the marginal statistics for each pair of variants and the correlation of the genotype of the same variants. The difference between the correlation of the marginal statistics and the correlation of the transformed genotype shown in Panel c is close to zero and their variance is much smaller than other cases as shown in Panels a and b. To compare the results, we plot the residual difference between −0.4 and 0.4, as a result some points for Panel b are not shown
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btv240-F3: CAVIAR-Gene adjusts for population structure. Panel a illustrates the case where the data have population structure and the statistics is not corrected for the population structure. Panels b and c illustrate the cases where we have corrected the statistics for the population structure. However, in Panel b, we compute the correlation between the original genotypes and in Panel c the correlation is computed from the transformed genotypes. Then, we calculate the difference between the correlation computed from the marginal statistics for each pair of variants and the correlation of the genotype of the same variants. The difference between the correlation of the marginal statistics and the correlation of the transformed genotype shown in Panel c is close to zero and their variance is much smaller than other cases as shown in Panels a and b. To compare the results, we plot the residual difference between −0.4 and 0.4, as a result some points for Panel b are not shown

Mentions: We use an HMDP dataset (Bennett et al., 2010) which we determine to have population structure. We generate phenotypes with population structure and compute the marginal statistics for each variant both corrected and not corrected for population structure. We then compute the correlation between each pair of marginal statistics and the correlation between each pair of variants for the original genotype and the transformed genotype. We calculate the difference between the correlation computed from the marginal statistics for each pair of variants and the correlation of the genotype of the same variants. The boxplot of these differences are shown in Figure 3.Fig. 3.


Identification of causal genes for complex traits.

Hormozdiari F, Kichaev G, Yang WY, Pasaniuc B, Eskin E - Bioinformatics (2015)

CAVIAR-Gene adjusts for population structure. Panel a illustrates the case where the data have population structure and the statistics is not corrected for the population structure. Panels b and c illustrate the cases where we have corrected the statistics for the population structure. However, in Panel b, we compute the correlation between the original genotypes and in Panel c the correlation is computed from the transformed genotypes. Then, we calculate the difference between the correlation computed from the marginal statistics for each pair of variants and the correlation of the genotype of the same variants. The difference between the correlation of the marginal statistics and the correlation of the transformed genotype shown in Panel c is close to zero and their variance is much smaller than other cases as shown in Panels a and b. To compare the results, we plot the residual difference between −0.4 and 0.4, as a result some points for Panel b are not shown
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4542778&req=5

btv240-F3: CAVIAR-Gene adjusts for population structure. Panel a illustrates the case where the data have population structure and the statistics is not corrected for the population structure. Panels b and c illustrate the cases where we have corrected the statistics for the population structure. However, in Panel b, we compute the correlation between the original genotypes and in Panel c the correlation is computed from the transformed genotypes. Then, we calculate the difference between the correlation computed from the marginal statistics for each pair of variants and the correlation of the genotype of the same variants. The difference between the correlation of the marginal statistics and the correlation of the transformed genotype shown in Panel c is close to zero and their variance is much smaller than other cases as shown in Panels a and b. To compare the results, we plot the residual difference between −0.4 and 0.4, as a result some points for Panel b are not shown
Mentions: We use an HMDP dataset (Bennett et al., 2010) which we determine to have population structure. We generate phenotypes with population structure and compute the marginal statistics for each variant both corrected and not corrected for population structure. We then compute the correlation between each pair of marginal statistics and the correlation between each pair of variants for the original genotype and the transformed genotype. We calculate the difference between the correlation computed from the marginal statistics for each pair of variants and the correlation of the genotype of the same variants. The boxplot of these differences are shown in Figure 3.Fig. 3.

Bottom Line: As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD.Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches.Software is freely available for download at genetics.cs.ucla.edu/caviar.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Inter-Departmental Program in Bioinformatics, Department of Human Genetics and Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.

Show MeSH
Related in: MedlinePlus