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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 is computationally more efficient than CAVIAR. Running time comparison between CAVIAR and CAVIAR-Gene. The experiments are run on a 64 bit Intel(R) Xeon(R) 2 G with 5 GB RAM
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btv240-F1: CAVIAR-Gene is computationally more efficient than CAVIAR. Running time comparison between CAVIAR and CAVIAR-Gene. The experiments are run on a 64 bit Intel(R) Xeon(R) 2 G with 5 GB RAM

Mentions: CAVIAR and CAVIAR-Gene at high level can consider all possible causal combinations for variants and genes, respectively. However, considering all possible causal combinations is intractable. In CAVIAR, we make an assumption that in each locus we have at most six causal variants. However, in CAVIAR, in order to detect the ρ causal variants, we consider all possible causal sets which can be very slow depending on the number of variants selected in the ρ causal variant set. In the worst case, the running time of CAVIAR can be , where m is the total number of variants in a region. In CAVIAR-Gene, we use the proposed greedy method which is mentioned in Section 2.8. This greedy algorithm reduces the complexity of CAVIAR from to . Applying CAVIAR on loci with 100 of variants will take around 30 h. However, it will take 2 h for CAVIAR-Gene to finish on the same loci and 3 h for CAVIAR-Gene to finish on loci with 200 variants. Figure 1 indicates the running time compression between CAVIAR and CAVIAR-Gene for different number of variants in a region.Fig. 1.


Identification of causal genes for complex traits.

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

CAVIAR-Gene is computationally more efficient than CAVIAR. Running time comparison between CAVIAR and CAVIAR-Gene. The experiments are run on a 64 bit Intel(R) Xeon(R) 2 G with 5 GB RAM
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btv240-F1: CAVIAR-Gene is computationally more efficient than CAVIAR. Running time comparison between CAVIAR and CAVIAR-Gene. The experiments are run on a 64 bit Intel(R) Xeon(R) 2 G with 5 GB RAM
Mentions: CAVIAR and CAVIAR-Gene at high level can consider all possible causal combinations for variants and genes, respectively. However, considering all possible causal combinations is intractable. In CAVIAR, we make an assumption that in each locus we have at most six causal variants. However, in CAVIAR, in order to detect the ρ causal variants, we consider all possible causal sets which can be very slow depending on the number of variants selected in the ρ causal variant set. In the worst case, the running time of CAVIAR can be , where m is the total number of variants in a region. In CAVIAR-Gene, we use the proposed greedy method which is mentioned in Section 2.8. This greedy algorithm reduces the complexity of CAVIAR from to . Applying CAVIAR on loci with 100 of variants will take around 30 h. However, it will take 2 h for CAVIAR-Gene to finish on the same loci and 3 h for CAVIAR-Gene to finish on loci with 200 variants. Figure 1 indicates the running time compression between CAVIAR and CAVIAR-Gene for different number of variants in a region.Fig. 1.

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