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Imputation without doing imputation: a new method for the detection of non-genotyped causal variants.

Howey R, Cordell HJ - Genet. Epidemiol. (2014)

Bottom Line: This observation motivates popular but computationally intensive approaches based on imputation or haplotyping.These two SNPs are used as predictors in linear or logistic regression analysis to generate a final significance test.Previous analysis showed that fine-scale sequencing of a Gambian reference panel in the region of the known causal locus, followed by imputation, increased the signal of association to genome-wide significance levels.

View Article: PubMed Central - PubMed

Affiliation: Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, United Kingdom.

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Powers and type I errors for Scenarios 1–3, windows defined by number of SNPs. Shown are bar plots of the calculated powers (for P-values 10−8, 10−6, and 10−4) and type I errors (for P-values 10−4,  and 10−3) for Scenarios 1–3 for imputation (Imp), haplotype analysis (Hap), single-SNP logistic regression (LR), and the AI test with different SNP window sizes. The standard multiplicative model and correlation metrics were used in the AI test. Rows 1-3 show Scenarios 1-3, respectively.
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fig01: Powers and type I errors for Scenarios 1–3, windows defined by number of SNPs. Shown are bar plots of the calculated powers (for P-values 10−8, 10−6, and 10−4) and type I errors (for P-values 10−4, and 10−3) for Scenarios 1–3 for imputation (Imp), haplotype analysis (Hap), single-SNP logistic regression (LR), and the AI test with different SNP window sizes. The standard multiplicative model and correlation metrics were used in the AI test. Rows 1-3 show Scenarios 1-3, respectively.

Mentions: We used computer simulations to evaluate the performance of our proposed AI test and to compare it to other approaches. Figure 1 (left hand panels) shows a comparison of the powers to achieve various P-values in three different simulation scenarios for imputation, haplotype analysis, single-SNP logistic regression in PLINK, and the AI test (with the AI test taking different SNP window sizes, see Methods) respectively. Imputation was carried out using the program IMPUTE2 [Howie et al., 2009; Marchini et al., 2007] with prephasing [Howie et al., 2012] in SHAPEIT [Delaneau et al., 2012], using data from the 1000 Genomes Project [1000 Genomes Project Consortium et al., 2012] (Phase I interim data, updated release April 2012) as a reference panel. Haplotype analysis was carried out using the haplotype regression approach implemented in the program UNPHASED [Dudbridge, 2008; Dudbridge et al., 2011], using a sliding window of 5-SNP haplotypes.


Imputation without doing imputation: a new method for the detection of non-genotyped causal variants.

Howey R, Cordell HJ - Genet. Epidemiol. (2014)

Powers and type I errors for Scenarios 1–3, windows defined by number of SNPs. Shown are bar plots of the calculated powers (for P-values 10−8, 10−6, and 10−4) and type I errors (for P-values 10−4,  and 10−3) for Scenarios 1–3 for imputation (Imp), haplotype analysis (Hap), single-SNP logistic regression (LR), and the AI test with different SNP window sizes. The standard multiplicative model and correlation metrics were used in the AI test. Rows 1-3 show Scenarios 1-3, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150535&req=5

fig01: Powers and type I errors for Scenarios 1–3, windows defined by number of SNPs. Shown are bar plots of the calculated powers (for P-values 10−8, 10−6, and 10−4) and type I errors (for P-values 10−4, and 10−3) for Scenarios 1–3 for imputation (Imp), haplotype analysis (Hap), single-SNP logistic regression (LR), and the AI test with different SNP window sizes. The standard multiplicative model and correlation metrics were used in the AI test. Rows 1-3 show Scenarios 1-3, respectively.
Mentions: We used computer simulations to evaluate the performance of our proposed AI test and to compare it to other approaches. Figure 1 (left hand panels) shows a comparison of the powers to achieve various P-values in three different simulation scenarios for imputation, haplotype analysis, single-SNP logistic regression in PLINK, and the AI test (with the AI test taking different SNP window sizes, see Methods) respectively. Imputation was carried out using the program IMPUTE2 [Howie et al., 2009; Marchini et al., 2007] with prephasing [Howie et al., 2012] in SHAPEIT [Delaneau et al., 2012], using data from the 1000 Genomes Project [1000 Genomes Project Consortium et al., 2012] (Phase I interim data, updated release April 2012) as a reference panel. Haplotype analysis was carried out using the haplotype regression approach implemented in the program UNPHASED [Dudbridge, 2008; Dudbridge et al., 2011], using a sliding window of 5-SNP haplotypes.

Bottom Line: This observation motivates popular but computationally intensive approaches based on imputation or haplotyping.These two SNPs are used as predictors in linear or logistic regression analysis to generate a final significance test.Previous analysis showed that fine-scale sequencing of a Gambian reference panel in the region of the known causal locus, followed by imputation, increased the signal of association to genome-wide significance levels.

View Article: PubMed Central - PubMed

Affiliation: Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, United Kingdom.

Show MeSH
Related in: MedlinePlus