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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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Related in: MedlinePlus

Q-Q plots of the AI test statistics for Scenarios 1–3. The plots on the left show results from 1,000 replicates under the  hypothesis with no causal variants. The plots on the right show results from 20 replicates of the whole of chromosome 11 under the alternative hypothesis, where crosses denote SNPs that are within 110 K base pair positions of the causal SNP.
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fig04: Q-Q plots of the AI test statistics for Scenarios 1–3. The plots on the left show results from 1,000 replicates under the hypothesis with no causal variants. The plots on the right show results from 20 replicates of the whole of chromosome 11 under the alternative hypothesis, where crosses denote SNPs that are within 110 K base pair positions of the causal SNP.

Mentions: As an additional check on the type I error rate for AI, we constructed quantile-quantile (Q-Q) plots and calculated genomic control inflation factors [Devlin and Roeder, 1999], λ, for the AI test statistics from Scenarios 1-3. Figure 4 (left hand plots) shows the test statistics obtained within the 110 kb detection window for 1,000 replicates simulated under the hypothesis. Figure 4 (right hand plots) shows the results for 20 replicates of the whole of chromosome 11 simulated under the alternative hypothesis, where gray crosses indicate test statistics of SNPs that lie within the 110 kb detection window, which might therefore be considered as “true” findings. The Q-Q plots of the test statistics show an acceptable distribution, indicating that the AI approach provides inference that can be considered to have essentially the same properties as inference from standard single-SNP testing in GWAS.


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

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

Q-Q plots of the AI test statistics for Scenarios 1–3. The plots on the left show results from 1,000 replicates under the  hypothesis with no causal variants. The plots on the right show results from 20 replicates of the whole of chromosome 11 under the alternative hypothesis, where crosses denote SNPs that are within 110 K base pair positions of the causal SNP.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig04: Q-Q plots of the AI test statistics for Scenarios 1–3. The plots on the left show results from 1,000 replicates under the hypothesis with no causal variants. The plots on the right show results from 20 replicates of the whole of chromosome 11 under the alternative hypothesis, where crosses denote SNPs that are within 110 K base pair positions of the causal SNP.
Mentions: As an additional check on the type I error rate for AI, we constructed quantile-quantile (Q-Q) plots and calculated genomic control inflation factors [Devlin and Roeder, 1999], λ, for the AI test statistics from Scenarios 1-3. Figure 4 (left hand plots) shows the test statistics obtained within the 110 kb detection window for 1,000 replicates simulated under the hypothesis. Figure 4 (right hand plots) shows the results for 20 replicates of the whole of chromosome 11 simulated under the alternative hypothesis, where gray crosses indicate test statistics of SNPs that lie within the 110 kb detection window, which might therefore be considered as “true” findings. The Q-Q plots of the test statistics show an acceptable distribution, indicating that the AI approach provides inference that can be considered to have essentially the same properties as inference from standard single-SNP testing in GWAS.

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