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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

Manhattan plots of each chromosome for the case-parent trio severe malaria dataset from The Gambia. The top plot shows P-values using the transmission disequilibrium test as implemented in PLINK. The lower plot shows AI test P-values. The black and gray points highlight different chromosomes.
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fig06: Manhattan plots of each chromosome for the case-parent trio severe malaria dataset from The Gambia. The top plot shows P-values using the transmission disequilibrium test as implemented in PLINK. The lower plot shows AI test P-values. The black and gray points highlight different chromosomes.

Mentions: Figure 6 shows Manhattan plots across the whole genome for the Gambian case-parent trio dataset, using either the TDT implemented in PLINK (top panel) or the AI test implemented in SnipSnip applied to cases and pseudocontrols (bottom panel). The overall genomic control inflation factor was 1.02 for the TDT and 1.00 for the AI test. Both methods gave reasonably compelling results in the vicinity of the known causal SNP (rs334) on chromosome 11, but the top AI result ( at rs979752) was an order of magnitude more significant than the top TDT result (). Standard logistic regression applied to same case/pseudocontrol data gave similar results to the TDT but with less significant P-values. The top anchor (and partner) SNP in the case-parent trio study differed from that found in the case/control study, most likely due to the different genotyping arrays used in the two studies, which contain SNPs that show varying patterns of LD with the causal SNP, rs334.


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

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

Manhattan plots of each chromosome for the case-parent trio severe malaria dataset from The Gambia. The top plot shows P-values using the transmission disequilibrium test as implemented in PLINK. The lower plot shows AI test P-values. The black and gray points highlight different chromosomes.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig06: Manhattan plots of each chromosome for the case-parent trio severe malaria dataset from The Gambia. The top plot shows P-values using the transmission disequilibrium test as implemented in PLINK. The lower plot shows AI test P-values. The black and gray points highlight different chromosomes.
Mentions: Figure 6 shows Manhattan plots across the whole genome for the Gambian case-parent trio dataset, using either the TDT implemented in PLINK (top panel) or the AI test implemented in SnipSnip applied to cases and pseudocontrols (bottom panel). The overall genomic control inflation factor was 1.02 for the TDT and 1.00 for the AI test. Both methods gave reasonably compelling results in the vicinity of the known causal SNP (rs334) on chromosome 11, but the top AI result ( at rs979752) was an order of magnitude more significant than the top TDT result (). Standard logistic regression applied to same case/pseudocontrol data gave similar results to the TDT but with less significant P-values. The top anchor (and partner) SNP in the case-parent trio study differed from that found in the case/control study, most likely due to the different genotyping arrays used in the two studies, which contain SNPs that show varying patterns of LD with the causal SNP, rs334.

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