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SNP imputation bias reduces effect size determination.

Khankhanian P, Din L, Caillier SJ, Gourraud PA, Baranzini SE - Front Genet (2015)

Bottom Line: When healthy controls were used as reference for imputation, a significant bias was observed, particularly in the disease-associated markers.Using cases as reference significantly attenuated this bias.We found that the bias is inherent to imputation as using different methods did not alter the results.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, University of California San Francisco San Francisco, CA, USA.

ABSTRACT
Imputation is a commonly used technique that exploits linkage disequilibrium to infer missing genotypes in genetic datasets, using a well-characterized reference population. While there is agreement that the reference population has to match the ethnicity of the query dataset, it is common practice to use the same reference to impute genotypes for a wide variety of phenotypes. We hypothesized that using a reference composed of samples with a different phenotype than the query dataset would introduce imputation bias. To test this hypothesis we used GWAS datasets from Amyotrophic Lateral Sclerosis (ALS), Parkinson Disease (PD), and Crohn's Disease (CD). First, we masked and then performed imputation of 100 disease-associated markers and 100 non-associated markers from each study. Two references for imputation were used in parallel: one consisting of healthy controls and another consisting of patients with the same disease. We assessed the discordance (imprecision) and bias (inaccuracy) of imputation by comparing predicted genotypes to those assayed by SNP-chip. We also assessed the bias on the observed effect size when the predicted genotypes were used in a GWAS study. When healthy controls were used as reference for imputation, a significant bias was observed, particularly in the disease-associated markers. Using cases as reference significantly attenuated this bias. For nearly all markers, the direction of the bias favored the non-risk allele. In GWAS studies of the three diseases (with healthy reference controls from the 1000 genomes as reference), the mean OR for disease-associated markers obtained by imputation was lower than that obtained using original assayed genotypes. We found that the bias is inherent to imputation as using different methods did not alter the results. In conclusion, imputation is a powerful method to predict genotypes and estimate genetic risk for GWAS. However, a careful choice of reference population is needed to minimize biases inherent to this approach.

No MeSH data available.


Related in: MedlinePlus

Imputation bias vs. odds ratio of association in ALS. Each circle represents one of the 100 DAM in ALS. For each SNP, the odds ratio (OR) of association (x-axis) indicates whether the minor allele (OR > 1) or the major allele (OR < 1) is the susceptibility allele (the allele more prevalent in cases than controls). The imputation bias (y-axis) indicates whether imputation error favors the major allele (positive values) or the minor allele (negative values). When controls were used as the reference for imputation, imputation is biased against the susceptibility allele. When an independent set of cases was used as the reference for imputation, the bias is significantly decreased. For reference, the 100 NAM (OR ≈ 1) are shown as boxes. Points are shaded by the log10 p-value of association with disease. The odds ratios of NAM are exaggerated for visual clarity.
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Figure 1: Imputation bias vs. odds ratio of association in ALS. Each circle represents one of the 100 DAM in ALS. For each SNP, the odds ratio (OR) of association (x-axis) indicates whether the minor allele (OR > 1) or the major allele (OR < 1) is the susceptibility allele (the allele more prevalent in cases than controls). The imputation bias (y-axis) indicates whether imputation error favors the major allele (positive values) or the minor allele (negative values). When controls were used as the reference for imputation, imputation is biased against the susceptibility allele. When an independent set of cases was used as the reference for imputation, the bias is significantly decreased. For reference, the 100 NAM (OR ≈ 1) are shown as boxes. Points are shaded by the log10 p-value of association with disease. The odds ratios of NAM are exaggerated for visual clarity.

Mentions: Figure 1 shows the bias (Y-axis) when controls (left) or cases (right) are used as a reference to impute SNPs in the ALS dataset. With either reference population the bias is consistently against the risk allele and can be observed for all DAM (circles) including the most significantly associated SNPs (dark gray) as well as for more modestly associated (light gray). However, the magnitude of the bias is lower when cases are used as reference. We observed similar results in the PD and CD data sets (Supplementary Figures S1, S2). Of note, results were largely unchanged when the call tolerance parameter T was changed from 0.5 to 0.3 or 0.1 (data not shown), or when fractional genotypes were used (Supplementary Figures S3–S5).


SNP imputation bias reduces effect size determination.

Khankhanian P, Din L, Caillier SJ, Gourraud PA, Baranzini SE - Front Genet (2015)

Imputation bias vs. odds ratio of association in ALS. Each circle represents one of the 100 DAM in ALS. For each SNP, the odds ratio (OR) of association (x-axis) indicates whether the minor allele (OR > 1) or the major allele (OR < 1) is the susceptibility allele (the allele more prevalent in cases than controls). The imputation bias (y-axis) indicates whether imputation error favors the major allele (positive values) or the minor allele (negative values). When controls were used as the reference for imputation, imputation is biased against the susceptibility allele. When an independent set of cases was used as the reference for imputation, the bias is significantly decreased. For reference, the 100 NAM (OR ≈ 1) are shown as boxes. Points are shaded by the log10 p-value of association with disease. The odds ratios of NAM are exaggerated for visual clarity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4321633&req=5

Figure 1: Imputation bias vs. odds ratio of association in ALS. Each circle represents one of the 100 DAM in ALS. For each SNP, the odds ratio (OR) of association (x-axis) indicates whether the minor allele (OR > 1) or the major allele (OR < 1) is the susceptibility allele (the allele more prevalent in cases than controls). The imputation bias (y-axis) indicates whether imputation error favors the major allele (positive values) or the minor allele (negative values). When controls were used as the reference for imputation, imputation is biased against the susceptibility allele. When an independent set of cases was used as the reference for imputation, the bias is significantly decreased. For reference, the 100 NAM (OR ≈ 1) are shown as boxes. Points are shaded by the log10 p-value of association with disease. The odds ratios of NAM are exaggerated for visual clarity.
Mentions: Figure 1 shows the bias (Y-axis) when controls (left) or cases (right) are used as a reference to impute SNPs in the ALS dataset. With either reference population the bias is consistently against the risk allele and can be observed for all DAM (circles) including the most significantly associated SNPs (dark gray) as well as for more modestly associated (light gray). However, the magnitude of the bias is lower when cases are used as reference. We observed similar results in the PD and CD data sets (Supplementary Figures S1, S2). Of note, results were largely unchanged when the call tolerance parameter T was changed from 0.5 to 0.3 or 0.1 (data not shown), or when fractional genotypes were used (Supplementary Figures S3–S5).

Bottom Line: When healthy controls were used as reference for imputation, a significant bias was observed, particularly in the disease-associated markers.Using cases as reference significantly attenuated this bias.We found that the bias is inherent to imputation as using different methods did not alter the results.

View Article: PubMed Central - PubMed

Affiliation: Department of Neurology, University of California San Francisco San Francisco, CA, USA.

ABSTRACT
Imputation is a commonly used technique that exploits linkage disequilibrium to infer missing genotypes in genetic datasets, using a well-characterized reference population. While there is agreement that the reference population has to match the ethnicity of the query dataset, it is common practice to use the same reference to impute genotypes for a wide variety of phenotypes. We hypothesized that using a reference composed of samples with a different phenotype than the query dataset would introduce imputation bias. To test this hypothesis we used GWAS datasets from Amyotrophic Lateral Sclerosis (ALS), Parkinson Disease (PD), and Crohn's Disease (CD). First, we masked and then performed imputation of 100 disease-associated markers and 100 non-associated markers from each study. Two references for imputation were used in parallel: one consisting of healthy controls and another consisting of patients with the same disease. We assessed the discordance (imprecision) and bias (inaccuracy) of imputation by comparing predicted genotypes to those assayed by SNP-chip. We also assessed the bias on the observed effect size when the predicted genotypes were used in a GWAS study. When healthy controls were used as reference for imputation, a significant bias was observed, particularly in the disease-associated markers. Using cases as reference significantly attenuated this bias. For nearly all markers, the direction of the bias favored the non-risk allele. In GWAS studies of the three diseases (with healthy reference controls from the 1000 genomes as reference), the mean OR for disease-associated markers obtained by imputation was lower than that obtained using original assayed genotypes. We found that the bias is inherent to imputation as using different methods did not alter the results. In conclusion, imputation is a powerful method to predict genotypes and estimate genetic risk for GWAS. However, a careful choice of reference population is needed to minimize biases inherent to this approach.

No MeSH data available.


Related in: MedlinePlus