26 research outputs found

    UK Biobank data interpretation.

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    We analyzed four traits from the UK Biobank using five methods: Single Trait GWAS, MTAG, MI GWAS, HIPO, and PAT and show the variants associated with each trait. For Single Trait GWAS and MTAG, the per trait association was directly computed. For MI GWAS, HIPO and PAT, an omnibus association was first performed. The significant variants were then interpreted using the m-value framework using 0.9 as the threshold.</p

    Additional simulations and a table of the real data results.

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    (PDF)</p

    Replication power in the GIANT consortium for BMI and height.

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    We tested the novel associations in the UK Biobank discovered by PAT and HIPO for replication in the GIANT consortium. We separately clumped using the lead variant as determined by the m-value. For each variant, we calculate replication power and bin the variants into deciles. The first column lists the trait. The second column is the decile while the third and fourth column are the average power within the set for each respective method. The number of variants tested for replication, the expected number of replications, and the number of variants that replicated are reported in the next six columns. The final two columns contain the number of variants with effect sizes from the GIANT consortium in the same direction seen in the UK Biobank. A binomial test on whether the proportion of effect sizes in the same direction across studies is greater than 50% of all tested variants in the set. A single asterisks means the results are significant at the nominal α = 0.05 and two asterisks indicates significance at .</p

    Using importance sampling for setting critical values.

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    We simulated data according to two univariate Gaussian distributions and and show the densities. We show the critical value z ≈ 1.96 for α = 0.05. We would expect to see the critical value |z| or larger more often when simulating data according to v than when simulated under the distribution of s.</p

    Comparison of the rejection regions for MI GWAS and PAT.

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    We simulated 100,000 summary statistics for two traits with the genetic variance () and the sample size (N = 25,000) equal for both traits. We varied the genetic and environmental correlation between traits and used α = 0.05 for the level of significance. Each row corresponds to one set of simulations highlighting three points. The left column shows the rejection region of MI GWAS, the middle column has PAT’s rejection region while the third column provides a comparison of the two methods. The simulations used in A-C have no environmental or genetic correlation while the data in D-F has no environmental correlation and 67% genetic correlation. For the third row of G-I, the environmental correlation was 67% while there was no genetic correlation between traits. The last row of simulations assumed an environmental and genetic correlation of 67%.</p

    Four multi-trait GWAS methods with per trait interpretation.

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    1.5 million variants were simulated with z-scores for four traits with 10% of variants being truly associated. The first column lists which trait has a genetic effect. The second column is the number of variants simulated under this specific model. The third column is the genetic effect size of the variant. The remaining columns are split by trait where the performance of the four methods are shown for each trait. These 16 columns present the number of variants identified as associated by each method for the specific trait. MTAG uses p-values, ASSET uses the optimal subset, while PAT and HIPO use the m-value framework to provide per trait associations.</p

    Interpreting m-values using a P-M plot.

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    Along the x-axis is the per trait m-value and the y-axis shows the p-value from the original single trait GWAS. Region A is when the original association is significant, but the m-value interpretation is ambiguous. There should not be data points in this region. Region B and D are associations with an m-value greater than 0.9, so the interpretation is that there is a genetic effect in this trait. In Region C, the m-value interpretation is left ambiguous.</p

    Interpreting per trait associations from omnibus significant variants.

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    We simulated one million variants for four traits under two models. The first set of simulations assumed there was a genetic effect in every trait (A), while the second model only has a genetic effect in body mass index and height (B). The associated traits are noted with an asterisks (*). The results for each trait were split based on the absolute value of the z-score and showed the interpretation as either ambiguous or associated. The threshold for associated is an m-value greater than 0.9.</p

    Comparison of multi-trait GWAS methods.

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    1.5 million variants were simulated with z-scores for four traits with 10% of variants as truly associated. The first column lists which trait has a genetic effect. The second column is the number of variants simulated under this specific model. The third column is the genetic effect size. The remaining four columns contain the number of variants identified as associated by four methods: PAT, HIPO, MTAG, and ASSET. The final row of the table contains each methods running time.</p
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