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Statistical Methods for Rare Variant Association Test Using Summarized Data

Statistical Methods for Rare Variant Association Test Using Summarized Data

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## Statistical Methods for Rare Variant Association Test Using Summarized Data

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**Statistical Methods for Rare Variant Association Test Using**Summarized Data Qunyuan Zhang Ingrid Borecki, Michael A. Province Division of Statistical Genomics**Next generation sequencing => rare variants**Two types of data Motivation Summarized level • Pooled DNA sequencing • Public data (as control) Individual level**EFTTFT**QQ Plots of Existing Methods(under the null) EFT and C-alpha inflated with false positives TFT and CAST no inflation, but assuming single effect-direction Objective More general, powerful methods … CAST C-alpha**variant 1**variant 2 … … variant 3 variant k variant i Structure of Summarized data Strategy Instead of testing total freq./number, we test the randomness of all tables.**Exact Probability Test (EPT)**1.Calculating the probability of each table based on hypergeometric distribution 2. Calculating the logarized joint probability (L) for all k tables 3. Enumerating all possible tables and L scores 4. Calculating p-value P= Prob.( )**Likelihood Ratio Test (LRT)**Binomial distribution**EPT**N=500 LRT N=500 Q-Q Plots of EPT and LRT(under the null) LRT N=3000 EPT N=3000**Power Comparison significance level=0.00001**Variant proportion Positive causal 80% Neutral 20% Negative Causal 0% Power Power Power Sample size Sample size Sample size**Power Comparisonsignificance level=0.00001**Variant proportion Positive causal 60% Neutral 20% Negative Causal 20% Power Sample size**Power Comparison significance level=0.00001**Variant proportion Positive causal 40% Neutral 20% Negative Causal 40% Power Sample size**Power Comparison individual-level data vs. summarized**dataN=1000, significance level=0.00001 Power CMC Li & Leal, 2008 SKAT Wu et al., 2011 Variant proportion positive : neutral : negative (%)**Cases: 460 ovarian cancer cases, germline exome data, from**TCGA Controls: ~3500 individuals, exome data, from NHBLI Application -LOG10 p-values of 933 cancer-related genes**Conclusions**EFT and C-alpha produce inflated p-value. TFT and CAST produce correct p-value, but lose power in detecting bi-directional effects. EPT produces correct p-value and maintains power regardless of effect directions, more computer time. LRT produces slightly biased p-value for small N, can be improved by larger N, similar power of EPT, less computer time, a good alternative for large datasets. If no confounders need to be modeled, there is no significant loss of power in the use of summarized data**Acknowledgements**Dr. Li Ding Charles Lu Krishna-Latha Kanchi (for providing the TCGA and NHBLI exome data)