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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제32권 제1호
- 발행연도
- 2025.1
- 수록면
- 47 - 79 (33page)
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초록· 키워드
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Estimating the proportion of signals is a fundamental subject in a large scale inference for genome-wide association study (GWAS). In GWAS, hundreds of thousands of single-nucleotide polymorphisms (SNPs) are identified and often only p-values of each SNP are reported. The goal in GWAS is to test if any of these SNPs are associated with some disease or a phenotype of interest. When correlation structures of p-values are unknown, p-values under the null are no longer uniformly distributed and most existing methods become instable and inconsistent. To reduce these instability and inconsistency, we propose a new method that estimates the proportion of signals under arbitrary dependency structures of input data. We use a conditional distribution approach to account for the correlation information of observed p-values. We show the consistency of the proposed method under arbitrarily correlated p-values. We investigate the proposed method with extensive numerical studies and real data analysis to compare the performance of our method with existing methods under various dependency structures.
목차
Abstract
1. Introduction
2. Estimating the proportion of null hypotheses under arbitrarily correlated p-values
3. Numerical studies
4. Case study
5. Concluding remarks
References
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