Improved Estimation of Phenotypic Correlations Using Summary Association Statistics

Frontiers in Genetics

Ting Li¹  Xia Shen²  Zheng Ning³ 

[1] Biostatistics Group, State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China;Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom;Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;

关键词: phenotypic correlation;    genome-wide association;    low MAF estimator;    LD score regression;    genetic correlation;    minor allele frequency;   

DOI  :  10.3389/fgene.2021.665252

来源: DOAJ

【 摘 要 】

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.

【 授权许可】

Unknown