Scalable Summary Statistics-Based Heritability Estimation Method with Individual Genotype Level Accuracy

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原文链接

NETL
Springer Nature

外文摘要：SNP heritability， the proportion of phenotypic variation explained by genotyped SNPs， is an important parameter in understanding the genetic architecture underlying various diseases and traits。 Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale datasets and by the restrictions in access to individual-level data。 These limitations have motivated the development of methods that only require summary statistics。 While the availability of publicly accessible summary statistics makes them widely applicable， these methods lack the accuracy of methods that utilize individual genotypes。 Here we present a SUMmary statistics-based Randomized Haseman-Elston regression (SUM-RHE)， a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes， while exclusively relying on summary statistics。 SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population， which can be efficiently estimated and readily shared for public use。 Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared to other summary statistic methods and on par with methods that rely on individual-level data。

外文关键词：

HeritabilitySummary statisticsBiobankScalability

作者：

Moonseong Jeong、Ali Pazokitoroudi、Zhengtong Liu、Sriram Sankararaman

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作者单位：

Department of Computer Science, UCLA, Los Angeles, CA, USA

Department of Computer Science, UCLA, Los Angeles, CA, USA##Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

Department of Computer Science, UCLA, Los Angeles, CA, USA##Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA##Deparment of Epidemiology, Harvard School of Public Health, Boston, MA, USA##Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

会议名称：

Annual International Conference on Research in Computational Molecular Biology

会议地点：

Cambridge(US)

会议母体文献：

Research in Computational Molecular Biology

页码：

475-478

出版时间：

2024

DOI：

10.1007/978-1-0716-3989-4_56