Abstract
BackgroundData from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability.ResultsIn this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy.ConclusionsThe proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.
Highlights
Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years
In modern GWASs, designs based on a population sample of unrelated people help to overcome the confounding of genes and environment, with the Single nucleotide polymorphism (SNP) heritability being viewed as a lower bound for the narrow-sense heritability
Researchers in [9] developed the software genomewide complex trait analysis (GCTA) to estimate the SNP heritability without the requirement that individual SNPs are significant, arriving at a higher lower bound for the narrow-sense heritability [10]
Summary
Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. This has been referred to as the “missing heritability” problem [7, 8] To address this gap, researchers in [9] developed the software genomewide complex trait analysis (GCTA) to estimate the SNP heritability without the requirement that individual SNPs are significant, arriving at a higher lower bound for the narrow-sense heritability [10]. Researchers in [9] developed the software genomewide complex trait analysis (GCTA) to estimate the SNP heritability without the requirement that individual SNPs are significant, arriving at a higher lower bound for the narrow-sense heritability [10] Computing tools such as BOLT-REML [11], BayesR [12], and massively expedited genome-wide heritability analysis (MEGHA) [13] have been developed to achieve a higher speed. These works make use of the linear mixed model (LMM) to consider all SNPs across the genome-wide average, assuming that the genetic effects are random variables and the genotypes are fixed quantities
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
