Abstract
In a recent publication in PNAS, Krishna Kumar et al. (1) claim that “GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability.” We show below that those claims are false due to their misunderstanding of the theory and practice of random-effect models underlying genome-wide complex trait analysis (GCTA) (2). GCTA, more precisely, the genomic-relatedness-based restricted maximum-likelihood (GREML) approach (3) implemented in GCTA (4), is a method to estimate the proportion of phenotypic variation that can be explained by all genome-wide SNPs ( h g 2 ) using an SNP-derived genetic relationship matrix. Krishna Kumar et al. (1) claim that the estimate of h g 2 from GCTA-GREML is unreliable based on the observations that the observed variance explained per SNP ( σ 2 = h g 2 / m , where m is the number of SNPs) from simulations is inconsistent with their expectation. … [↵][1]1To whom correspondence may be addressed. Email: jian.yang{at}uq.edu.au or peter.visscher{at}uq.edu.au. [1]: #xref-corresp-1-1
Sign-in/Register to access full text options 
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.
