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# Computational inefficiency: The original GCTA implementation scales poorly with increasing data size (<math>\mathcal{O}(\text{SNPs} \cdot n^2)</math>), so even if enough data is available for precise GCTA estimates, the computational burden may be unfeasible. GCTA can be meta-analyzed as a standard precision-weighted fixed-effect meta-analysis,<ref>[http://gcta.freeforums.net/thread/213/analysis-greml-results-multiple-cohorts "Meta-analysis of GREML results from multiple cohorts"], Yang 2015</ref> so research groups sometimes estimate cohorts or subsets and then pool them meta-analytically (at the cost of additional complexity and some loss of precision). This has motivated the creation of faster implementations and variant algorithms which make different assumptions, such as using [[Method of moments (statistics)|moment matching]].<ref>[http://biorxiv.org/content/early/2016/08/18/070177 "Phenome-wide Heritability Analysis of the UK Biobank"], Ge et al 2016</ref>
# Need for raw data: GCTA requires genetic similarity of all subjects and thus their raw genetic information; due to privacy concerns, individual patient data is rarely shared. GCTA cannot be run on the summary statistics reported publicly by many GWAS projects, and if pooling multiple GCTA estimates, a [[meta-analysis]] must be performed. <br> In contrast, there are alternative techniques which operate on summaries reported by GWASes without requiring the raw data<ref>Pasaniuc & Price 2016, [https://www.dropbox.com/s/4mgmun29xbund7z/2016-pasaniuc.pdf "Dissecting the genetics of complex traits using summary association statistics"]</ref> e.g. "[[Linkage disequilibrium score regression|LD score regression]]"<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4495769/ "LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies"], Bulik-Sullivan et al 2015</ref> contrasts [[linkage disequilibrium]] statistics (available from public datasets like [[1000 Genomes]]) with the public summary effect-sizes to infer heritability and estimate genetic correlations/overlaps of multiple traits. The [[Broad Institute]] runs [http://ldsc.broadinstitute.org/about/ LD Hub] {{Webarchive|url=https://web.archive.org/web/20160511100955/http://ldsc.broadinstitute.org/about/ |date=2016-05-11 }} which provides a public web interface to >=177 traits with LD score regression.<ref>[http://biorxiv.org/content/biorxiv/early/2016/05/03/051094.full.pdf "LD Hub: a centralized database and web interface to LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis"], Zheng et al 2016</ref> Another method using summary data is HESS.<ref>[http://biorxiv.org/content/early/2016/01/14/035907 "Contrasting the genetic architecture of 30 complex traits from summary association data"], Shi et al 2016</ref>
# Confidence intervals may be incorrect, or outside the 0-1 range of heritability, and highly imprecise due to asymptotics.<ref>
# Underestimation of SNP heritability: GCTA implicitly assumes all classes of SNPs, rarer or commoner, newer or older, more or less in linkage disequilibrium, have the same effects on average; in humans, rarer and newer variants tend to have larger and more negative effects<ref>[https://www.dropbox.com/s/idh2vm1dkar3qho/2017-gazal.pdf "Linkage disequilibrium–dependent architecture of human complex traits shows action of negative selection"], Gazal et al 2017</ref> as they represent [[mutation load]] being purged by [[Negative selection (natural selection)|negative selection]]. As with measurement error, this will bias GCTA estimates towards underestimating heritability.
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