Genome-wide complex trait analysis: Difference between revisions

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{{short description|Statistical method for genetic variance component estimation}}

{{Redirect|GCTA|the TV camera used in the Apollo space program|Apollo TV camera#RCA J-Series Ground-Commanded Television Assembly (GCTA){{!}}Apollo TV camera}}

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{{technical|date=January 2017}}

{{Original research|date=September 2019}}

'''Genome-wide complex trait analysis (GCTA) Genome-based [[restricted maximum likelihood]] (GREML)''' is a statistical method for [[variance]] component estimation in genetics which quantifies the total narrow-sense (additive) contribution to a trait's [[heritability]] of a particular subset of genetic variants (typically limited to [[Single-nucleotide polymorphism|SNPs]] with [[Minor allele frequency|MAF]] >1%, hence terms such as "chip heritability"/"SNP heritability"). This is done by directly quantifying the chance genetic similarity of unrelated individuals and comparing it to their measured similarity on a trait; if two unrelated individuals are relatively similar genetically and also have similar trait measurements, then the measured genetics are likely to causally influence that trait, and the correlation can to some degree tell how much. This can be illustrated by plotting the squared pairwise trait differences between individuals against their estimated degree of relatedness.<ref>[https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3232052/figure/F3/ Figure 3] of Yang et al 2010, or Figure 3 of Ritland & Ritland 1996</ref> The GCTA framework can be applied in a variety of settings. For example, it can be used to examine changes in heritability over aging and development.<ref name="Deary2012">[https://www.researchgate.net/profile/David_Dave_Liewald/publication/221760226_Genetic_contributions_to_stability_and_change_in_intelligence_from_childhood_to_old_age/links/02e7e52ca9a723a8fa000000.pdf "Genetic contributions to stability and change in intelligence from childhood to old age"], Deary et al 2012</ref>. It can also be extended to analyse bivariate [[genetic correlation]]s between traits.<ref name="Lee2012">Lee et al 2012, [http://bioinformatics.oxfordjournals.org/content/28/19/2540.full "Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood"]</ref> There is an ongoing debate about whether GCTA generates reliable or stable estimates of heritability when used on current SNP data.<ref>{{Cite journal |last=Krishna Kumar |first=Siddharth |last2=Feldman |first2=Marcus W. |last3=Rehkopf |first3=David H. |last4=Tuljapurkar |first4=Shripad |date=2016-01-05 |title=Limitations of GCTA as a solution to the missing heritability problem |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=113 |issue=1 |pages=E61–70 |doi=10.1073/pnas.1520109113 |issn=1091-6490 |pmc=4711841 |pmid=26699465}}</ref> The method is based on the outdated and false dichotomy of genes versus the environment. It also suffers from serious methodological weaknesses, such as susceptibility to [[population stratification]].<ref>{{cite journal |last1=BURT |first1=CALLIE H. |last2=SIMONS |first2=RONALD L. |title=HERITABILITY STUDIES IN THE POSTGENOMIC ERA: THE FATAL FLAW IS CONCEPTUAL |journal=Criminology |date=February 2015 |volume=53 |issue=1 |pages=103–112 |doi=10.1111/1745-9125.12060}}</ref>