Genome-wide complex trait analysis: Difference between revisions

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{{Original research|date=September 2019}}

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'''Genome-wide complex trait analysis''' ('''GCTA''') '''Genome-based [[restricted maximum likelihood]]''' ('''GREML''') is a statistical method for [[heritability]] estimation in genetics, which quantifies the total additive contribution of a set of genetic variants to a trait. GCTA is typically applied to common single nucleotide polymorphisms ([[SNPs]]) on a genotyping array (or "chip") and thus termed "chip" or "SNP" heritability.

'''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, [https://web.archive.org/web/20121016034953/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 |last1=Krishna Kumar |first1=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|bibcode=2016PNAS..113E..61K |doi-access=free }}</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. |title=Heritability Studies in the Postgenomic Era: The Fatal Flaw is Conceptual |last2=SIMONS |first2=RONALD L. |journal=Criminology |date=February 2015 |volume=53 |issue=1 |pages=103–112 |doi=10.1111/1745-9125.12060}}</ref>

GCTA operates 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> GCTA makes a number of modeling assumptions and whether/when these assumptions are satisfied continues to be debated.

GCTA heritability estimates are useful because they provide lower bounds<ref>{{Cite journal |last1=Duncan |first1=L. E. |last2=Ratanatharathorn |first2=A. |last3=Aiello |first3=A. E. |last4=Almli |first4=L. M. |last5=Amstadter |first5=A. B. |last6=Ashley-Koch |first6=A. E. |last7=Baker |first7=D. G. |last8=Beckham |first8=J. C. |last9=Bierut |first9=L. J. |date=March 2018 |title=Largest GWAS of PTSD (N=20 070) yields genetic overlap with schizophrenia and sex differences in heritability |journal=Molecular Psychiatry |volume=23 |issue=3 |pages=666–673 |doi=10.1038/mp.2017.77 |issn=1476-5578 |pmc=5696105 |pmid=28439101 |quote="A common misconception about SNP-chip heritability estimates calculated with GCTA and LDSC is that they should be similar to twin study estimates, when in reality twin studies have the advantage of capturing all genetic effects—common, rare and those not genotyped by available methods. Thus, the assumption should be that h2SNP < h2TWIN when using GCTA and LDSC, and this is what we observe for PTSD, as has been observed for many other phenotypes.}}</ref> for the genetic contributions to traits such as [[Heritability of IQ|intelligence]] without relying on the assumptions used in [[twin study|twin studies]] and other family and [[Genealogy|pedigree]] studies, thereby corroborating them<ref>Eric Turkheimer ([http://people.virginia.edu/~ent3c/papers2/StillMissingFinal.pdf "Still Missing"], Turkheimer 2011) discusses the GCTA results in the context of the twin study debate: "Of the three reservations about quantitative genetic heritability that were outlined at the outset—the assumptions of twin and family studies, the universality of heritability, and the absence of mechanism—the new paradigm has put the first to rest, and before continuing to explain my skepticism about whether the most important problems have been solved, it is worth appreciating what a significant accomplishment this is. Thanks to the Visscher program of research, it should now be impossible to argue that the whole body of quantitative genetic research showing the universal importance of genes for human development was somehow based on a sanguine view of the equal environments assumption in twin studies, putting an end to an entire misguided school of thought among traditional opponents of classical quantitative (and by association behavioral) genetics (e.g., Joseph, 2010; Kamin & Goldberger, 2002)"; see also [https://www.vox.com/the-big-idea/2017/5/18/15655638/charles-murray-race-iq-sam-harris-science-free-speech Turkheimer, Harden, & Nisbett]: "These methods have given scientists a new way to compute heritability: Studies that measure DNA sequence variation directly have shown that pairs of people who are not relatives, but who are slightly more similar genetically, also have more similar IQs than other pairs of people who happen to be more different genetically. These “DNA-based” heritability studies don’t tell you much more than the classical twin studies did, but they put to bed many of the lingering suspicions that twin studies were fundamentally flawed in some way. Like the validity of intelligence testing, the heritability of intelligence is no longer scientifically contentious."</ref><ref>"This finding of strong genome-wide pleiotropy across diverse cognitive and learning abilities, indexed by general intelligence, is a major finding about the origins of individual differences in intelligence. Nonetheless, this finding seems to have had little impact in related fields such as cognitive neuroscience or experimental cognitive psychology. We suggest that part of the reason for this neglect is that these fields generally ignore individual differences.65,66 Another reason might be that the evidence for this finding rested largely on the twin design, for which there have always been concerns about some of its assumptions;6 we judge that this will change now that GCTA is beginning to confirm the twin results." --[http://www.nature.com/mp/journal/vaop/ncurrent/full/mp2014105a.html "Genetics and intelligence differences: five special findings"], Plomin & Deary 2015</ref><ref>[http://www.gwern.net/docs/genetics/2016-plomin.pdf "Top 10 Replicated Findings From Behavioral Genetics"], Plomin et al., 2016: "This research has primarily relied on the twin design in which the resemblance of identical and fraternal twins is compared and the adoption design in which the resemblance of relatives separated by adoption is compared. Although the twin and adoption designs have been criticized separately (Plomin et al., 2013), these two designs generally converge on the same conclusion despite being based on very different assumptions, which adds strength to these conclusions...GCTA underestimates genetic influence for several reasons and requires samples of several thousand individuals to reveal the tiny signal of chance genetic similarity from the noise of DNA differences across the genome (Vinkhuyzen, Wray, Yang, Goddard, & Visscher, 2013). Nonetheless, GCTA has consistently yielded evidence for significant genetic influence for cognitive abilities (Benyamin et al., 2014; Davies et al., 2015; St. Pourcain et al., 2014), psychopathology (L. K. Davis et al., 2013; Gaugler et al., 2014; Klei et al., 2012; Lubke et al., 2012, 2014; McGue et al., 2013; Ripke et al., 2013; Wray et al., 2014), personality (C. A. Rietveld, Cesarini, et al., 2013; Verweij et al., 2012; Vinkhuyzen et al., 2012), and substance use or drug dependence (Palmer et al., 2015; Vrieze, McGue, Miller, Hicks, & Iacono, 2013), thus supporting the results of twin and adoption studies."</ref> and enabling the design of well-[[statistical power|powered]] [[genome-wide association study]] (GWAS) designs to find the specific genetic variants involved. For example, a GCTA estimate of 30% SNP heritability is consistent with a larger total genetic heritability of 70%. However, if the GCTA estimate was ~0%, then that would imply one of three things: a) there is no genetic contribution, b) the genetic contribution is entirely in the form of genetic variants not included, or c) the genetic contribution is entirely in the form of non-additive effects such as [[epistasis]]/[[Dominance (genetics)|dominance]]. Running GCTA on individual chromosomes and regressing the estimated proportion of trait variance explained by each chromosome against that chromosome's length can reveal whether the responsible genetic variants cluster or are distributed evenly across the genome or are [[sex-linked]]. Chromosomes can of course be replaced by more fine-grained or functionally informed subdivisions. Examining genetic correlations can reveal to what extent observed correlations, such as between intelligence and socioeconomic status, are due to the same genetic traits, and in the case of diseases, can indicate shared causal pathways such as can be inferred from the genetic variation jointly associated with schizophrenia and other mental diseases or reduced intelligence.

The GCTA framework has also been extended in a number of ways: quantifying the contribution from multiple SNP categories (i.e. functional partitioning); quantifying the contribution of Gene-Environment interactions; quantifying the contribution of non-additive/non-linear effects of SNPs; and bivariate analyses of multiple phenotypes to quantify their genetic covariance (co-heritability or [[genetic correlation]]).

GCTA estimates have implications for the potential for discovery from [[Genome-wide association study|Genome-wide Association Studies (GWAS)]] as well as the design and accuracy of [[polygenic scores]]. GCTA estimates from common variants are typically substantially lower than other estimates of total or narrow-sense heritability (such as from twin or kinship studies), which has contributed to the debate over the [[Missing heritability problem]].

== History ==