Proportion of variance that two traits share due to genetic causes
In multivariate quantitative genetics, a genetic correlation (denoted or ) is the proportion of variance that two traits share due to genetic causes,[1][2][3] the correlation between the genetic influences on a trait and the genetic influences on a different trait[4][5][6][7][8][9] estimating the degree of pleiotropy or causal overlap. A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical. The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across > 2 traits using factor analysis. Genetic correlation models were introduced into behavioral genetics in the 1970s–1980s.
Genetic correlations have applications in validation of genome-wide association study (GWAS) results, breeding, prediction of traits, and discovering the etiology of traits & diseases.
They can be estimated using individual-level data from twin studies and molecular genetics, or even with GWAS summary statistics.[10][11] Genetic correlations have been found to be common in non-human genetics[12] and to be broadly similar to their respective phenotypic correlations,[13] and also found extensively in human traits, dubbed the 'phenome'.[14][15][16][17][18][19][20][21][22][23][24]
This finding of widespread pleiotropy has implications for artificial selection in agriculture, interpretation of phenotypic correlations, social inequality,[25] attempts to use Mendelian randomization in causal inference,[26][27][28][29] the understanding of the biological origins of complex traits, and the design of GWASes.
A genetic correlation is to be contrasted with environmental correlation between the environments affecting two traits (e.g. if poor nutrition in a household caused both lower IQ and height); a genetic correlation between two traits can contribute to the observed (phenotypic) correlation between two traits, but genetic correlations can also be opposite observed phenotypic correlations if the environment correlation is sufficiently strong in the other direction, perhaps due to tradeoffs or specialization.[30][31] The observation that genetic correlations usually mirror phenotypic correlations is known as "Cheverud's Conjecture"[32] and has been confirmed in animals[33][34] and humans, and showed they are of similar sizes;[35] for example, in the UK Biobank, of 118 continuous human traits, only 29% of their intercorrelations have opposite signs,[23] and a later analysis of 17 high-quality UKBB traits reported correlation near-unity.[36]
^Loehlin & Vandenberg (1968) "Genetic and environmental components in the covariation of cognitive abilities: An additive model", in Progress in Human Behaviour Genetics, ed. S. G. Vandenberg, pp. 261–278. Johns Hopkins, Baltimore.
^Cite error: The named reference Solovieff2013 was invoked but never defined (see the help page).
^Cotsapas, Chris; Voight, Benjamin F.; Rossin, Elizabeth; Lage, Kasper; Neale, Benjamin M.; Wallace, Chris; Abecasis, Gonçalo R.; Barrett, Jeffrey C.; Behrens, Timothy; Cho, Judy; De Jager, Philip L.; Elder, James T.; Graham, Robert R.; Gregersen, Peter; Klareskog, Lars; Siminovitch, Katherine A.; Van Heel, David A.; Wijmenga, Cisca; Worthington, Jane; Todd, John A.; Hafler, David A.; Rich, Stephen S.; Daly, Mark J.; FOCiS Network of Consortia (2011). "Pervasive sharing of genetic effects in autoimmune disease". PLOS Genetics. 7 (8): e1002254. doi:10.1371/journal.pgen.1002254. PMC3154137. PMID21852963.
^Chambers, J. C.; Zhang, W.; Sehmi, J.; Li, X.; Wass, M. N.; Van Der Harst, P.; Holm, H.; Sanna, S.; Kavousi, M.; Baumeister, S. E.; Coin, L. J.; Deng, G.; Gieger, C.; Heard-Costa, N. L.; Hottenga, J. J.; Kühnel, B.; Kumar, V.; Lagou, V.; Liang, L.; Luan, J.; Vidal, P. M.; Leach, I. M.; O'Reilly, P. F.; Peden, J. F.; Rahmioglu, N.; Soininen, P.; Speliotes, E. K.; Yuan, X.; Thorleifsson, G.; et al. (2011). "Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma". Nature Genetics. 43 (11): 1131–1138. doi:10.1038/ng.970. PMC3482372. PMID22001757.
^Hemani, Gibran; Bowden, Jack; Haycock, Philip; Zheng, Jie; Davis, Oliver; Flach, Peter; Gaunt, Tom; Smith, George Davey (2017). "Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome". doi:10.1101/173682. S2CID8865889. {{cite journal}}: Cite journal requires |journal= (help)
^Socrates, Adam; Bond, Tom; Karhunen, Ville; Auvinen, Juha; Rietveld, Cornelius A.; Veijola, Juha; Jarvelin, Marjo-Riitta; o'Reilly, Paul F. (2017). "Polygenic risk scores applied to a single cohort reveal pleiotropy among hundreds of human phenotypes". doi:10.1101/203257. S2CID90474334. {{cite journal}}: Cite journal requires |journal= (help)
^Falconer, p. 315 cites the example of chicken size and egg laying: chickens grown large for genetic reasons lay later, fewer, and larger eggs, while chickens grown large for environmental reasons lay quicker and more but normal sized eggs; Table 19.1 on p. 316 also provides examples of opposite-signed phenotypic & genetic correlations: fleece-weight/length-of-wool & fleece weight/body-weight in sheep, and body-weight/egg-timing & body-weight/egg-production in chicken. One consequence of the negative chicken correlations was that, despite moderate heritabilities and a positive phenotypic correlation, selection had begun to fail to yield any improvements (p. 329) according to "Genetic slippage in response to selection for multiple objectives", Dickerson 1955.