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Animal Trait Correlation Database |
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Frequently Asked Questions
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Correlation Coefficient (r) is a statistical parameter that describes the degree as how closely the pairs of variables are related.
R-square: The square of the coefficient
(
![]() ![]() ![]() For genetic analysis, the geneticists partition the correlation into phenotypic correlations and genetic correlations. The phenotypic correlation is the correlation between records of two traits on the same animal and is usually estimated by the product-moment correlation statistic (or Pearson correlation coefficient, for short). The genetic correlation is the correlation between an animal's genetic value for one trait and the same animal's genetic value for the other trait.
![]() VP = VG + VE + VGE
where VP = total phenotypic variation
VG = total genetic factor variation
VE = total environmental factor variation
VGE = genetic X environmental factor interaction variation
Genetic variance = additive genetic variance
+ dominant genetic variance
+ epestatic genetic variance
+ interaction between/among all previous genetic variances
Non-genetic variance = variances due to environmental factors + Error. ![]()
VG = VA + VD + VI
∴ VP = VA + VD + VI + VE + VGE
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![]() In classical genetic analysis, the residual variance is often conveniently used to represent environmental variations, referring to "everything else" after the explained variations. It is worth to note that, in a more resent study, Huang and Mackay (2016) showed evidences to indicate that variance component analysis should not be used to infer genetic architecture of quantitative traits.
H2 = VG / VP
This is called heritability in the broad sense
because it is a rather crude measure that includes reasons for the genetic
variation that are not necessarily passed on to the next generation.
Narrow sense heritability gives the ratio of additive genetic variance/ phenotypic variance:
h2 = VA / VP
The reason why the additive genetic variance matters here is because what's passed
on to the next generation are only the alleles (NOT the dominance interaction NOR
the epistatic interaction). The allele sets to be passed on are formed newly at
each generation. For example, at generation one, some offspring may have alleles
A1/A3 and B2/B4. They are new combinations not seen in either parent, therefore the
dominance and epistatic interactions will be new. In general, greater the additive
genetic variability VA in a population, greater the diversity it, thus
greater selection potentials (greater the narrow-sense heritability);
There could have been a confusion between "environmental veriance" and "residual
variance" as they both serve as "the other", or "everything else", less important
variance component when study focus is mostly on genetic variances. Although
"environmental veriance" and "residual variance" may pretty much overlap, they
are not the same. The "environmental veriance" is a genetic concept (or method
for variance partitions), whereas the "residual variance" is a statistical concept
(or method for variance partitions).
It is not uncommon to see in publications that some only report "genetic + environment", and some others report "genetic + residual" variances. When they are curated into the CorrDB, we record they as they are (i.e. "residual" variance into a "residual" field and "environment" variance into a "environment" field. It will be up to users how these data will be looked at.
Genomic heritability (or
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SNP-based heritability (or
One can estimate the relationships between individuals based on their genotypes and use a
linear mixed model to estimate the variance explained by the genetic markers. This gives
a genomic heritability estimate based on the variance captured by common genetic variants.
Other types of estimates include using GCTA approch (
![]() All in all, various types of heritability come from our dissection of inheritable genetic elements each of which contributes to the total heritability in general terms. e.g. h2g ≤ h2 ≤ H2
Yes, the CorrDB supports dbxref to facilitate universal links with specific CorrID. This facility was introduced in 2020 that provides links to each correlation and heritability record in the CorrDB. The syntax for the specific URL link is in the form of
https://www.animalgenome.org/CorrDB/q?id=[CorrID] , where CorrID is a numeric stable ID for each correlation record in the CorrDB. This is often used by web tools, API tools, or database dbxref references. There is a section for Animal CorrDB in the GeneOntology db-xrefs list to describe the syntax to use in order to establish stable links to CorrDB.
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First draft: January 9, 2018 Last update: October 28 2022 15:31:19. |
By Zhiliang Hu CorrDB Developer Associate Scientist Dept of Animal Science Iowa State University | ||||||||||||
References
Douglas S. Falconer, Trudy F.C. Mackay (1996), Introduction to Quantitative Genetics. Published by Pearson, Edinburgh Gate, Harlowm Essex CM20 2JE, England. Wen Huang and Trudy F.C.Mackay (2016), "The Genetic Architecture of Quantitative Traits Cannot Be Inferred from Variance Component Analysis". PLoS Genet. 12(11). Peter M. Visscher, William G. Hill and Naomi R. Wray, (2008), "Heritability in the genomics era — concepts and misconceptions". Nat Rev Genet. 9(4):255-66. Jian Yang, Jian Zeng, Michael E Goddard, Naomi R Wray & Peter M Visscher (2017), "Concepts, estimation and interpretation of SNP-based heritability". Nature Genetics, 49:1304–1310. John Stanton-Geddes, Jeremy B. Yoder, Roman Briskine, Nevin D. Young, and Peter Tiffin (2013), "Estimating heritability using genomic data". Methods in Ecology and Evolution, 4:1151–1158. Raymond Walters with contributions from Claire Churchhouse and Rosy Hosking (2017). "Heritability 201: Types of Heritability and How We Estimate It". Web page last visited on April 14, 2022 at address: http://www.nealelab.is/blog/2017/9/13/heritability-201-types-of-heritability-and-how-we-estimate-it. John Hunt and Leigh W. Simmons. (2002). "The genetics of maternal care: Direct and indirect genetic effects on phenotype in the dung beetle Onthophagus taurus". Proc Natl Acad Sci U S A. 2002 May 14; 99(10): 6828–6832. |
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