contents: BreedeR: a R-package implementing statistical models specifically suited for forest genetic resources analysts
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1 contents: definitions components of phenotypic correlations causal components of genetic correlations pleiotropy versus LD scenarios of correlation computing genetic correlations why genetic correlations are important BreedeR: a R-package implementing statistical models specifically suited for forest genetic resources analysts
2 Multiple trait analysis and breedr Multiple trait analyses are not yet available in current breedr release From now onwards & up to the end of the project there will be new releases at regular basis Next release after the workshop will have the feature of multiple trait analyses breedr is based on BLUPF90 family of programs, which already support multiple trait models. BreedR needs a refactoring of the interface to have full benefit of this BLUPF90 feature.
3 Lynch & Walsh (1998): Deciphering the relative contributions of environmental and genetic factors to phenotypic correlations is one of the most powerful and revealing applications of QG [Genetics and Analysis of Quantitative Traits] multiple natures? environmental correlations p.e. availability of same basic resources during development genetic correlations o pleiotropy o linkage (by distance, by sampling)
4 trait X trait Y α locus 1 d e environmental correlation locus 2 α d e G E genetic correlation (linkage) add-dom
5 trait X trait Y locus 1 α locus 3 α e e genetic correlation (pleiotropy) locus 2
6 Nature of genetic correlations: linkage vs. pleiotropy Mather & Jinks (1982): linkage is the more likely explanation [Biometrical Genetics, London] Jinks et al. (1985):...either or both factors may be important, depending on the pair of traits [Heredity, 55] Falconer & Mackay (1996): the genetic cause of correlation is chiefly pleiotropy, linkage is a cause of transient correlation Linkage is of minor importance in equilibrium populations Tureli (1985), Hastings (1989), Bürger (1989) [Genetics, 111; 121; 121]
7 Correlations and gene frequencies Génotype A1A1 A1A2 A2A2 B1B1 B1B2 B2B2 caractère X caractère Y Génotype A1A1 A1A2 A2A2 B1B1 B1B2 B2B2 caractère X caractère Y Génotype A1A1 A1A2 A2A2 B1B1 B1B2 B2B2 caractère X caractère Y corr q corr q 0.01 p corr q 0.01 p p
8 Pleiotropy: several sources Hodgkin (1998): pleiotropy is for the geneticist what ambiguity is for the critic, multiple functions embedded in the same object [Journal of Developmental Biology, 42] Artefactual (, mutagenesis affecting adjacent but unrelated genes) Adoptive & opportunistic (, 1 gene different purposes) Parsimonious (1 gene same purposes different pathways) Combinatorial (genes as bricks of larger functional products) Unifying (several genes coding a common biol. function)...
9 Pleiotropy: resources model Sheridan and Barker (1974) [Aust. J. biol. Sci., 27] trait X trait Y gene P (+/-) gene Q (-/+) substrate resources gene R (+/+)
10 Genetic correlations and evolution Armbruster & Schwaegerle (1996): covariation of traits that are functionally related, adaptative character complexes, that have evolved by natural selection favouring certain combinations of genetically independent traits. [J.Evol.Biol., 9] Pleiotropy is a constraint in evolution (Arnold, 1992) [American Naturalist, 140]
11 computing genetic correlations y i X b y j X b y k X b y l X b y i X b x j X b y k X b x l X b
12 scenarios of genetic & phenotypic correlations
13 scenarios of genetic & phenotypic correlations
14 scenarios of genetic & phenotypic correlations time lapse of genetic correlations over t=8
15 scenarios of genetic & phenotypic correlations Sanchez, L., Yanchuk, A., King, J., Gametic models for multitrait selection schemes to study variance of response and drift under adverse genetic correlations. Tree Genetics & Genomes.
16 Index selection Independent culling x x y t y t I=b T z=(p -1 Ga) T z Optimal if known G and P Inefficient if poor estimations of G et P drift of genetic parameters over generations selected x selected y simple economic advantages (multistage) cumbrous computing of optimal thresholds with many traits
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