Management Embryocentre Ltd of genetic variation in local breeds Asko Mäki-Tanila Reykjavik 30/4/2009 based on collaboration with T Meuwissen, J Fernandez and M Toro within EURECA project
Approach in two stages: -present state of genetic variation -management of variation Existence of pedigree recording essential! Molecular tools are used more and more to support pedigree information - efficient assessment of variation with genetic markers is still expensive.
Assessment of genetic variation
Measures of diversity - proportion of variable loci - expected vs observed heterozygosity - number of alleles per locus (long-term potential for selection) - OR: parameters based on pedigree give a cheap and holistic picture Inbreeding coefficient related to mean Coancestry measures drift and changes in variance Inbreeding is unavoidable. values of inbreeding depend on the depth of pedigree recording BETTER CONCEPT: Proportional increment or rate of inbreeding ( F = 1 / 2 N e ) - stays constant over generations - useful parameter for comparing populations
Reasoning minimum N e = 50 or rate of inbreeding ΔF = 1/2N e < 0.01 amount and nature of available variation new variation mutations vs losses in small populations h 2 =.33 (.50) N e = 50 (100) maintaining neutral alleles segregating (potential for future) not loosing beneficial alleles negative pleiotropic effects increases variation harmful alleles mutation meltdown: fixation reduces overall fitness and repr rate and pop size inbreeding depression - sufficient genetic variation for natural selection to balance reduced performance certainty in achieving the predicted response
Realisation of predicted change less certain in small populations 20 10 keskiarvo (σ p ) mean (phenotypic sd) 0-10 1 2 3 4 5 sukupolvi generation 20 10 0-10 1 2 3 4 5 N e halved Strandén MTT
Estimation of proportional increment ( F t -F t-1 ) ΔF = ( 1 - F t-1 ) or similarly we could compute the changes in coancestry (more stable and gives early signals about undesirable trends) So we need individual F (or pairwise coancestry of two individuals or relationship matrix A) lots of methods to compute F - Wright s path method (Colleau, GENUP, PEDIGREEVIEWER) - tabular method (used in BLUP; e.g. Meuwissen; A given by EVA, ENDOG, PEDIG) - other methods: contribution, gene dropping
Yrjö Tuunanen / MTT Without pedigree information N e = 4 N m N f / (N m + N f ) (augmented by variation in family size, ENDOG) - molecular methods - block structure within genome related to N e - linkage disequilibrium 1 / ( 4 N e c + 1 ) (c ~ map distance) - longer blocks from earlier bottlenecks - coancestry based on lots of molecular markers (even over breeds)
Distances of Dutch chicken lines 46 lines, 17 markers Crooijmans et al Poultry Sci 75, 904-909 Example on estimating coancestries Eding et al Gen Sel Evol
cont. Sumatra Layer 17 Layer 20 Layer 18 Layer 56 Welsummer Broiler CD Broiler CT Broiler GB Broiler CQ Broiler DE Broiler CG Broiler DA Broiler CP Broiler DD Broiler CH Broiler CK Broiler CO Broiler CZ Broiler DB Broiler CR Broiler CV Broiler EE Barneveld B Barneveld A Booted bantam Layer 25 Layer 27 Layer 26 Layer 57 Layer 29 NH hoen Gron.Mew Polish brd Owl beard Polish non-brd Brabanter Frisian Breda Assendelft Lakenvelder Hamburgh Drents Dutch bantam Kraienkoppe 0.600-0.700 0.500-0.600 0.400-0.500 0.300-0.400 0.200-0.300 0.100-0.200 0.000-0.100-0.100-0.000.6 -.7.5 -.6.4 -.5.3 -.4.2 -.3.1 -.2 0 -.1 Sumatra Layer 17 Layer 20 Layer 18 Layer 56 Welsummer Broiler CD Broiler CT Broiler GB Broiler CQ Broiler DE Broiler CG Broiler DA Broiler CP Broiler DD Broiler CH Broiler CK Broiler CO Broiler CZ Broiler DB Broiler CR Broiler CV Broiler EE Barneveld B Barneveld A Booted bantam Layer 25 Layer 27 Layer 26 Layer 57 Layer 29 NH hoen Gron.Mew Polish brd Owl beard Polish non-brd Brabanter Frisian Breda Assendelft Lakenvelder Hamburgh Drents Dutch bantam Kraienkoppe Bankiva estimated coancestries within and between populations
Sumatra Layer 17 Layer 20 Layer 18 Layer 56 Welsummer Broiler CD Broiler CT Broiler GB Broiler CQ Broiler DE Broiler CG Broiler DA Broiler CP Broiler DD Broiler CH Broiler CK Broiler CO Broiler CZ Broiler DB Broiler CR Broiler CV Broiler EE Barneveld B Barneveld A Booted bantam Layer 25 Layer 27 Layer 26 Layer 57 Layer 29 NH hoen Gron.Mew Polish brd Owl beard Polish non-brd Brabanter Frisian Breda Assendelft Lakenvelder Hamburgh Drents Dutch bantam Kraienkoppe 0.600-0.700 0.500-0.600 0.400-0.500 0.300-0.400 0.200-0.300 0.100-0.200 0.000-0.100-0.100-0.000 Sumatra Layer 17 Layer 20 Layer 18 Layer 56 Welsummer Broiler CD Broiler CT Broiler GB Broiler CQ Broiler DE Broiler CG Broiler DA Broiler CP Broiler DD Broiler CH Broiler CK Broiler CO Broiler CZ Broiler DB Broiler CR Broiler CV Broiler EE Barneveld B Barneveld A Booted bantam Layer 25 Layer 27 Layer 26 Layer 57 Layer 29 NH hoen Gron.Mew Polish brd Owl beard Polish non-brd Brabanter Frisian Breda Assendelft Lakenvelder Hamburgh Drents Dutch bantam Kraienkoppe Bankiva cont.
Management of genetic variation Tapio Tuomela / MTT
Management of genetic variation or maximising Ne This is achieved by equalising the contributions from the parents or, in selected populations, optimising the contributions with respect to genetic gain and rate of inbreeding (e.g. software GENCONT). In long-term choosing parents and their contributions is more important - if avoidance of (short-term) inbreeding depression is important, then extra attention should be paid to mating pairs
There are many simplified mating strategies - compromising tools with penalising average coancestry EVA (Berg et al), TGRM (Kinghorn et al) - hierarchical mating strategies (one male from each sire, one female from each dam) - factorial mating (requires large families) - avoiding matings with relatives (inbreeding depression) avoidance of sibs reducing co-selection of relatives ( inflated h2) more weight to individuals vs family When pedigree information is missing, e.g. circular mating could be practiced over herds or coancestries could be approximated by resorting to genomics.
K V circular mating maximising variation G S M important to recognise important sub-populations, herds or family lines within a breed matings within sub-populations rotational matings across sub-populations to maintain performance (vs inbr depr.)
CONCLUSIONS estimate rate of inbreeding or effective population size rather than level of inbreeding future variation guaranteed by maximising effective population size optimising contributions from individuals (families) still need to develop user-friendly tools missing pedigree information: cyclic mating large-scale genomic methods history of populations supporting pedigree predictions differentiated approach over genome EAAP Barcelona