in Aquaculture Statistical methods for GS 2/3/2015 Introduction How to use new genomics technology? Aim Why not Marker Assisted Selection?

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1 /3/015 Genomic selection in Aquaculture Theo Meuwissen Norwegian University of Life Sciences Ås, Norway Introduction Genome sequence available in humans, livestock, and cod, trout, salmon (By)product: massive numbers of SNPs available through nd generation sequencing techn. Dramatic improvement of SNP genotyping ~00$ for 50k SNPs (for cattle: 800k SNPs) Future: whole genome sequence for $ Aim How are we going to use this new technology in fish breeding? For the prediction of genetic value Same aim: in plant & animal breeding (outcross species) Human genetic counselling for diseases Assuming complex trait: How to use new genomics technology? Genomic selection: Using dense SNP genotyping/ resequencing Every gene perfectly predicted by SNP(s) Genome sequence => all genes covered Simultaneously estimate the effect of all causative and non-causative SNPs Select for SNP effects without significance testing no phenotypes required 3 4 Why not Marker Assisted Selection? Recipe for MAS: Find most significant QTL underlying traits Select for QTL effects Drawbacks: QTL explain only fraction of Vg: limits value of MAS (traditional) selection for polygenes stays important Within familiy estimation of marker effects: Limits the value of the marker estimates Statistically complicated Low uptake in breeding schemes Statistical methods for GS 5 1

2 /3/015 Method for GEBV estimation GBLUP: normal prior with constant variance BayesB-type: Prior has a spike at 0 (or very close around 0) t-distribution prior for effects 0 LASSO: uses mode instead of mean of poster. using mode makes many SNP effects 0 Bayesian LASSO (BLASSO): Double-exponential prior (no spike at 0) Semi-parametric (eg RKHS): Handles epistatic effects BLUP of marker effects (GBLUP) Estimate effect of every SNP Prior distribution: normal with constant variance Model: y = Xm + e Var(m) = Is m Var(y) = XX s m + Is e = G s m + Is e (Var(y) = A ped s a + Is e Trad-BLUP) (Habier et al. 007; Goddard & Hayes 007) 7 8 BayesA (Meuwissen et al., 001) Same model : y = Xm + e Prior distribution: t-distribution (more fat-tailed than normal) Gibbs-sampling implementation: Sample variance of SNP effect from inverse-c Sample SNP effect given this variance So: relaxes the assumption of constant variance BayesB Prior distribution for SNP effect m i : m i ~ t-distribution with prob. p m i = 0 with prob. (1-p) Makes biologically sense: Many SNPs are not close to QTL so no effect Sequence data: only causative SNPs have effect 9 10 BayesC GBLUP vs. BayesA/B/C Prior distribution for SNP effect m i : m i ~ N(0,s ) with prob. p m i = 0 with prob. p Implementable by Gibbs and iterative methods Simulation studies: GBLUP < BayesA < BayesB/C Real data studies: Differences small Often GBLUP is as good as BayesA/B Theoretically: If few genes & dense SNPs: BayesB best If many genes / not enough density: GBLUP best 11 1

3 /3/015 Within family GS Possible in large fullsib families Use (sibtest) fullsibs as training animals Me=~70 segments Few SNPs needed to trace these few segments Can also apply linkage analysis (LA): Use markers to trace inheritance of parental chromosome segm. Set up G matrix based on LA Apply GBLUP (BayesA/B/C not good here) The accuracy of GS 13 Factors affecting accuracy of GS Marker density & LD between SNPs Approximates LD between SNPs and QTL Number of phen & gen in training population h (for phenotypes) or r (for DYDs) Genome size Historical Ne Relationships between trainees & candidates Number of genes and distribution of effects Method used for GEBV estimation Reliability of GBLUP = reliability of segment BLUP theory : n => T = number of training records σ e => 1 phenotypic variance (arbitrarily set to 1) σ g => h /M e = the genetic var due to segment Daetwyler et al. 008 How many effective segments? Goddard et al. 011 N e = effective population size L = size of a chromosome (in Morgan) k= # of chromosomes (assumed of equal size) Note: M e increases approx. proportional with: L*k = the genome size (expected) N e = the effective size (less expected) Conclusion on reliability Reliabil. increases with Th = information in data Reliabil. decreases with M e = number of segm. L*k = Genome size N e = effective population size BayesB: tries to find segments with effects Thereby reduces M e => increases reliability Only works if the number of genes < M e Otherwise: all segments have effects (GBLUP is best) 3

4 /3/015 r function of Th /M e Extend results to different genome size double L requires double T Assumes constant SNP density (twice as many SNPs) Can extend results to different heritability If h halved, T needs to be doubled Application in Aquaculture if Ne doubles: need twice as big T No of SNPs changes proportionally Pros & Cons of GS in Aquaculture Pros: Many traits are not recorded on the selection candidates Ne is often small WithinFamGS Eliminate expensive family based breeding structures Breeding nucleus is often quite centrally controlled Company may decide to set up training population Cons: Generation interval reduction is often not possible Number of individuals involved is very large Structure of family based aquaculture breeding Each family is split into: Candidates (+100) Parents (0-10) Sibtest-trait1 (+0) Family mean r max =0.7 Sibtest-traitx (+0) 1 Each family is split into: Candidates (+100) Parents (0-10) With GS Family mean + within family deviation Sibtest-trait1 (?) r max =1.0 Sibtest-traitx (?) Reducing genotyping costs Within family GS (uses linkage analysis within fams) Sparse marker panels Preselection of candidates based on TradEBV Eliminates entire fams from genotyping Pooling of testsibs based on high-low performance Requires accurate estimation of allele frequencies within pools Molecular genetic strategies RAD sequencing 3 4 4

5 Accuracy of selection /3/015 Accuracy of within fam. GS Genetic gains : SIB & CAND trait A, H=30% 0.4 Gla, H=30% Number of full-sibs Ødegård & Meuwissen, SNPs/Morgan: Conv Gswfam Gspop CAND SIB TOT (Lillehammer et al., 14) 5 6 Within fam GS: SNP density Preselection on TradEBV SNPs/M Acc CAND SIB TOT Lillehammer et al., 14 Total genetic gain: SNPs/M Preselect Fract_gen % 15 % % 31 % % 55 % % 100 % Lillehammer et al., Conclusions GS is MAS on a genome-wide scale By using GW-dense markers : use all Vg Trad. MAS : ~10% of Vg GS predicts GEBV without trait recording of candidate i.e. Sibtest traits Functional traits -> sustainable breeding Statistical methods: GBLUP => BayesB/C/etc Conclusions () GS in aquaculture: costs reduction needed Within family GS: sparse SNP maps (5 SNPs/M) Preselection based on TradEBV (70% cost reduction) No more family based housing Not in combination with preselection on TradEBV Pooling of testsibs may reduce costs RADsequencing Cheap method for sparse genotyping All-in-all: Good opportunities for GS in aquaculture GS can be used to control inbreeding (Sonesson, et al 011)

6 /3/015 Acknowledgements EU-7thFramework: FISHBOOST (grant No ) Norwegian Research Council 31 6