Mrode, Raphael A.

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Genomic selection with weighted GBLUP and APY single stepRaphael Mrode (ILRI)Third ACGG Program Management Team Meeting  Abuja, Nigeria, 28-30 November 2016Rationale for the topic• Various ecotypes of chicken are being tested with expected differences in performance in different agro-ecological zones• Need to understand the genetic basis of such differences in performance• This may involve GWAS  or other candidate gene approach• Marked Assisted selection or other approaches may be employed to use such information in selective breeding• This is usually faced with a lot of practical problem• An alternative approach could be the use of weighted GBLUP which is readily easily to implementRationale for the topic• Data will be collected at various levels of  the project• Same ecotypes in several countries• In stations within countries  with opportunity for more detailed phenotyping• At farm  level within countries• Only a proportion of these will be genotyped• Single Step  offers us the framework to account for all various data structure within  and across countries using phenotypes, pedigree and genotypes in a single analysis• APY offers us the framework to reduce the dimensionality of the analysisWeighted GBLUP• Genomic selection with GBLUP involves constructing a genomic selection relationship  G among animals • G is plugged into the normal MME equations to get estimates of direct genomic breeding valuesyRWyRXabGWRWXRWWRXXRX111111ˆˆ1αWeighted GBLUP• The major assumptions of G and GBLUP is that all markers contribute equally to the genetic variance of a trait. • This assumption is violated for traits that are controlled by a small number of quantitative trait loci (QTL) or individual QTL with large effects.• Larger gains can be expected for traits with few QTL if we modify GBLUP to account for this• Weighted GBLUP (wGBLUP)  involves weighting SNPs or regions of the genome based on prior information on SNP effectsPossible weights in GBLUP• Weights for G  - based on SNP  effects• Single –marker regression effects (P <0.05)• Use Log10 P values from GWAS• Estimates from Bayesians approaches• Tiezzi and Maltecca (2015 –GSE 47:27) used several approached but will focus on results from Single –marker regression effects• SNPs were weighted by the expected variance of SNPs to  compute Gw:wi = 2Pi (1 – Pi)Si with Si = 1 if SNP is significant or zeroAccuracies  for Milk Yield and Fat percentMilk yield --- very polygenic traits Fat percent – QTL – DGAT in chromosome 14Impact of WGLUP depends on heritability of trait  and where few QTL accounted for  up to 25% the genetic varianceMilk yield Fat percentA matrix 0.404 0.350G matrix 0.560 0.548Gw Matrix 0.560 0.580Genomic prediction with Single –Step • ssBLUP similar to GBLUP  but with G replaced with matrix H• H is formed by  incorporating  both pedigree-derived and genomic-derived relationships in to single matrix22AG000AH1111Advantages and disadvantages• Advantages include• Easy to implement with existing genetic evaluation software• Account for bias to some degree due to selective genotyping• Propagate genomic information across the whole population• Useful in combining information when recording and genotyping  is only on sub-sets of the populationAdvantages and disadvantages• Can be used to analyze  ACGG data pooled across stations, farms and countries with genotypic information incorporated• Disadvantage • Obtaining the inverse of H with large population. A direct inverse has a cubic cost and can be computed efficiently for perhaps up to 150,000 individuals.• APY  constituent one of the ways to overcome this obstacle Algorithm for Proven and young animals• It involves• Partition of genotyped animals to core (c) and non-core animals (n)• Core animals could only be sires; sires and cows• Better properties when animals treated as core  are well related to animals treated as non-core• Computing G-1 needed for H-1 that only involves the inverse of core animalsAlgorithm for Proven and young animalsThe inverse needed  for all genotyped animals is: Note that it involves only  the inverse of G for core animals IGGMIGGGG ccnc1nn1cc1cc 11000   cnAPYCorrelations of GEBVs between ssGBLUPand APY-ssGBLUPFragomeni et al 2015 (JDS : 98:4090)Definition of sub-setAnimals in sub-setCorrelationsRounds top convergenceAll 100,000 1.00 567Sires 23174 0.994 432Sires + cows 50389 0.995 428Cows 27215 0.992 797Sires > 5 progeny 16434 0.992 415Theory of APY• Theory for the APY assumes that the optimal size of the subset (maximizing accuracy of genomic predictions) is due to a limited dimensionality of the G• With large numbers of SNP markers and genotyped animals , the dimensionality of genomic information defined by the eigenvalue of G which  is approximately a linear function of effective population size. • Consequently, an inverse of the G based on limited recursion can be computed inexpensively for a large number of individuals.Conclusion• Weighted GBLUP offers a quick route to put more selective pressure on identified genomic regions with more effect on traits of interest• APY-ssGBLUP could be a framework  to implement genomic prediction for ACGG data across stations, farms and countriesAcknowledgementsThis work is financed by the Bill & Melinda Gates FoundationIt contributes to the CGIAR Research Program on Livestock and FishThis presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.better lives through livestockilri.orgILRI thanks all donors and organizations who globally supported its work through their contributions to the CGIAR system

Genomic selection with weighted GBLUP and APY single step

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Genomic selection with weighted GBLUP and APY single step

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