14 th QTL-MAS Workshop, Poznań University <strong>of</strong> Life Sciences, Poland 2010Haplotype inference based on Hidden Markov Models in the QTL-MASmulti-generational datasetCarl Nettelblad 1∗1Department <strong>of</strong> Information Technology, Uppsala University∗ Presenting author: Carl Nettelblad, email: carl.nettelblad@it.uu.seBackground. In previous work (Nettelblad et al., LNCS 6462, 2009, Springer Verlag) wedemonstrated an approach for efficient computation <strong>of</strong> genotype probabilities, and moregenerally probabilities <strong>of</strong> allele inheritance in inbred as well as outbred populations. Th<strong>is</strong>work also included an extension for haplotype inference, or phasing, using Hidden MarkovModels. <strong>The</strong> code was able to phase > 99.0 % <strong>of</strong> all heterozygous markers in the dataset fromthe 12 th QTL-MAS correctly.Computational phasing <strong>of</strong> multi-thousand marker datasets has not become common as <strong>of</strong> yet.In th<strong>is</strong> work, we elaborate on the differences in allele probabilities, as well as QTL detectionperformance, when considering an identical workflow with and without phasing based onexpectation-maximization.Results. Using phasing for 20 iterations, almost all heterozygous markers in all generationsconverged. Th<strong>is</strong> makes actual allele origin traceable, back to the founder generation, in over99 % <strong>of</strong> all locus-individual pairs. Some specific regions turned out to be hard to track, but theartifacts were minor in a dense marker set <strong>of</strong> the current type. Haldane mapping d<strong>is</strong>tanceswere also computed during the phasing.A litter/parental effect was fitted for each pair <strong>of</strong> parents for the scalar phenotype in thedataset, explaining about 27.7 % <strong>of</strong> the total phenotypic variance in that phenotype.Obviously, some genetic variance <strong>is</strong> also included in th<strong>is</strong> number.<strong>The</strong> residuals from the litter fitting were fitted against loci, with an indicator vector for eachindividual indicating the allele origin probability for each <strong>of</strong> the 40 founder alleles. Whenusing consecutive forward selection, 5 QTL were identified above a threshold derived fromrandom permutations, accounting for 14.30 % <strong>of</strong> the phenotypic variance.Corresponding results for an identical workflow without phasing, unable to d<strong>is</strong>tingu<strong>is</strong>hbetween founder alleles resulted in a total explainable variance <strong>of</strong> only 6.97 % for the 5highest ranking QTL, although not all <strong>of</strong> those were found significant.Conclusions. Using inferred phasing can greatly increase power and accuracy in multigenerationalQTL detection, at least when simple models are employed.14
14 th QTL-MAS Workshop, Poznań University <strong>of</strong> Life Sciences, Poland 2010QTL-MAS 2010: Simulated DatasetMaciej Szydlowski 1∗1 Poznań University <strong>of</strong> Life Sciences, Poznan, Poland∗ Presenting author: Maciej Szydłowski, email: mcszyd@jay.au.poznan.plHypothetical pedigree data set was simulated for a population cons<strong>is</strong>ting <strong>of</strong> 3226 individualsin 4 generations with large full-sib groups (about 30 <strong>of</strong>fspring per mating).Genome size was about 500 mln bp and it cons<strong>is</strong>ted <strong>of</strong> 5 chromosomes, each about 100 mlnbp long. <strong>The</strong> alleles for 20 founders were generated by the use <strong>of</strong> mh s<strong>of</strong>tware (Hudson 2002)and then dropped down the pedigree assuming incomplete interference. <strong>The</strong> SNP markerswere a random sample from all biallelic sites with MAF>0.1. For each individual a set <strong>of</strong>10031 unordered genotypes was <strong>available</strong>.Two traits were generated: a quantitative trait (Q) and a binary trait (B). <strong>The</strong> Q trait wassimulated assuming 30 random biallelic additive QTLs, two pairs <strong>of</strong> ep<strong>is</strong>tatic QTLs and 3imprinted loci (active paternal allele) and the narrow-sense heritability <strong>of</strong> 33% (30 add loci).<strong>The</strong> B trait was generated under pure additive threshold model, assuming 22 functional SNPsand heritability <strong>of</strong> 50%. <strong>The</strong> two traits shared 22 additive QTLs. Genotypes for 10 functionalSNPs were <strong>available</strong> in the data set. No systematic environmental effect was generated,residuals were normal and uncorrelated. Phenotypes for generation 4 was removed from thedataset.Hudson, R. R. (2002) Generating samples under a Wright-F<strong>is</strong>her neutral model. Bioinformatics 18:337-8.15
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