Using a MQTL matrix to test for pleiotropic effects of Mendelian trait loci on quantitative traits. C. Scheper1. S. König1   1Institute of Animal Breeding and Genetics. Justus-Liebig-University Gießen. 35390 Gießen Dear Ladies and Gentleman. My name is Carsten Scheper. I am a Phd Student at the Institute of Animal Breeding and Genetics at the Justus Liebig University in Gießen. The topic i am speaking about today is an approach to test for pleiotropic effects of a Mendelian trait in cattle that is viewed as beneficial rather than detrimental. bovine polledness. However this approach could also be a feasible tool to test secondary effects of other Mendelian traits or detrimental genetic defects on other important traits as well.

Background | Research question Introduction Background | Research question Managing a growing number of genetic characteristics (detrimental as well as beneficial) has become a major task in modern breeding programs (e.g. Cole 2015, Segelke et al 2016) focus has shifted from asap elimination to short/mid term management and long-term elimination for detrimental recessives negative secondary effects of recessives on other traits in breeding goals become potentially more important (e.g. Cole et al 2016), especially for beneficial characteristics (e.g. polledness (Götz et al 2015)) Are these observations due to direct pleiotropic effects or linkage to causal variants (QTL) of secondary traits? Search for methods to estimate these direct effects of the Mendelian trait polledness on secondary quantitative traits in German Simmental cattle kürzen. mehr Fokus auf Krankheiten

Variance component estimation methods to test for QTL effects Introduction | Material and Methods Variance component estimation methods to test for QTL effects methods for mapping QTLs based on marker information via variance component estimation (i.e. Fernando & Grosman 1989. van Arendonk et al. 1994 ...) inclusion of QTL effects using special gametic ( 𝑮 𝒗 ) or numerator ( 𝑨 𝒗 ) relationship matrices derived from single- or multiple marker information separation of polygenic (𝑨) effects and QTL-effects of the polled locus ( 𝑨 𝒗 ) for other traits Procedure: Variance component estimation for: basic model with a pedigree-based animal effect -> Basic(univariate) basic model + random QTL-effect based on 𝑨 𝒗 -> QTL (univariate) bivariate model with the same model structure as QTL (univariate) but also modelling the Mendelian trait with a simple linear model including only the QTL-effect: 𝒚 𝒊 = 𝒗 𝒊 + 𝒆 𝒊 (numeric genotypes (0,1,2) as phenotypes) (referring to e.g. Sörensen et al 2003) i) Validation in simulated data ii) Application to a real dataset from German Simmental kürzen

Relationship Matrix 𝑨 𝒗 based on marker genotypes (MQTL-matrix) Material and Methods Relationship Matrix 𝑨 𝒗 based on marker genotypes (MQTL-matrix) = gametes calculation is based on reliably reconstructed polled genotypes in the German Simmental pedigree calculation of transmission probabilites between gametes based on given (polled) marker genotypes using the recursive algorithm of van Arendonk et al. 1994 the relationship of polled animals among each other is enhanced compared to the numerator relationship matrix 𝑨 (𝐚𝐛𝐨𝐯𝐞 𝐝𝐢𝐚𝐠) 𝑨 𝒗 (𝒅𝒊𝒂𝒈+𝒃𝒆𝒍𝒐𝒘) P p p p P p P p überarbeiten P P p p 2695|2694 and 2517|2516 = FS (same dam) 2695, 2694, 2517, 2516 = HS (same sire)

Validation using stochastic simulation (i) Material and Methods Validation using stochastic simulation (i) Stochastic simulation with QMSim: Population:3400 animals (3000 females with phenotypes. 400 males). 5 generations. random mating and selection 2 simulated quantitative traits: 1. h2 = 0.30 2. h2 = 0.05 single QTL with a defined heritability of 0.025 simulated as a Mendelian trait locus on 1 chromosome with the same size as BTA1 variance component estimation for the 3 mentioned models using DMU (Madsen et al. 2006) 𝑨 𝒗 precalculated using a self-written R function

Validation using stochastic simulation (i) Results Validation using stochastic simulation (i) Trait Model σ2a σ2v σ2e h2 (polygenic + QTL) QTL-h2 SE(h2) logLik h2 = 0.30 QTL-h2 = 0.025 Basic(univariate) 0.0030 0.0072 0.2918 0.0374 -10852.7091 QTL (univariate) 0.0019 0.0001 0.2832 0.0944 0.0508 -8863.6053 QTL (bivariate) 0.0025 0.0005 0.2909 0.0465 0.0464 -5978.2035 h2 = 0.05 0.1168 2.9110 0.0386 0.0207 12643.2372 0.3538e-06 0.1409 2.8775 0.0467 0.0281 16637.3472 0.0169 0.1177 2.8755 0.0447 0.0391 0.0280 22484.3273 simulated QTL-effects are detected, however they are overestimated overestimation is smaller using bivariate models (in accordance with Sörensen et al 2003) overestimation higher in trait with h2 = 0.05

Material and Methods (ii) – dataset and models Performance Traits Traits: MY (milk yield), F% (fat percent), P% (protein percent), SCS (somatic cell score) Dataset: 48046 test-day records from 1746 German Simmental cows (12 farms) Model: 𝒚 𝒊𝒋𝒌𝒍 = 𝑳𝒂𝒄𝒕 𝒊 + 𝑯𝑻𝑫 𝒋 + 𝑪𝑺 𝒌 + 𝒃 𝟎 𝑷𝒓𝒆𝒈𝒅 𝒊𝒋𝒌𝒍 + 𝒃 𝑰 𝑪𝑨 𝒊𝒋𝒌𝒍 + 𝑰𝑰=𝟏 𝟑 𝒃 𝑰𝑰 𝑿 𝒊𝒋𝒌𝒍 + 𝒂 𝒍 + 𝒗 𝒍 + 𝑷𝑬 𝒍 + 𝒆 𝒍 fixed effects: lactation, herd-test-day, calving season | covariables: days pregnant, calving age, legendre polynoms 1-3 random effects in all models: animal (a), permanent environment (PE), residual error (e) QTL-effect polled locus (v)→ Base models for comparison estimated without QTL-effects Pedigree: 8624 animals with polled genotypes (5 generations back) -> basis for the calculation of 𝑨 𝒗 variance component estimation for the 3 mentioned models using DMU (Madsen et al. 2006)

Results (ii) – Performance Traits Variance Components Trait Model σ2a σ2v σ2PE σ2e h2 (polygenic + QTL) QTL-h2 SE(h2) logLik MY Basic (univariate) 5.771 5.504 15.482 0.216   0.030 165473.579 QTL (univariate) 0.313e-03 0.117e-04 0.037 169866.939 QTL (bivariate) 5.613 0.019 5.574 15.483 0.211 0.704e-03 0.014 143671.333 F% 0.100 0.025 0.257 0.262 0.024 -5699.313 0.147e-04 0.384e-04 0.029 -1305.932 0.975e-04 0.261 0.255e-03 0.015 -27497.858 P% 0.035 0.003 0.038 0.498 -84666.300 0.033 0.001 0.457 -80274.303 0.034 0.912e-03 0.463 0.012 -106462.543 SCS 0.255 0.570 1.645 0.103 0.021 70851.929 0.240 0.010 0.573 0.101 0.004 75244.980 0.249 0.200e-03 0.612e-04 0.011 49051.027 no evidence for substantial QTL-effects of the polled locus for MY, F%, SCS small QTL effect detected for P% (~2.6% of total heritability in bivariate model)

Material and Methods (ii) – dataset and models Reproduction Traits Traits: NRR-56 (non return rate 56, cow), DFS (days to first service, cow), DO (days open, cow) Dataset: 3834 records from 1368 German Simmental cows (12 farms) Models: NRR-56 𝒍𝒐𝒈𝒊𝒕 𝒚 𝒊𝒋𝒌 = 𝑭𝒀 𝒊 + 𝑻𝒚𝑰𝒀 𝒋 + 𝑳𝑨𝑪𝑨 𝒌 ++ 𝒂 𝒍 + 𝒗 𝒍 + 𝑷𝑬 𝒍 + 𝒆 𝒍 DFS | DO 𝒚 𝒊𝒋𝒌𝒍 = 𝑭𝒀 𝒊 +𝑹𝒀𝑴 𝒋 + 𝒂 𝒍 + 𝒗 𝒍 + 𝑷𝑬 𝒍 + 𝒆 𝒍 fixed effects: farm-year, type of insemination-year, lactation-calving age class, region-year-month random effects in all models: animal (a), permanent environment (PE), residual error (e) QTL-effect polled locus (v)→ Base models for comparison estimated without QTL-effects Pedigree: 8624 animals with polled genotypes (5 generations back) -> basis for the calculation of 𝑨 𝒗 variance component estimation for the 3 mentioned models using DMU (Madsen et al. 2006)

Results (ii) – Reproduction Traits Variance Components Trait Model σ2a σ2v σ2PE σ2e h2 (polygenic + QTL) QTL-h2 SE(h2) logLik NRR-56 Basic (univariate) 0.081 0.191 3.290 0.023   0.029 -14767.987 QTL (univariate) 0.063 0.011 0.196 0.021 0.003 0.050 -5741.005 QTL (bivariate) 0.066 0.100e-03 0.204 0.019 0.387e-04 0.036 -33092.975 DFS 63.636 71.755 2282.596 0.026 0.027 22589.368 55.629 23.449 70.368 2265.483 0.033 0.010 0.022 27785.658 17.784 0.800e-03 2002.926 0.009 0.115e-04 0.047 -5254.544 DO 132.647 191.765 2451.121 0.048 0.032 22901.908 125.813 24.778 187.219 2433.323 0.054 28111.071 47.923 0.299 2335.931 0.020 0.001 -4969.551 no evidence for substantial QTL-effects, however considering the small overall heritability they account for a higher relative fraction bivariate models tend to underestimate the overall genetic variance in contrast to performance traits

Results (ii) 𝒓 𝑷 𝒓 𝒈 (𝒗) Genetic Correlations genetic correlations from bivariate models show no unfavourable antagonistic relationships 𝒓 𝒈 (𝒗) should be interpreted in relation to the size of estimated QTL-effects a proposed procedure could be: estimate QTL-effects of a Mendelian trait on other traits results: substantial QTL-effect present QTL-effect not present / very small 2. utilize 𝒓 𝒈 (𝒗) 𝒓 𝒈 (𝒗) are negligible MY F% P% SCS NRR56 DFS DO 𝒓 𝑷 -0.193 -0.025 -0.067 0.012 -0.041 -0.059 𝒓 𝒈 (𝒗) -0.020 -0.005 -0.026 -0,042 -0,078 -0,035

Conclusions We found evidence for a small pleiotropic or linked effect of the polled locus on P%. However the genetic correlation based on the bivariate QTL-model shows no antagonistic relationship Given the presented results there is no evidence for substantial direct negative effects of the polled locus in German Simmental cattle on performance and reproduction traits The presented approach could potentially be a helpful tool for the initial evaluation of (beneficial and detrimental) mendelian traits/genetic characteristics to infer their potential effects on other important traits In presence of substantial QTL-effects of a Mendelian trait, genetic correlations based on bivariate QTL-models can be utilized in breeding plan evaluations / indexes

Thank you for your attention Acknowledgments we´d like to thank: The present study was supported by funds of the German Government´s Special Purpose Fund held at Landwirtschaftliche Rentenbank. The authors gratefully thank the Landwirtschaftliche Rentenbank for funding this project. Prof. Dr. Götz and Dr. Emmerling from the Institute of Animal Breeding at the Bavarian State Research Centre for Agriculture. for preparing and providing the data. our Project Partner: Group of Prof. Swalve at University Halle-Wittenberg/Germany Thank you for your attention