Abstracts 2020 Abstract book sponsored by Talks Page Session 1 …………………………………………………2 Session 2 …………………………………………………3 Session 3 …………………………………………………6 Session 4 …………………………………………………9 Session 5 ………………………………………………..11 Session 6 ………………………………………………..15 Session 7 ………………………………………………..20 Session 8 ………………………………………………..23 Session 9 ………………………………………………..27 Session 10 ………………………………………………..33 Session 11 ………………………………………………..39 Session 12 ………………………………………………..43 Session 13 ………………………………………………..49 Session 14 ………………………………………………..53 Session 15 ………………………………………………..54 Session 16 ………………………………………………..58 Session 17 ………………………………………………..61 Session 18 ………………………………………………..64 Session 19 ………………………………………………..70 Session 20 ………………………………………………..76 Session 21 ……………………………………………… 80 Session 22 ………………………………………………..85 Session 23 ………………………………………………..88 Session 24 ……………………………………………… 91 Session 25 ………………………………………………. 94 Poster Presentations……………………………97-279 1 316 Quantitative Genetics Isn’t Dead Yet Professor Peter M. Visscher1 1Institute for Molecular Bioscience, University of Queensland, ST LUCIA, Australia Session 1, November 3, 2020, 7:00 AM - 8:30 AM Biography: Peter Visscher FRS is a quantitative geneticist with research interests focussed on a better understanding of genetic variation for complex traits in human populations, including quantitative traits and disease, and on systems genomics. The first half of his research career to date was predominantly in livestocK genetics (animal breeding is applied quantitative genetics), whereas the last 15 years he has contributed to methods, software and applications to quantify and dissect genetic variation in human traits. The discipline of quantitative genetics (QG) was established more than a century ago, and most agree that RA Fisher’s 1918 publication, which reconciled Mendelian genetics with the resemblance between relatives for quantitative traits, is its foundation paper. It was mostly theoretical for the first half of the 20th century but became much more empirical in the latter half, with empirical tests of theory using selection experiments and applications in plant and animal breeding. As eloquently summarised by Bill Hill in a 2010 Royal Society publication, QG as a discipline has been written off many times in the last 40 years, in particular when the molecular biology revolution started to flex its muscles in the 1980s. Surely it was just a matter of little time before the few relevant mutations for each trait would be found and quantitative genetics would become a mere sub-branch of molecular genetics? If only. I will show that not only is QG alive and kicking, it has revolutionised many fields of research in the last ~15 years, including plant and animal breeding, human genetics, and evolutionary and ecological genetics. Examples will be given of how the combination of whole-genome genetic data and trait information has led to new insights into (i) the polygenic basis of complex traits (including common disease), (ii) the partitioning of genetic variation between and within families and (iii) the effects of natural selection and non-random mating on trait variation. 2 417 The Genetic Architecture of Drosophila Lifespan Professor Trudy Mackay1 1Center for Human Genetics and Self Family Endowed Chair, Clemson University, Greenwood, 5a806b70-ae39-4973-b97f- 43620ad3b71d Session 2, November 3, 2020, 12:00 PM - 1:25 PM Biography: Trudy MacKay is the Director of the Center for Human Genetics, the Self Family Endowed Chair of Human Genetics and Professor of Genetics and Biochemistry at Clemson University. Her laboratory focuses on understanding the genetic and environmental factors affecting variation in quantitative traits, using Drosophila as a translational model system. Her laboratory seeKs to identify the genetic loci at which segregating and mutational variation occurs, allelic effects and environmental sensitivities, and the causal molecular variants. Her research utilizes mutagenesis to identify candidate genes and pathways, quantitative trait locus mapping of alleles segregating in nature, systems genetics analyses to provide biological context and identify transcriptional and genetic networks affecting complex traits; and germline gene editing to prove causal associations. She is a Fellow of the American Academy of Arts and Sciences and the Royal Society, a member of the US National Academy of Sciences, the 2016 Wolf Prize Laureate for Agriculture and the 2018 Dawson Prize recipient, Trinity College, Dublin. The world population is rapidly growing older, and population aging will be one of the most important social and health problems in the coming half-century. Lifespan is a typical quantitative trait, with natural variation attributable to segregating variants at multiple interacting loci, the effects of which are sensitive to the environment to which the individuals are exposed. However, only a handful of candidate genes associated with natural variation in lifespan in human populations have been identified. Drosophila melanogaster is a powerful genetic model system for identifying evolutionarily conserved genes and genetic networks causally associated with variation in lifespan, due to the ability to accurately measure lifespan while precisely controlling both genetic background and environmental conditions, and publicly available genetic resources. I will describe the properties of allelic variants affecting D. melanogaster lifespan from analyses of new mutations; as well as naturally occurring variants in the Drosophila Genetic Reference Panel (DGRP), a population of 205 sequenced inbred strains; a large outbred, advanced intercross population derived from a subset of DGRP lines; and a classic laboratory evolution experiment. Lifespan is highly polygenic, and naturally segregating alleles have effects that are largely context-dependent, varying according to sex, environment, and genetic background. These context-dependent effects may explain why variation for lifespan is maintained in natural populations. Further, many of the implicated genes are evolutionarily conserved and have human orthologs, facilitating direct tests for effects on lifespan in human populations. 3 311 Estimation of realized rates of genetic gain for breeding program assessment: Insights from rice research at IRRI Dr Jessica Rutkoski1 1University of Illinois, Urbana, United States Session 2, November 3, 2020, 12:00 PM - 1:25 PM Biography: Jessica received a Bachelor of Science degree in genetics from the University of Wisconsin-Madison in 2009. During her time at Wisconsin she worked for Professor Bill Tracy’s sweetcorn breeding program where she developed a love for plant breeding. Shortly after graduation, Jessica began her PhD work at Cornell University under the direction of wheat breeder and plant breeding professor Mark Sorrells. Jessica’s PhD research focused on genomic selection for quantitative disease resistance in wheat, and it included one of the first empirical genomic selection experiments in plants. After receiving her PhD in 2014, Jessica tooK a position as an assistant professor at Cornell and an adjunct associate scientist at CIMMYT working on integrating genomic selection and high-throughput phenotyping to predict breeding values for yield in wheat. Jessica is now leading the quantitative genetics cluster at the international rice research institute (IRRI) where her research currently focuses on improving rice breeding efficiency and monitoring breeding program effectiveness. Advancements in quantitative genetics have promised to revolutionize plant breeding, but most plant breeding programs continue to use traditional methods. More intensive monitoring and evaluation of breeding programs, especially those at CGIAR centers, based on realized rates of genetic gain (ΔGt) has been suggested to promote the adoption of improved breeding methods. However, methods to estimate realized ΔGt from plant breeding datasets have not been systematically evaluated. To develop recommendations for routine estimation of ΔGt in plant breeding. I conducted a stochastic simulation study of 80 rice breeding programs over 28 years and used the simulated data to compare five different methods for estimating realized ΔGt. At best, estimates of realized ΔGt were under or overestimated by 15% when using all 28 years of data and by 27% when using the most recent 15 years of data. On average, the best methods were the control population, estimated breeding value, and era trial methods. All methods led to estimates that were biased, and the direction of this bias was associated with the breeding program that was simulated. I conclude that that estimates of realized ΔGt can be accurate in some cases, but they are not useful for comparing the effectiveness of different breeding programs. In light of these challenges, I will discuss other means of monitoring breeding program performance which I used at the International Rice Research Institute (IRRI), and I will share my perspective on how the quantitative plant breeding revolution will unfold. 4 242 The role of cross-sex genetic covariances in the evolution of sexual dimorphism among species Jacqueline Sztepanacz1 1University of Toronto, Canada Session 2, November 3, 2020, 12:00 PM - 1:25 PM Biography: Dr. Jacqueline Sztepanacz is an Assistant Professor in the Department of Ecology and Evolutionary Biology at the University of Toronto. Her research focuses on the evolution of genetic variation, with a particular interest in how traits evolve (or don't) under stabilizing selection. Prior