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Computer Simulation of Breeding Strategies & Inbreeding Depression Adam Festa 5/12/16 General Background • The most costly part of running a tree breeding program is maintaining a large base population and conducting progeny testing • A large base population is maintained in order to keep a wide range of genetic diversity in order to continue making genetic and phenotypic gain while not running into the dilemma of inbreeding depression • Consequences of inbreeding depression on economical traits in loblolly can be severe, therefore proper management of future breeding strategies is critical to the longevity of economic and genetic gain in a tree breeding program Overall Objective • Evaluate the ability of different breeding strategies to mitigate the risk of long-term inbreeding depression • Identifying the most efficient breeding strategies to maintain or increase genetic gain while keeping inbreeding depression to a minimum can potentially have a large beneficial impact in breeding programs • Computer simulations can help to accomplish this by providing a cost effective (“free”) way to probe the genetic architecture of economical traits, study long term impacts of breeding strategies and evaluate their potential for inbreeding depression Causes of inbreeding depression • Generally speaking, inbreeding depression is defined as a reduction in fitness traits or mean phenotype (ex. seed # or survival rate) among progeny of a given cross • There are multiple hypothesis on what can give rise to inbreeding depression such as the dominance or over-dominance models • All of these models may hold true for different traits, however we chose to model the expression of major deleterious recessive genes as this type of effect has been observed in previous loblolly pine studies • While inbreeding depression can occur among full sibs or other related individuals, selfing is the fastest way to achieve homozygous expression of deleterious alleles. Inbreeding Depression: Example • negative allele • positive allele outcross self dead alive • Ex: Hunting dogs which are inbred have a much higher susceptibility of decreased litter size or to display diseases such as hip dysplasia Inbreeding Depression Management • After thousands of generations, natural selection has worked to purge the majority of these harmful deleterious alleles from loblolly pine populations • However, some recessive alleles are still hiding out within families • If these alleles could be purged, then potentially placing restrictions on relatedness when making future crosses among selections may not be as important Supporting Evidence Effects of Inbreeding on Loblolly pine traits • Recently published work by Graham Ford assessed the effects of inbreeding depression on growth and quality traits for 9 families from the Coastal & Piedmont Region • Reported results suggest that families may differ in the amount of “genetic load” (harmful alleles) they carry Effect of breeding strategies on deleterious allele fixation • Work published by Wu et al 2016. simulated 6 different breeding strategies over 20 generations using varying levels of dominance coefficients and allele frequency distributions • Results indicated that different breeding strategies lead to different rates at which detrimental or harmful allele fixation may occur Effects of Inbreeding on Growth and Quality Traits in Loblolly Pine (Ford et al 2015) • Changes in family rankings due to the various levels of inbreeding • Magnitude of inbreeding depression can be seen to be dependent on the trait • Some families show relatively little response to high amounts of inbreeding, suggesting that these families may have less deleterious alleles Breeding strategies and their effect on fixation of detrimental alleles • • Fig. 5 from their publication, shown on the left, assessed 6 different breeding strategies over 20 generations • Each strategy showed different rates at which deleterious alleles responsible for inbreeding depression may reach fixation within the population However, the underlying assumptions of genetic architecture and breeding strategies utilized in the Wu et al paper doesn’t reflect that of loblolly pine Current Breeding Population Management • NCSUTIP 4th-cycle breeding population is currently managed using the differential evolution algorithm • This algorithm uses a pedigree to put constraints on relatedness among selections which are to be mated while maintaining genetic gain • A potential consequence of this approach is that more crosses are made with individuals which have superior breeding values, leading to a higher coancestry in future generations • Our goal is to develop a simulator whose genetic assumptions and breeding strategies more accurately reflect that of loblolly pine and can potentially be used by other crop or animal breeding programs Simulator Overview • The foundation of the simulator was built by Ross Whetten and included: – creation of a genetic map with user defined map length and # of chromosomes – simulated genotypes – creation of progeny with simulated recombination – scaled phenotypes • A multitude of user adjustable inputs were added; including but not limited to: – Dominance co-efficient, Deleterious SNP frequencies and effects, # of Quantitative Trait Loci (QTL), # of Selections, and an array of built-in breeding and selection strategies • The final resulting simulator can be adjusted to have a genetic architecture, population structure, and input of breeding strategies designed to the users preference Proof of Concept: Effects of Inbreeding Depression on Loblolly Pine Growth & Quality Traits • Our simulator can produce different outcomes depending on parameters set • The overall objective is to better understand genetic architecture seen in real populations Total QTL SNP QTL Dominance Coefficient Major Allele Value Minor Allele Value Straightness Height Volume 1000 900 0 1 0 1000 950 1 1 -1.5 1000 960 1 1 -0.1 Proof of Concept: Impact of breeding strategies on genetic gain • Similarly, setting parameters to those found in Wu et al., (with the exception of linked loci) we can reproduce the same rates of genetic gain for 4 out of 6 of their breeding strategies over 20 generations. Breeding Strategies • While each breeding strategy results in different rates of genetic gain, rank of breeding strategy doesn’t change given different assumptions of trait control (additive, partial dominance, complete dominance) • Significant reduction in expected genetic gain from traits under complete dominance • Knowing the type of genetic control a trait is influenced by is important in understanding the expected genetic gain Summary of Population Structure for All Designs • Initial founder population of 300 unrelated individuals • First generation includes an input cross design of 150 single pairwise crosses • Parental phenotypes are simulated to have an individual-tree heritability h2=0.3. The same variance is used for all following simulated generations • 3,000 progeny are simulated each generation with 300 selections made • All following generations include 150 crosses with 20 progeny each Proposed Population Breeding and Selection Strategies to assess Breeding strategies • Random Mating • Mate-Select type approach: Mating pairs are first determined by which selections pass a co-ancestry threshold and then the top mating pair is selected based upon estimated mid-parent mean, which can be done using: • Single pair mating OR.. • Multiple pair mating scheme with individuals who have top estimated breeding values (EBV’s) Selection strategies • ABLUP based on Pedigree • GBLUP based on Genomic relationship matrix • Phenotypic • Selfing strategy: Approach that makes selections from each generation based on assessment of marker effects using GBLUP from selfed parents • Future Selections are made based on which progeny share the least amount of those markers found in linkage with the deleterious allele Summary of Genetic Architecture for Founder Population • Map size: 1800 cM • 12 linkage groups • 60,000 total loci • 500 SNP QTLs • 200 random valued QTLs • Dominance coefficient = 1 Distribution of Harmful Alleles Among Parents • Each individual in the population carries a different frequency of recessive deleterious alleles • The number each parent carries can impact the probability of making crosses which result in harmful alleles being in the homozygous state • Potentially purging or reducing these recessive alleles in the population could allow for the reduction of inbreeding when mating closely related individuals Example Result: Comparison of Population vs. Selections Inbreeding Levels Example Result: Effects of Breeding Strategies on Genetic and Phenotypic Gain Work In Progress… • Current version of simulator is computationally challenging, working to lower run time to allow for carrying out more generations • Incorporation of multiple pair mating strategy similar to that used in TIP • Assessment of selfing strategy for ability to purge harmful alleles from the population – Is it possible? – If so, how many generations may it take? Take-home Message & Prospective Impact • Breeding & testing is the most expensive part of maintaining a breeding program, therefore numerous benefits exist in being able to potentially limit population size while maintaining genetic gain • Proper management of inbreeding is an important factor to long-term economic and genetic gain in a loblolly pine breeding program • Families may differ in their frequency of carrying recessive deleterious alleles • Identifying the most efficient breeding strategies to maintain or increase genetic gain while keeping inbreeding depression to a minimum can have a large positive benefit on loblolly pine breeding programs • A potential outcome of this project is identifying a prospective breeding strategy that can purge deleterious alleles from the population allowing for a smaller test population while maintaining an increase in genetic gain. Acknowledgements • PINEMAP for supporting the funding of this project • All the members of NCSU Cooperative Tree Improvement Program for their hard work and ongoing contributions!