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Population, Quantitative and Comparative Genomics of Adaptation in Forest Trees Quantitative Genetics CEA International Workshop August 3-5, 2008 1 Quantitative Genetics The branch of genetics concerned with metric traits Traits that: Show continuous variation – are not discrete Are affected by the environment (to a large extent) Traits such as: Growth, Survival, Reproductive ability Cold hardiness, Drought hardiness Wood quality, Disease resistance Economic Traits! Adaptive Traits! Applied & Evolutionary Genetic Principles: Underlying the inheritance of metric traits are those of population genetics but historically we could not follow the segregation of multiple genes, so the concepts of QG or biometrical genetics were developed. CEA International Workshop August 3-5, 2008 2 Distinctions • How does a trait become metrical (measured in continuous fashion rather than counted) when it is a function of segregation of genes (intrinsically discontinuous variation)? – Simultaneous segregation of many genes – Non-genetic or environmental variation (truly continuous effects) • Mendelian vs metrical – Lies in the magnitude of effect. – Recognizable discontinuity – mendelian (major) – Non-recognizable discontinuity – metrical (minor) CEA International Workshop August 3-5, 2008 3 CEA International Workshop August 3-5, 2008 4 CEA International Workshop August 3-5, 2008 5 Phenotypic Expression of a Metrical Trait CEA International Workshop August 3-5, 2008 6 Properties of Populations We Can Measure (for metrical traits) • Means • Variances • Covariances Subdividing populations into families allows for estimation of variance components (genetic and environmental) which in turn allow for measurements of degree of resemblance between relatives (heritability estimates), breeding values, genetic correlations and so forth. CEA International Workshop August 3-5, 2008 7 Properties of Genes • • • • • Dominance – allelic interactions at a locus) Epistasis (non-allelic interactions) Pleiotrophy Linkage Fitness CEA International Workshop August 3-5, 2008 8 Describing a Population CEA International Workshop August 3-5, 2008 9 Phenotypic Variance Partitioning Var (P) = Var (µ) + Var (A) + Var (I) + Var (E) Or σ2p = σ2A +σ2I + σ2E Where A = Additive genetic variance (breeding value) I = Non-additive variance E = Environmental Variance Pop mean = 0, random mating CEA International Workshop August 3-5, 2008 10 CEA International Workshop August 3-5, 2008 11 Additive Variance Breeding Value: The sum of all average allelic effect at each locus influencing the trait(s) of interest. (Alleles, not genotypes are passed on to the next generation) Breeding value is a concept associated with parents in a sexually breeding population. It can be measured. Historically, average allelic effects could not be measured. Now they can. How? What is effect of population gene frequencies on average effect? CEA International Workshop August 3-5, 2008 12 Non-Additive Genetic Variance This is really a catch-all for “dominance” variance and “epistatic” variance. Thus, σ2I = σ2D + σ2Є Where Dominance variance arises from interaction of alleles within a locus and Epistatic variation arises from interaction of alleles between loci CEA International Workshop August 3-5, 2008 13 Genetics and the Environment Most variation among trees is environmental, not genetic It’s hard to judge the genetic value of a tree just by looking at it Heritability (h2) – the percentage of variation among trees that is genetic • h2 ranges from 0 to 100% (0.00 to 1.00) • Heritability for growth is often only 10-30% (0.10 – 0.30) • Low heritabilities make genetic improvement difficult CEA International Workshop August 3-5, 2008 14 2 Heritability (h ) P = G + E h2 = σ2G /σ2P P E G CEA International Workshop August 3-5, 2008 15 Heritability: narrow sense • Heritability is mathematically defined in terms of population variance components. It can only be estimated from experiments that have a genetic structure: sexually produced offspring in this case. • Heritability is the proportion of total phenotypic variance that is due to additive genetic affects. Var (P) = Var (µ) + Var (A) + Var (I) + Var (E) Or σ2p = σ2A +σ2I + σ2E CEA International Workshop August 3-5, 2008 16 More h2 • Thus, narrow sense heritability can be written as h2 = σ2A/ (σ2A + σ2I + σ2E) Where σ2A is the additive genetic variance (variance among breeding values in a reference population); σ2I is the interaction or non-additive genetic variance (which includes both dominance variance and epistatic variance) σ2E is the variance associated with environment CEA International Workshop August 3-5, 2008 17 Broad Sense Heritability (H2) • Broad sense heritability is used when we deal with clones! Clones can capture all of genetic variance due to both the additive breeding value and the nonadditive interaction effects. Thus, H2 = (σ2A + σ2I) / (σ2A + σ2I + σ2E) Consequently, broad sense heritability is typically larger than narrow sense heritability and progress in achieving genetic gain can be faster when clonal selection is possible. What might be a drawback to clonal based programs? CEA International Workshop August 3-5, 2008 18 Estimating Genetic Gain • Predicted genetic gain – Are forward looking, and are calculated using formulae derived form quantitative genetic theory and results of young field tests, with small plot sizes (dozens of trees) – These are used extensively in TI to guide programs and strategies – Gains of 0 to 10% in mass selection, and 10-20% in subsequent generations of selection are common. • Realized genetic gain – Retrospective estimate based on large field trials comparing improved lots with control lots. (hundreds of trees per plot) CEA International Workshop 19 – Less common, more expensive. August 3-5, 2008 Factors Affecting Genetic Gain (Mass Selection) • Selection Intensity (i): The proportion of trees selected of trees measured for each trait. • Heritability of the trait (h2 or H2): this is a measure of the variability in a trait that is under genetic control and can be passed on to progeny or vegetative propagules. – h2, or narrow sense measures additive genetic variance as seen with offspring; H2 or broad sense, measures both additive and dominance variance, as experienced with clones. • Phenotypic standard deviation of a trait (σp). CEA International Workshop August 3-5, 2008 20 Calculating Genetic Gain ΔG = i h2 σp Thus, gain can be improved by manipulating any of the 3 variables: selection intensity, heritability or population phenotypic standard variation. CEA International Workshop August 3-5, 2008 21 A Little More on Selection Intensity • The factor most under breeders control • i increases as the fraction of trees selected decreases • Law of diminishing returns takes hold. • Intensity drops rapidly with increasing number of traits selected simultaneously (See White et al. 2007 p. 342) CEA International Workshop August 3-5, 2008 From White et al 2007 22 How to Estimate the Genotype of a Tree? By measuring: The average performance of many “copies” of the same tree (i.e., the same genotype) Clones can be produced via rooted cuttings or tissue culture The average performance of its offspring The average performance of its siblings (i.e., “brothers and sisters”) CEA International Workshop August 3-5, 2008 23 1. Open-pollinated family (may include selfs & sib-matings) Types of Families 3. Full-sib family 2. Half-sib family Pollen from a single tree Equal pollen from many trees CEA International Workshop August 3-5, 2008 24 Progeny Tests Common-garden experiments can be used to separate genetic from environmental effects Plantation #1 Plantation #2 Block #1 Block #1 Block #2 Block #2 Family 8 Family 6 Family 3 Family 8 Family 7 Family 2 Family 7 Family 5 Family 3 Family 9 Family 9 Family 1 Family 4 Family 8 Family 8 Family 9 Family 9 Family 5 Family 4 Family 4 Family 6 Family 1 Family 1 Family 6 Family 2 Family 7 Family 2 Family 2 Family 1 Family 4 Family 5 Family 3 Family 5 Family 3 Family 6 Family 7 CEA International Workshop August 3-5, 2008 25 How to Estimate the Genotype of a Tree? • Genetic Dissection of Complex Traits: – QTL mapping in pedigreed populations – Association genetics CEA International Workshop August 3-5, 2008 26 3-generation pedigree and mapping populations Maternal Grandfather (early flushing) Maternal Grandmother (late flushing) Paternal Grandmother (late flushing) F1 Parent F1 Parent (1994) (1991) clonally replicated progeny linkage map (Jermstad et al. 1998) Twin Harbors, WA test site (n=224) (Jermstad et al. 2001a, 2001b) clonally replicated progeny Turner,OR test site (n=78) (Jermstad et al. 2001a) Growth cessation experiment (357< n <407) Bud flush experiment (n=429) Winter chill (WC) hours Daylength (DL) 1500 750 NDL Flushing temperature (FT) oC Longview, WA test site (n=408) Springfield, OR test site (n=408) EDL NMS MS CEA International Workshop August 3-5, 2008 NMS EDL_NMS MS EDL_MS 20 NDL_NMS 15 NDL_MS (WC750_FT20) 10 (WC1500_FT20) 20 (WC1500_FT15) 15 Field Experiment Moisture stress (MS) (WC1500_FT20) 10 Paternal Grandfather (early flushing) 27 Fig. 2 Bud flush QTLS in Douglas-fir Verification pop. Detection pop. LG1 LG2 LG3 gfl 9* wtr 7* ofl 1* wlt 7* wfl 1* wfl 8* gc 9* gfl 9* wlt 6 LG4 wtr 5 LG5 gfl 9* wlt 5,7 wtr 6* wfl 8* wlt 6* wtr 5 otr 6 oqy* otr 6 wtr 5* LG7 gfl 9* gc 9* wfl 1* ofl 1* wfl 1* wlt 5* ofl 1* gfl 9* wlt 5* LG6 wlt 5,6*7 wtr 5,6* wfl 8* wfl 1* ofl 1* LG8 wlt 7 wlt 7 wfl 1* wfl 1* wfl 1* ofl 1* wtr 7 wfl 1* qs 5* wlt 5*6*7* wtr 5*6* wfl 8* wqy* qs 8 wlt 6,7* wtr 7* wfl 8* Jermstad et al 2003. Genetics 165: 1489-1506 wfl 8* ofl 8* wlt 7 ofl 8* ofl 8 qs 8* gc 9* gh 9* wlt 6* wtr 5* wfl 8* wfl 1* ofl 1* LG13 LG15 LG17 LG11 wlt 7 ofl 8* wfl 8* qs 8* qs 6*8* wqy oqy* ofl 8 qs 5* gfl 9* gc 9* ofl 1* LG10 wlt 5*6* wtr 5* wfl 8* wqy* wtr 5* wlt 5* wqy* LG9 LG16 wfl 1* CEA International Workshop August 3-5, 2008 wtr 6* otr 6 wtr 6* otr 6 wqy oqy LG14 gfl 9* wlt 5* wlt 5* gfl 9* gc 9* gh 9* LG12 wfl 1* ofl 1* wfl 8* qs 8* gfl 9* qs 6* ofl 8 ofl 8 wfl 1* ofl 1* 28 Three Approaches to MAS LE MAS LD MAS Gene MAS (GAS) From Grattapaglia 2007 (modified) CEA International Workshop August 3-5, 2008 29 Figure 1 CEA International Workshop August 3-5, 2008 30 SNPs markers are in linkage disequilibrium and can be used for family selection Tree 1 - Discovery A1 A Q1 Tree 2 - Application B1 G A1 T Q2 x x A2 T Q2 C B2 A2 B1 C A Q1 G B2 QTL Genotype Phenotypic Value Q1Q1 Q1Q2 Q2Q2 CEA International Workshop August 3-5, 2008 31 The aims of an association study include G P (phenotypes) (genotypes for SNPs or single genes) Genotyping service provider e.g. Illumina Research organization (e.g. University) P = f(G) + E estimating a function of the SNP genotypes, f(G), International Workshop - merit which can be used toCEA predict genetic August 3-5, 2008 32 f(G) are included in analyses as ‘pseudo phenotypes’ Pedigree Phenotypes Pseudo phenotypes* Parameters TREEPLAN® (BLUP) Now including •Variances for f(G) •Residual variance will reflect accuracy with which f(G) is correlated to true genetic value *Note: f(G) is an attribute of the genotype •Covariances of f(G) with other •Measured traits •Traits in the breeding objective CEA International Workshop August 3-5, 2008 TREEPLAN 33 Estimated Breeding Values are significantly enhanced by the genotypic data CEA International Workshop August 3-5, 2008 34 WHAT FORMS DO THE f(G) TAKE? One simple form m f (G ) ij xik bˆ jk k 1 Where f (G ) ij is the pseudo phenotype for the jth trait observed on the ith individual b̂ jk is the coefficient for the regression of allele content at the kth marker on the jth trait xik is the regression variable taking the value 0, 1, or 2 (depending on whether the ith individual is 00, 01 or 11 at the kth marker CEA International Workshop August 3-5, 2008 35 Summary • Quantitative genetics deals with metrical traits (two or more loci, their interactions with each other and their environment) • Properties of populations and genes • Crop improvement programs use basic parameters of means, variances, covariances to calculate relevant heritabilities, gain, etc • Traditional methods for characterizing genotypes require breeding and testing • QTL and association mapping offer alternatives CEA International Workshop August 3-5, 2008 36