CEA International Workshop - August 3-5, Population, Quantitative and Comparative Genomics of Adaptation in Forest Trees Quantitative Genetics

CEA International Workshop - August 3-5, Quantitative Genetics The branch of genetics concerned with metric traits Traits such as: Growth, Survival, Reproductive ability Cold hardiness, Drought hardiness Wood quality, Disease resistance Economic Traits! Adaptive Traits! Applied & Evolutionary Traits that: Show continuous variation – are not discrete Are affected by the environment (to a large extent) 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, 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,

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6 Phenotypic Expression of a Metrical Trait

CEA International Workshop - August 3-5, 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, Properties of Genes Dominance – allelic interactions at a locus) Epistasis (non-allelic interactions) Pleiotrophy Linkage Fitness

CEA International Workshop - August 3-5, Describing a Population

CEA International Workshop - August 3-5, Phenotypic Variance Partitioning Var (P) = Var (µ) + Var (A) + Var (I) + Var (E) Or σ 2 p = σ 2 A +σ 2 I + σ 2 E 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,

CEA International Workshop - August 3-5, 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, Non-Additive Genetic Variance This is really a catch-all for “dominance” variance and “epistatic” variance. Thus, σ 2 I = σ 2 D + σ 2 Є 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, It’s hard to judge the genetic value of a tree just by looking at it Heritability (h 2 ) – the percentage of variation among trees that is genetic h 2 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 Genetics and the Environment Most variation among trees is environmental, not genetic

CEA International Workshop - August 3-5, P G E P = G + E h 2 = σ 2 G /σ 2 P Heritability (h 2 )

CEA International Workshop - August 3-5, 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 σ 2 p = σ 2 A +σ 2 I + σ 2 E

CEA International Workshop - August 3-5, More h 2 Thus, narrow sense heritability can be written as h 2 = σ 2 A / (σ 2 A + σ 2 I + σ 2 E ) Where σ 2 A is the additive genetic variance (variance among breeding values in a reference population); σ 2 I is the interaction or non-additive genetic variance (which includes both dominance variance and epistatic variance) σ 2 E is the variance associated with environment

CEA International Workshop - August 3-5, Broad Sense Heritability (H 2 ) 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 non- additive interaction effects. Thus, H 2 = (σ 2 A + σ 2 I ) / (σ 2 A + σ 2 I + σ 2 E ) 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, 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) –Less common, more expensive.

CEA International Workshop - August 3-5, Factors Affecting Genetic Gain (Mass Selection) Selection Intensity (i): The proportion of trees selected of trees measured for each trait. Heritability of the trait (h 2 or H 2 ): 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. –h 2, or narrow sense measures additive genetic variance as seen with offspring; H 2 or broad sense, measures both additive and dominance variance, as experienced with clones. Phenotypic standard deviation of a trait (σ p ).

CEA International Workshop - August 3-5, Calculating Genetic Gain ΔG = i h 2 σ p 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, 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 p. 342) From White et al 2007

CEA International Workshop - August 3-5, 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”) How to Estimate the Genotype of a Tree?

CEA International Workshop - August 3-5, Open-pollinated family (may include selfs & sib-matings) 2. Half-sib family Equal pollen from many trees Types of Families Pollen from a single tree 3. Full-sib family

CEA International Workshop - August 3-5, Common-garden experiments can be used to separate genetic from environmental effects Plantation #1 Block #1 Block #2 Family 8Family 6 Family 7Family 2 Family 3Family 9 Family 4Family 8 Family 9Family 5 Family 6Family 1 Family 2Family 7 Family 1Family 4 Family 5Family 3 Plantation #2 Block #1 Block #2 Family 3Family 8 Family 7Family 5 Family 9Family 1 Family 8Family 9 Family 4 Family 1Family 6 Family 2 Family 5Family 3 Family 6Family 7 Progeny Tests

CEA International Workshop - August 3-5, 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, Maternal Grandmother (late flushing) Maternal Grandfather (early flushing) Paternal Grandmother (late flushing) Paternal Grandfather (early flushing) F 1 Parent (1991) (1994) clonally replicated progeny linkage map (Jermstad et al. 1998) Turner,OR test site (n=78) (Jermstad et al. 2001a) clonally replicated progeny Bud flush experiment (n=429) Field Experiment Longview, WA test site (n=408) Springfield, OR test site (n=408) 750 NDLEDL Moisture stress (MS) MS NMS Twin Harbors, WA test site (n=224) (Jermstad et al. 2001a, 2001b) (WC750_FT10) (WC750_FT15) (WC750_FT20)(WC1500_FT15)(WC1500_FT20)NDL_NMSNDL_MSEDL_NMSEDL_MS Flushing temperature (FT) o C 3-generation pedigree and mapping populations Daylength (DL) Winter chill (WC) hours (WC1500_FT20) Growth cessation experiment (357< n <407)

CEA International Workshop - August 3-5, Fig. 2 Bud flush QTLS in Douglas-fir Verification pop. Detection pop. ofl 1* wfl 1* ofl 1* wfl 1* ofl 1* wfl 1* ofl 1* wfl 1* ofl 1* wfl 1* ofl 1* wfl 1* ofl 1* wfl 1* ofl 1* wfl 1* ofl 1* gfl 9* gc 9* gfl 9* gc 9* gfl 9* gc 9* gh 9* gfl 9* gc 9* gfl 9* gc 9* gh 9* Jermstad et al Genetics 165:

CEA International Workshop - August 3-5, Three Approaches to MAS LE MAS LD MAS Gene MAS (GAS) From Grattapaglia 2007 (modified)

CEA International Workshop - August 3-5, Figure 1

CEA International Workshop - August 3-5, SNPs markers are in linkage disequilibrium and can be used for family selection A1A1 A2A2 A1A1 A2A2 B1B1 B2B2 B1B1 B2B2 A Q1Q1 T Q2Q2 Q2Q2 Q1Q1 G C AG TC QTL Genotype Q 1 Q 1 Q 2 Q 2 Phenotypic Value Tree 1 - Discovery Tree 2 - Application x x

CEA International Workshop - August 3-5, G (genotypes for SNPs or single genes) Genotyping service provider e.g. Illumina P (phenotypes) Research organization (e.g. University) P = f(G) + E The aims of an association study include estimating a function of the SNP genotypes, f(G), which can be used to predict genetic merit

CEA International Workshop - August 3-5, f(G) are included in analyses as ‘pseudo phenotypes’ TREEPLAN® (BLUP ) Pedigree Phenotypes Pseudo phenotypes* Parameters Now including Variances for f(G) Residual variance will reflect accuracy with which f(G) is correlated to true genetic value Covariances of f(G) with other Measured traits Traits in the breeding objective *Note: f(G) is an attribute of the genotype TREEPLAN

CEA International Workshop - August 3-5, Estimated Breeding Values are significantly enhanced by the genotypic data

CEA International Workshop - August 3-5, WHAT FORMS DO THE f(G) TAKE? One simple form Whereis the pseudo phenotype for the jth trait observed on the ith individual is the coefficient for the regression of allele content at the kth marker on the jth trait 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, 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