1 / 14

Ch.4 Genetic analysis of quantitative traits

Ch.4 Genetic analysis of quantitative traits. 1. Component of quantitative genetic variation 2. Variance component of a quantitative trait in the populations 3. Heritability ( 유전력 , 유전율 ) 4. QTL analysis. Quantitative traits

kolton
Télécharger la présentation

Ch.4 Genetic analysis of quantitative traits

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Ch.4 Genetic analysis of quantitative traits 1. Component of quantitative genetic variation 2. Variance component of a quantitative trait in the populations 3. Heritability (유전력, 유전율) 4. QTL analysis

  2. Quantitative traits - described in terms of the degree of expression: metric traits - continuous variation - under polygenic control - influenced by environments - population genetics 1. Component of quantitative genetic variation (1) Types of gene action a. an additive portion : describing the difference between homozygotes at any single locus (Aa = ½(AA + aa) b. a dominance components : arising from interaction of alleles ---> intra-locus interaction (Aa > ½(AA + aa) c. an epistatic part ; associated with interaction of non-alleles ---> inter-locus interaction or epistasis * Application to plant breeding : in choosing parents for crossing and breeding methods to maximize the selection gain

  3. Gene models (Allard) for the expression of a quantitative trait a. Model 1 : additive model b. Model 2 : dominance model c. Model 3 : Epistasis --- complementary model d. Model 4 complex model

  4. 2. Variance component of a quantitative trait in the populations • 1) F2 • - Genotypic value • Frequency ¼ ½ ¼ • - Generation mean of F2 = ¼(-da) + ½(ha) + ¼(da) = ½ha • - Variance of F2 = ¼(da-½ha)2 + ½(ha-½ha)2 + ¼(-da-½ha)2 = ½da2 + ¼ha2 • * Many independent loci are involved for the trait ; A-a, B-b, C-c, ... • (Assume that the effects of the genes are cumulative) • da2 + db2 + dc2 + ... = D (Variance for additive effects of genes) • ha2 + hb2 + hc2 + ... = H (Variance for dominant effects of genes) • Then, VF2 = ½ D+ ¼ H + E1 (environmental variance among individuals) Epiatisis, GxE  not included 2) VB1, VB2 VB1 : F1 (Aa) x P1 (AA) ---> AA : Aa= 1 : 1 Genotype Freq. Genotypic value Generation mean of B1 AA ½ da ½da+ ½ha Aa ½ ha VB1 = ½ (da-½da-½ha)2 + ½ (ha-½da-½ha)2 = (½da-½ha)2 + E1 Aa AA aa -da ha VB2 : F1 (Aa) x P2 (aa) ---> Aa : aa = 1 : 1 Genotype Freq. Genotypic value Generation mean of B2 Aa ½ ha ½ha - ½da aa ½ -da VB2 = ½ (ha-½ha+½da)2 + ½ (-da-½ha+½da)2 = (½da+½ha)2 + E1 ==> VB1 + VB2 = ½da2 + ½ha2 + 2E1 = ½ D + ½ H + 2E1 +da 0

  5. 3) VF3 (Variance of F3 population) Genotype Freq. Genotypic value Mean AA 3/8 da Aa 2/8 ha ¼ ha (=3/8 da + 2/8 ha -3/8 da) aa3/8 -da VF3 = ⅜(da-¼ha)2 + ¼(ha-¼ha)2 + ⅜(-da-¼ha)2 = ¾da2 + 3/16 ha2 + E1 = ¾ D+ 3/16 H+ E1 4) VF3(Variance among F3 lines) (F2) Genotype of F3 lines Freq. Mean of each line Grand mean of lines AA ---> AA ¼ da ¼ha Aa ---> ¼AA+½Aa+¼aa ½ ½ha(=¼da+½ha-¼da) (= ¼da+½(½ha)-¼da) aa---> aa ¼ -da VF3 = ¼(da-¼ha)2 + ½(½ha-¼ha)2 + ¼(-da-¼ha)2 = ½da2 + 1/16 ha2 = ½ D + 1/16 H + E2 (Environm. variation among lines) 5) VF3(Mean of variance of each F3 line) Genotype of lines Freq. Mean of each line Variance within line AA ¼ da 0 ¼AA+½Aa+¼aa ½ ½ha(=¼da+½ha-¼da) ½da2+¼ha2 aa ¼ -da 0 VF3 = ¼(0) + ½ (½da2+¼ha2) + ¼(0) = ¼da2 + ⅛ha2 = ¼ D + ⅛ H + E3 (Mean of environmental variance within line ( ≒ E1)) 6) WF2/F3 (Covcariance between a F2 individual and the F3 line) F2 ind. Value F3 line Value 1/n {∑(x-x)(y-y)} ¼ AA da ¼ AA da ¼ (da-½ha)(da-¼ha) ½ Aa ha ½ (¼AA+½Aa+¼aa) ½ha ½ (ha-½ha)(½ha-¼ha) ¼ aa -da ¼ aa -da ¼ (-da-½ha)(-da-¼ha) Mean ½ha ¼ha +)_______________________ ½ da2 + ⅛ ha2 W F2/F3 = ½ D+ ⅛ H

  6. 3. Heritability (유전력, 유전율) Phenotype = Genotype + Environment + GxE Vph= VG + VE + VGxE = VA + VD + VE + VGxE(+ VEpi + Vinteractions) * V: variance, Ph: phenotypic, G: genotypic, E: environmental A: additive, D: dominant, Epi: epistatic Broad sense heritability: the proportion of observed variation in a particular trait that can be attributed to inherited genetic factors in contrast to environmental ones (h2B) h2B = (x 100 %) Narrow sense heritability (h2N) : more useful h2N = *rice 20 vars. 3 repl. IITA, Afr. J. Plant Sci. 5(3): 207-212,

  7. - Heritability for a certain trait is a measure of the response to selection of the trait. The higher the heritability of a trait, the easier it is to modify that trait by selection. If heritability is very high, then phenotypic value is a good estimator of genotypic value. - Although the heritability of a trait depends on how it is measured, in what environment(s) it is measured, and which plant materials are measured. Qualitative traits, such as flower color, often have a value of heritability close to 100. Calculation of Heritability (1) Use of variances in parents, F1, F2: VP , VF1 , VF2 --- h2B VF2 : Variance of F2 population VE : Variance caused by environments -- variation among individuals of the same genotype

  8. (2) Heritability estimation by ANOVA analysis --- h2B n varieties, r replication  Ex) barly20 vars. 3 repl(RBD)--- no. of kernels per spike ANOVA df           MS       F      EMS             Total    59        530 Variety19      410       **    σE2 + rσg2             Block      2        70    ns     σE2 + nσB2             Error   38         50             σE2            σg2 = = 120   h2 = = 0.706 (= 70.6 %)                      3             

  9. (3) Heritability estimation by variance component method E1 : Variance among individuals of parents E2 : Variance among lines of parents E3 : Mean variance among lines of parents Ex) Oats seed length data collected in an F2 population and F3 lines E1 = 0.3427, E2 =0,0495, E3 =0.3110 Since there are experimental errors in each components across populations  calculate an optimum estimates of each component using least square method

  10. i) D ii) For other components, H, E1, E2, E3 iii) Optimum estimate of each components iv) Heritability D = 1.3211 H = 1.0694 E1 = 0.3653 E2 = 0.1015 E3 = 0.3169 In F2 pop. In F3 lines

  11. (4) Use of parent-offspring regression b: regression coefficient rxy: probability which a specific gene of offspring is identical to that of parents h2N= b : in completely heterozygous parents vs offsprings h2N = 2b : in bisexual populations Parent-offspring Regression rxyh2N F1 --> F2 1/2 b F2 --> F3 3/4 2/3 b F3 --> F4 7/8 4/7 b F4 --> F5 15/16 8/15 b F5 --> F6 31/32 16/31 b Ex) In a soybean population (Wiggins, 2012)  h2N= 16/31 b = 0.222 (22.2%)

  12. (5) Selection experiment = M’’ – M : response to selection (Rs); selection gain (Gs) ; genetic advance after selection = M’ – M : Selection differential (선발차) = q = selection intensity (out of total) : standard deviation of the trait in the pop. Base population q Freq. M’ M Population after selection value determined by q M Character value M’’

  13. Application of heritability in plant breeding heritability of target traits  Prediction of expected gain after selection - across environments - under different experimental designs - using several type of populations + information about the relative costs  to determine the optimal selection strategy to make the breeding scheme efficient

  14. 4. QTL (quantitative trait loci) analysis

More Related