Introduction to Biometrical Genetics

Mather, Kenneth; Jinks, J L

doi:10.1007/978-94-009-5787-9

Cited by 329 publications

(260 citation statements)

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“…Total phenotypic variance is defined by the equation V P ϭ a 2 ϩ d 2 ϩ c 2 ϩ e 2 ϭ 1, where genotypic effects (h) comprise a and any effects of d. Additive genetic influences are those in which the total genetic effect is simply the sum of effects at individual loci, with no interaction. Nonadditive genetic effects comprise dominance, which involves allelic interaction, and epistasis, which involves nonallelic interaction between genes at different loci on one or more chromosomes (45). Genetic nonadditivity and shared environment are completely confounded in data on twin pairs reared together (43, 44, 46 -48), and only one of them can be estimated.…”

Section: Discussionmentioning

confidence: 99%

Genetic influences on endometriosis in an Australian twin sample

Treloar

O’Connor

et al. 1999

Fertility and Sterility

299

208

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Result(s):Three thousand ninety-six (94%) of the twins and 145 (82%) of the physicians responded to the survey. Two hundred fifteen twins reported endometriosis, for a prevalence rate of .07 among question respondents. Tetrachoric twin pair correlations for self-reported endometriosis (MZ: n ϭ 854 and DZ: n ϭ 493) were r MZ ϭ .46 Ϯ .09 and r DZ ϭ .28 Ϯ .13. When available medical and pathology reports were included, they changed to r MZ ϭ .52 Ϯ .08 and r DZ ϭ .19 Ϯ .16, suggesting that 51% of the variance of the latent liability to endometriosis may be attributable to additive genetic influences. Conclusion(s):These findings support the hypothesis that genes influence liability to endometriosis. (Fertil Steril 1999;71:701-10.

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Section: Discussionmentioning

confidence: 99%

Genetic influences on endometriosis in an Australian twin sample

Treloar

O’Connor

et al. 1999

Fertility and Sterility

299

208

View full text Add to dashboard Cite

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“…In the complete model (m, a, d, aa, ad, dd), the parameters were estimated by the method of ordinary least squares. In the additive-dominant model (m, a, d), the parameters were estimated by the weighted least squares method proposed by Mather & Jinks (1982). The adequacy of complete and additive-dominant model was evaluated using the coefficient of determination (R 2 ), which expresses the degree of similarities of the estimates between the estimated values and the observed ones.…”

Section: Methodsmentioning

confidence: 99%

“…If the goal of the breeding program is to develop superior inbred lines, the magnitude and sign of additive effects and additive x additive effects need to be evaluated. When the goal is to develop a hybrid, it is necessary to perform analysis of contributions of genetic effects of dominance, dominance x dominance, additive x dominance, selecting for heterosis in the desired direction, with greater heterosis expected when such effects are predominantly directional (Mather & Jinks, 1982;Marame et al, 2009a).…”

Section: Palavras-chavementioning

confidence: 99%

Determining the inheritance of agronomic traits in chili pepper

et al. 2016

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The Capsicum genus is widely studied in many countries but most of research focuses on C. annuum. Although its importance as potential genetic resource for breeding to disease resistance and fruit quality, studies on genetics and breeding in C. baccatum var. pendulum are still scarce and it still remains as an underexploited crop. This paper aimed to study the inheritance of agronomic characteristics, to estimate the genetic parameters for seven agronomic traits in C. baccatum var. pendulum, using generation mean analysis and to verify the existence of major genes and polygenes with additive and non-additive effects, using the method of maximum likelihood with the aid of Monogen software. The agronomic traits evaluated were: number of fruits per plant (NFP); average fruit mass per plant (AFM); fruit length (FRL); fruit diameter (FRD); fruit pulp thickness (FPT); soluble solids content (SSC) and fruit dry mass (FDM). There was a significant difference among generations (P1, P2, F1, F2, BC1 and BC2) for all traits evaluated. Narrow sense heritability estimates ranged from 45% for NFP to 75% for FRD. By estimating the average degree of dominance (ADD), we found that partial dominance is the gene action involved in the genetic control for FRL and FRD, while additive effect was observed for NFP, FPT and SSC, complete dominance for FDM and overdominance for AFM. The additive-dominant model was sufficient to explain the results obtained for all evaluated traits. Maximum likelihood method identified the existence of polygenes with additive and dominance effects in SSC, FPT and FDM. Major gene effect was identified for FRL and NFP characteristics. The results indicated the complexity of the inheritance of these traits with the presence of major gene involved.

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“…Environmental variance (σ 2 E ) was estimated as the geometric mean of the variances of P 1 , P 2 and F 1 generations. Genetic variance (σˆ 2 G ), and its additive (σˆ 2 A ) and dominance (σˆ 2 D ) components, as well as broad sense heritability (H 2 ) were estimated (Mather and Jinks 1977). Generation mean analysis was carried out based on the data by the weighted least squares method (Mather and Jinks 1977), in order to test the fitness of a simple additive-dominant model, and to estimate the mean degree of dominance (MDD).…”

Section: Methodsmentioning

confidence: 99%

“…Based on the means and variances components (Mather and Jinks 1977), data were subjected to a normal distribution, as follows: Tests using maximum likelihood were made via LR (Gonçalves et al 2004):…”

Section: Methodsmentioning

confidence: 99%