Association and heritability of traits of milk productivity and blood in the dairy cows
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Numerous studies have shown that there is a certain association between the biochemical parameters of blood and milk. It is of big practical importance to determine the heritability of traits involved in the selection process, which gives breeders the opportunity to choose the direction of selection for the fastest solution of the tasks. The purpose of the researches was to establish the association between the indicators of the biochemical composition of milk and blood in the dairy cows, as well as to determine the heritability of the studied traits. It has been found in the course of researches that in most cases the correlations between the biochemical parameters of blood and milk were insignifi cant and unreliable. It can be seen that there is a slight correlation between the main indicators of blood and milk, which should be taken into account by breeders when conducting breeding to improve the quality of milk. The highest regression association has been observed between the content of carotene, calcium and phosphorus in blood and milk. If you increase these indicators in the blood by the corresponding unit, their content in milk will also increase by 0,90, 0,31 and 0,42, respectively. The heritability of milk productivity traits in most cases was at medium and low levels and ranged from 0,03 to 0,66 (straight-line correlation method) and from 0,03 to 0,44 (straight-line regression method). The heritability of hematological traits in most cases was at an average level and ranged from 0,14 to 0,98 (straight-line correlation method) and from 0,11 to 0,76 (straight-line regression method). The total protein content in the blood (h² =0,98 и 0,76) and the number of monocytes (h² =0,82 и 0,60) depended more on the genotypic features of the animal. The average level of heritability coeffi cients has been observed in the blood content of total lipids, phosphorus, eosinophils, young neutrophils, and lymphocytes.The goal of this review article is to provide a conceptual based summary of how heritability estimates for complex traits such as obesity are determined and to explore the future directions of research in the heritability field. The target audience are researchers who use heritability data rather than those conducting heritability studies. The article provides an introduction to key concepts critical to understanding heritability studies including: i) definitions of heritability: broad sense versus narrow sense heritability; ii) how data for heritability studies are collected: twin, adoption, family and population-based studies; and iii) analytical techniques: path analysis, structural equations and mixed or regressive models of complex segregation analysis. For each section, a discussion of how the different definitions and methodologies influence heritability estimates is provided. The general limitations of heritability studies are discussed including the issue of "missing heritability" in which heritability estimates are significantly higher than the variance explained by known genetic variants. Potential causes of missing heritability include restriction of many genetic association studies to single nucleotide polymorphisms, gene by gene interactions, epigenetics, and gene by environment interactions. Innovative strategies of accounting for missing heritability including modeling techniques and improved software are discussed.
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IT IS now recognized that aside from its genetic basis, the phenotypic expression of a characteristic depends on the environmental modification of the genetic potential for that characteristic. In this connection, several methods of estimating heritability have been devised to evaluate the relative importance of environmental and hereditary influences in determining the phenotype. By a broad definition, heritability is that fraction of the observed phenotypic variance which can be ascribed to known genetic differences between individuals. A narrow definition of heritability can be limited to include only the average gene effects, such as would be expected to appear if the genes were acting additively. This latter definition is a more useful one from the standpoint of applied genetics because it excludes genetic effects that are not likely to be recovered in successive generations. In terms of variances, heritability in the narrow sense can be expressed as while the corresponding broader . . .
Quantitative Genetics
Missing heritability problem
Variance components
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Heritability was estimated in nine rice populations (three crosses and three methods of selection). Heritability increased progressively from F2 to F6 generations. in general, by F6 almost all the characters in all the three crosses showed about 80% heritability with proportionate increase from generation to generation. The increase in heritability was maximum in early generations, i.e., F3 and F4 and less in later generations. in early generations, the breeder may depend on the characters having high heritability for advancing the lines.
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【Objective】 Under the assumption that there were no new variation caused by mutation and migration,and the environment remained unchanged,the selection efficiency in quantitative genetics was studied.【Method】 According to the selection theory to improve quantitative characters,generation index of selection efficiency was constructed with the ratio of the genetic progress of generations to the genetic progress on the first generation in order to measure the selection effect and potential.【Result】 If the heritability of zero generation of the breeding population was known,the generation index of generations can be calculated:On the first generation it was 1,and on later generations it can be calculated by heritability.When the selection proportion was invariant and the heritability of zero generation was in different situations,the corresponding generation index changed fast on the former three generations of selection,but quickly stabilized.When the heritability was fixed in low,medium and high levels respectively,the genetic progress on the first generation of the population,when in different selection proportion,decreased as the selection proportion increased,and the higher the level of heritability,the greater the genetic progress and also the larger the generation index of selection efficiency.【Conclusion】 The main factor which could influence selection efficiency was heritability,and the greater the heritability,the higher the selection efficiency.And the selection potential of the population was only 2 to 3 generations,so new selection response could be brought into breeding.
Index selection
Genetic correlation
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In developed countries, selection indices is usually used in the genetic improvement of sheep. One of the most critical assiunptions used in constnicting multiple trait selection indices is that parameters such as genetic and phenotypic variances and covariances are known . without error. In practise, however, only estimates of these parame-ters are used, and these are subject to sampling error. The objective of the present paper is to calculate genetic selection indices in sheep breeding using the bending technique develoved by Gunawan (1986). In the final model, 11 production traits are used in the breeding objective and 10 characters in tho index. The results show that the accuracy of selection ranged fi'om about 0.62 to 0.80. The efficiencies of selection indices including all traits relative to single trait selection are also calculated. The results show that selection indices are always more cfficient than single trait selection. The efiiciency ranged from about 102% to 176%. The highest relative efiiciency is for traits with low heritability (h2=0.l4-0.20), with efficiencies of 176% and 139% respectively. For traits with moderate heritability (117-=0.2l-0.40) the efficiencies ranged from 115% to 132%, and for traits with high heritability (h2>0.40) efficiencies are between 102% and 107%. It is concluded that a selection index is very efficient if the trait has a low heritability but also relatively high correlations with characters of high heritability in the index.
Trait
Genetic correlation
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In developed countries, selection indices is usually used in the genetic improvement of sheep. One of the most critical assiunptions used in constnicting multiple trait selection indices is that parameters such as genetic and phenotypic variances and covariances are known . without error. In practise, however, only estimates of these parame-ters are used, and these are subject to sampling error. The objective of the present paper is to calculate genetic selection indices in sheep breeding using the bending technique develoved by Gunawan (1986). In the final model, 11 production traits are used in the breeding objective and 10 characters in tho index. The results show that the accuracy of selection ranged fi'om about 0.62 to 0.80. The efficiencies of selection indices including all traits relative to single trait selection are also calculated. The results show that selection indices are always more cfficient than single trait selection. The efiiciency ranged from about 102% to 176%. The highest relative efiiciency is for traits with low heritability (h2=0.l4-0.20), with efficiencies of 176% and 139% respectively. For traits with moderate heritability (117-=0.2l-0.40) the efficiencies ranged from 115% to 132%, and for traits with high heritability (h2>0.40) efficiencies are between 102% and 107%. It is concluded that a selection index is very efficient if the trait has a low heritability but also relatively high correlations with characters of high heritability in the index.
Trait
Genetic correlation
Index selection
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Estimates of heritability (narrow sense) and genetic advance for nine characters including seed yield have been reported by studying F2, BC1 and BC2 generations in eight crosses of Linum usitatissimum L. High estimates of heritability were recorded for all the traits in cross combinations of T. 477 × IPI 6 and Afg. 8 × Neelam. Population of T. 397 × Mukta and EC. 22609 × No. 55 expressed high estimates of heritability for all the characters except for primary branches, secondary branches (low heritability) and capsule per plant (moderate heritability). Cross NP (RR) 9 × NP (RR412 and GLC. 2 - 1 × GLC. 6 showed low heritability only for 1,000 seed weight and moderate heritability for yield. Low heritability for primary branches, secondary branches and moderate heritability for seeds per capsule was exhibited in EC.22609 × No. 55, EC. 22585 × R. 17, EC9832 × Hira. The population of GLC. 1 - 1 × GLC. 6 showed lowest heritability for yield. Jn general, the estimates of genetic advance followed the estimates of heritability in all the crosses.
Linum
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Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.
Missing heritability problem
Trait
Epistasis
Genetic correlation
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Abstract The heritability of a trait is the proportion of variation in a population that can be attributed to genetic differences among individuals. Although usually applied to continuously varying traits, such as milk yield in dairy cows or blood pressure in humans, the concept of heritability can also be applied to discontinuous traits that have complex patterns of inheritance, such as the presence or the absence of medical conditions such as diabetes. The heritability can be estimated by measuring the degree of resemblance, quantified by the covariance, between relatives of various types, although special care must be taken when estimating heritability in humans.
Trait
Inheritance
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Genetic correlation
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