Population Genetic Models

2016 ◽  
pp. 87-117
Author(s):  
John Buckleton ◽  
Duncan Taylor ◽  
James Curran ◽  
Jo-Anne Bright
Keyword(s):  
Population Genetic ◽  
Genetic Models

scholarly journals Linking Dynamical and Population Genetic Models of Persistent Viral Infection

2003 ◽  
Vol 162 (1) ◽  
pp. 14-28 ◽  
Author(s):  
John K. Kelly ◽  
Scott Williamson ◽  
Maria E. Orive ◽  
Marilyn S. Smith ◽  
Robert D. Holt
Keyword(s):  
Viral Infection ◽  
Population Genetic ◽  
Genetic Models ◽  
Persistent Viral Infection

Population Genetic Models

2018 ◽  
pp. 87-117
Author(s):  
John S. Buckleton ◽  
Duncan Taylor ◽  
James M. Curran ◽  
Jo-Anne Bright
Keyword(s):  
Population Genetic ◽  
Genetic Models

Amplifiers and the handicap principle in sexual selection: a different emphasis

1989 ◽  
Vol 235 (1281) ◽  
pp. 383-406 ◽  
Keyword(s):  
Sexual Selection ◽  
Genetic Association ◽  
Female Choice ◽  
Mating Success ◽  
Population Genetic ◽  
Genetic Model ◽  
Genetic Models ◽  
Handicap Principle ◽  
Resolution Power ◽  
Male Display

Population genetic models have shown that female choice is a potential cause of the evolution of male display. In these models the display is assumed to be the immediate object of female choice. Here I present an explicit genetic model that shows that male display can evolve as a consequence of female choice even when the display is not the immediate object of choice. When females initially base their preferences on the existence of variance in a cue that is correlated with male viability, a rare display can evolve to fixation if it amplifies the previously recognized differences in males, (i. e. if it increases the resolution power of females with respect to the original cue). By definition, amplifying displays (or amplifiers) increase mating success of the more viable males and decrease mating success of the less viable males. Therefore, the higher the frequency of the preferred, more viable males, the more likely it is that amplifiers will evolve to fixation. The evolution of an amplifier is further facilitated by a genetic association that is built up between the amplifier allele and the more viable allele. If the expression of the amplifier is limited to the more viable males, the amplifier will evolve to fixation provided only that the change in total fitness to the more viable males (higher mating success, lower viability), is positive.

Risk estimation for multifactorial diseases

1999 ◽  
Vol 29 (3-4) ◽  
pp. 1-2 ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Coronary Heart Disease ◽  
Population Genetic ◽  
Threshold Model ◽  
Genetic Models ◽  
Multifactorial Disease ◽  
Induced Mutations ◽  
Multifactorial Diseases ◽  
Radiation Induced ◽  
The Impact

This report reviews data on naturally-occurring multifactorial diseases and develops a mathematical model to predict the impact of radiation-induced mutations on the frequencies of these diseases in the population. It provides an outline of the aetiological features and examples of multifactorial diseases, interpreted to arise as a result of the joint action of genetic and environmental factors. Examples include common congenital abnormalities (such as neural tube defects, cardiovascular malformations, cleft lip±palate etc.) and chronic diseases (such as coronary heart disease, essential hypertension, diabetes mellitus etc.). These diseases are not readily explained on the basis of simple mendelian patterns of inheritance. The report considers the concepts and models used to explain the inheritance patterns of multifactorial diseases with particular emphasis on the multifactorial threshold model (MTM) of disease liability. The MTM is useful for predicting risk to relatives of those affected from information on their population frequencies. In these predictions, the heritability ( h2) provides a measure of the relative importance of transmissible genetic effects in the overall phenotypic variation. Conceptual differences between mendelian and multifactorial diseases are discussed. The genetic basis of a multifactorial disease is that a genetically susceptible individual may or may not develop the disease depending on the interaction of a number of risk factors, both genetic and environmental. Three chronic multifactorial disease entities are reviewed in depth, viz. diabetes mellitus, essential hypertension, and coronary heart disease. The report considers briefly mechanistic population genetic models developed to explain polygenic variation. The basic conclusion is that the concepts of liability and threshold (underlying the MTM model) and that of mutation-selection balance (from population genetic models) together provide a basis for developing a model for assessing the impact of radiation-induced mutations on the frequencies of multifactorial diseases in the population. The mutation component (MC) of genetic diseases quantifies the responsiveness of the genetic component of a disease to an increase in mutation rate (e.g. after radiation exposure). This report integrates the concepts of liability and threshold (from the MTM model) and of mutation-selection equilibrium (from mechanistic population genetic models) into the ‘Finite Locus Threshold Model’ (FLTM) for estimating MC for multifactorial diseases and the relationship between MC and h 2 of these diseases. Computer simulation studies illustrate the effects of one-time or a permanent increase in mutation rate on MC for multifactorial diseases. Finally, the report addresses the estimation of the radiation risk of multifactorial diseases. A formal revision of the estimates of risk of multifactorial diseases (and also of mendelian diseases) contained in the 1990 Recommendations of ICRP, Publication 60, must await the results of studies currently underway. While future genetic risk estimates are likely to be lower than those in current use, until the new ones become available, those provided in Publication 60 may be regarded as being adequate for use in radiological protection- they are unlikely to underestimate risk.

scholarly journals Exploring Population Genetic Models With Recombination Using Efficient Forward-Time Simulations

Genetics ◽  
2008 ◽  
Vol 178 (4) ◽  
pp. 2417-2427 ◽  
Author(s):  
Badri Padhukasahasram ◽  
Paul Marjoram ◽  
Jeffrey D. Wall ◽  
Carlos D. Bustamante ◽  
Magnus Nordborg
Keyword(s):  
Population Genetic ◽  
Genetic Models
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