Year: 1997
Type: article
Abstract: Bennett Dyke, Ph.D., and Michael C. Mahaney, Ph.D. are in the Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas. Bennett Dyke, Ph.D., and Michael C. Mahaney, Ph.D. are in the Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Texas. The application of numerical modeling to distributions of traits as they are passed from parents to their offspring began with Mendel, who realized that proportions of offspring phenotypes produced by the various mating types followed a binomial distribution and therefore implied that an underlying binary sampling process governs inheritance. The power of this observation was confirmed repeatedly in the early years of this century, and to this day the standard test for evidence of genetic control of discrete traits is conformity to the familiar Mendelian ratios in offspring sibships--a test that is fundamentally a process of model fitting. Although there are many discrete traits of interest in biomedical science that are amenable to simple Mendelian analysis, the great majority of common disease-related phenotypes, such as blood pressure, serum cholesterol levels, and body mass index, do not exhibit discrete states of presence or absence, but rather a degree of variability that is expressed in quantitative terms. Moreover, most of these quantitative traits do not show clear evidence of Mendelian patterns of inheritance. Much of biomedical research in the past has concentrated on the many environmental risk factors for disease, such as diet, smoking, and chemical pollutants. However, a strictly environmental approach has not been satisfactory, because degree of exposure often fails to correlate strongly with susceptibility. These apparent anomalies, combined with the observation that many diseases (or resistance to them) tend to run in families, suggest that genes, as well as environment, play a role. The same conclusion applies to quantitative phenotypes that are not directly related to disease. The preponderance of genetic research involving nonhuman mammals has been done with highly inbred strains of rodents, where underlying genetic variability is largely removed by enforcing a mating structure of generations of close inbreeding so that individuals belonging to each strain are nearly identical both genotypically and phenotypically. Under these conditions, genetic effects may be determined experimentally by introducing or removing genetic material by selective mating between dissimilar strains, or by physical manipulation at the cellular or molecular level. These methods are not practicable with larger, relatively noninbred animals such as primates because of their longer generation times, lower reproductive rates, and expensive husbandry. Conditions for genetic research in these species contrast with highly inbred strains--individuals tend to be quite variable both phenotypically and genotypically, so that isolating the effects of particular genes must be done in a heterogeneous and often noisy background. The larger, noninbred species, however, are often chosen as animal models of human disease, as well as for basic studies of metabolism, morphology, reproduction, and behavior, because of considerations of scale (for example, dogs and swine) or close evolutionary relationships to humans (in the case of nonhuman primates). Increasingly, the potential importance of the effects of genetic factors on phenotypes of interest in animal models is being recognized. This is because The genes responsible for most of the common single gene defects are rapidly being discovered, and the human gene map is filling out. As a consequence, human geneticists are turning their attention to the search for genes that influence common diseases such as cardiovascular disease, cancer, and diabetes. The value of animal models of these diseases is increasingly a function of the extent to which effects of genes can be investigated in experimental animals. It is apparent that testing drugs, vaccines, gene therapy, and diagnostic methods without making a distinction between inherited and environmental effects on the phenotypes of interest can lead to misinterpretation. Genetic research is the source of much of the current progress in biology and medicine. Application of genetic analysis can make a profound contribution to our knowledge of basic physiology, morphology, reproduction, and behavior in all experimental organisms. The challenge has been to devise methods that make it possible to determine the effects of genes on complex traits (particularly quantitative traits) in noninbred animal models. The fundamental approach has been a statistical one that looks at the distribution of phenotypes in families. Methods for statistical genetic analysis of quantitative phenotypes come originally from 2 sources. One approach, classical quantitative genetics, was developed largely for use in breeding for desirable traits in domestic animals. The other approach, segregation and linkage analysis, was originally developed to detect the influence of genetic factors in common human diseases (such as heart disease, hypertension, diabetes, and cancers) and their risk factors in human families. These are complementary approaches, appropriate for the study of quantitative phenotypes under conditions that are typical of noninbred species. They have the potential to significantly increase the value of many animal models of human disease, and to make a profound contribution to the study of basic mammalian biology. The first step in statistical genetic analysis of quantitative traits is simply to determine the extent to which they are influenced by genes. The underlying assumption made in basic quantitative genetic analysis is that any phenotypic measurement P is expressed as the sum of 2 components: where G is the component determined by genes, and E is the nongenetic (or environmental) portion. Estimates of these components are based on the assumption that the greater the effect of genes on a trait, the greater the resemblance between relatives compared to randomly selected population members. Measurements of the phenotype are made on a relatively large sample of families, and evaluation of the relative effect of the genetic and environmental components is done in terms of the variance: where Vp is the phenotvpic variance, or simply the variance of the quantitative trait as measured in the population sample, VG is the genetic variance estimated from correlations between various classes of relatives, and VE is the residual environmental variance . The most commonly used variance-based measure of genetic contribution to a trait is the heritability (abbreviated as h2 ) which is simply the ratio of the genetic variance to the total phenotypic variance. We have calculated heritability for high density lipoprotein cholesterol (HDL-C) in the pedigreed baboon colony at the Southwest Foundation for Biomedical Research (SFBR). In humans, high levels of HDL-C are thought to be protective against atherosclerosis. This work was done as part of a large project aimed at understanding genetic effects on cholesterol metabolism under contrasting high- and low-fat diets ( MacCluer 1993 ). In this study, HDL-C values ranged from 16 to 130 mg/dl (mean 61.96 and variance 233.40) in 373 animals in the pedigree when they were measured on a normal monkey chow diet. An analysis done with the Pedigree Analysis Package (PAP) ( Hasstedt 1989 ) gave a rough estimate for the heritability of this trait of h 2 = 0.48, that is, about 48% (a substantial proportion) of the variability in the trait on this diet is attributable to genotype. These basic quantitative genetic methods have important applications in plant and animal breeding, but are relatively new to laboratory animal and biomedical science. One deficiency of these methods is that, while they can elucidate the role of genetics in a general sense, they do not evaluate the effects of individual gene loci. It is in this crucial area that statistical modeling methods become most important. As mentioned above, the key to inferring genetic effects on a phenotype rests on measuring the resemblance between relatives. A complicating factor, of course, is the fact that family members typically share a number of environmental features as well as genes. For example, human families typically share residence and diet, which may make them alike in the way they express a given phenotype. Such similarities are also found in nonhuman species, but often can be manipulated experimentally more readily than is the case with humans. Segregation analysis is a statistical modeling method aimed at decomposing the sources of family resemblance, with the particular aim of detecting the effects of single gene loci on a trait. It is based on the idea that there are several possible sources of family resemblance. Three basic sources are Nongenetic factors, called shared environmental factors, may contribute to family resemblance. Individually unidentifiable genes each having a very small effect, working together with a large number of other such genes, can influence the phenotype. Differences in the composition of this set of polygenes might explain phenotypic differences between families. A single very influential gene may affect the phenotype. Such a gene may be present in some individuals in some families, but completely absent in other families, differences which might explain phenotypic variability both within and between families. A single gene of this sort is termed a major gene . Each of these 3 sources of similarity (if operating independently) results in a unique distribution of phenotypes in a family. A phenotype influenced solely by a major gene is distributed according to Mendel's laws, which give rise to predictable ratios of individuals with different phenotypes among the members of the same family. Mendel's laws also can account for differences in phenotypic ratios between males and females, and the tendency for the phenotype sometimes to skip generations. For many qualitative traits, family members tend either to have the phenotype, or not, because they either have inherited the gene responsible for it from a parent, or they have not. Major genes can also influence quantitative traits, although their effect in such cases may require statistical analysis to detect. A phenotype caused by polygenes tends to have a wide range of expression. Offspring of the same parents tend to be affected similarly, and the expression of their phenotypes usually lies between that of their parents. An environmentally caused phenotype is less likely than one that is inherited to show differences in incidence and expression based on family relationship. Such a phenotype is more likely to be shared by both related and unrelated members of the same household, for example. This simple model can be extended in a fashion analogous to Equation (3) so that the phenotypic distribution is modeled as a linear function of these 3 variance components, plus the effects of selected covariates. In operation, the technique of segregation analysis is basically one of systematic trial and error: models consisting of various major gene/polygene/environmental combinations are tried until one is found that best fits the phenotype pattern observed in the sample of families being analyzed. An understanding of the contributions of polygenes and environment is important, but discovery of a major gene is by far the most significant and exciting result of these analyses, because it is a first step toward the application of molecular genetic analysis and the potential for sophisticated new methods of diagnosis and treatment. We again use the example of HDL-C in baboons to show the application of segregation analysis. In this analysis, MacCluer and others (1988) tested a series of models using PAP ( Hasstedt 1989 ). Underlying the method is the assumption that the distribution of the trait in the population can be decomposed into the sum of 3 separate phenotypic distributions, each with its own mean (μ 2 and 3 ). with each of these phenotypic distributions is a for a major A A A 2 and A 2 A 2 ). with each is a 2 and 3 which is the that A is passed from to The phenotypic distributions share a common variance and a common or the of the variability to polygenes for each models to be tested are by various combinations of some of which may be set at while others are that is, they are to the of the until they values that make the model most to the observed phenotypic In this study, models were of which show that best the The or general model used These are the of the 3 distributions, their single common variance and the 3 of the underlying and the of is all that is to the 3 under of it the greater number of this model to the observed phenotypic distribution than models based on Although the general model is the one against which all others are it is usually in to in and to work to more complex models with more this a model was by the 3 distribution to be to each other to the to the heritability to and the of A to This that of a single distribution are estimated without the influence of any genetic factors making all phenotypic a function of environmental factors In this study, the estimated values in (mean variance the observed phenotypic mean and variance. are the by which the model is and the 2 values from with the general Segregation analysis for models For A A A A 2 A 2 A 2 2 and 3 are the of the phenotypic distributions 2 is the common variance of the distributions h 2 is the shared heritability of the distributions ) is the of A in the population 2 and 3 ( are the that is passed from to offspring For each model in values is the of the model tested Segregation analysis for models For A A A A 2 A 2 A 2 2 and 3 are the of the phenotypic distributions 2 is the common variance of the distributions h 2 is the shared heritability of the distributions ) is the of A in the population 2 and 3 ( are the that is passed from to offspring For each model in values is the of the model tested A model was tested were set in the same way as in the that in to the mean and variance of the single the heritability was estimated is, and to The of this model is greater than that for the and a test 2 = P a significantly to the observed distribution when genetics is into This analysis the estimate of heritability 2 = This is an estimate of the total it is based on the assumption that the genetic contribution to the trait is which may not be the case evidence for a major gene from of the segregation analysis. A tested a major gene to the model (a gene is one in which the has an expression between the is estimated as in the but phenotypic values are longer to a single and 3 separate to A A A A and A 2 A 2 are as is the of A . for this are set to the Mendelian ratios and these values the of the A to an offspring when the is A A = A A 2 2 = or A 2 A 2 3 = The heritability 2 = is an estimate of the effect of polygenes on each of the separate distributions, rather than on the phenotypic distribution as a of a significantly of this model than the model 2 = P Although this result is of the presence of a major evidence 2 more of the model must be close to of the general The general model from the model in that it all which are longer to the Mendelian of the for the 2 models in to be in A statistical is that the of the model must not be significantly than the general this is so can be from the 2 value of P = for the to be that the was not simply the result of the number of phenotypic distributions from to a environmental model is This model is the that are although they are all to be to the of A . This 3 phenotypic distributions, but that they are the result of some major rather than a major As in the the of each of the distributions can be influenced by The of the environmental model must be significantly than the general the 2 value of P that this is the have evidence for the of a major gene plus polygenes to HDL-C levels on a chow diet in 2 from the are proportions from the of A and the number of animals is based on the 373 members of the study Although the are not given from the in it is quite simple to estimate the relative contributions of the major and environment to the total phenotypic variance. In this the major gene for of the total variance in polygenes for and the is to environmental factors that are with to genotype. from the model from the model A of this analysis is that of the major gene can be on a for each individual in the In animal this it possible to individuals for of or breeding that are likely to be genes that influence the trait in the function of the gene has not been For example, in and molecular studies of high and of animals from the A A high and the A 2 A 2 HDL-C distributions the that differences are rather than to environmental This example is a As above, for of have our of the of models tested in the search for an HDL-C major the models developed in this study were relatively simple compared to many other segregation analyses, which may more than one trait or major and such as and The to model such the method a and for analysis of any or trait that can be expressed as a quantitative Segregation analysis can detect the presence of a major but its function or in the the method has important applications in noninbred species for which a gene map has not been trait linkage analysis is the step in genetic on distributed genetic linkage analysis statistical methods to determine the that 2 or more genetic loci simple Mendelian are on the same These may be either genes with or function is trait linkage analysis these making it possible to determine linkage relationships between discrete loci and a quantitative trait (or . are 2 general to quantitative trait linkage and linkage analysis is an of segregation analysis, and usually with of a major gene in the The best of the segregation model (such as and major gene and are used as values for a new combined segregation and linkage analysis that such as the gene and the (a measure of the relative between the and the major In the test for evidence of a model in which the is to of is compared to that of a model in which the is A significant between the of these models is as evidence for linkage between the 2 loci. A combined segregation and linkage analysis was used in a study of the sources of in density lipoprotein cholesterol in pedigreed baboons at Segregation analysis found evidence for a major gene for this phenotype ( and others and the combined analysis tested for linkage between the major and a in the A ratio test a model with linkage against one with linkage to the linkage model at = to a ratio of which is highly of linkage in this linkage analysis methods have been developed to test for linkage between loci and in the of statistical evidence for a major These are often to as methods because they on the that any 2 individuals share that come from a common is, that are identical by or which is a function of their For example, have a of that are than do The best application of methods to quantitative trait linkage analysis is the test ( and ). This test the of the in trait values between of and the that the are at a A significantly less than linkage of the and a the trait. The method has been in studies of human linkage because it of from of and their rather than from extended The power of the method on the of large of however, and the in may be by of statistical power to detect extended are readily for study is the case in many laboratory animal an analysis to the in all other classes of relatives. A more approach to quantitative trait linkage analysis is an of the variance component methods used in quantitative genetic analysis. combined linkage and segregation analysis, these methods ( and ) do not estimate the of a major gene and but they do require of for the family (such as and developed ( and others ) have the of this approach to complex that animal have to map and the baboon in search of genes density and other phenotypes related to using variance component linkage for quantitative trait linkage analysis are greater than of segregation or quantitative genetic analysis, it knowledge of loci that are to as well as on phenotypes of As of more than one of the loci in the human been and it is that a map be done by the ( and others ). gene of this are not likely to be for most animal models of human in the gene have been for at nonhuman animal species, and gene are under way for a of species baboons and laboratory Moreover, and the fact that many developed for humans work well with related species make it possible to map genes to small without the for gene as have been with density genes in baboons at the of animals used in genetic research may require more attention to colony breeding and structure than is common for animals used in other of biomedical large of animals with both parents are often for analysis, with phenotypic made on as many of these individuals families be so that relatively large both are individuals one are but to analyses, an animal that has with more than one not be to an animal that also has with more than one for many species there are usually more breeding than males in a in it may be to of 3 to generations in are although in most tend to become complex and may require by between families to analysis. of breeding structure may on the of analysis being Segregation analysis a normal range of variability in the This that animals not be for affected or phenotypes, these are the phenotypes to be analyzed. For segregation analysis it is best that inbreeding be although mating between relatives can be by pedigree the other the power of linkage analysis can be by the appropriate use of as well as by for a breeding that inbreeding and for phenotypes is the for the This is because segregation analysis is likely to be an important method of genetic analysis in the of gene and because such a the that a given breeding colony be for the analysis of a of The may in the however, as gene for experimental animals become more and linkage analysis the method of for genes underlying disease-related The way in which pedigreed animals are used as research also to some extent on the of the species in and having large and generation (such as it may be possible to that of key family members is not a In with species (such as nonhuman that relatively key family members often must be for breeding making it to for or or for of experimental which can a breeding In the case a is usually on animals in the pedigree their in a with experimental are from or other and and other such It is important to however, that all breeding can more offspring than are to as an source of for animals used in with a of selected on the of family relationships and for breeding and genetic and the a substantial to the biomedical at by and by and of the Southwest Foundation are
Source: ILAR Journal
DOI: 10.1093/ilar.38.2.76
Authors: B. Dyke, M. C. Mahaney
Institution: Texas Biomedical Research Institute
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Funder: National Institutes of Health
OpenAlex ID: W2117896801