skeletal maturity)

Abbreviations: Mo = mother; Fa = father; Dau = daughter; MZ = monozygotic twins; DZ = dizygotic twins; rpc = parent-child correlation; rsib = sibling correla- n tion; TO = "takeoff" (height velocity minimum); TOV = velocity at "takeoff" (height velocity minimum); PHV = peak height velocity. t secular trends that may reduce correlations between parents and offspring, and the shared environments of siblings, especially twins, that may inflate correlations between them. Second, specific environmental sources of variation, such as diet and disease, usually have not been incorporated into the analyses. Not accounting for the variance in a trait attributable to such environmental factors can lead to erroneous estimation of the heritability of the trait. Third, the majority of studies have focused solely on height at a given point in time (mostly adult height). A much smaller number of studies have examined variation in other anthropometrics. Fourth, the majority of studies are based on cross-sectional data. Only a very few studies have longitudinal growth and development data from related individuals that permit examination of genetic influences on patterns of change in height, weight, and other measures over time. And fifth, almost all of the studies have focused solely on the magnitude of genetic effects. There are very few multivariate quantitative genetic analyses of measures of growth and development; analyses of genotype-by-environment, -sex, or -age interactions; association studies; or linkage analyses.

Birth Weight

The genetics of prenatal growth has largely been approached by examining the heritability of birth weight. Initially, genetic influences on birth weight were deduced from the known effects of quantitative changes in chromosomes. For example, supernumerary autosomes (trisomy 21, 18, and 13) and abnormal numbers of X chromosomes (as in Turner syndrome) all result in growth retardation. Formal quantitative genetic analyses of birth weight find somewhat lower heritability estimates than for body weight and length in postnatal life, which are both highly heritable (see later). Assessment of genetic influences on birth weight is complicated, however, because prenatal growth (at least as measured by birth weight) is influenced by both the genetic makeup of the fetus and the maternal intrauterine environment, and there is no fully satisfactory way to partition these two sources of variation. Therefore, not surprisingly, estimates of the influences of fetal genes, maternal genes, nongenetic maternal factors, and random environmental effects on fetal growth vary considerably across studies. The role of fetal genes varies from 0 to 50%, maternal factors from 27 to 50%, and random environmental factors from 8 to 43% in the variation in birth weight.7

A classic study by Penrose8 attempted to partition the variance in birth weight among fetal genes, maternal genes, nongenetic maternal factors, and random environmental effects. He concluded that fetal genes accounted for approximately 18% of the phenotypic variance, while "maternal factors" (a combination of both genetic and uterine environment) explained approximately 40% of the phenotypic variance. The importance of uterine environment in the control of prenatal growth is also demonstrated by the changes in twin correlations from birth onward (e.g., Wilson9). Intrapair differences in the birth weight of MZ twins are often significant at birth (tending to be larger than differences between DZ twins), because MZ twins compete for placental resources. Differences in weight between MZ twins decreases over time. By 3 years of age, the MZ twin correlation is about 0.80-0.90 and the DZ twin correlation is about 0.40-0.50.

A problem with the use of birth weight as a measure of prenatal growth is that it represents growth status at a variety of maturational ages depending on gesta-tional age. Most studies of the genetics of birth weight have not controlled for ges-tational age. This flaw has likely led to underestimates of genetic influences. Indeed, using a variance components method for pedigree data and modeling a gestational age covariate effect, we found a high heritability of birth weight in the Fels Longitudinal Study population (h2 = 0.80; Demerath et al., unpublished results). Continued work along these lines will help identify specific factors influencing fetal growth and development. However, progress depends on measurement strategies that better capture the process of fetal development (e.g., serial ultrasound biometry).


Data from nearly 4000 individuals in 1100 nuclear families in England analyzed by Pearson and Lee10 provide perhaps the earliest evidence for the inheritance of height. In this landmark study, Pearson and Lee found a significant correlation between spouses (0.28), which shows positive assortative mating for height, but higher correlations between siblings (0.54) and between parents and offspring (0.50). Since the expected correlation between full siblings and between parents and offspring would be 0.50 if the h2 of the trait was 1.0, they concluded that the population variation in height was highly determined by genetic factors. These early results have been corroborated by hundreds of subsequent family studies. In populations around the world, the estimates of the h2 of height range from 0.6 to above 0.9, clearly showing that height is a highly heritable trait.

In a review of 24 studies of parent-child correlations of height and weight, however, Mueller11 indicated that population estimates of heritability tend to be systematically lower in developing countries than affluent countries. There are a number of reasons why this might be so. As mentioned earlier, according to classic quantitative genetic theory, the heritability of height or any trait is a function of the population in which the estimate is made, as well as of the trait itself. Heritabil-ity estimates tend to be higher if there is positive assortative mating (i.e., a significant phenotypic correlation between parents). And indeed, assortative mating for height has been found in European populations more frequently than in non-European populations. Also, non-European populations in the developing world tend to live under more nutritional and disease stress than European populations. In these populations, such environmental factors have the potential to affect a given trait more than in affluent populations. Since heritability is the proportion of variance due to genetic influences, a larger proportion of environmentally induced variation reduces the heritability. Additionally, many non-European populations are experiencing rapid economic change, which results in the growth environments of children differing quite markedly from that of their parents, thus decreasing parent-offspring correlations and the estimate of total variation attributable to genes.

Other Anthropometric Measurements

Whereas the heritability of skeletal length (e.g., height, sitting height) tends to be high, the h2 of skeletal breadth (e.g., biiliac and biacromial diameters) tends to be somewhat lower, averaging between 0.4 and 0.8. In turn, skeletal breadth tends to have higher heritability than weight, circumference, and skinfolds. It has been assumed that soft-tissue traits are more easily altered by the changing nutritional environment of individuals than skeletal tissues, which respond less quickly to changes in nutritional status, and, as a result, have a greater proportion of their variance explained by environmental, rather than genetic, factors. Nonetheless, growth status in all anthropometrics has been shown to have a significant heritable component.

Longitudinal Studies

As mentioned earlier, the vast majority of family studies of growth are cross-sectional. Only a few studies have longitudinal growth and development data from related children that permit genetic analyses of the processes of growth and development. Some of these longitudinal studies of the genetics of growth, for example, examined changes in parent-child or sibling correlations from age to age. Reports from the Fels Longitudinal Study,12 Poland,13 and elsewhere14,15 indicated that parent-child correlations for height increase during the first 4 years of life, decrease during adolescence (when heterogeneity of maturational tempo disrupt familial similarity), and subsequently rise above the prepubertal level.

Modern longitudinal studies of growth and development use various growth curve fitting methods to pinpoint maturational events, particularly of changes in the tempo of growth in stature, and then examine growth curve parameters in genetic analyses. For example, Buenen et al.16 report high heritability estimates for the ages at takeoff and peak height velocities, and the heights at those ages. Similar analyses of Fels Longitudinal Study data are discussed in more detail later.


Not only is physical size heritable, but the timing and tempo of maturation also are significantly controlled by genes. A number of early studies of dental development found that radiographic measures of the timing of tooth formation (calcification) and dental emergence were more highly correlated within MZ twin pairs than DZ twin pairs, suggesting a heritability of 0.85-0.90.17 Also, the number and pattern of dental cusps were found to be under genetic control. The rate of skeletal maturation has been compared in siblings over time in several reports, with the general finding being that there is a great deal of similarity between sibs in the age of ossification onset of bones in the hand and foot. The general pattern of skeletal maturation (i.e., the tendency to be an "early" or "late" maturing individual) also suggests that the tempo of development is highly heritable, with sib-sib correlations of 0.45.18

The process of maturation is commonly believed to be controlled, at least partially, by genes independent from those controlling final size. This conjecture stems from the observation that siblings may reach identical height even though they differed in the timing of maturational events.19 Further and more widespread use of the multivariate quantitative approaches discussed previously, in which genetic and environmental correlations between different traits may be calculated, allows for greater understanding of the extent of shared genetic and nongenetic factors underlying growth and development traits.

Age at menarche is one of the most studied developmental traits. Many early studies suggested that age at menarche has a genetic basis (e.g., Boas20). The mother-daughter and sister-sister correlations in the age at menarche were close to 0.50, indicating a high degree of genetic determination of age at menarche. These and later studies, however, relied primarily on recalled ages at menarche, and thus recall bias (greater in mothers than in daughters) is introduced into these estimates. Subsequent work has confirmed a strong genetic influence on age at menarche.21-25 Beyond the documentation of the magnitude of genetic influences on menarche, a recent study sought to decompose the known relationships between skeletal development, BMI, and the onset of menarche into their shared genetic and environmental components.26 Further work of this type will improve our understanding of the orchestration of changes in body size and maturation.

examples from the fels longitudinal study and the jiri growth study

This section highlights some of the topics discussed in the preceding sections through examples of genetic analyses conducted over the years in the Fels Longitudinal Study and those currently underway in the recently established Jiri Growth Study.

Fels Longitudinal Study

The Fels Longitudinal Study began in 1929 in Yellow Springs, Ohio. It was one of several longitudinal studies of child growth and development initiated in the United States between the end of World War I and the start of the Great Depression, and it is the only one that has survived to today. Although the Fels Longitudinal Study did not begin with an interest in genetics, familial data began to be collected soon after the study began. Most of the mothers who enrolled their children in the early years of the study had more children later, and many of those children subsequently became participants. Also, a set of monozygotic, dichori-onic triplets was recruited early in the study specifically to examine their similarities in growth and development. Another set of triplets and a few twin pairs also were recruited in later years. Over time, other relatives were incorporated into the study, the first of these being the offspring of study participants. The Fels

Longitudinal Study today has more than 1000 participants with extensive serial data from infancy and cross-sectional data from more than 2000 of their relatives. These individuals represent about 200 kindreds consisting of both nuclear and extended families.

The description of the Genetics Program of the Fels Longitudinal Study written by its first director, Lester W. Sontag, is remarkable for its modern sounding tone.27 Sontag noted that many aspects of growth and development are likely to have significant genetic determination but are influenced by environmental factors as well. He noted that the study included many families with two or more children, and that these "constitute the material for the study of inheritance of growth patterns as well as of metabolic characteristics."

For example, the set of monozygotic, dichorionic triplets just mentioned were the subject of three early reports that described their similarities in physical and mental traits as young children, striae in their bones, and the onset of ossification from infancy through pubescence.28-30 Soon after the triplet's 18th birthday,31 Reynolds and Schoen reported a description of their growth patterns. A paper by Reynolds32 is especially noteworthy because it used familial data from different types of relatives to examine the effects of degree of kinship on patterns of ossification. Included in this analysis were the set of identical triplets, as well as 3 pairs of identical twins, 22 pairs of siblings, 8 pairs of first cousins, and 18 unrelated children. Reynolds found that close relatives were very similar in pattern of ossification, distant relatives less so, and unrelated even less similar.

A series of studies from the late 1950s to the late 1960s by Garn and colleagues used data from siblings, parents, and offspring to examine patterns of familial correlations in traits pertaining especially to dental and skeletal maturation. An example of the analyses and sample sizes from this period is provided by Garn, Lewis, and Kerewsky,33 who examined ossification data from radiographs of the hand-wrist and chest for 72 parent-child pairs, 318 sibling pairs, 4 pairs of dizygotic twins, and 4 pairs of monozygotic twins. Since these were serial data taken at half-yearly intervals from ages 1 to 7, there were 1211 pairings of parent-child data, 6690 pairings of sibling data, 102 pairings of data from dizygotic twins, and 176 pairings of data from monozygotic twins. They concluded that, "In these well-nourished . . . Ohio-born white children, genes appear to account for a major proportion of ossification variance during growth." These investigators also examined the genetics of various dental traits, including the timing of stages of dental development,17 tooth morphology,34 and the appearance of discrete dental traits.35 The influence of familial factors on growth in body size also were examined.12

Genetic analyses of growth and development data from the Fels Longitudinal Study data have had a resurgence in recent years. This is due largely to advances in statistical genetic methods that maximize the amount of information available in longitudinal data from large numbers of relatives of varying degrees of relationship to one another, as well as advances in molecular genetic methodology that allow for relatively low-cost genotyping. For example, Towne et al.36 fitted a three-parameter function to serial recumbent lengths from 569 infants in order to characterize each individual's unique pattern of growth during infancy. Figure 6-2 shows the growth curves of two infant boys who differ in their patterns of growth. Boy #1 started out in life shorter than Boy #2, but had a rate of increase in recumbent length that was much greater than that of Boy #2. Both boys, however, experienced about the same amount of growth (~42 cm) from birth to age 2 years. In this study, substantial h2 estimates of 0.83 for recumbent length at birth, 0.67 for rate of increase in length, and 0.78 for a parameter describing the curvilinear shape of growth in recumbent length from birth to 2 years were found.

Towne et al.37 used the triple logistic model of Bock et al.38 to fit growth curves to serial stature data from 471 Fels Study participants, age 2-22 years, representing 188 kindreds, to conduct a multivariate quantitative genetic analysis of different parameters of the pubertal growth spurt. Figure 6-3 shows the growth and velocity curves of two girls with visibly different growth patterns. Girl #1 was only 9.37 years old when she was at the peak of her pubertal growth spurt, whereas the age at peak height velocity of Girl #2 was 13.09 years. At the time of peak height velocity, Girl #1 was shorter than Girl #2 (141.1 cm vs. 156.5 cm), which is expected given her younger age at peak height velocity; but at the age at peak height velocity, Girl #1 had a higher rate of growth than Girl #2 (8.8 cm/yr vs. 6.4 cm/yr). By the end of their growth, Girl #1 was a petite woman (158.5 cm) while Girl #2 was somewhat taller than average (170.6 cm). Highly significant h2 estimates—on the order of 0.85 for age at peak height velocity, 0.61 for growth rate at peak height

Peak Height Velocity Boys Girls
FIGURE 6-2 Height distance curves for two boys with differing growth patterns between birth and 24 months old.

velocity, and 0.96 for stature at the age of peak height velocity—were found. Especially interesting was the finding of additive genetic correlations between these pubertal growth spurt parameters that were significantly lower than 1.0, suggesting incomplete pleiotropic effects of genes on different aspects of growth. That is, these three different growth curve parameters may have, to some extent, unique genetic underpinnings.

In a recent association study, Towne et al.39 found evidence of the effects of a functional polymorphism in the P-subunit of the luteinizing hormone gene (LHP) on stature during childhood. A total of 736 individuals, from 137 nuclear and extended families, measured a total of 13,300 times between 2 and 18 years old, were genotyped for the LH-P polymorphism. Individuals with the less common LH-P allele were found to be shorter than those homozygous for the common LHP allele at all childhood ages.

With regard to the genetics of development, Towne et al.40 used a multivariate variance components method incorporating parametric correlation functions to model the heritability of skeletal maturity in children from 3 to 15 years old and the genetic and environmental correlations between skeletal maturity assessed across this age range. A total of 6893 annual skeletal age assessments, made from hand-wrist radiographs taken of 807 children from 192 nuclear and extended families, were simultaneously analyzed. The best-fitting model had 65 parameters and allowed for an exponential decay in genetic and environmental correlations as a function of chronological age differences. From this model, the h2 estimates of skeletal age

FIGURE 6-3 Height distance and velocity curves for two girls between 12 months and 20 years old.

with different growth patterns

FIGURE 6-3 Height distance and velocity curves for two girls between 12 months and 20 years old.

with different growth patterns at each chronological age were 3 = 0.71, 4 = 0.73, 5 = 0.77, 6 = 0.93, 7 = 0.78, 8 = 0.77, 9 = 0.73, 10 = 0.63, 11 = 0.45, 12 = 0.39, 13 = 0.34, 14 = 0.23, and 15 = 0.11. The genetic correlation matrix showed a pattern of decreasing correlations between skeletal age at different chronological ages as age differences increased (e.g., pG between skeletal age at 3 years old and skeletal age at 4 years old was 0.96, but between skeletal age at 3 years old and skeletal age at 15 years old, pG was 0.56). The random environmental correlation matrix showed an even more pronounced pattern of decreasing correlations between skeletal age at different chronological ages as age differences increased (e.g., pE between skeletal age at 3 years old and skeletal age at 4 years old was 0.77, but pE between skeletal age at 3 years old and skeletal age at 15 years old was only 0.12). These results show a high heritability of skeletal age through early puberty and suggest that skeletal maturation at different stages of development is influenced by different sets of genes and environmental factors.

Jiri Growth Study

The goal of the Jiri Growth Study is to elucidate the roles of genetic and environmental factors influencing processes of childhood growth and development. This is being accomplished by establishing a longitudinal study of the growth and development of a large cohort of related children living in rural Nepal, where gastrointestinal parasitic diseases (helminthic and protozoan infections in particular) are endemic. Initiated in 1998 by one of the authors (B.T.), the Jiri Growth Study is an infant compared to the 73-year-old Fels Longitudinal Study. But, by virtue of its extended pedigree study design and the use of modern statistical genetic methods, it will not take long to mature into a genetic epidemiological study of growth and development.

The Jiri Growth Study is an offshoot of the Jiri Helminth Project. The Jiri Helminth Project began in 1995 as a collaborative effort of U.S. and Nepali investigators, with primary grant funding from the U.S. National Institutes of Health to Sarah Williams-Blangero and John Blangero in the Department of Genetics at the Southwest Foundation for Biomedical Research. The goal of the Jiri Helminth Project is to examine both genetic and environmental factors that predispose individuals to helminthic infection.

Roundworm, hookworm, and whipworm are major health concerns in both tropical and temperate areas of the world. Worldwide, approximately one out of every four persons is infected by at least one of these three helminths. They are major causes of morbidity in developing nations and significant causes of mortality in areas with limited health care. Increasing urbanization in many areas of the developing world, usually without adequate infrastructure development (e.g., water and sanitation systems), has resulted in increasing rates of intestinal parasitic infections.

There has been increasing evidence over the last 20 years that susceptibility or predisposition to helminthic infection has a genetic component, with several studies finding familial aggregation of roundworm and whipworm infections. None of these studies, however, were conducted using data from large numbers of relatives and modern genetic epidemiological approaches. The Jirel ethnic group in the Jiri region of eastern Nepal is an ideal study population in which to examine the genetic epidemiology of helminthic infection. The Jirel population today numbers approximately 4000 individuals, who live in nine villages. The Jirels have been the focus of extensive anthropologic, population genetic, and genetic epidemiologic studies over the last 15 years. Over this time the complete genealogy of the Jirel population has been compiled. Almost all Jirels trace their ancestry back approximately 150 years to a population of some 200 individuals. Most Jirels today are members of one very large extended pedigree of approximately 3500 individuals.

The tremendous power that this population structure provides for genetic epi-demiologic research is evidenced in the tens of thousands of pairwise kin relationships that exist among the some 2000 Jirels participating in the Jiri Helminth Project. For example, Williams-Blangero et al.41 recently reported h2 estimates between 0.30 and 0.50 for different quantifications of roundworm burden (i.e., egg counts and worm counts). Similar heritabilities for hookworm and whipworm infection have been estimated in preliminary analyses (Williams-Blangero, personal communication). These results demonstrate a highly significant genetic basis to susceptibility to helminthic infection in the Jirel population.

Given the negative impact that helminthic infections have on growth and development, it is reasonable to hypothesize that genes predisposing for susceptibility to helminthic infection also negatively influence processes of growth and development. In an initial analysis of height and roundworm infection status data from 432 Jirel children, Towne, Blangero, and Williams-Blangero42 found a high h2 of 0.91 for height and negative associations of height with roundworm infection status, with the effect being more pronounced in males. For example, on average, a 12-year-old boy infected with roundworms would be 2.2 cm shorter than an uninfected 12-year-old boy, while an infected 12-year-old girl would be only 0.5 cm shorter than an uninfected 12-year-old girl.

Ultimately, some 1000 Jirel children will be examined on a regular basis as part of the Jiri Growth Study. In addition to annual quantitative determinations of helminthic and protozoan infections, a comprehensive battery of anthropometrics, body composition measures, developmental indicators, and blood biochemistries will be collected from each child. Extensive sociocultural survey data also will be collected to examine associations between growth and development and features of household environment. The Jiri Growth Study offers unique opportunities to quantify genetic and environmental factors influencing growth and development over the course of childhood and to explicitly examine genotype-by-environment interaction effects on human growth and development.


For over a century, there has been scientific interest in the genetic underpinnings of growth and development. But, as with any area of scientific inquiry, to one degree or another, all studies of the genetics of growth and development have been limited by the methods and technologies available to them at the time. For that reason, most of the literature is limited to heritability estimates of measures of growth and development gathered from first-degree relatives. The opportunities exist today, however, for much more sophisticated studies of both genetic and environmental factors that influence the processes of growth and development.

One problem, though, is that modern genetic epidemiological studies of growth and development can be expensive undertakings. Such studies are readily justified, however, on the very practical and applied grounds that growth and development of children can have health consequences later in life. Indeed, much of the current research emphasis in the Fels Longitudinal Study pertains to studies of the relationships between age-related changes in body composition (including those that occur during childhood) and the development and progression of cardiovascular disease (CVD) risk factors in later life, an area of active research today. A current Fels Longitudinal Study project, for example, is aimed at evaluating the role of birth weight in predisposing to adult CVD, taking into account the significant heritable components of both birth weight and various measures of adult CVD risk. Demerath et al.43 recently found that birth weight was negatively associated with fasting insulin concentration in adulthood after adjusting for BMI and age, but after taking into account the significant heritability of insulin concentration, birth weight only accounted for 1-2% of the remaining phenotypic variance of fasting insulin concentration. Another current Fels Longitudinal Study project is examining changes in serum lipids during growth and development. Although lipid and lipoprotein levels track from childhood to adulthood, Czerwinski et al.44 found higher heritabilities of lipids and lipoproteins after puberty than before, suggesting that the genetic control of lipid and lipoprotein levels may be influenced by maturational factors.

We hope this chapter has demonstrated that the processes of growth and development are to a large extent controlled by genes; therefore, the first task in establishing a genetic epidemiology of growth discussed in the Introduction: "characterizing the magnitude of genetic influences on growth and development phenotypes" has been essentially completed. Auxological genetics is now poised to move beyond this critical but basic assertion to a thorough understanding of the sources of genetic control over growth during prenatal and postnatal life. This will involve examining how those genetic influences operate over time, identifying and localizing specific genetic polymorphisms that contribute to variation in growth and development, and elucidating how genetic and environmental factors interact during growth and development. Given the advances in statistical and molecular genetics made over the last 20 years and the renewed interest in childhood growth due to its potential relationship with diseases of adulthood, these goals may now be achieved.


Allele: A variant of the DNA sequence at a particular locus. Typically, individuals possess two allelic variants at each locus, derived from the maternal and paternal chromosomes, respectively. The two alleles may be identical or different, making the individual homozygous or heterozygous, respectively, at that locus.

Assortative mating: Selection of a mate based on phenotypic characteristics. Positive assortative mating occurs when selection is based on a shared character. Negative assortative mating occurs when selection is based on an unshared character.

Complex trait or phenotype: Any phenotype whose expression is influenced by multiple genes, or by one or more genes and one or more environmental factors. Complex traits can be quantitative or discrete.

Epistasis: Interactions between alleles at different loci. Also known as gene x gene interaction.

Gene: A segment of DNA that codes for a specific protein or enzyme.

Genotype: The group of genes making up an organism. The genotype at a particular locus consists of the two alleles present at that locus.

Heritability: A measure that expresses the extent to which phenotypes are determined by genes transmitted from parents to their offspring. Heritability (in the narrow sense) is defined as the proportion of the total phenotypic variance attributable to the additive effects of genes.

Identity by descent (IBD): Identical alleles at the same locus found in two related individuals that are identical because they originated from a common ancestor.

Identity by state (IBS): Identical alleles found within two individuals. If the two individuals are related, the two alleles may also be identical by descent if they are replicates of the same ancestral allele from a previous generation.

Kinship coefficient: The probability that two genes from two individuals for a given locus are identical by descent. A general measure of relatedness.

Linkage analysis: A method of analysis used to localize the position of genes on a chromosome.

Linkage disequilibrium: Nonrandom association within a population of alleles at two or more linked loci. Linkage disequilibrium decays with increasing genetic (recombination) distance between loci.

Locus: The position of a gene on a chromosome.

Monogenic: A trait is monogenic if it is influenced primarily or entirely by only one genetic locus.

Mutation: Specific sequence variants in the nucleotide sequence of a gene. These variants may or may not be inherited.

Oligogenic: A trait is oligogenic if it is influenced by a few loci of significant, individually detectable effects.

Phenotype: The observable characteristics of an organism or a specific trait produced by the genotype in conjunction with the environment.

Polygenic: A phenotype is polygenic if it is influenced by many genes of relatively small individual effects, such that the influence of any single locus is very difficult or impossible to detect on its own.

Polymorphism: The joint occurrence in a population of two or more genetically determined alternative phenotypes, each occurring at an appreciable frequency (arbitrarily, 1% or higher). A polymorphism may be defined at either the protein level (e.g., Rh+ and Rh- red blood cell groups) or at the DNA level (alternative alleles at a locus).

Quantitative trait locus: Any locus that influences variation in a complex phenotype.

Recombination (crossover): The exchange of segments of homologous chromosomes following chromosomal duplication and synapse formation during meiosis. Recombination is responsible for the production of offspring with combinations of alleles at linked loci that differ from those possessed by the two parents.

Hartl DL. A Primer of Population Genetics. Sunderland, MA: Sinauer Associates, 1999.

Hartl DL, Clark AG. Principles of Population Genetics. Sunderland, MA: Sinauer Associates, 1997.

Khoury MJ, Cohen BH, Beaty TH. Fundamentals of Genetic Epidemiology. Oxford: Oxford University Press, 1993.

Lynch M, Walsh B. Genetic Analysis of Quantitative Traits. Sunderland, MA: Sinauer Associates, 1997.

Ott J. Analysis of Human Genetic Linkage. Baltimore: Johns Hopkins University Press, 1999.

Terwilliger JD, Ott J. Handbook of Human Genetic Linkage. Baltimore: Johns Hopkins University Press, 1994.

Weiss KM. Genetic Variation and Human Disease: Principles and Evolutionary Approaches. New York: Cambridge University Press, 1993.

suggested reading internet resources


Site Address

Center for Medical Genetics (Marshfield, WI) Cooperative Human Linkage Center GENATLAS QUERY


Site Address

Genetics-related resources list.html



OMIM Home Page—Online Mendelian Inheritance The Genome Database The Human Genetic Analysis Resource Genetic linkage analysis

National Human Genome Research Institute (NHGRI) Genome Research

National Center for Biotechnology Information Human obesity gene maps references

1. Neel J, Schull W. Human Heredity. Chicago; London: University of Chicago Press, 1954.

2. Falconer DS, Mackay TFC. Introduction to Quantitative Genetics, 4th ed. Harlow, UK: Longman, 1996.

3. Lynch M, Walsh B. Genetic Analysis of Quantitative Traits. Sunderland, MA: Sinauer Associates, 1997.

4. Fisher RA. The correlation between relatives on the supposition of Mendelian inheritance. Trans Royal Soc Edinburgh. 1918;52:399-433.

5. Eveleth PB, Tanner JM. Worldwide Variation in Human Growth, 2nd ed. Cambridge: Cambridge University Press, 1990.

6. Martorell R, Mendoza F, Castillo R. Poverty and stature in children. In: Waterlow JC (ed). Linear Growth Retardation in Less Developed Countries. New York: Raven Press, 1988:57-73.

7. Mueller WH. Genetic and environmental influences on fetal growth. In: Ulijaszek SJ, Johnston FE, Preece MA (eds). The Cambridge Encyclopedia of Human Growth and Development. Cambridge: Cambridge University Press, 1998:133-136.

8. Penrose L. Some recent trends in human genetics. Caryologia. 1954;6(Suppl):521.

9. Wilson R. Concordance in physical growth for monozygotic and dizygotic twins. Ann Hum Biol. 1976;3:1-10.

10. Pearson K, Lee A. On the laws of inheritance in man. I. Inheritance of physical characteristics. Biometr. 1903;2:356-462.

11. Mueller WH. Parent-child correlations for stature and weight among school-aged children: Areview of 24 studies. Hum Biol. 1976;48:379-397.

12. Garn SM, Rohmann CG. Interaction of nutrition and genetics in the timing of growth and development. Ped Clin N Amer. 1966;13:353-379.

13. Welon Z, Bielicki T. Further investigations of parent-child similarity in stature as assessed from longitudinal data. Hum Biol. 1971;43:517-525.

14. Tanner J, Goldstein H, Whitehouse R. Standards for children's height at ages 2-9 years allowing for height of the parents. Arch Dis Child. 1970;45:755-762.

15. Furusho T. On the manifestations of the genotypes responsible for stature. Hum Biol. 1968;40:437-455.

16. Beunen G, Maes H, Vlietinck R, et al. Univariate and multivariate genetic analysis of subcutaneous fatness and fat distribution in early adolescence. Behav Gen. 1998;28:279-288.

17. Garn S, Lewis A, Polacheck D. Sibling similarities in dental development. J Dent Res. 1960;39:170-175.

18. Hewitt D. Some familial correlations in height, weight and skeletal maturity. Ann Hum Genet. 1957;22:26-35.

19. Tanner JM. Growth at Adolescence, 2nd ed. Oxford: Blackwell Scientific Publications, 1962.

20. Boas F. Studies in growth. Hum Biol. 1932;4:307-350.

21. Damon A, Damon S, Pred R, Valadian I. Age at menarche of mothers and daughters with a note on accuracy of recall. Hum Biol. 1969;41:160-175.

22. Zacharias L, Rand W, Wurtman R. A prospective study of sexual development and growth in American girls: The statistics of menarche. Obstet Gynec Surv. 1976;31:325-337.

23. Meyer J, Eaves L, Heath A, Martin N. Estimating genetic influences on the age-at-menarche: A survival analysis approach. Amer J Med Genet. 1991;39:148-154.

24. Malina R, Ryan R, Bonci C. Age at menarche in athletes and their mothers and sisters. Ann Hum Biol. 1994;21:417-422.

25. Brooks-Gunn J, Warren MP. Mother-daughter differences in menarcheal age in adolescent girls attending national dance company schools and non-dancers. Ann Hum Biol. 1988;15:35-44.

26. Loesch D, Huggins R, Rogucka E, Hoang N, Hopper J. Genetic correlates of menarcheal age: A multivariate twin study. Ann Hum Biol. 1995;22:479-490.

27. Sontag L. The Fels Research Institute for the Study of Human Development. Yellow Springs, OH: Antioch College Press, 1946.

28. Sontag L, Nelson V. A study of identical triplets. Part I. Comparison of the physical and mental traits of a set of monozygotic dichorionic triplets. J Hered. 1933;24:473-480.

29. Sontag L, Comstock G. Striae in bones of a set of monozygotic triplets. Amer J Dis Child. 1938;56:301-308.

30. Sontag L, Reynolds E. Ossification sequences in identical triplets. A longitudinal study of resemblances and differences in the ossification patterns of a set of monozygotic triplets. J Hered. 1944;35:57-64.

31. Reynolds E, Schoen G. Growth patterns of identical triplets from 8 through 18 years. Child Devel. 1947;18:130-151.

32. Reynolds E. Degree of kinship and pattern of ossification. Amer J Phys Anthropol. 1943;1:405-416.

33. Garn S, Lewis A, Kerewsky R. Third molar agenesis and size reduction of the remaining teeth. Nature. 1963;200:488-489.

34. Garn S, Lewis A, Walenga A. The genetic basis of the crown-size profile pattern. J Dent Res. 1968;47:1190.

35. Garn S, Lewis A, Kerewsky R, Dahlberg A. Genetic independence of Carabelli's trait from tooth size or crown morphology. Arch Oral Biol. 1966;11:745-747.

36. Towne B, Guo S, Roche A, Siervogel R. Genetic analysis of patterns of growth in infant recumbent length. Hum Biol. 1993;65:977-989.

37. Towne B, Parks JS, Guo S, Siervogel RM. Quantitative genetic analysis of associations between pubertal growth spurt parameters. Amer J Hum Genet. 1995;57(Suppl):A173.

38. AUXAL. Auxological Analysis of Longitudinal Measurements of Human Stature program. Version: DOS Auxal 2. Chicago: Scientific Software International, 1994.

39. Towne B, Parks JS, Brown MR, Siervogel RM, Blangero J. Effect of a luteinizing hormone P-subunit polymorphism on growth in stature. Acta Medica Auxol (abstract). 2000;32(1):43.

40. Towne B, Siervogel RM, Parks JS, Brown MR, Roche AF, Blangero J. Genetic regulation of skeletal maturation from 3 to 15 years. Amer J Hum Genet. 1999;S65:A401.

41. Williams-Blangero S, Subedi J, Upadhayay R, et al. Genetic analysis of susceptibility to infection with Ascaris lumbricoides. Amer J Trop Med Hyg. 1999;60:921-926.

42. Towne B, Blangero J, Williams-Blangero S. Genetic and environmental influences on the growth status of Nepali children. Amer J Hum Biol. 2000;12(2):278.

43. Demerath E, Towne B, Czerwinski S, Siervogel R. Covariate effect of birth weight in a genetic analysis of fasting insulin and glucose concentrations in adulthood. Diabetes. 2000;49(Suppl 1):A183.

44. Czerwinski SA, Towne B, Guo S, Chumlea WC, Roche AF, Siervogel RM. Genetic and environmental influences on lipid and lipoprotein levels in pre- and post-pubertal children. Amer J Hum Biol. 2000;12:289.

45. Clausson B, Lichtenstein P, Cnattinigius S. Genetic influence on birthweight and gestational length determined by studies in offspring of twins. Brit J Genet. 2000;107:375-381.

46. Nance W, Kramer A, Corey L, Winter P, Eaves L. A causal analysis of birth weight in the offspring of monozygotic twins. Amer J Hum Genet. 1983;35:1211-1223.

47. Morton N. The inheritance of human birth weight. Ann Hum Genet. 1955;20:125.

48. Susanne C. Heritability of anthropological characters. Hum Biol. 1977;49:573-580.

49. Solomon P, Thompson E, Rissanen A. The inheritance of height in a Finnish population. Ann Hum Biol. 1983;10:247-256.

50. Malina R, Mueller W, Holman J. Parent-child correlations and heritability of stature in Philadelphia black and white children 6 to 12 years of age. Hum Biol. 1976;48:475-486.

51. Garn S, Bailey S, Cole P. Similarities between parents and their adopted children. Amer J Phys Anthropol. 1976;45:539-543.

52. Fischbein S. Intra-pair similarity in physical growth of monozygotic and of dizygotic twins during puberty. Ann Hum Biol. 1977;4:417-430.

53. Fischbein S, Nordqvist T. Profile comparisons of physical growth for monozygotic and dizygotic twin pairs. Ann Hum Biol. 1978;5:321-328.

54. Byard P, Guo S, Roche A. Family resemblance for Preece-Baines growth curve parameters in the Fels Longitudinal Study. Amer J Hum Biol. 1993;5:151-157.

55. Vandenberg S, Falkner F. Hereditary factors in human growth. Hum Biol. 1965;37:357-365.

56. Hauspie RC, Bergman P, Bielicki T, Susanne C. Genetic variance in the pattern of the growth curve for height: A longitudinal analysis of male twins. Ann Hum Biol. 1994;21:347-362.

57. Roberts D, Billewicz W, McGregor I. Heritability of stature in a West African population. Ann Hum Genet. 1978;42:15-24.

58. Mueller W, Titcomb M. Genetic and environmental determinants of growth of school-aged children in a rural Colombian population. Ann Hum Biol. 1977;4:1-15.

59. Devi M, Reddi G. Heritability of body measurements among the Jalari population of Cisakhapatnam. Ann Hum Biol. 1983;10:483-485.

60. Kaur D, Singh R. Parent-adult offspring correlations and heritability of body measurements in a rural Indian population. Ann Hum Biol. 1981;8:333-339.

61. Sharma K, Byard P, Russell J, Rao D. A family study of anthropometric traits in a Punjabi community: I. Introduction and familial correlations. Amer J Phys Anthropol. 1984;63:389-395.

62. Ikoma E, Kanda S, Nakata S, Wada Y, Yamazaki K. Quantitative genetic analysis of bi-iliac breadth. Amer J Phys Anthropol. 1988;77:295-301.

63. Orley J. Analysis of menarche and gynecological welfare of Budapest school girls. In: Eiben O (ed). Growth and Development: Physique. Budapest: Adademiai Kiado, 1977:191-194.

Supplements For Diabetics

Supplements For Diabetics

All you need is a proper diet of fresh fruits and vegetables and get plenty of exercise and you'll be fine. Ever heard those words from your doctor? If that's all heshe recommends then you're missing out an important ingredient for health that he's not telling you. Fact is that you can adhere to the strictest diet, watch everything you eat and get the exercise of amarathon runner and still come down with diabetic complications. Diet, exercise and standard drug treatments simply aren't enough to help keep your diabetes under control.

Get My Free Ebook

Post a comment