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Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.
Twin studies of child temperament using objective measures consistently suggest moderate heritability for most dimensions. However, parent rating measures produce unusual patterns of results. Intraclass correlations for identical (MZ) twins are typically high, whereas fraternal (DZ) twin intraclass correlations are much lower than would be predicted from an additive genetic model. The ‘too low’ DZ correlations can be explained by parent-rating biases that either exaggerate the differences between DZ twins (contrast effects) or that inflate the similarity of MZ twins (assimilation effects), or by the presence of non-additive genetic variance. To evaluate the three possible explanations, we used model-fitting procedures applied to parent-rating data averaged across 14, 20, 24, and 36 months of age in a sample of 196 twin pairs participating in the MacArthur Longitudinal Twin Study. The data were best described by a model that included contrast effects. Implications for non-twin research are discussed. Twin Research (2000) 3, 224–233.
Body-mass index (BMI), total cholesterol (TC), lowdensity lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels are known to be highly heritable. We evaluated the genetic and environmental relationships of these measures over time in an analysis of twin pairs. Monozygotic (235 pairs) and dizygotic (260 pairs) male twins were participants in the National Heart Lung and Blood Institute Veteran Twin Study, and were followed with three clinical exams from mean age 48 years to mean age 63 years. Structural equation modeling (SEM) with adjustment for APOE genotype (a significant contributor to TC and LDL-C) was used to assess longitudinal patterns of heritability. Results indicated a contribution of genetic factors to BMI, TC, LDL-C, HLD-C, and TG. Modest increases over time were observed in the heritability of BMI (from 0.48 to 0.61), TC (from 0.46 to 0.57), LDL-C (from 0.49 to 0.64), and HDL-C (from 0.50 to 0.62), but this trend was not present for TG. There was a corresponding decrease in shared environmental influences over time for these traits, although shared environment was a significant contributor only for HDL-C. Moreover, we observed that genetic influences for all measures were significantly correlated over time, and we found no evidence of age-specific genetic effects. In summary, longitudinal analyses of twin data indicate that genetic factors do not account for a significant proportion of the variation in age-related changes of BMI or lipid and lipoprotein levels.
Dense maps of short-tandem-repeat polymorphisms (STRPs) have allowed genome-wide searches
for genes involved in a great variety of diseases with genetic influences, including common complex
diseases. Generally for this purpose, marker sets with a 10 cM spacing are genotyped in hundreds
of individuals. We have performed power simulations to estimate the maximum possible inter-marker distance that still allows for sufficient power. In this paper we further report on
modifications of previously published protocols, resulting in a powerful screening set containing
229 STRPs with an average spacing of 18·3 cM. A complete genome scan using our protocol
requires only 80 multiplex PCR reactions which are all carried out using one set of conditions and
which do not contain overlapping marker allele sizes. The multiplex PCR reactions are grouped by
sets of chromosomes, which enables on-line statistical analysis of a set of chromosomes, as sets of
chromosomes are being genotyped. A genome scan following this modified protocol can be
performed using a maximum amount of 2·5 μg of genomic DNA per individual, isolated from
either blood or from mouth swabs.
General cognitive ability, or general intelligence, may be the area in which we know the most from a behavior-genetic perspective. Bouchard and McGue (1981) noted over 140 studies of this domain, which yielded the largely consistent result that genetic differences account for approximately 50% of the observed variability in general cognitive ability. However, it was not until recently that we began to learn more about cognitive ability than this simple, albeit important, finding.
The conceptualization of general cognitive ability as variance shared by a number of tests of various specific abilities dates back to Spearman (1904). The development of general cognitive ability, conceptualized in this manner, from infancy through middle childhood, is the focus of this chapter. Longitudinal twin and sibling data can be used to address two aspects of the developmental process. The first aspect concerns the sources of observed variation in individual differences in general cognitive ability at each age of assessment. By comparison of correlations from identical (monozygotic) and fraternal (dizygotic) twins, as well as adoptive and biological siblings, behavior-genetic methodology allows us to partition the observed variation into variation due to differences between individuals in genetic makeup and environmental differences between individuals. These environmental differences can be further subdivided into those shared by members of the family and those nonshared environmental influences that are unique to individuals. We can then examine whether heritable and environmental contributions change across the developmental period in question.
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