A note on the statistical power in extended twin designs

this paper. a trait can not be ascribed to genes because the statistical power to detect sources of genetic variation is insufficient (Svikis, Velz & Pickens, 1994; Pickens, Svikis, McGue, Lykken, et al., 1991). This will preclude further searching for effects of QTL's on that particular trait, even though such QTL's may be presen

Introduction to the Special Issue: Human Linkage Studies for Behavioral Traits

In the post Genome era, the aim of behavior genetics has shifted from estimating the relative contributions of genes and environmental factors to (co-)variation in human complex traits, to localization of genes and identification of functional genetic variants. This special issue reflects this transition and presents fifteen papers that report on genome-wide linkage scans for complex traits in humans and on methodological tools and innovations. Six papers focus on cognition and report overlapping linkage peaks on chromosomes 6p and 14p. Papers on addictive behavior, i.e. smoking and alcohol dependence and its endophenotypes, find moderate LOD scores on chromosomes 6p, 5q, 4p and 7q, respectively. Three papers concentrate on emotionality, depression and loneliness and examine chromosomes 2q and 12q. The papers in this issue represent a summary of the first large scale linkage enterprises of human behavioral traits. © 2006 Springer Science+Business Media, Inc.link_to_subscribed_fulltex

Progress in the molecular-genetic study of intelligence

The past decade has seen a major shift in the genetic study of human intelligence; where classic studies aimed to quantify the heritability of intelligence, current studies aim to dissect this heritability into its molecular-genetic components. Five whole-genome linkage scans have been published in the past year, converging on several chromosomal (or genomic) regions important to intelligence. A handful of candidate genes, some of which lie in these genomic regions, have shown significant association to intelligence and the associations have been replicated in independent samples. Finding genes brings us closer to an understanding of the neurophysiological basis of human cognition. Furthermore, when genes are no longer latent factors in our models but can actually be measured, it becomes feasible to identify those environmental factors that interact and correlate with genetic makeup. This will supplant the long nature-nurture debate with actual understanding. Copyright © 2006 Association for Psychological Science

Environmental Factors Determine Where the Dutch Live: Results From the Netherlands Twin Register

The heritability of the degree of residential area urbanization in twins and their siblings in the Dutch population was examined. The postal code was known for 6879 twins and 2724 siblings registered with the Netherlands Twin Register and born between 1940 and 1983. Using data from Statistics Netherlands (Centraal Bureau voor de Statistiek, 2001), these postal codes could be related to residential area characteristics, including urbanization level. The degree of urbanization was assessed on a 5-point scale: very heavy, heavy, moderate, low and not urbanized. Genetic model-fitting was carried out in three age cohorts: young adults (born 1975 to 1983), adults (born 1965 to 1974) and older adults (born 1940 to 1964). Twin and sibling resemblance in urbanization level was expressed in polychoric correlations. These correlations decreased from the youngest cohort (.66 to .86) to the oldest cohort (.20 to .58). In all 3 age cohorts, genetic factors did not contribute to familial resemblance. The influence of common environment decreased in importance from the young cohort (70% to 83%) to the old cohort (46% to 47%) and was lower in women than in men in all but the oldest age cohort. This study did not replicate Australian findings of a genetic contribution in the older cohorts; common environmental factors and, increasingly with age, unique environmental factors determine where the Dutch live. Future studies in European and other populations will reveal whether these results are specific to the Dutch population

Perceptual speed and IQ are associated through common genetic factors

Individual differences in inspection time explain about 20% of IQ test variance. To determine whether the association between inspection time and IQ is mediated by common genes or by a common environmental factor, inspection time and IQ were assessed in an extended twin design. Data from 688 participants from 271 families were collected as part of a large ongoing project on the genetics of adult brain function and cognition. The sample consisted of a young adult cohort (mean age 26.2 years) and an older adult cohort (mean age 50.4 years). IQ was assessed with the Dutch version of the WAIS-3R. Inspection time was measured in the so-called II-paradigm, in which a subject is asked to decide which leg of the II-figure is longest at varying display times of the II-figure. The number of correct inspections per second (i.e., the reciprocal of inspection time) was used to index perceptual speed. For Verbal IQ and Performance IQ, heritabilities were 85% and 69%, respectively. For perceptual speed, 46% of the total variance was explained by genetic variance. No differences in heritability estimates across age cohorts or sexes were found. Across the whole sample, a significant phenotypic correlation was found between perceptual speed and Verbal IQ (0.19) and between perceptual speed and Performance IQ (0.27). These correlations were entirely due to a common genetic factor that accounted for 10% of the genetic variance in verbal IQ and for 22% of the genetic variance in performance IQ. This factor is hypothesized to reflect the influence of genetic factors that determine axonal myelination in the central nervous system

A longitudinal genetic study of vocabulary knowledge in adults.

Vocabulary test scores were obtained from a total of 997 adults, all twins or a sibling of twins in this study. Some (N = 217) individuals were tested twice, around 6 years apart. Heritability varied from 50% at the first test occasion to 63% at the second test occasion. The correlation of scores across time was .74. Structural equation modelling showed that stability in vocabulary knowledge over time can largely (around 76%) be explained by genetic factors. Part of the non-shared environmental variance was stable over time also. Any influence from shared environmental factors could not be detected. Results were similar for the two sexes, except that males generally outperformed females. Results were also similar for two age cohorts, except that the older cohort generally outperformed the younger cohort

ADHD: Sibling interaction or dominance: An evaluation of statistical power

Sibling interaction effects are suggested by a difference in phenotypic variance between monozygotic (MZ) twins and dizygotic (DZ) twins, and a pattern of twin correlations that is inconsistent with additive genetic influences. Notably, negative sibling interaction will result in MZ correlations which are more than twice as high as DZ correlations, a pattern also seen in the presence of genetic dominance. Negative sibling interaction effects have been reported in most genetic studies on Attention Deficit Hyperactivity Disorder (ADHD) and related phenotypes, while the presence of genetic dominance is not always considered in these studies. In the present paper the statistical power to detect both negative sibling interaction effects and genetic dominance is explored. Power calculations are presented for univariate models including sources of variation due to additive genetic influences, unique environmental influences, dominant genetic influences and a negative sibling interaction (i.e., contrast effect) between phenotypes of twins. Parameter values for heritability and contrast effects are chosen in accordance with published behavior genetic studies on ADHD and associated phenotypes. Results show that when both genetic dominance and contrast effects are truly present and using a classical twin design, genetic dominance is more likely to go undetected than the contrast effect. Failure to detect the presence of genetic dominance consequently gives rise to slightly biased estimates of additive genetic effects, unique environmental effects, and the contrast effect. Contrast effects are more easily detected in the absence of genetic dominance. If the significance of the contrast effect is evaluated while also including genetic dominance, small contrast effects are likely to go undetected, resulting in a relatively large bias in estimates of the other parameters. Alternative genetic designs, such as adding pairs of unrelated siblings reared together to a classical twin design, or adding non-twin siblings to twin pairs, greatly enhances the statistical power to detect contrast effects as well as the power to distinguish between genetic dominance and contrast effects

Are smarter brains running faster? Heritability of alpha peak frequency, IQ and their interrelation

The idea that faster central nervous system (CNS) processing may amount to a smarter brain has been proposed in earlier studies (e.g., Vernon, 1987) and has recently been supported by studies reporting positiv

Heritability of background EEG across the power spectrum

We estimated the genetic and nongenetic (environmental) contributions to individual differences in the background EEG power spectrum in two age cohorts with mean ages of 26.2 and 49.4 years. Nineteen-lead EEG was recorded with eyes closed from 142 monozygotic and 167 dizygotic twin pairs and their siblings, totaling 760 subjects. We obtained power spectra in 24 bins of 1 Hz ranging from 1.0 to 25.0 Hz. Generally, heritability was highest around the alpha peak frequency and lower in the theta and delta bands. In the beta band heritability gradually decreased with increasing frequency, especially in the temporal regions. Genetic correlations between power in the classical broad bands indicated that half to three-quarters of the genetic variance can be attributed to a common source. We conclude that across the scalp and most of the frequency spectrum, individual differences in adult EEG are largely determined by genetic factors. Copyright © 2005 Society for Psychophysiological Research