Multivariate analysis of craniofacial measurements in twin and family data

Thirty-three cephalometric variables and height have been measured on each of 630 individuals (316 male and 314 female) from 157 families. After age and sex differences were adjusted for each measurement, a rotated factor analysis was undertaken to account for the variation by a limited number of linear combinations of the adjusted measurements. It was found that most of the variation could be explained by nine independent factors. Finally, correlation coefficients were computed on twins, siblings and parent-offspring data for factor scores. The results suggest that each factor which is measured by a linear combination of a set of variables could result from the interaction of independent sets of genes with the environment.     

Statistical inference in mixed models and analysis of twin and family data

Analysis of data from twin and family studies provides the foundation for studies of disease inheritance. The development of advanced theory and computational software for general linear models has generated considerable interest for using mixed-effect models to analyze twin and family data, as a computationally more convenient and theoretically more sound alternative to the classical structure equation modeling. Despite the long history of twin and family data analysis, some fundamental questions remain unanswered. We addressed two important issues. One is to determine the necessary and sufficient conditions for the identifiability in the mixed-effects models for twin and family data. The other is to derive the asymptotic distribution of the likelihood ratio test, which is novel due to the fact that the standard regularity conditions are not satisfied. We considered a series of specific yet important examples in which we demonstrated how to formulate mixed-effect models to appropriately reflect the data, and our key idea is the use of the Cholesky decomposition. Finally, we applied our method and theory to provide a more precise estimate of the heritability of two data sets than the previously reported estimate.     

Resolution of genetic and cultural inheritance in twin families by path analysis: application to HDL-cholesterol.

A path model and associated statistical method for the analysis of data on twin families are introduced and applied to high density lipoprotein cholesterol (HDL-c) observations in the Swedish Twin Family Study. The proposed path model incorporates both genetic and environmental sources of familial resemblance, maternal environmental effects, intergenerational differences in heritabilities, marital resemblance due to either primary or secondary phenotypic homogamy, and twin residual environmental correlations. Application of the model to HDL-c levels resulted in parameter estimates consistent with those reported in earlier reviews and in the analysis of nuclear family and twin data. Genetic heritability was estimated as h2 = .363 +/- .243, cultural heritability as c2 = .187 +/- .082, and the proportion of phenotypic variance due to residual environmental effects as r2 = .450 +/- .207. Although the parameter estimates were comparable, the statistical tests of hypotheses were, relative to other designs, of low statistical power. It appears that environmental indices are necessary for powerful tests of hypotheses.     

Biometrical modeling of twin and family data using standard mixed model software

Summary .   Biometrical genetic modeling of twin or other family data can be used to decompose the variance of an observed response or 'phenotype' into genetic and environmental components. Convenient parameterizations requiring few random effects are proposed, which allow such models to be estimated using widely available software for linear mixed models (continuous phenotypes) or generalized linear mixed models (categorical phenotypes). We illustrate the proposed approach by modeling family data on the continuous phenotype birth weight and twin data on the dichotomous phenotype depression. The example data sets and commands for Stata and R/S-PLUS are available at the Biometrics website.     

Genetic analysis of febrile convulsions: twin and family studies

Summary Thirty-two twin pairs and 673 sibship-cases with febrile convulsions (FC) were studied. Twin study: (1) The pairwise concordance rate for FC was 56% (10/18 pairs) in monozygotic and 14% (2/14 pairs) in dizygotic twins (P<0.05). (2) Intra-pair similarity of clinical symptoms in concordant twin pairs was greater than that in sibship-cases. Sibship-pair study (population): (3) In sibship-pair study a large positive correlation of some clinical symptoms — in particular, age at onset of FC, exogenous factors, and degree of fever (P<0.001 for each) — was indicated. (4) Compared with FC children with no family history, those with such family history had a higher frequency of age at onset between 8 and 19 months, exogenous factors, low degree of fever before onset of convulsions, many recurrences, and recurrence after age 3 (P<0.01–0.001 for each). (5) Morbidity risk among near relatives was highest in first-degree relatives (16%) than in second (4.0%) or third-degree relatives (4.1%). The following differences were found: siblings (24%)>parents (12%), uncles (4.5%)>aunts (3.5%), male cousins (4.4%)>female cousins (3.8%). Segregation ratio, influence by affection of father or mother, and maternal preponderance were analysed. (6) Similar findings were also observed in the clinic study. (7) A multifactorial mode of inheritance for FC receives some support from this study, and the heritability was estimated to be 75% in the population study. The results may be useful for genetic counselling for FC.     

Coevolutionary genetics of hosts and parasites with quantitative inheritance

Summary A model of host—parasite coevolution is analysed. A host resistance trait and a parasite virulence trait interact to determine the outcome of a parasitic attack, where each trait is determined by quantitative genetic variation. The resistance and virulence traits are assumed to have a fitness cost. Each host and parasite genotype is treated as a separate species in a multidimensional Lotka—Volterra system in which the numerical abundance of each genotype is free to change. Thus, the epidemiological effects of fluctuating population sizes are analysed jointly with changes in genotype frequencies. Population sizes fluctuate increasingly as the parasites' reproductive capacity increases and as resistance and virulence benefits per unit cost decline. The patterns of genetic variability depend mainly on the stability of population sizes and on the shape of the relationship between the costs and benefits of a trait.     

On the application of queuing theory for analysis of twin data.

A mathematical model based on queuing theory is used to study the dynamics of environmental influence on twin pairs. The model takes into consideration genetic factors and effects of nonshared environment. Histograms are exploited as base analysed characteristics, with the method of minimum chi-square yielding estimated characteristics. The proposed technique was applied to analysis of longitudinal data for MZ and DZ twins. It was shown that the same environment impact may yield different contributions to final variances of the IQ parameters under investigation. Magnitudes of these contributions depend on the genetic factor represented by distributions of an analysed parameter at the point of birth.     

Capture and analysis of quantitative proteomic data

Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding MS scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.     

The Fanconi family adds a fraternal twin

The Fanconi anemia (FA) pathway plays an important role in DNA repair. In a recent issue of Cell, Smogorzewska et al. (2007) now demonstrate that FANCD2 has a paralog, FANCI. FANCI and FANCD2 form the "ID" complex that loads onto chromatin after DNA damage. Like FANCD2, monoubiquitination of FANCI requires the FA core complex. Importantly, FANCI and FANCD2 monoubiquitination is co-dependent, suggesting a novel mechanism in ubiquitin conjugation.     

Estimation of penetrance from twin data.

A simple method for estimating the gene frequency p and the penetrance value K from data on polymorphic monogenic characteristics on monozygotic twin pairs is presented. In spite of the method here presented having limited value because the results it yields cannot be evaluated on their own, the estimates of p and K it provides can be indirectly tested by comparing them to the ones obtained in familial aggregates through classical segregation analysis or by using the latter to calculate the expected proportions of dominant-dominant, dominant-recessive and recessive-recessive monozygotic twin pairs. When the method is applied to data on tongue-rolling ability published in the literature, a good agreement is observed between twin and familial estimates, thus indicating that the method is reliable and that it can be used as an ancillary way of corroborating or otherwise evidence of monogenic autosomal dominant mechanism inferred from the analysis of familial data.     

Parallel evolution and inheritance of quantitative traits

Parallel phenotypic evolution, the independent evolution of the same trait in closely related lineages, is interesting because it tells us about the contribution of natural selection to phenotypic evolution. Haldane and others have proposed that parallel evolution also results from a second process, the similarly biased production of genetic variation in close relatives, an idea that has received few tests. We suggest that influence of shared genetic biases should be detectable by the disproportionate use of the same genes in independent instances of parallel phenotypic evolution. We show how progress in testing this prediction can be made through simple tests of parallel inheritance of genetic differences: similar additive, dominance, and epistasis components in analysis of line means and similar effective numbers of loci. We demonstrate parallel inheritance in two traits, lateral plate number and body shape, in two lineages of threespine stickleback that have adapted independently to freshwater streams on opposite sides of the Pacific Ocean. Notably, reduction of plate number in freshwater involves a substitution at the same major locus in both lineages. Our results represent only a first step in the study of the genetics of parallel phenotypic evolution in sticklebacks. Nevertheless, we have shown how such studies can be employed to test the genetic hypothesis of parallel evolution and how study of parallel evolution might yield insights into the roles of both selection and genetic constraint in phenotypic evolution.     

Structured exploratory data analysis (SEDA) for determining mode of inheritance of quantitative traits. I. Simulation studies on the effect of background distributions.

We examine through simulations the effectiveness of a new methodology to help distinguish among monogenic, multifactorial, and sporadic trait transmission from parents to offspring in nuclear family data sets. The major gene index (MGI), which compares the deviation of the offspring from the midparental value with a function of the individual deviations between parents and offspring, aids in the discrimination of multifactorial from sporadic and monogenic models. In contrast with other methodologies, the ability of the MGI to separate multifactorial, monogenic, and sporadic models improves with increased skewness in the trait distribution. The midparental correlation coefficient serves as a further guide for indicating mode of inheritance. A new class of techniques, the offspring between parents function (OBP), is introduced that provides a more sensitive tool to help in assessing mode of transmission through the analysis of the level, shape, and undulation characteristics of the curves. Four data examples are used to illustrate the methodology: erythrocyte catechol-O-methyltransferase (COMT) activity, height, weight, and triglyceride measurements. Height appears largely multifactorial, and weight appears to be mostly sporadic, while COMT and triglyceride measurements suggest the presence of some major gene influences.