Genetic analysis of systolic blood pressure in Melbourne families

Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks

BACKGROUND

The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered.

DEVELOPMENT

We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors.

APPLICATION

For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors.

CONCLUSION

VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.

Antihypertensive treatments obscure familial contributions to blood pressure variation

The linkage and association between inherent blood pressure and underlying genotype is potentially confounded by antihypertensive treatment. We estimated blood pressure variance components (genetic, shared environmental, individual-specific) in 767 adult volunteer families by using a variety of approaches to adjusting blood pressure of the 244 subjects (8.2%) receiving antihypertensive medications. The additive genetic component of variance for systolic pressure was 73.9 mm Hg(2) (SE, 8.8) when measured pressures (adjusted for age by gender within each generation) were used but fell to 61.4 mm Hg(2) (SE, 8.0) when treated subjects were excluded. When the relevant 95th percentile values were substituted for treated systolic pressures, the additive genetic component was 81.9 mm Hg(2) (SE, 9.5), but individual adjustments in systolic pressure ranged from -53.5 mm Hg to +64.5 mm Hg (mean, +17.2 mm Hg). Instead, when 10 mm Hg was added to treated systolic pressure, the additive genetic component rose to 86.6 mm Hg(2) (SE, 10.1). Similar changes were seen in the shared environment component of variance for systolic pressure and for the combined genetic and shared environmental (ie, familial) components of diastolic pressure. There was little change in the individual-specific variance component across any of the methods. Therefore, treated subjects contribute important information to the familial components of blood pressure variance. This information is lost if treated subjects are excluded and obscured by treatment effects if unadjusted measured pressures are used. Adding back an appropriate increment of pressure restores familial components, more closely reflects the pretreatment values, and should increase the power of genomic linkage and linkage disequilibrium analyses.

Emerging patterns of hookworm infection: influence of aging on the intensity of Necator infection in Hainan Province, People's Republic of China

We examined risk factors associated with Necator americanus infection among persons aged > or =50 years in Hainan Province, People's Republic of China. Age and sex made the most important contributions to the variation in infection intensity (28%-30%), with age alone responsible for 27% of this variation. When stratified by 20-year age intervals, the influence of shared residence was 23% for persons aged > or =50 years and 27% for those aged <20 years, who had the highest and lowest levels of infection intensity, respectively. This points to shared residence as a means of capturing the complex relationship between aging and shared socioeconomic, environmental, and behavioral factors that influence transmission of Necator infection. None of the other 26 personal or 32 household risk factors were found to be significant. The importance of aging in Necator infection reveals an emerging public health problem among the elderly population of developing countries.

Exposure to Schistosoma mansoni infection in a rural area in Brazil. II: household risk factors

A number of studies have pointed out the potential importance of the household in the transmission of schistosomiasis. The clustering of domestic activities associated with water collection, storage, and usage can result in the sharing of transmission sites and infective water contact behaviours. In this study, we employed a variance component method to estimate effects due to individual risk factors and shared residence on the variance in faecal egg counts during Schistosoma mansoni infection. A suite of covariates, which included demographic, socioeconomic, water supply, and water contact behaviour terms, contributed 15% to the variance in faecal egg counts. Shared residence alone accounted for 28% of the variance in faecal egg excretion. When both the suite of covariates and shared residence were considered in the same model, shared residence still contributed 22% to the variance in infection intensity. These results point to the importance of shared residence as a means of capturing the complex interrelationship between shared demographic, socioeconomic, physical environmental, and behavioural factors that influence transmission of schistosomiasis at the household level.

Variance components for statistical genetics: applications in medical research to characteristics related to human diseases and health

RA Fisher introduced variance components in 1918. He synthesized Mendelian inheritance with Darwin's theory of evolution by showing that the genetic variance of a continuous trait could be decomposed into additive and non-additive components. The model can be extended to include environmental factors, interactions, covariation, and non-random mating. Identifiability depends critically on design. Methods of analysis include modelling the mean squares from a fixed effects analysis of variance, and covariance structure modelling, which can be extended to multivariate traits and has been used to study ordinal traits by reference to postulated, unmeasured, latent 'liabilities'. These methods operate on dependent observations within independent groups of the same size and structure, and therefore require balanced designs ('regular' pedigrees). A multivariate normal model handles data in its generic form, utilizes data efficiently from all members of pedigrees of unequal size or varying structure, accommodates individuals missing at random, and allows flexible modelling with tests of distributional assumptions and fit. Most analytical methods use least squares or maximum likelihood under normal theory. Robust methods, scale transformation, ascertainment, path diagrams and correlational path models (popular in behavioural genetics through addressing nonrandom mating and social interactions), 'heritability', and the contribution and limitations of statistical modelling to the 'nature-nurture' debate, are discussed.

Pedigree analysis of blood pressure in subjects from rural Greece and relatives who migrated to Melbourne, Australia

Diastolic blood pressure readings taken in 1983-1984 on 1,474 Greek individuals (628 living on the island of Levkada, 846 relatives having migrated to Melbourne, Australia) from 204 two generational pedigrees were analysed. Blood pressure was regressed as a quadratic in age by sex and migrant status, and on temperature. Variance increased with age and was greater in migrant males. The covariance between relatives in different countries was significant. Variation was modeled by a multivariate normal model for pedigree analysis in terms of genetic effects, a common environment effect, and effects particular to an individual. The genetic component was 25.9 mm Hg2, independent of sex and migrant status. Importantly, the common environment component was not significant. The third component was greatest in migrant males. Spouse correlation was -0.09 (SE = 0.03). Exclusion of 86 individuals who reported currently receiving medication for elevated blood pressure stabilised the variance and decreased the genetic component. The data suggest that familial aggregation of diastolic blood pressure is due to genetic factors which produce the same variation in males and females, living on Levkada or in Melbourne. Nongenetic factors explain the greater variation in blood pressure of migrant males living in Melbourne.

Innovations in the statistical analysis of twin studies

Advances in computer technology have made possible a greater sophistication in the statistical analysis of pedigree data, however this is not necessarily manifest by fitting more comprehensive causative models. Planned twin and family studies measure numerous explanatory variables, including perhaps genetic and DNA marker information status on all pedigree members, and the cohabitation of all pairs of individuals. A statistical analysis should examine the contribution of these measured factors on individual means, and in explaining the variation and covariation between individuals, concurrently with the postulated effect of unmeasured factors such as polygenes. We present two models that meet this requirement: the Multivariate Normal Model for Pedigree Analysis for quantitative traits, and a Log-Linear Model for Binary Pedigree Data. For both models, important issues are examination of fit, detection of outlier pedigrees and outlier individuals, and critical examination of the model assumptions. Procedures for fulfilling these needs and examples of modelling are discussed.

The utility of a multivariate normal model for studying familial patterns in medical and psychiatric data

Quantitative traits (such as blood pressure, height or anxiety level) are usually more similar in related than in unrelated individuals. A positive correlation between two family members indicates that there are common factors (genetic and/or environmental) which predispose to the trait values in this pair of individuals. A negative correlation can indicate that there is (environmental) competition or some other cause for negative interaction between the pair of relatives. Using a flexible method for the analysis of quantitative data measured over pedigrees, it is possible to estimate the magnitude of the correlations between family members as a function of both their genetic relationship and their cohabitation history. This computer-based method can distinguish the effects of shared genes from some of the effects of shared family environment, and can identify negative interactions between family members which are likely to be of particular interest in studies of behavioural and psychiatric traits. The utility of this method is illustrated with pedigree data on blood lead levels, blood pressure levels and psychological traits.

Extensions to multivariate normal models for pedigree analysis

Lange, Westlake & Spence (1976) used the assumption of multivariate normality to apply a likelihood method to the analysis of quantitative traits measured over pedigrees. We now introduce a test of the assumption of multivariate normality and methods for the detection of outlying families and outlying individuals. We also introduce a method for the estimation of effects of measured genetic markers as variance components, a flexible parameterization to estimate effects of shared family environment, and a method to allow for the ascertainment of pedigrees through probands. These innovations have been applied using numerical methods for maximization of the likelihood. Simulation studies and available theory suggest that the likelihood ratio criterion used in significance testing follows the expected asymptotic distribution with sample sizes encountered in typical applications.