Biomedical subjects

Katrina J Scurrah

Publications and source records attributed to Katrina J Scurrah.

9 recordsLinked to original sources

PubMed2006

Comprehensive multi-stage linkage analyses identify a locus for adult height on chromosome 3p in a healthy Caucasian population.

There have been a number of genome-wide linkage studies for adult height in recent years. These studies have yielded few well-replicated loci, and none have been further confirmed by the identification of associated gene variants. The inconsistent results may be attributable to the fact that few studies have combined accurate phenotype measures with informative statistical modelling in healthy populations. We have performed a multi-stage genome-wide linkage analysis for height in 275 adult sibling pairs drawn randomly from the Victorian Family Heart Study (VFHS), a healthy population-based Caucasian cohort. Height was carefully measured in a standardised fashion on regularly calibrated equipment. Following genome-wide identification of a peak Z-score of 3.14 on chromosome 3 at 69 cM, we performed a fine-mapping analysis of this region in an extended sample of 392 two-generation families. We used a number of variance components models that incorporated assortative mating and shared environment effects, and we observed a peak LOD score of approximately 3.5 at 78 cM in four of the five models tested. We also demonstrated that the most prevalent model in the literature gave the worst fit, and the lowest LOD score (2.9) demonstrating the importance of appropriate modelling. The region identified in this study replicates the results of other genome-wide scans of height and bone-related phenotypes, strongly suggesting the presence of a gene important in bone growth on chromosome 3p. Association analyses of relevant candidate genes should identify the genetic variants responsible for the chromosome 3p linkage signal in our population.

Adolescent↗

PubMed2006

Genome-wide linkage analysis of population variation in high-density lipoprotein cholesterol.

Lower plasma levels of high-density lipoprotein cholesterol (HDL-C) are associated with the metabolic syndrome (insulin resistance, obesity, hypertension) and higher cardiovascular risk. Recent association studies have suggested rare alleles responsible for very low HDL-C levels. However, for individual cardiovascular risk factors, the majority of population-attributable deaths are associated with average rather than extreme levels. Therefore, genetic factors that determine the population variation of HDL-C are particularly relevant. We undertook genome-wide and fine mapping to identify linkage to HDL-C in healthy adult nuclear families from the Victorian Family Heart Study. In 274 adult sibling pairs (average age 24 years, average plasma HDL-C 1.4 mmol/l), genome-wide mapping revealed suggestive evidence for linkage on chromosome 4 (Z score = 3.5, 170 cM) and nominal evidence for linkage on chromosomes 1 (Z = 2.1, 176 cM) and 6 (Z = 2.6, 29 cM). Using genotypes and phenotypes from 932 subjects (233 of the sibling pairs and their parents), finer mapping of the locus on chromosome 4 strengthened our findings with a peak probability (Z score = 3.9) at 169 cM. Our linkage data suggest that chromosome 4q32.3 is linked with normal population variation in HDL-C. This region coincides with previous reports of linkage to apolipoprotein AII (a major component of HDL) and encompasses the gene encoding the carboxypeptidase E, relevant to the metabolic syndrome and HDL-C. These findings are relevant for further understanding of the genetic determinants of cardiovascular risk at a population level.

Adult↗

PubMed2006

Sex differences in genetic and environmental determinants of pulse pressure.

Pulse pressure (PP) is an independent risk factor for cardiovascular disease. PP rises with age, more so in women. We examined sex differences in the correlations and variance components of PP in adult subjects from 767 nuclear families, enriched with those containing twins, from the Victorian Family Heart Study. After adjusting for age, we found no significant differences in the means or variances of PP in males and females. Under the assumption of no sex differences, the proportions of variance due to shared genes, shared environment, and individual-specific environment were 20%, 23% and 57%, respectively. However, same-sex relative pairs had significantly higher correlations than opposite-sex pairs (P = 0.005), implying the existence of sex-dependent effects. Extensions to the simple variance components model suggested three possible explanations for these differences: smaller genetic correlation between opposite-sex pairs (rhoG,MF = 0.45, P = 0.007); smaller environmental correlation between opposite-sex pairs (P = 0.0003); or different environmental and genetic correlations obtained by estimating genetic, environmental, and individual variance components separately for males and females (not nested, Akaike's Information Criterion (AIC) smaller by 6.69). Under the last model, the genetic component of PP variance is greater for males (1.62 vs 0.33) while the environmental component is greater for females (1.84 vs 0), which would have implications for the planning of gene discovery studies, since heritability would be higher in males. However, the second (environmental) approach best fits the data according to the AIC. Genetic explanations for sex differences in phenotypic correlations may be misleading unless shared environmental factors are also considered. PP illustrates a phenotype in which sex dependency represents an important component of phenotypic determination that can be revealed by detailed variance components modelling.

Adult↗

PubMed2005

Tutorial in biostatistics: spline smoothing with linear mixed models.

The semi-parametric regression achieved via penalized spline smoothing can be expressed in a linear mixed models framework. This allows such models to be fitted using standard mixed models software routines with which many biostatisticians are familiar. Moreover, the analysis of complex correlated data structures that are a hallmark of biostatistics, and which are typically analysed using mixed models, can now incorporate directly smoothing of the relationship between an outcome and covariates. In this paper we provide an introduction to both linear mixed models and penalized spline smoothing, and describe the connection between the two. This is illustrated with three examples, the first using birth data from the U.K., the second relating mammographic density to age in a study of female twin-pairs and the third modelling the relationship between age and bronchial hyperresponsiveness in families. The models are fitted in R (a clone of S-plus) and using Markov chain Monte Carlo (MCMC) implemented in the package WinBUGS.

Adolescent↗

PubMed2005

Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure.

A population-based study of a quantitative trait may be seriously compromised when the trait is subject to the effects of a treatment. For example, in a typical study of quantitative blood pressure (BP) 15 per cent or more of middle-aged subjects may take antihypertensive treatment. Without appropriate correction, this can lead to substantial shrinkage in the estimated effect of aetiological determinants of scientific interest and a marked reduction in statistical power. Correction relies upon imputation, in treated subjects, of the underlying BP from the observed BP having invoked one or more assumptions about the bioclinical setting. There is a range of different assumptions that may be made, and a number of different analytical models that may be used. In this paper, we motivate an approach based on a censored normal regression model and compare it with a range of other methods that are currently used or advocated. We compare these methods in simulated data sets and assess the estimation bias and the loss of power that ensue when treatment effects are not appropriately addressed. We also apply the same methods to real data and demonstrate a pattern of behaviour that is consistent with that in the simulation studies. Although all approaches to analysis are necessarily approximations, we conclude that two of the adjustment methods appear to perform well across a range of realistic settings. These are: (1) the addition of a sensible constant to the observed BP in treated subjects; and (2) the censored normal regression model. A third, non-parametric, method based on averaging ordered residuals may also be advocated in some settings. On the other hand, three approaches that are used relatively commonly are fundamentally flawed and should not be used at all. These are: (i) ignoring the problem altogether and analysing observed BP in treated subjects as if it was underlying BP; (ii) fitting a conventional regression model with treatment as a binary covariate; and (iii) excluding treated subjects from the analysis. Given that the more effective methods are straightforward to implement, there is no argument for undertaking a flawed analysis that wastes power and results in excessive bias.

Aged↗

PubMed2005

Covariance components models for longitudinal family data.

A longitudinal family study is an epidemiological design that involves repeated measurements over time in a sample that includes families. Such studies, that may also include relative pairs and unrelated individuals, allow closer investigation of not only the factors that cause a disease to arise, but also the genetic and environmental determinants that modulate the subsequent progression of that disease. Knowledge of such determinants may pay high dividends in terms of prognostic assessment and in the development of new treatments that may be tailored to the prognostic profile of individual patients. Unfortunately longitudinal family studies are difficult to analyse. They conflate the complex within-family correlation structure of a cross-sectional family study with the correlation over time that is intrinsic to longitudinal repeated measures. Here we describe an approach to analysis that is relatively straightforward to implement, yet is flexible in its application. It represents a natural extension of a Gibbs-sampling-based approach to the analysis of cross-sectional family studies that we have described previously. The approach can be applied to pedigrees of arbitrary complexity. It is applicable to continuous traits, repeated binary disease states, and repeated counts or rates with a Poisson distribution. It not only supports the analysis of observed determinants, including measured genotypes, but also allows decomposition of the correlation structure, thereby permitting conclusions to be drawn about the effect of unobserved genes and environment on key features of disease progression, and hence to estimate the heritability of these features. We demonstrate the efficacy of our methods using a range of simulated data analyses, and illustrate its practical application to longitudinal blood pressure data measured in families from the Framingham Heart Study.

Blood Pressure↗

PubMed2004

Risk of wrist fracture in women is heritable and is influenced by genes that are largely independent of those influencing BMD.

UNLABELLED: Using a classical twin design study, we estimated the genetic contribution to liability of wrist fracture in women to be statistically and clinically significant. BMD is highly heritable, but statistical models showed very little overlap of shared genes between the two traits. INTRODUCTION: Studies have observed contradictory evidence for genetic effects influencing the outcome of osteoporotic fracture, in part because of the methodological problems involved in analyzing age-related "censored" outcomes. Although a shared genetic etiology is often assumed between fracture and low BMD, this has not been shown to be the case. MATERIALS AND METHODS: In a study of 6570 white healthy female volunteer twins between 18 and 80 years of age, we identified and validated 220 nontraumatic wrist fracture cases. From this we estimated the population prevalence, case-wise twin concordance, heritability in liability to wrist fracture (WF), and the genetic contribution to WFs controlling for age by analyzing the survival outcome using generalized linear mixed models implemented in Winbugs software. We included forearm BMD as a co-variate in some of the models to test whether there is a shared genetic etiology between WFs and BMD. RESULTS: The prevalence of WFs in women was estimated to be 3.3% with a case-wise concordance in monozygotic twins of 0.28 and 0.11 in dizygotic twins. The additive polygenic heritability in liability was approximately 54%, and a significant genetic etiology was confirmed by analyzing WFs as a survival outcome. The magnitude of the genetic influence on risk of WFs reduced very little when BMD was included as a co-variate in the survival analysis model. CONCLUSIONS: There is an important genetic contribution to the risk of WFs, but for the most part, these genes are unlikely to play a direct etiological role in the development of low BMD. If these results are confirmed for other sites, fracture and low BMD will have their own specific genetic risk factors that are unlikely to be shared between the two traits. This has important clinical and research implications.

Adolescent↗

PubMed2003

Genome-wide linkage analysis of longitudinal phenotypes using sigma2A random effects (SSARs) fitted by Gibbs sampling.

The study of change in intermediate phenotypes over time is important in genetics. In this paper we explore a new approach to phenotype definition in the genetic analysis of longitudinal phenotypes. We utilized data from the longitudinal Framingham Heart Study Family Cohort to investigate the familial aggregation and evidence for linkage to change in systolic blood pressure (SBP) over time. We used Gibbs sampling to derive sigma-squared-A-random-effects (SSARs) for the longitudinal phenotype, and then used these as a new phenotype in subsequent genome-wide linkage analyses. Additive genetic effects (sigma2A.time) were estimated to account for approximately 9.2% of the variance in the rate of change of SBP with age, while additive genetic effects (sigma2A) were estimated to account for approximately 43.9% of the variance in SBP at the mean age. The linkage results suggested that one or more major loci regulating change in SBP over time may localize to chromosomes 2, 3, 4, 6, 10, 11, 17, and 19. The results also suggested that one or more major loci regulating level of SBP may localize to chromosomes 3, 8, and 14. Our results support a genetic component to both SBP and change in SBP with age, and are consistent with a complex, multifactorial susceptibility to the development of hypertension. The use of SSARs derived from quantitative traits as input to a conventional linkage analysis appears to be valuable in the linkage analysis of genetically complex traits. We have now demonstrated in this paper the use of SSARs in the context of longitudinal family data.

Adult↗

PubMed2003

Longitudinal variance components models for systolic blood pressure, fitted using Gibbs sampling.

This paper describes an analysis of systolic blood pressure (SBP) in the Genetic Analysis Workshop 13 (GAW13) simulated data. The main aim was to assess evidence for both general and specific genetic effects on the baseline blood pressure and on the rate of change (slope) of blood pressure with time. Generalized linear mixed models were fitted using Gibbs sampling in WinBUGS, and the additive polygenic random effects estimated using these models were then used as continuous phenotypes in a variance components linkage analysis. The first-stage analysis provided evidence for general genetic effects on both the baseline and slope of blood pressure, and the linkage analysis found evidence of several genes, again for both baseline and slope.

Adult Children↗