Biomedical subjects

John C Whittaker

Publications and source records attributed to John C Whittaker.

14 recordsLinked to original sources

PubMed2007

Family-based association analysis with ordered categorical phenotypes, covariates and interactions.

Genetic association analyses of family-based studies with ordered categorical phenotypes are often conducted using methods either for quantitative or for binary traits, which can lead to suboptimal analyses. Here we present an alternative likelihood-based method of analysis for single nucleotide polymorphism (SNP) genotypes and ordered categorical phenotypes in nuclear families of any size. Our approach, which extends our previous work for binary phenotypes, permits straightforward inclusion of covariate, gene-gene and gene-covariate interaction terms in the likelihood, incorporates a simple model for ascertainment and allows for family-specific effects in the hypothesis test. Additionally, our method produces interpretable parameter estimates and valid confidence intervals. We assess the proposed method using simulated data, and apply it to a polymorphism in the c-reactive protein (CRP) gene typed in families collected to investigate human systemic lupus erythematosus. By including sex interactions in the analysis, we show that the polymorphism is associated with anti-nuclear autoantibody (ANA) production in females, while there appears to be no effect in males.

Autoantibodies↗

PubMed2006

Bayesian graphical models for genomewide association studies.

As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being collected in such studies. We use discrete graphical models as a data-mining tool, searching for single- or multilocus patterns of association around a causative site. The approach is fully Bayesian, allowing us to incorporate prior knowledge on the spatial dependencies around each marker due to linkage disequilibrium, which reduces considerably the number of possible graphical structures. A Markov chain-Monte Carlo scheme is developed that yields samples from the posterior distribution of graphs conditional on the data from which probabilistic statements about the strength of any genotype-phenotype association can be made. Using data simulated under scenarios that vary in marker density, genotype relative risk of a causative allele, and mode of inheritance, we show that the proposed approach has better localization properties and leads to lower false-positive rates than do single-locus analyses. Finally, we present an application of our method to a quasi-synthetic data set in which data from the CYP2D6 region are embedded within simulated data on 100K single-nucleotide polymorphisms. Analysis is quick (<5 min), and we are able to localize the causative site to a very short interval.

Bayes Theorem↗

PubMed2006

Limits to causal inference based on Mendelian randomization: a comparison with randomized controlled trials.

"Mendelian randomization" refers to the random assortment of genes transferred from parent to offspring at the time of gamete formation. This process has been compared to a randomized controlled trial of genetic variants. This could greatly aid observational epidemiology by potentially allowing an unbiased estimate of the effects of gene products on disease outcomes. However, studies utilizing Mendelian randomization to estimate effects of gene products on outcomes should be interpreted with caution. In this paper, the authors discuss some of the challenges facing epidemiologists in the analysis and interpretation of Mendelian randomization studies, particularly those that become apparent when the analogy with randomized controlled trials is closely examined. The authors conclude that Mendelian randomization is a powerful addition to etiologic research tools. However, care must be taken, because drawing valid causal inferences from its application depends upon more extensive assumptions than are required in randomized controlled trials.

Causality↗

PubMed2006

A Bayesian toolkit for genetic association studies.

We present a range of modelling components designed to facilitate Bayesian analysis of genetic-association-study data. A key feature of our approach is the ability to combine different submodels together, almost arbitrarily, for dealing with the complexities of real data. In particular, we propose various techniques for selecting the "best" subset of genetic predictors for a specific phenotype (or set of phenotypes). At the same time, we may control for complex, non-linear relationships between phenotypes and additional (non-genetic) covariates as well as accounting for any residual correlation that exists among multiple phenotypes. Both of these additional modelling components are shown to potentially aid in detecting the underlying genetic signal. We may also account for uncertainty regarding missing genotype data. Indeed, at the heart of our approach is a novel method for reconstructing unobserved haplotypes and/or inferring the values of missing genotypes. This can be deployed independently or, alternatively, it can be fully integrated into arbitrary genotype- or haplotype-based association models such that the missing data and the association model are "estimated" simultaneously. The impact of such simultaneous analysis on inferences drawn from the association model is shown to be potentially significant. Our modelling components are packaged as an "add-on" interface to the widely used WinBUGS software, which allows Markov chain Monte Carlo analysis of a wide range of statistical models. We illustrate their use with a series of increasingly complex analyses conducted on simulated data based on a real pharmacogenetic example.

Bayes Theorem↗

PubMed2005

Statistical design and analysis of pharmacogenetic trials.

Pharmacogenetic trials investigate the effect of genotype on treatment response. When there are two or more treatment groups and two or more genetic groups, investigation of gene-treatment interactions is of key interest. However, calculation of the power to detect such interactions is complicated because this depends not only on the treatment effect size within each genetic group, but also on the number of genetic groups, the size of each genetic group, and the type of genetic effect that is both present and tested for. The scale chosen to measure the magnitude of an interaction can also be problematic, especially for the binary case. Elston et al. proposed a test for detecting the presence of gene-treatment interactions for binary responses, and gave appropriate power calculations. This paper shows how the same approach can also be used for normally distributed responses. We also propose a method for analysing and performing sample size calculations based on a generalized linear model (GLM) approach. The power of the Elston et al. and GLM approaches are compared for the binary and normal case using several illustrative examples. While more sensitive to errors in model specification than the Elston et al. approach, the GLM approach is much more flexible and in many cases more powerful.

Clinical Trials as Topic↗

PubMed2005

Bayesian modelling of multivariate quantitative traits using seemingly unrelated regressions.

We investigate a Bayesian approach to modelling the statistical association between markers at multiple loci and multivariate quantitative traits. In particular, we describe the use of Bayesian Seemingly Unrelated Regressions (SUR) whereby genotypes at the different loci are allowed to have non-simultaneous effects on the phenotypes considered with residuals from each regression assumed correlated. We present results from simulations showing that, under rather general conditions that are likely to hold in real situations, the Bayesian SUR approach has increased probability of selecting the true model compared to univariate analyses. Finally, we apply our methods to data from subjects genotyped for 12 SNPs in the apolipoprotein E (APOE) gene. Phenotypes relate to response to treatment with atorvastatin and include changes in total cholesterol, low-density lipoprotein cholesterol, and triglycerides. Missing genotype data are naturally accommodated in our Bayesian framework by imputing them using a nested haplotype phasing algorithm.

Algorithms↗

PubMed2005

On the structural differences between markers and genomic AC microsatellites.

AC microsatellites have proved particularly useful as genetic markers. For some purposes, such as in population biology, the inferences drawn depend on the quantitative values of their mutation rates. This, together with intrinsic biological interest, has led to widespread study of microsatellite mutational mechanisms. Now, however, inconsistencies are appearing in the results of marker-based versus non-marker-based studies of mutational mechanisms. The reasons for this have not been investigated, but one possibility, pursued here, is that the differences result from structural differences between markers and genomic microsatellites. Here we report a comparison between the CEPH AC marker microsatellites and the global population of AC microsatellites in the human genome. AC marker microsatellites are longer than the global average. Controlling for length, marker microsatellites contain on average fewer interruptions, and have longer segments, than their genomic counterparts. Related to this, marker microsatellites show a greater tendency to concentrate the majority of their repeats into one segment. These differences plausibly result from scientists selecting markers for their high polymorphism. In addition to the structural differences, there are differences in the base composition of flanking sequences, marker flanking regions being richer in C and G and poorer in A and T. Our results indicate that there are profound differences between marker and genomic microsatellites that almost certainly affect their mutation rates. There is a need for a unified model of mutational mechanisms that accounts for both marker-derived and genomic observations. A suggestion is made as to how this might be done.

Base Composition↗

PubMed2004

Are reported preterm birth rates reliable? An analysis of interhospital differences in the calculation of the weeks of gestation at delivery and preterm birth rate.

We investigated the possibility of preterm birth misclassification as a determinant of variation in its reported rates. Using a database of 497,105 deliveries from 17 hospitals, the best estimate of gestational age made at delivery and entered into the database at that time was recalculated from the menstrual dates and mid-trimester ultrasound scan. The recalculated completed weeks of gestation at delivery was compared with that made at birth. Calculation of estimated gestational age varied between hospitals due to inconsistencies in 'rounding' and 'truncating' the weeks of gestation at delivery. This resulted in preterm birth misclassification rates of up to 10.1%.

Birth Rate↗

PubMed2003

Variance components linkage analysis for adjusted systolic blood pressure in the Framingham Heart Study.

We performed variance components linkage analysis in nuclear families from the Framingham Heart Study on nine phenotypes derived from systolic blood pressure (SBP). The phenotypes were the maximum and mean SBP, and SBP at age 40, each analyzed either uncorrected, or corrected using two subsets of epidemiological/clinical factors. Evidence for linkage to chromosome 8p was detected with all phenotypes except the uncorrected maximum SBP, suggesting this region harbors a gene contributing to variation in SBP.

Adult↗

PubMed2003

Multipoint linkage-disequilibrium mapping narrows location interval and identifies mutation heterogeneity.

Single-nucleotide polymorphism (SNP) genotypes were recently examined in an 890-kb region flanking the human gene CYP2D6. Single-marker and haplotype-based analyses identified, with genomewide significance (P < 10-7), a 403-kb interval displaying strong linkage disequilibrium (LD) with predicted poor-metabolizer phenotype. However, the width of this interval makes the location of causal variants difficult: for example, the interval contains seven known or predicted genes in addition to CYP2D6. We have developed the Bayesian fine-mapping software coldmap, which, applied to these genotype data, yields a 95% location interval covering only 185 kb and establishes genomewide significance for a causal locus within the region. Strikingly, our interval correctly excludes four SNPs, which individually display association with genomewide significance, including the SNP showing strongest LD (P < 10-34). In addition, coldmap distinguishes homozygous cases for the major CYP2D6 mutation from those bearing minor mutations. We further investigate a selection of SNP subsets and find that previously reported methods lead to a 38% savings in SNPs at the cost of an increase of <20% in the width of the location interval.

Bayes Theorem↗

PubMed2003

Estimation and testing of parent-of-origin effects for quantitative traits.

Recent progress in developing family-based association methods has extended their use to the analysis of quantitative traits in the offspring and to the estimation, for dichotomous traits, of the relative contribution of genetic and environmental mechanisms for parent-of-origin effects. However, many traits of interest are not naturally measured on a binary scale yet are suspected or known to be influenced by imprinted genes, and there is consequent interest in seeking evidence for parent-of-origin effects at these loci. Here we show how simple linear models can be used to estimate these parent-of-origin effects for a broad class of phenotypes; in particular, normally distributed quantitative traits are easily dealt with.

Female↗

PubMed2003

A Bayesian approach to disease gene location using allelic association.

A Bayesian approach to analysing data from family-based association studies is developed. This permits direct assessment of the range of possible values of model parameters, such as the recombination frequency and allelic associations, in the light of the data. In addition, sophisticated comparisons of different models may be handled easily, even when such models are not nested. The methodology is developed in such a way as to allow separate inferences to be made about linkage and association by including theta, the recombination fraction between the marker and disease susceptibility locus under study, explicitly in the model. The method is illustrated by application to a previously published data set. The data analysis raises some interesting issues, notably with regard to the weight of evidence necessary to convince us of linkage between a candidate locus and disease.

Alleles↗

PubMed2003

Likelihood-based estimation of microsatellite mutation rates.

Microsatellites are widely used in genetic analyses, many of which require reliable estimates of microsatellite mutation rates, yet the factors determining mutation rates are uncertain. The most straightforward and conclusive method by which to study mutation is direct observation of allele transmissions in parent-child pairs, and studies of this type suggest a positive, possibly exponential, relationship between mutation rate and allele size, together with a bias toward length increase. Except for microsatellites on the Y chromosome, however, previous analyses have not made full use of available data and may have introduced bias: mutations have been identified only where child genotypes could not be generated by transmission from parents' genotypes, so that the probability that a mutation is detected depends on the distribution of allele lengths and varies with allele length. We introduce a likelihood-based approach that has two key advantages over existing methods. First, we can make formal comparisons between competing models of microsatellite evolution; second, we obtain asymptotically unbiased and efficient parameter estimates. Application to data composed of 118,866 parent-offspring transmissions of AC microsatellites supports the hypothesis that mutation rate increases exponentially with microsatellite length, with a suggestion that contractions become more likely than expansions as length increases. This would lead to a stationary distribution for allele length maintained by mutational balance. There is no evidence that contractions and expansions differ in their step size distributions.

Alleles↗

PubMed2003

The structure of interrupted human AC microsatellites.

Microsatellite lengths change over evolutionary time through a process of replication slippage. A recently proposed model of this process holds that the expansionary tendencies of slippage mutation are balanced by point mutations breaking longer microsatellites into smaller units and that this process gives rise to the observed frequency distributions of uninterrupted microsatellite lengths. We refer to this as the slippage/point-mutation theory. Here we derive the theory's predictions for interrupted microsatellites comprising regions of perfect repeats, labeled segments, separated by dinucleotide interruptions containing point mutations. These predictions are tested by reference to the frequency distributions of segments of AC microsatellite in the human genome, and several predictions are shown not to be supported by the data, as follows. The estimated slippage rates are relatively low for the first four repeats, and then rise initially linearly with length, in accordance with previous work. However, contrary to expectation and the experimental evidence, the inferred slippage rates decline in segments above 10 repeats. Point mutation rates are also found to be higher within microsatellites than elsewhere. The theory provides an excellent fit to the frequency distribution of peripheral segment lengths but fails to explain why internal segments are shorter. Furthermore, there are fewer microsatellites with many segments than predicted. The frequencies of interrupted microsatellites decline geometrically with microsatellite size measured in number of segments, so that for each additional segment, the number of microsatellites is 33.6% less. Overall we conclude that the detailed structure of interrupted microsatellites cannot be reconciled with the existing slippage/point-mutation theory of microsatellite evolution, and we suggest that microsatellites are stabilized by processes acting on interior rather than on peripheral segments.

DNA, Satellite↗