Biomedical subjects

J C Whittaker

Publications and source records attributed to J C Whittaker.

At least 19 recordsLinked to original sources

PubMed2006

Elevated placental expression of the imprinted PHLDA2 gene is associated with low birth weight.

The identification of genes that regulate fetal growth will help establish the reasons for intrauterine growth restriction. Most autosomal genes are expressed biallelically, but some are imprinted, expressed only from one parental allele. Imprinted genes are associated with fetal growth and development. The growth of the fetus in utero relies on effective nutrient transfer from the mother to the fetus via the placenta. Some current research on the genetic control of fetal growth has focused on genes that display imprinted expression in utero. The expression levels of four imprinted genes, the paternally expressed insulin growth factor 2 (IGF2), the mesoderm-specific transcript isoform 1 (MEST); the maternally expressed pleckstrin homology-like domain, family A, member 2 (PHLDA2); and the polymorphically imprinted insulin-like growth factor 2 (IGF2R) gene are all known to have roles in fetal growth and were studied in the placentae of 200 white European, normal term babies. The quantitative expression analysis with real-time PCR showed the maternally expressing PHLDA2 but not the paternally expressing IGF2 and MEST, nor the polymorphic maternally expressing IGF2R placental levels to have a statistically significant effect on birth weight. PHLDA2 expression levels are negatively correlated with size at birth. These data implicate PHLDA2 as an imprinted gene important in fetal growth and also as a potential marker of fetal growth.

Female↗

PubMed2006

Evidence for unique association signals in SLE at the CD28-CTLA4-ICOS locus in a family-based study.

CD28, CTLA4 (cytotoxic T lymphocyte-associated protein 4) and ICOS (inducible T cell co-stimulator) are good candidate genes for systemic lupus erythematosus (SLE) because of their role in regulating T cell activation. CTLA4 inhibits CD28-mediated T cell activation. CTLA4 is expressed on CD4+ and CD8+ activated T cells, and also B cells, but CD28 and ICOS are largely restricted to T cells. An interval encompassing the CD28-CTLA4-ICOS locus on chromosome 2q33 was linked to lupus in two genome-wide linkage scans. This large family-based association study in 532 UK SLE families represents the first high-density genetic screen of 80 SNPs at this locus. There are seven haplotype blocks across the locus. In CTLA4, the strongest signal comes from two variants, located 2.1 kb downstream from the 3'-UTR. These polymorphisms, rs231726 (SNP 43) and rs231726 (SNP 44), are in complete linkage disequilibrium (LD) (r(2)=1) and are associated with SLE P=0.0008 (GH) and P=0.01 (family-based association test). There is also a signal in the distal 3' flanking region of CTLA4/ICOS promoter (P=0.003). There was no confirmation of published associations for SLE in the promoter or coding region of CTLA4. These SLE risk alleles are more distal than those identified in Graves' disease and are in LD with Graves' disease protective alleles identified in both of these regions of CTLA4 (Ueda et al. 2003). These factors suggest an SLE-specific pattern of association. The functional consequences of the associated polymorphisms are likely to influence CTLA4 expression, although it is possible that genetically modulated ICOS expression is involved in SLE susceptibility.

3' Flanking Region↗

PubMed2006

Fine mapping of disease genes via haplotype clustering.

We propose an algorithm for analysing SNP-based population association studies, which is a development of that introduced by Molitor et al. [2003: Am J Hum Genet 73:1368-1384]. It uses clustering of haplotypes to overcome the major limitations of many current haplotype-based approaches. We define a between-haplotype score that is simple, yet appears to capture much of the information about evolutionary relatedness of the haplotypes in the vicinity of a (unobserved) putative causal locus. Haplotype clusters can then be defined via a putative ancestral haplotype and a cut-off distance. The number of an individual's two haplotypes that lie within the cluster predicts the individual's genotype at the causal locus. This predicted genotype can then be investigated for association with the phenotype of interest. We implement our approach within a Markov-chain Monte Carlo algorithm that, in effect, searches over locations and ancestral haplotypes to identify large, case-rich clusters. The algorithm successfully fine-maps a causal mutation in a test analysis using real data, and achieves almost 98% accuracy in predicting the genotype at the causal locus. A simulation study indicates that the new algorithm is substantially superior to alternative approaches, and it also allows us to identify situations in which multi-point approaches can substantially improve over single-SNP analyses. Our algorithm runs quickly and there is scope for extension to a wide range of disease models and genomic scales.

Algorithms↗

PubMed2006

A likelihood ratio approach to family-based association studies with covariates.

We introduce a procedure for association based analysis of nuclear families that allows for dichotomous and more general measurements of phenotype and inclusion of covariate information. Standard generalized linear models are used to relate phenotype and its predictors. Our test procedure, based on the likelihood ratio, unifies the estimation of all parameters through the likelihood itself and yields maximum likelihood estimates of the genetic relative risk and interaction parameters. Our method has advantages in modelling the covariate and gene-covariate interaction terms over recently proposed conditional score tests that include covariate information via a two-stage modelling approach. We apply our method in a study of human systemic lupus erythematosus and the C-reactive protein that includes sex as a covariate.

Epidemiologic Methods↗

PubMed2005

No association between E- and L-selectin genes and SLE: soluble L-selectin levels do correlate with genotype and a subset in SLE.

Altered function of selectin glycoprotein adhesion molecules may modulate severity and organ-specific manifestations of autoimmune and inflammatory disease via changes in leukocyte trafficking. Serum concentrations of selectin molecules have been suggested as useful biomarkers in systemic lupus erythematosus (SLE). We identified increased levels of soluble L-selectin (sL-selectin), but not soluble E-selectin (sE-selectin) in 278 European-Caucasian lupus patients compared to 230 healthy siblings (P=0.002). sL-selectin levels were markedly elevated in patients with IgG antiphospholipid autoantibodies (P=0.002), suggesting that perhaps sL-selectin defines a subgroup of lupus with vasculopathy. sL-selectin level was also influenced by two L-selectin polymorphisms: 665C>T, F206L in the epidermal growth factor-like domain (P=0.015) and rs12938 in the 3'-untranslated region (P=0.06). Having shown increased sL-selectin levels in lupus patients, we used genetics to investigate whether this was a secondary phenomena or the result of an underlying genetic mechanism. The inheritance of nine single-nucleotide polymorphisms (SNP) spanning the selectin locus was tested in 523 UK simplex SLE families. No association with SLE, or related phenotypes, was evident with any single SNP, or haplotype in family-based tests of association. Selectin polymorphisms are, therefore, unlikely to be independent factors in SLE susceptibility.

Adult↗

PubMed2005

SNP selection for association studies: maximizing power across SNP choice and study size.

Selection of single nucleotide polymorphisms (SNPs) is a problem of primary importance in association studies and several approaches have been proposed. However, none provides a satisfying answer to the problem of how many SNPs should be selected, and how this should depend on the pattern of linkage disequilibrium (LD) in the region under consideration. Moreover, SNP selection is usually considered as independent from deciding the sample size of the study. However, when resources are limited there is a tradeoff between the study size and the number of SNPs to genotype. We show that tuning the SNP density to the LD pattern can be achieved by looking for the best solution to this tradeoff. Our approach consists of formulating SNP selection as an optimization problem: the objective is to maximize the power of the final association study, whilst keeping the total costs below a given budget. We also propose two alternative algorithms for the solution of this optimization problem: a genetic algorithm and a hill climbing search. These standard techniques efficiently find good solutions, even when the number of possible SNPs to choose from is large. We compare the performance of these two algorithms on different chromosomal regions and show that, as expected, the selected SNPs reflect the LD pattern: the optimal SNP density varies dramatically between chromosomal regions.

Gene Frequency↗

PubMed2004

Little loss of information due to unknown phase for fine-scale linkage-disequilibrium mapping with single-nucleotide-polymorphism genotype data.

We present the results of a simulation study that indicate that true haplotypes at multiple, tightly linked loci often provide little extra information for linkage-disequilibrium fine mapping, compared with the information provided by corresponding genotypes, provided that an appropriate statistical analysis method is used. In contrast, a two-stage approach to analyzing genotype data, in which haplotypes are inferred and then analyzed as if they were true haplotypes, can lead to a substantial loss of information. The study uses our COLDMAP software for fine mapping, which implements a Markov chain-Monte Carlo algorithm that is based on the shattered coalescent model of genetic heterogeneity at a disease locus. We applied COLDMAP to 100 replicate data sets simulated under each of 18 disease models. Each data set consists of haplotype pairs (diplotypes) for 20 SNPs typed at equal 50-kb intervals in a 950-kb candidate region that includes a single disease locus located at random. The data sets were analyzed in three formats: (1). as true haplotypes; (2). as haplotypes inferred from genotypes using an expectation-maximization algorithm; and (3). as unphased genotypes. On average, true haplotypes gave a 6% gain in efficiency compared with the unphased genotypes, whereas inferring haplotypes from genotypes led to a 20% loss of efficiency, where efficiency is defined in terms of root mean integrated square error of the location of the disease locus. Furthermore, treating inferred haplotypes as if they were true haplotypes leads to considerable overconfidence in estimates, with nominal 50% credibility intervals achieving, on average, only 19% coverage. We conclude that (1). given appropriate statistical analyses, the costs of directly measuring haplotypes will rarely be justified by a gain in the efficiency of fine mapping and that (2). a two-stage approach of inferring haplotypes followed by a haplotype-based analysis can be very inefficient for fine mapping, compared with an analysis based directly on the genotypes.

Algorithms↗

PubMed2003

SNP subset selection for genetic association studies.

Association studies for disease susceptibility genes rely on the high density of SNPs within candidate genes. However, the linkage disequilibrium between SNPs imply that not all SNPs identified in the candidate region need be genotyped. Here we develop several approaches to SNP subset selection, which can substantially reduce the number of SNPs to be genotyped in an association study. We apply clustering algorithms to pairwise linkage disequilibrium measures, with SNP subsets determined for different cut-off values of Delta using nearest and furthest neighbour clusters. Alternatively, SNP subsets may be determined by the proportion of haplotypes they identify. We also show how power calculations, based on the average power to identify a SNP as the disease susceptibility mutation using haplotype-based or logistic regression based statistical analyses, can be used to choose SNP subsets. All these methods provide a ranking method for subsets of a specific size, but do not provide criteria for overall choice of SNP subset size. We develop such criteria by incorporating power calculations into a decision analysis, where the choice of SNP subset size depends on the genotyping costs and the perceived benefits of identifying association. These methods are illustrated using eleven SNPs in the MMP2 gene.

Cluster Analysis↗

PubMed2002

Fine-scale mapping of disease loci via shattered coalescent modeling of genealogies.

We present a Bayesian, Markov-chain Monte Carlo method for fine-scale linkage-disequilibrium gene mapping using high-density marker maps. The method explicitly models the genealogy underlying a sample of case chromosomes in the vicinity of a putative disease locus, in contrast with the assumption of a star-shaped tree made by many existing multipoint methods. Within this modeling framework, we can allow for missing marker information and for uncertainty about the true underlying genealogy and the makeup of ancestral marker haplotypes. A crucial advantage of our method is the incorporation of the shattered coalescent model for genealogies, allowing for multiple founding mutations at the disease locus and for sporadic cases of disease. Output from the method includes approximate posterior distributions of the location of the disease locus and population-marker haplotype proportions. In addition, output from the algorithm is used to construct a cladogram to represent genetic heterogeneity at the disease locus, highlighting clusters of case chromosomes sharing the same mutation. We present detailed simulations to provide evidence of improvements over existing methodology. Furthermore, inferences about the location of the disease locus are shown to remain robust to modeling assumptions.

Algorithms↗

PubMed2001

Family-based tests of association and/or linkage.

The large literature on family-based tests of association and/or linkage is reviewed, concentrating on the underlying principles and on recent methodological developments. We explain the distinction between testing for association and testing for linkage, and give our views on the circumstances in which each is the appropriate null hypothesis.

Alleles↗

PubMed2001

Mapping quantitative trait Loci using generalized estimating equations.

A number of statistical methods are now available to map quantitative trait loci (QTL) relative to markers. However, no existing methodology can simultaneously map QTL for multiple nonnormal traits. In this article we rectify this deficiency by developing a QTL-mapping approach based on generalized estimating equations (GEE). Simulation experiments are used to illustrate the application of the GEE-based approach.

Algorithms↗

PubMed2001

On prediction of genetic values in marker-assisted selection.

We suggest a new approximation for the prediction of genetic values in marker-assisted selection. The new approximation is compared to the standard approach. It is shown that the new approach will often provide substantially better prediction of genetic values; furthermore the new approximation avoids some of the known statistical problems of the standard approach. The advantages of the new approach are illustrated by a simulation study in which the new approximation outperforms both the standard approach and phenotypic selection.

Computer Simulation↗

PubMed2001

Controlling misdiagnosis errors in preimplantation genetic diagnosis: a comprehensive model encompassing extrinsic and intrinsic sources of error.

We have developed a mathematical model to explore accuracy of preimplantation genetic diagnosis (PGD) using single cell polymerase chain reaction (PCR). The model encompasses both extrinsic technical errors and intrinsic errors related to nuclear and chromosomal abnormalities. Using estimates for these errors, we have calculated the probability of a serious error (affected embryo diagnosed as unaffected) using a variety of strategies designed to increase the accuracy of PGD. Additional information from genotyping a linked marker or a second biopsied cell reduces the probability of replacing an affected embryo, while ensuring that sufficient unaffected embryos can be replaced. For a recessive disease, two genotypes are required to ensure a low probability of replacing an affected embryo (<1%) with a high proportion of unaffected embryos eligible for replacement (68%). These genotypes may be from a single cell with linked marker, or disease genotypes from two cells. PGD of a dominant disease is more difficult, as it relies on the amplification of a single copy of the mutation. Genotypes from two biopsied cells are required to ensure that a high proportion of unaffected embryos are eligible for replacement. This model can be used as a clinical tool to prioritize embryos for transfer in a PGD cycle.

Bayes Theorem↗

PubMed2001

A maximum-likelihood approach to fitting equilibrium models of microsatellite evolution.

Here, we develop a new approach to Markov chain modeling of microsatellite evolution through polymerase slippage and introduce new models: a "constant-slippage-rate" model, in which there is no dependence of slippage rate on microsatellite length, as envisaged by Moran; and a "linear-with-constant" model, in which slippage rate increases linearly with microsatellite length, but the line of best fit is not constrained to go through the origin. We show how these and a linear no-constant model can be fitted to data hierarchically using maximum likelihood. This has advantages over previous methods in allowing statistical comparisons between models. When applied to a previously analyzed data set, the method allowed us to statistically establish that slippage rate increases with microsatellite length for dinucleotide microsatellites in humans, mice, and fruit flies, and suggested that no slippage occurs in very short microsatellites of one to four repeats. The suggestion that slippage rates are zero or close to zero for very short microsatellites of one to four repeats has important implications for understanding the mechanism of polymerase slippage.

Animals↗

PubMed2000

The problems of using the transmission/disequilibrium test to infer tight linkage.

Family-based association methods such as the transmission/disequilibrium test (TDT) have become very popular during the past few years, often being preferred to case-control studies because family-based approaches avoid the difficulties of ascertainment of appropriate populations of cases and controls for case-control studies. Significant TDT results indicate both linkage and allelic association. However, significant TDT results are often interpreted as implying tight linkage of marker and disease locus, and we shall argue here that, in general, this interpretation is not justified.

Alleles↗

PubMed2000

Bayesian fine-scale mapping of disease loci, by hidden Markov models.

We present a new multilocus method for the fine-scale mapping of genes contributing to human diseases. The method is designed for use with multiple biallelic markers-in particular, single-nucleotide polymorphisms for which high-density genetic maps will soon be available. We model disease-marker association in a candidate region via a hidden Markov process and allow for correlation between linked marker loci. Using Markov-chain-Monte Carlo simulation methods, we obtain posterior distributions of model parameter estimates including disease-gene location and the age of the disease-predisposing mutation. In addition, we allow for heterogeneity in recombination rates, across the candidate region, to account for recombination hot and cold spots. We also obtain, for the ancestral marker haplotype, a posterior distribution that is unique to our method and that, unlike maximum-likelihood estimation, can properly account for uncertainty. We apply the method to data for cystic fibrosis and Huntington disease, for which mutations in disease genes have already been identified. The new method performs well compared with existing multi-locus mapping methods.

Alleles↗

PubMed2000

Fine scale association mapping of disease loci using simplex families.

We present a new method for the fine scale mapping of disease loci based on samples of simplex families, each containing an affected child. The method is based on a generalisation of a single locus allele transmission model to multiple marker loci. The model is developed under the assumption of a single ancestral mutation and allows for the calculation of posterior probabilities that each allele at a particular marker was present on the founder chromosome. We illustrate the method using simulated family data for cystic fibrosis and Huntingtons disease, for which the locations of mutations in the disease genes are now known. For both diseases, our new method provides good estimates of the location of the mutations.

Alleles↗

PubMed2000

Marker-assisted selection using ridge regression.

In cross between inbred lines, linear regression can be used to estimate the correlation of markers with a trait of interest; these marker effects then allow marker assisted selection (MAS) for quantitative traits. Usually a subset of markers to include in the model must be selected: no completely satisfactory method of doing this exists. We show that replacing this selection of markers by ridge regression can improve the mean response to selection and reduce the variability of selection response.

Genetic Markers↗