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Stuart Macgregor

Publications and source records attributed to Stuart Macgregor.

16 recordsLinked to original sources
PubMed2006

Bias, precision and heritability of self-reported and clinically measured height in Australian twins.

Many studies of quantitative and disease traits in human genetics rely upon self-reported measures. Such measures are based on questionnaires or interviews and are often cheaper and more readily available than alternatives. However, the precision and potential bias cannot usually be assessed. Here we report a detailed quantitative genetic analysis of stature. We characterise the degree of measurement error by utilising a large sample of Australian twin pairs (857 MZ, 815 DZ) with both clinical and self-reported measures of height. Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature. In our twin sample the heritability estimate for clinical height exceeded 90%. Repeated measures analysis shows that 2-3 times as many self-report measures are required to recover heritability estimates similar to those obtained from clinical measures. Bivariate genetic repeated measures analysis of self-report and clinical height measures showed an additive genetic correlation >0.98. We show that the accuracy of self-report height is upwardly biased in older individuals and in individuals of short stature. By comparing clinical and self-report measures we also showed that there was a genetic component to females systematically reporting their height incorrectly; this phenomenon appeared to not be present in males. The results from the measurement error analysis were subsequently used to assess the effects of error on the power to detect linkage in a genome scan. Moderate reduction in error (through the use of accurate clinical or multiple self-report measures) increased the effective sample size by 22%; elimination of measurement error led to increases in effective sample size of 41%.

Adult↗
PubMed2006

Convergent evidence that oligodendrocyte lineage transcription factor 2 (OLIG2) and interacting genes influence susceptibility to schizophrenia.

Abnormal oligodendrocyte function has been postulated as a primary etiological event in schizophrenia. Oligodendrocyte lineage transcription factor 2 (OLIG2) encodes a transcription factor central to oligodendrocyte development. Analysis of OLIG2 in a case-control sample (n = approximately 1,400) in the U.K. revealed several SNPs to be associated with schizophrenia (minimum P = 0.0001, gene-wide P = 0.0009). To obtain independent support for this association, we sought evidence for genetic interaction between OLIG2 and three genes of relevance to oligodendrocyte function for which we have reported evidence for association with schizophrenia: CNP, NRG1, and ERBB4. We found interaction effects on disease risk between OLIG2 and CNP (minimum P = 0.0001, corrected P = 0.008) for interaction with ERBB4 (minimum P = 0.002, corrected P = 0.04) but no evidence for interaction with NRG1. To investigate the biological plausibility of the interactions, we sought correlations between the expression of the genes. The results were similar to those of the genetic interaction analysis. OLIG2 expression significantly correlated in cerebral cortex with CNP (P < 10(-7)) and ERBB4 (P = 0.002, corrected P = 0.038) but not NRG1. In mouse striatum, Olig2 and Cnp expression also was correlated, and linkage analysis for trans-effects on gene expression suggests that each locus regulates the other's expression. Our data provide strong convergent evidence that variation in OLIG2 confers susceptibility to schizophrenia alone and as part of a network of genes implicated in oligodendrocyte function.

2',3'-Cyclic-Nucleotide Phosphodiesterases↗
PubMed2006

Analysis of pooled DNA samples on high density arrays without prior knowledge of differential hybridization rates.

Array based DNA pooling techniques facilitate genome-wide scale genotyping of large samples. We describe a structured analysis method for pooled data using internal replication information in large scale genotyping sets. The method takes advantage of information from single nucleotide polymorphisms (SNPs) typed in parallel on a high density array to construct a test statistic with desirable statistical properties. We utilize a general linear model to appropriately account for the structured multiple measurements available with array data. The method does not require the use of additional arrays for the estimation of unequal hybridization rates and hence scales readily to accommodate arrays with several hundred thousand SNPs. Tests for differences between cases and controls can be conducted with very few arrays. We demonstrate the method on 384 endometriosis cases and controls, typed using Affymetrix Genechip(c) HindIII 50 K arrays. For a subset of this data there were accurate measures of hybridization rates available. Assuming equal hybridization rates is shown to have a negligible effect upon the results. With a total of only six arrays, the method extracted one-third of the information (in terms of equivalent sample size) available with individual genotyping (requiring 768 arrays). With 20 arrays (10 for cases, 10 for controls), over half of the information could be extracted from this sample.

Case-Control Studies↗
PubMed2006

GAIA: an easy-to-use web-based application for interaction analysis of case-control data.

BACKGROUND: The advent of cheap, large scale genotyping has led to widespread adoption of genetic association mapping as the tool of choice in the search for loci underlying susceptibility to common complex disease. Whilst simple single locus analysis is relatively trivial to conduct, this is not true of more complex analysis such as those involving interactions between loci. The importance of testing for interactions between loci in association analysis has been highlighted in a number of recent high profile publications. RESULTS: Genetic Association Interaction Analysis (GAIA) is a web-based application for testing for statistical interactions between loci. It is based upon the widely used case-control study design for genetic association analysis and is designed so that non-specialists may routinely apply tests for interaction. GAIA allows simple testing of both additive and additive plus dominance interaction models and includes permutation testing to appropriately correct for multiple testing. The application will find use both in candidate gene based studies and in genome-wide association studies. For large scale studies GAIA includes a screening approach which prioritizes loci (based on the significance of main effects at one or both loci) for further interaction analysis. CONCLUSION: GAIA is available at http://www.bbu.cf.ac.uk/html/research/biostats.htm.

Case-Control Studies↗
PubMed2006

Variation at the DAOA/G30 locus influences susceptibility to major mood episodes but not psychosis in schizophrenia and bipolar disorder.

CONTEXT: Variation at the DAOA/G30 locus has been described to be associated with both schizophrenia and bipolar disorder, but there is little consistency between studies of the tested polymorphisms or variants showing association. OBJECTIVES: To obtain a stringent replication of association in large samples of both disorders using consistent clinical and laboratory methods, and to test the hypothesis that association at DAOA/G30 identifies an underlying domain of psychopathological abnormalities that cuts across traditional diagnostic categories. DESIGN: A systematic study of polymorphisms at DAOA/G30 using genetic case-control association analysis. SETTING: Subjects were unrelated and ascertained from general psychiatric inpatient and outpatient services. PARTICIPANTS: White persons from the United Kingdom meeting criteria for DSM-IV schizophrenia (n = 709) or bipolar I disorder (n = 706) and 1416 ethnically matched controls. METHODS: Nine polymorphisms that tag common genetic variations at DAOA/G30 were genotyped in all of the individuals, and comparisons were made between affected and unaffected individuals. RESULTS: We identified significant association (P = .01-.047) between 3 single-nucleotide polymorphisms and bipolar disorder but failed to find association with schizophrenia. Analyses across the traditional diagnostic categories revealed significant evidence (P = .002-.02) for association with 4 single-nucleotide polymorphisms in the subset of cases (n = 818) in which episodes of major mood disorder had occurred (gene-wide P = .009). We found a similar pattern of association in bipolar cases and in schizophrenia cases in which individuals had experienced major mood disorder. In contrast, we found no evidence for association in the subset of cases (n = 1153) in which psychotic features occurred (all P>.08). CONCLUSIONS: Despite being originally described as a schizophrenia susceptibility locus, our data suggest that variation at the DAOA/G30 locus does not primarily increase susceptibility for prototypical schizophrenia or psychosis. Instead, our results imply that variation at the DAOA/G30 locus influences susceptibility to episodes of mood disorder across the traditional bipolar and schizophrenia categories.

Bipolar Disorder↗
PubMed2006

Use of phenotypic covariates in association analysis by sequential addition of cases.

Optimal use of phenotype information is important in complex disease gene mapping. We describe a method, sequential addition, for the analysis of case-control data by taking into account of a quantitative trait that is measured in cases but not in controls. The method also provides an estimate of the best phenotype definition for future studies. We demonstrate proof of principle, using an example of incorporation of age-at-onset data into a study of a small sample for association between APOE and late-onset Alzheimer's disease. The sequential addition method finds evidence of association when conventional case-control methods fail. We also illustrate the use of the method for taking account of a dimensional measure of psychosis in a study of the schizophrenia susceptibility gene, dysbindin, in bipolar disorder.

Age of Onset↗
PubMed2006

A single nucleotide polymorphism in CHAT influences response to acetylcholinesterase inhibitors in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a devastating neurodegeneration with a characteristic deficit in cholinergic neurotransmission. Treatment with acetylcholinesterase (AChE) inhibitors aims to reverse this deficit and does ameliorate the decline in cognition in some AD patients, although response is variable. OBJECTIVE: To examine whether sequence variation in the gene encoding choline acetyltransferase (CHAT), which encodes the major catalytic enzyme of the cholinergic pathway, predicts response to AChE inhibitors. METHODS: Alzheimer's disease patients (121) were treated with cholinesterase inhibitors and the effect of treatment on cognition was measured using the Mini Mental State Examination (MMSE). Six polymorphisms in CHAT were analysed for association with change in MMSE score. RESULTS: After correction for multiple testing, we found one SNP, rs733722, in a promoter region of CHAT, is associated with response of AD patients to cholinesterase inhibitors (P = 0.03) and accounts for 6% of the variance in response to AChE inhibitors. CONCLUSION: Rs733722 represents a putative marker of response to AChE inhibitors in AD patients.

Aged↗
PubMed2006

Familiality of postpartum depression in unipolar disorder: results of a family study.

OBJECTIVE: The authors previously reported strong evidence for familial aggregation of postpartum (puerperal) psychotic episodes in women with bipolar disorder. The authors here examine whether vulnerability to postpartum triggering of depressive episodes aggregates in families and assess how this aggregation varies with the definition of postpartum onset. METHOD: Postpartum depression occurrence was studied in the female members of 120 sibling pairs recruited at a site within an international multicenter study of sibling pairs with recurrent unipolar depression. Employing a range of definitions of postpartum onset, the authors examined concordance for postpartum episode status between sisters. RESULTS: Episodes of depression with onset within 4 weeks of delivery clustered in families, but there was no significant evidence of familial clustering of broadly defined postpartum depression (onset within 6 months). Among women with a family history of narrowly defined postpartum episodes, 42% experienced depression following their first delivery, whereas only 15% of women with no such family history experienced depression following first delivery. The evidence for familiality maximized with a postpartum onset definition of 6-8 weeks. CONCLUSIONS: These results implicate familial factors in susceptibility to the triggering of narrowly defined postpartum depressive episodes in women with recurrent major depression. They suggest that a postnatal onset definition of within 6-8 weeks of delivery may be optimal in studies of the triggering of depressive illness by childbirth.

Adult↗
PubMed2006

Genetic variation of brain-derived neurotrophic factor (BDNF) in bipolar disorder: case-control study of over 3000 individuals from the UK.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) influences neuronal survival, proliferation and plasticity. Three family-based studies have shown association of the common Valine (Val) allele of the Val66Met polymorphism of the BDNF gene with susceptibility to bipolar disorder. AIMS: To replicate this finding. METHOD: We genotyped the Val66Met polymorphism in our UK White bipolar case-control sample (n=3062). RESULTS: We found no overall evidence of allele or genotype association. However, we found association with disease status in the subset of 131 individuals that had experienced rapid cycling at some time (P=0.004). We found a similar association on re-analysis of our previously reported family-based association sample (P < 0.03, one-tailed test). CONCLUSIONS: Variation at the Val66Met polymorphism of BDNF does not play a major role in influencing susceptibility to bipolar disorder as a whole, but is associated with susceptibility to the rapid-cycling subset of the disorder.

Bipolar Disorder↗
PubMed2006

False disease region identification from identity-by-descent haplotype sharing in the presence of phenocopies.

Linkage analysis (either parametric or nonparametric) is commonly applied to identify chromosomal regions using related individuals affected by disease. In complex disease the incomplete relationship between phenotype and genotype can be modeled using a phenocopy parameter, the probability that an individual is affected given they do not carry the disease mutation of interest, and a nonpenetrance parameter, the probability that an individual is not affected given they do carry the disease mutation of interest. If the linkage phase between multiple markers and a putative disease locus is known, then haplotypes carrying the mutation can, in principle, be identified by comparing the chromosome segments that are shared identical-by-descent (IBD) across affected individuals. We consider here the effect of a nonzero phenocopy rate on the linkage peak and hence upon the identification of disease haplotypes that are shared IBD between affected individuals. We show, by theory and computer simulation, that in diseases for which there is a nonzero phenocopy rate, the chromosomal regions identified may not include the true disease locus. We utilize a LOD-1 confidence interval for a widely used nonparametric linkage statistic. We find that in small/moderate samples this confidence interval may be inappropriate. We give specific examples where the phenocopy rates are nonnegligible in some complex diseases. The success of further work to identify the causal mutations underlying the linkage peaks in these diseases will depend on researchers allowing for the presence of phenocopies by examining appropriately wide regions around the initial positive linkage finding.

Alleles↗
PubMed2005

Quantitative trait locus analysis of longitudinal quantitative trait data in complex pedigrees.

There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.

Chromosome Mapping↗
PubMed2005

Operation of the schizophrenia susceptibility gene, neuregulin 1, across traditional diagnostic boundaries to increase risk for bipolar disorder.

CONTEXT: Family and twin data suggest that, in addition to susceptibility genes specific for bipolar disorder or schizophrenia, genes exist that contribute to susceptibility across the traditional kraepelinian divide. Several studies have provided evidence that variation at the neuregulin 1 (NRG1) gene on chromosome 8p12 influences susceptibility to schizophrenia. The most consistent finding has been that one particular haplotype (the "core" haplotype) is overrepresented in cases compared with control subjects. OBJECTIVE: To investigate the possible role of NRG1 in bipolar disorder. DESIGN: Genetic case-control association analysis. SETTING: Subjects were unrelated and ascertained from general psychiatric inpatient and outpatient services. PARTICIPANTS: Five hundred twenty-nine patients with DSM-IV bipolar I disorder and 1011 controls from the United Kingdom (100% white). METHODS: We genotyped the markers constituting the NRG1 core haplotype in cases and controls and reanalyzed our existing data from 573 DSM-IV schizophrenia cases with this larger set of controls. RESULTS: We found a significant difference in haplotype distribution between bipolar cases and controls globally (P = .003) and specifically for the core haplotype. Frequencies were 10.2% for bipolar cases and 7.8% for controls (effect size, as measured by odds ratio [OR], 1.37; 95% confidence interval [CI], 1.03-1.80; P = .04). The effect size in our bipolar sample was similar to that in our schizophrenia sample (OR, 1.22; 95% CI, 0.92-1.61). In the bipolar cases with predominantly mood-incongruent psychotic features (n = 193), the effect was greater (OR, 1.71; 95% CI, 1.29-2.59; P = .009), as was the case in the subset of schizophrenia cases (n = 27) who had experienced mania (OR, 1.64; 95% CI, 0.54-5.01). CONCLUSIONS: Our findings suggest that neuregulin 1 plays a role in influencing susceptibility to bipolar disorder and schizophrenia and that it may exert a specific effect in the subset of functional psychosis that has manic and mood-incongruent psychotic features.

Bipolar Disorder↗
PubMed2003

Longitudinal variance-components analysis of the Framingham Heart Study data.

The Framingham Heart Study offspring cohort, a complex data set with irregularly spaced longitudinal phenotype data, was made available as part of Genetic Analysis Workshop 13. To allow an analysis of all of the data simultaneously, a mixed-model- based random-regression (RR) approach was used. The RR accounted for the variation in genetic effects (including marker-specific quantitative trait locus (QTL) effects) across time by fitting polynomials of age. The use of a mixed model allowed both fixed (such as sex) and random (such as familial environment) effects to be accounted for appropriately. Using this method we performed a QTL analysis of all of the available adult phenotype data (26,106 phenotypic records). In addition to RR, conventional univariate variance component techniques were applied. The traits of interest were BMI, HDLC, total cholesterol, and height. The longitudinal method allowed the characterization of the change in QTL effects with aging. A QTL affecting BMI was shown to act mainly at early ages.

Adult↗
PubMed2003

Genome scan meta-analysis of schizophrenia and bipolar disorder, part III: Bipolar disorder.

Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for "very narrow" (i.e., BP-I and schizoaffective disorder-BP) and "narrow" (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A "broad" model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.

Bipolar Disorder↗
PubMed2003

Longitudinal data analysis in pedigree studies.

Longitudinal family studies provide a valuable resource for investigating genetic and environmental factors that influence long-term averages and changes over time in a complex trait. This paper summarizes 13 contributions to Genetic Analysis Workshop 13, which include a wide range of methods for genetic analysis of longitudinal data in families. The methods can be grouped into two basic approaches: 1) two-step modeling, in which repeated observations are first reduced to one summary statistic per subject (e.g., a mean or slope), after which this statistic is used in a standard genetic analysis, or 2) joint modeling, in which genetic and longitudinal model parameters are estimated simultaneously in a single analysis. In applications to Framingham Heart Study data, contributors collectively reported evidence for genes that affected trait mean on chromosomes 1, 2, 3, 5, 8, 9, 10, 13, and 17, but most did not find genes affecting slope. Applications to simulated data suggested that even for a gene that only affected slope, use of a mean-type statistic could provide greater power than a slope-type statistic for detecting that gene. We report on the results of a small experiment that sheds some light on this apparently paradoxical finding, and indicate how one might form a more powerful test for finding a slope-affecting gene. Several areas for future research are discussed.

Cardiovascular Diseases↗