Biomedical subjects

Bernie Devlin

Publications and source records attributed to Bernie Devlin.

25 records · Page 2Linked to original sources

PubMed2004

Alcohol use disorder comorbidity in eating disorders: a multicenter study.

BACKGROUND: Eating disorders and alcohol use disorders (AUDs) commonly co-occur, although the patterns of comorbidity differ by eating disorder subtype. Our aim was to explore the nature of the co-morbid relation between AUDs and eating disorders in a large and phenotypically well-characterized group of individuals. METHOD: We compared diagnostic and personality profiles of 97 women with lifetime anorexia nervosa only, 282 women with lifetime bulimia nervosa only, and 293 women with a lifetime history of both anorexia nervosa and bulimia nervosa or anorexia nervosa with binge eating (ANBN) (DSM-IV criteria). All individuals were participants in a multicenter study of the genetics of anorexia nervosa and bulimia nervosa. We explored pattern of onset, Axis I and II comorbidity, and personality characteristics of individuals with and without AUDs by eating disorder subtype. Personality characteristics were assessed with the Multidimensional Perfectionism Scale, the Temperament and Character Inventory, and the Barratt Impulsivity Scale. RESULTS: Alcohol use disorders were significantly more prevalent in women with ANBN and bulimia nervosa than in women with anorexia nervosa (p =.0001). The majority of individuals reported primary onset of the eating disorder, with only one third reporting the onset of the AUD first. After eating disorder subtype was controlled for, AUDs were associated with the presence of major depressive disorder, a range of anxiety disorders, and cluster B personality disorder symptoms. In addition, individuals with AUDs presented with personality profiles marked by impulsivity and perfectionism. CONCLUSIONS: Individuals with eating disorders and AUDs exhibit phenotypic profiles characterized by both anxious, perfectionistic traits and impulsive, dramatic dispositions. These traits mirror the pattern of control and dyscontrol seen in individuals with this comorbid profile and suggest that anxiety modulation may be related to alcohol use in this group.

Age of Onset↗

PubMed2003

Integration of association statistics over genomic regions using Bayesian adaptive regression splines.

In the search for genetic determinants of complex disease, two approaches to association analysis are most often employed, testing single loci or testing a small group of loci jointly via haplotypes for their relationship to disease status. It is still debatable which of these approaches is more favourable, and under what conditions. The former has the advantage of simplicity but suffers severely when alleles at the tested loci are not in linkage disequilibrium (LD) with liability alleles; the latter should capture more of the signal encoded in LD, but is far from simple. The complexity of haplotype analysis could be especially troublesome for association scans over large genomic regions, which, in fact, is becoming the standard design. For these reasons, the authors have been evaluating statistical methods that bridge the gap between single-locus and haplotype-based tests. In this article, they present one such method, which uses non-parametric regression techniques embodied by Bayesian adaptive regression splines (BARS). For a set of markers falling within a common genomic region and a corresponding set of single-locus association statistics, the BARS procedure integrates these results into a single test by examining the class of smooth curves consistent with the data. The non-parametric BARS procedure generally finds no signal when no liability allele exists in the tested region (ie it achieves the specified size of the test) and it is sensitive enough to pick up signals when a liability allele is present. The BARS procedure provides a robust and potentially powerful alternative to classical tests of association, diminishes the multiple testing problem inherent in those tests and can be applied to a wide range of data types, including genotype frequencies estimated from pooled samples.

Algorithms↗

PubMed2002

No evidence for linkage of liability to autism to HOXA1 in a sample from the CPEA network.

A recent study by Ingram et al. [2000b: Teratology 62:393-405] suggests a (His)73(Arg) polymorphism (A:G) in HOXA1 contributes substantially to a liability for autism. Using 68 individuals diagnosed with Autism Spectrum Disorders, they found a significant dearth of G homozygotes and biased transmission of G alleles from parents to affected offspring, especially from mothers. Because the connection between HOXA1 and liability to autism is compelling, we attempted to replicate their finding using a larger, independent sample from the Collaborative Programs of Excellence in Autism (CPEA) network. In our data, genotype frequencies conform to Hardy-Weinberg equilibrium; allele transmissions meet Mendelian expectations; and there is no obvious sex-biased allele transmission. Based on our sample size, calculations suggest that we would have at least 95% power to detect linkage and association even if the A:G polymorphism were to account for only 1% of the heritability of autism. Therefore, although we cannot exclude the possibility that the samples in the two studies are intrinsically different, our data from our sample argue against a major role for HOXA1 (His)73(Arg) in liability to autism.

Asperger Syndrome↗

PubMed2002

Association and linkage analyses of RGS4 polymorphisms in schizophrenia.

Gene expression analyses of postmortem cerebral cortex suggest that transcription of the regulator of G-protein signaling 4 (RGS4) is decreased in a diagnosis-specific manner in subjects with schizophrenia. To evaluate the possible role of RGS4 in the pathogenesis of schizophrenia, we conducted genetic association and linkage studies using samples ascertained independently in Pittsburgh and New Delhi and by the NIMH Collaborative Genetics Initiative. Using the transmission disequilibrium test, significant transmission distortion was observed in the Pittsburgh and NIMH samples. Among single-nucleotide polymorphisms (SNPs) spanning approximately 300 kb, significant associations involved four SNPs localized to a 10 kb region at RGS4, but the associated haplotypes differed. A trend for transmission distortion was also present in the Indian sample for haplotypes incorporating the same SNPs. Consistent with the linkage/association observed from the family-based tests, samples with affected siblings (NIMH, India) showed higher levels of allele sharing, identical by descent, at RGS4. When the US patients were contrasted to two population-based control samples, however, no significant differences were observed. To check the specificity of the transmission bias, we therefore examined US families with bipolar I disorder (BD1) probands. This sample also showed a trend for transmission distortion, and differed significantly from the population-based controls for the four-SNP haplotypes tested in the other samples. The transmission distortion is unlikely to be due to chance, but its mechanism and specificity require further study. Our results illustrate the potential power of combining gene expression profiling and genomic analyses to identify susceptibility genes for genetically complex disorders.

Bipolar Disorder↗

PubMed2002

Linkage analysis of anorexia nervosa incorporating behavioral covariates.

Eating disorders, such as anorexia nervosa (AN) and bulimia nervosa (BN), have genetic and environmental underpinnings. To explore genetic contributions to AN, we measured psychiatric, personality and temperament phenotypes of individuals diagnosed with eating disorders from 196 multiplex families, all accessed through an AN proband, as well as genotyping a battery of 387 short tandem repeat (STR) markers distributed across the genome. On these data we performed a multipoint affected sibling pair (ASP) linkage analysis using a novel method that incorporates covariates. By exploring seven attributes thought to typify individuals with eating disorders, we identified two variables, drive-for-thinness and obsessionality, which delimit populations among the ASPs. For both of these traits, or covariates, there were a cluster of ASPs who have high and concordant values for these traits, in keeping with our expectations for individuals with AN, and other clusters of ASPs who did not meet those expectations. When we incorporated these covariates into the ASP linkage analysis, both jointly and separately, we found several regions of suggestive linkage: one close to genome-wide significance on chromosome 1 (at 210 cM, D1S1660; LOD = 3.46, P = 0.00003), another on chromosome 2 (at 114 cM, D2S1790; LOD = 2.22, P = 0.00070) and a third region on chromosome 13 (at 26 cM, D13S894; LOD = 2.50, P = 0.00035). By comparing our results to those implemented using more standard linkage methods, we find the covariates convey substantial information for the linkage analysis.

Anorexia Nervosa↗

PubMed2002

Mixture models for linkage analysis of affected sibling pairs and covariates.

To determine the genetic etiology of complex diseases, a common study design is to recruit affected sib/relative pairs (ASP/ARP) and evaluate their genome-wide distribution of identical by descent (IBD) sharing using a set of highly polymorphic markers. Other attributes or environmental exposures of the ASP/ARP, which are thought to affect liability to disease, are sometimes collected. Conceivably, these covariates could refine the linkage analysis. Most published methods for ASP/ARP linkage with covariates can be conceptualized as logistic models in which IBD status of the ASP is predicted by pair-specific covariates. We develop a different approach to the problem of ASP analysis in the presence of covariates, one that extends naturally to ARP under certain conditions. For ASP linkage analysis, we formulate a mixture model in which a disease mutation is segregating in only a fraction alpha of the sibships, with 1 - alpha sibships being unlinked. Covariate information is used to predict membership within groups; in this report, the two groups correspond to the linked and unlinked sibships. For an ASP with covariate(s) Z = z and multilocus genotype X = x, the mixture model is alpha(z)g(x; lambda) + [1 - alpha(z)]g(0)(x), in which g(0)(x) follows the distribution of genotypes under the null IBD distribution and g(x; lambda) allows for increased IBD sharing. Two mixture models are developed. The pre-clustering model uses covariate information to form probabilistic clusters and then tests for excess IBD sharing independent of the covariates. The Cov-IBD model determines probabilistic group membership by joint consideration of covariate and IBD values. Simulations show that incorporating covariates into linkage analysis can enhance power substantially. A feature of our conceptualization of ASP linkage analysis, with covariates, is that it is apparent how data analysis might evaluate covariates prior to the linkage analysis, thus avoiding the loss of power described by Leal and Ott [2000] when data are stratified.

Algorithms↗

PubMed2002

Association studies for quantitative traits in structured populations.

Association between disease and genetic polymorphisms often contributes critical information in our search for the genetic components of common diseases. Devlin and Roeder [1999: Biometrics 55:997-1004] introduced genomic control, a statistical method that overcomes a drawback to the use of population-based samples for tests of association, namely spurious associations induced by population structure. In essence, genomic control (GC) uses markers throughout the genome to adjust for any inflation in test statistics due to substructure. To date, genomic control (GC) has been developed for binary traits and bi- or multiallelic markers. Tests of association using GC have been limited to single genes. In this report, we generalize GC to quantitative traits (QT) and multilocus models. Using statistical analysis and simulations, we show that GC controls spurious associations in reasonable settings of population substructure for QT models, including gene-gene interaction. Through simulations, we explore GC power for both random and selected samples, assuming the QT locus tested is causal and its specific heritability is 2.5-5%. We find that GC, combined with either random or selected samples, has good power in this setting, and that more complex models induce smaller GC corrections. The latter suggests greater power can be achieved by specifying more complex genetic models, but this observation only follows when such models are largely correct and specified a priori.

Alleles↗