Biomedical subjects

Neil Shephard

Publications and source records attributed to Neil Shephard.

4 recordsLinked to original sources

PubMed2004

Whole-genome scan, in a complex disease, using 11,245 single-nucleotide polymorphisms: comparison with microsatellites.

Despite the theoretical evidence of the utility of single-nucleotide polymorphisms (SNPs) for linkage analysis, no whole-genome scans of a complex disease have yet been published to directly compare SNPs with microsatellites. Here, we describe a whole-genome screen of 157 families with multiple cases of rheumatoid arthritis (RA), performed using 11,245 genomewide SNPs. The results were compared with those from a 10-cM microsatellite scan in the same cohort. The SNP analysis detected HLA*DRB1, the major RA susceptibility locus (P=.00004), with a linkage interval of 31 cM, compared with a 50-cM linkage interval detected by the microsatellite scan. In addition, four loci were detected at a nominal significance level (P<.05) in the SNP linkage analysis; these were not observed in the microsatellite scan. We demonstrate that variation in information content was the main factor contributing to observed differences in the two scans, with the SNPs providing significantly higher information content than the microsatellites. Reducing the number of SNPs in the marker set to 3,300 (1-cM spacing) caused several loci to drop below nominal significance levels, suggesting that decreases in information content can have significant effects on linkage results. In contrast, differences in maps employed in the analysis, the low detectable rate of genotyping error, and the presence of moderate linkage disequilibrium between markers did not significantly affect the results. We have demonstrated the utility of a dense SNP map for performing linkage analysis in a late-age-at-onset disease, where DNA from parents is not always available. The high SNP density allows loci to be defined more precisely and provides a partial scaffold for association studies, substantially reducing the resource requirement for gene-mapping studies.

Arthritis, Rheumatoid↗

PubMed2004

Investigation of susceptibility loci identified in the UK rheumatoid arthritis whole-genome scan in a further series of 217 UK affected sibling pairs.

OBJECTIVE: A previous whole-genome scan (WGS) of 182 UK rheumatoid arthritis (RA) affected sibling pair (ASP) families suggested linkage to HLA and 11 other chromosome regions. Replication of such findings in an independent cohort can help to distinguish true linkages from false-positive linkages. Since RA is a heterogeneous disease, some loci may be linked only in subsets of patients. Thus, the aim of this study was to investigate in an additional set of RA ASP families linkage to regions showing deviation in expected allele-sharing ratios in the UK WGS and to perform subset analysis on the combined cohort. METHODS: Twenty loci were investigated for linkage in 217 Caucasian UK RA ASPs. Stratification analysis was performed on the combined cohort of 377 RA ASP families to account for sex, RA severity, and the shared epitope (SE). RESULTS: None of the regions of linkage identified in the initial WGS achieved statistical significance in the second cohort. In contrast, after stratification analysis, 14 regions showed nominal evidence of linkage (logarithm of odds score >0.8) in one or more subgroups. In particular, the strength of evidence for linkage to chromosome 16p was increased in subsets of ASPs with younger age at disease onset (LOD score 2.38) and for linkage to chromosome 6q in female-female ASPs (LOD score 2.31) and in ASPs in which both siblings had 2 copies of the SE (LOD score 3.03). CONCLUSION: These results support the evidence for heterogeneity of RA. This information will inform the future design of association-based investigations as the search for disease genes in the linked regions begins.

Age of Onset↗

PubMed2003

Linkage analysis of cross-sectional and longitudinally derived phenotypic measures to identify loci influencing blood pressure.

BACKGROUND: The design of appropriate strategies to analyze and interpret linkage results for complex human diseases constitutes a challenge. Parameters such as power, definition of phenotype, and replicability have to be taken into account in order to reach meaningful conclusions. Incorporating data on repeated phenotypic measures may increase the power to detect linkage but requires sophisticated analysis methods. Using the simulated Genetic Analysis Workshop 13 data set, we have estimated a variety of systolic blood pressure (SBP) phenotypic measures and examined their performance with respect to consistency among replicates and to true and false positive linkage signals. RESULTS: The whole-genome scan conducted on a dichotomous hypertension phenotype indicated the involvement of few true loci with nominal significance and gave rise to a high rate of false positives. Analysis of a cross-sectional quantitative SBP measure performed better, although genome-wide significance was again not reached. Additional phenotypic measures were derived from the longitudinal data using random effects modelling for censored data with varying levels of covariate adjustment. These models provided evidence for significant linkage to most genes influencing SBP and produced few false positive results. Overall, replicability of results was poor for loci, representing weak effects. CONCLUSION: Longitudinally derived phenotypes performed better than cross-sectional measures in linkage analyses. Bearing in mind the sample design and size of these data, linkage results that fail to replicate should not be dismissed; instead, different lines of evidence derived from complementary analysis methods should be combined to prioritize follow up.

Adult Children↗

PubMed2002

Computationally intensive econometrics using a distributed matrix-programming language.

This paper reviews the need for powerful computing facilities in econometrics, focusing on concrete problems which arise in financial economics and in macroeconomics. We argue that the profession is being held back by the lack of easy-to-use generic software which is able to exploit the availability of cheap clusters of distributed computers. Our response is to extend, in a number of directions, the well-known matrix-programming interpreted language Ox developed by the first author. We note three possible levels of extensions: (i) Ox with parallelization explicit in the Ox code; (ii) Ox with a parallelized run-time library; and (iii) Ox with a parallelized interpreter. This paper studies and implements the first case, emphasizing the need for deterministic computing in science. We give examples in the context of financial economics and time-series modelling.

Computer Communication Networks↗