Mathew J Barber

University of Cambridge, Cambridge, England, United Kingdom

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Publications (3)8.26 Total impact

  • Mathew J Barber · John A Todd · Heather J Cordell
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    ABSTRACT: We address the analytical problem of evaluating the evidence for linkage at a test locus while taking into account the effect of a known linked disease locus. The method we propose is a multimarker regression approach that models the identity-by-descent states for affected sib-pairs at a series of linked markers in terms of the identity-by-descent state at the known disease locus. Our method allows analysis to be performed at a test location (or a series of locations) without the requirement that identity-by-descent be directly observed at either the test or the known conditioning locus. An advantage of our method is that identity-by-descent states from multiple markers are included simultaneously in the test of linkage, without recourse to multipoint imputation. The properties and power of the method are examined under various null and alternative hypotheses. The method is applied to data from a study of 1,056 type 1 diabetes families to examine the evidence for an additional putative locus (IDDM15) on chromosome 6q, linked to IDDM1 in the HLA region on chromosome 6p. After accounting for the strong effect of IDDM1 and the differing rates of male and female recombination in the region, we find only marginal evidence for IDDM15 (P = 0.03 to 0.002, using different methods) approximately 15 cM centromeric of the original localisation.
    Genetic Epidemiology 04/2006; 30(3):191-208. DOI:10.1002/gepi.20137 · 2.95 Impact Factor
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    Mathew J Barber · Eleanor Wheeler · Heather J Cordell
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    ABSTRACT: The purposes of this study were 1) to examine the performance of a new multimarker regression approach for model-free linkage analysis in comparison to a conventional multipoint approach, and 2) to determine the whether a conditioning strategy would improve the performance of the conventional multipoint method when applied to data from two interacting loci. Linkage analysis of the Kofendrerd Personality Disorder phenotype to chromosomes 1 and 3 was performed in three populations for all 100 replicates of the Genetic Analysis Workshop 14 simulated data. Three approaches were used: a conventional multipoint analysis using the Zlr statistic as calculated in the program ALLEGRO; a conditioning approach in which the per-family contribution on one chromosome was weighted according to evidence for linkage on the other chromosome; and a novel multimarker regression approach. The multipoint and multimarker approaches were generally successful in localizing known susceptibility loci on chromosomes 1 and 3, and were found to give broadly similar results. No advantage was found with the per-family conditioning approach. The effect on power and type I error of different choices of weighting scheme (to account for different numbers of affected siblings) in the multimarker approach was examined.
    BMC Genetics 01/2006; 6 Suppl 1(Suppl 1):S40. DOI:10.1186/1471-2156-6-S1-S40 · 2.36 Impact Factor
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    ABSTRACT: Existing standard methods of linkage analysis for quantitative phenotypes rest on the assumptions of either ordinary least squares (Haseman and Elston [1972] Behav. Genet. 2:3-19; Sham and Purcell [2001] Am. J. Hum. Genet. 68:1527-1532) or phenotypic normality (Almasy and Blangero [1998] Am. J. Hum. Genet. 68:1198-1199; Kruglyak and Lander [1995] Am. J. Hum. Genet. 57:439-454). The limitations of both these methods lie in the specification of the error distribution in the respective regression analyses. In ordinary least squares regression, the residual distribution is misspecified as being independent of the mean level. Using variance components and assuming phenotypic normality, the dependency on the mean level is correctly specified, but the remaining residual coefficient of variation is constrained a priori. Here it is shown that these limitations can be addressed (for a sample of unselected sib-pairs) using a generalized linear model based on the gamma distribution, which can be readily implemented in any standard statistical software package. The generalized linear model approach can emulate variance components when phenotypic multivariate normality is assumed (Almasy and Blangero [1998] Am. J. Hum Genet. 68: 1198-1211) and is therefore more powerful than ordinary least squares, but has the added advantage of being robust to deviations from multivariate normality and provides (often overlooked) model-fit diagnostics for linkage analysis.
    Genetic Epidemiology 02/2004; 26(2):97-107. DOI:10.1002/gepi.10299 · 2.95 Impact Factor