Biomedical subjects

H J Cordell

Publications and source records attributed to H J Cordell.

At least 19 recordsLinked to original sources

PubMed2006

Genomic DNA pooling for whole-genome association scans in complex disease: empirical demonstration of efficacy in rheumatoid arthritis.

A pragmatic approach that balances the benefit of a whole-genome association (WGA) experiment against the cost of individual genotyping is to use pooled genomic DNA samples. We aimed to determine the feasibility of this approach in a WGA scan in rheumatoid arthritis (RA) using the validated human leucocyte antigen (HLA) and PTPN22 associations as test loci. A total of 203 269 single-nucleotide polymorphisms (SNPs) on the Affymetrix 100K GeneChip and Illumina Infinium microarrays were examined. A new approach to the estimation of allele frequencies from Affymetrix hybridization intensities was developed involving weighting for quality signals from the probe quartets. SNPs were ranked by z-scores, combined from United Kingdom and New Zealand case-control cohorts. Within a 1.7 Mb HLA region, 33 of the 257 SNPs and at PTPN22, 21 of the 45 SNPs, were ranked within the top 100 associated SNPs genome wide. Within PTPN22, individual genotyping of SNP rs1343125 within MAGI3 confirmed association and provided some evidence for association independent of the PTPN22 620W variant (P=0.03). Our results emphasize the feasibility of using genomic DNA pooling for the detection of association with complex disease susceptibility alleles. The results also underscore the importance of the HLA and PTPN22 loci in RA aetiology.

Arthritis, Rheumatoid↗

PubMed2006

Genome-wide scan for loci influencing quantitative immune response traits in the Belém family study: comparison of methods and summary of results.

Here we report the results from a genome-wide linkage scan to identify genes and chromosomal regions that influence quantitative immune response traits, using multi-case leprosy and tuberculosis families from north-eastern Brazil. Total plasma IgE, antigen-specific IgG to Mycobacterium leprae soluble antigen (MLSA), M. tuberculosis soluble antigen (MTSA) and M. tuberculosis purified protein derivative (PPD), and antigen-specific lymphocyte proliferation (stimulation index or SI) and interferon-gamma (IFN-gamma) release to MLSA and PPD, were measured in 16 tuberculosis (184 individuals) and 21 leprosy (177 individuals) families. The individuals were genotyped at 382 autosomal microsatellite markers across the genome. The adjusted immune-response phenotypes were analysed using a variety of variance components and regression-based methods. These analyses highlighted a number of practical issues and problems with regard to implementation of the methods and, interestingly, differences were observed between several standard statistical and genetic analysis packages used. From this we determined that, for this set of traits in these pedigrees, significant p values for linkage using variance components analysis, supported by significance using the Visscher-Hopper modification of the Haseman-Elston method, provided the most compelling evidence for linkage. Using these criteria, linkage (5.8 x 10(-5) < p < 0.008) was seen for: total plasma IgE on chromosome 2; IgG to MLSA on chromosomes 8, 17 and 21; IgG to PPD on chromosome 12; SI to PPD on chromosome 1; IFN-gamma to MLSA on chromosomes 6, 7, 10, 12 and 14; and IFN-gamma to PPD on chromosomes 1, 16 and 19.

Analysis of Variance↗

PubMed2004

Evidence for a cluster of genes on chromosome 17q11-q21 controlling susceptibility to tuberculosis and leprosy in Brazilians.

The region of conserved synteny on mouse chromosome 11/human 17q11-q21 is known to carry a susceptibility gene(s) for intramacrophage pathogens. The region is rich in candidates including NOS2A, CCL2/MCP-1, CCL3/MIP-1alpha, CCL4/MIP-1beta, CCL5/RANTES, CCR7, STAT3 and STAT5A/5B. To examine the region in man, we studied 92 multicase tuberculosis (627 individuals) and 72 multicase leprosy (372 individuals) families from Brazil. Multipoint nonparametric analysis (ALLEGRO) using 16 microsatellites shows two peaks of linkage for leprosy at D17S250 (Z(lr) score 2.34; P=0.01) and D17S1795 (Z(lr) 2.67; P=0.004) and a single peak for tuberculosis at D17S250 (Z(lr) 2.04; P=0.02). Combined analysis shows significant linkage (peak Z(lr) 3.38) at D17S250, equivalent to an allele sharing LOD score 2.48 (P=0.0004). To determine whether one or multiple genes contribute, 49 informative single nucleotide polymorphisms were typed in candidate genes. Family-based allelic association testing that was robust to family clustering demonstrated significant associations with tuberculosis susceptibility at four loci separated by intervals (NOS2A-8.4 Mb-CCL18-32.3 kb-CCL4-6.04 Mb-STAT5B) up to several Mb. Stepwise conditional logistic regression analysis using a case/pseudo-control data set showed that the four genes contributed separate main effects, consistent with a cluster of susceptibility genes across 17q11.2.

Animals↗

PubMed2004

Genome-wide scans for leprosy and tuberculosis susceptibility genes in Brazilians.

Genome-wide scans were conducted for tuberculosis and leprosy per se in Brazil. At stage 1, 405 markers (10 cM map) were typed in 16 (178 individuals) tuberculosis and 21 (173 individuals) leprosy families. Nonparametric multipoint analysis detected 8 and 9 chromosomal regions respectively with provisional evidence (P<0.05) for linkage. At stage 2, 58 markers from positive regions were typed in a second set of 22 (176 individuals) tuberculosis families, with 22 additional markers typed in all families; 42 positive markers in 50 (192 individuals) new leprosy families, and 30 additional markers in all families. Three regions (10q26.13, 11q12.3, 20p12.1) retained suggestive evidence (peak LOD scores 1.31, 1.85, 1.78; P=0.007, 0.0018, 0.0021) for linkage to tuberculosis, 3 regions (6p21.32, 17q22, 20p13) to leprosy (HLA-DQA, 3.23, P=5.8 x 10(-5); D17S1868, 2.38, P=0.0005; D20S889, 1.51, P=0.004). The peak at D20S889 for leprosy is 3.5 Mb distal to that reported at D20S115 for leprosy in India. (151 words).

Brazil↗

PubMed2003

Genetic susceptibility to visceral leishmaniasis in The Sudan: linkage and association with IL4 and IFNGR1.

Longitudinal studies in Sudan show ethnic differences in incidence and clinical phenotypes associated with Leishmania donovani. Immunologically, bias in type 1 vs type 2 cytokine responses is important. To determine whether polymorphisms at IL4/IL9 or IFNGR1 contribute to susceptibility, we examined 59 multicase families of visceral leishmaniasis (VL) with/without post Kala-azar dermal leishmaniasis (PKDL). Multipoint nonparametric analysis (Allegro) linked IL4/IL9 to VL per se (P=0.002). Transmission disequilibrium testing with robust variance estimates confirmed association in the presence of linkage between VL per se and IL4 (P=0.008) but not IL9. Stepwise logistic regression analysis showed both IL4RP2 and IL4RP1 markers contributed significantly to the association, suggesting a common disease-associated haplotype. In contrast, IFNGR1 was linked (P=0.031) and associated (P=0.007) to PKDL but not VL or VL per se. Hence, polymorphism in a type 2 cytokine gene influences underlying susceptibility to VL, whereas IFNGR1 is specifically related to susceptibility to PKDL.

Alleles↗

PubMed2001

Linkage analysis of Genetic Analysis Workshop 12 simulated data based on affected individuals only.

A variety of allele-sharing methods was used to assess linkage to disease for a whole-genome screen in each of ten replicates from the Genetic Analysis Workshop 12 simulated data. Analysis of each replicate produced many false positive results in addition to a few "true" positives. Conditioning on the strongest initial linkage and reanalyzing using a variety of conditional methods did not improve the power for detection or help discriminate between true and false positive signals.

Alleles↗

PubMed2001

Overview of model-free methods for linkage analysis.

Methods of model-free linkage analysis do not require a detailed specification for the mode of inheritance of the trait locus being linked. Beginning with methods proposed by Penrose in the 1930s, which allowed detection of linkage only, these methods now allow one to use multipoint analysis both to locate trait genes and to estimate variance components that give information on the genetic mechanism underlying the trait. The newer methods can utilize data on multiple types of pairs of relatives other than just sibpairs, and they can detect multiple trait loci. In combination with special sampling schemes, these methods give hope that they may play a crucial role in unraveling the genetic etiology of multifactorial traits, regardless of whether epistatic interactions are present. The results of such analyses can guide the use of more powerful model-based linkage analyses.

Chromosome Mapping↗

PubMed2001

Sample size requirements to control for stochastic variation in magnitude and location of allele-sharing linkage statistics in affected sibling pairs.

Typically, genome scans for complex disease have produced linkage peaks which have proved difficult to replicate in additional independent studies. Here we confirm that this may be due to the large variance in magnitude and position of the linkage statistics when maximized across a region. Simulations suggest that for genes of moderate effect (locus-specific sibling relative risks lambda s in the range 1.23-1.39), sample sizes of less than 500 affected sib pairs will give unacceptably large standard errors in the magnitudes and locations of significant linkage results. For genes of small effect (lambda s < or = 1.13), sample sizes in the region of 1000-2000 pairs may be required to achieve consistency of results between different studies. These figures have important implications for our confidence in location estimates for disease genes obtained from linkage studies of modest size. In particular, collection of larger data sets and/or analysis strategies such as conditioning or narrowing the phenotype definition, in order to increase the relative effect size, may be required before embarking on positional cloning.

Alleles↗

PubMed2001

Haplotype tagging for the identification of common disease genes.

Genome-wide linkage disequilibrium (LD) mapping of common disease genes could be more powerful than linkage analysis if the appropriate density of polymorphic markers were known and if the genotyping effort and cost of producing such an LD map could be reduced. Although different metrics that measure the extent of LD have been evaluated, even the most recent studies have not placed significant emphasis on the most informative and cost-effective method of LD mapping-that based on haplotypes. We have scanned 135 kb of DNA from nine genes, genotyped 122 single-nucleotide polymorphisms (SNPs; approximately 184,000 genotypes) and determined the common haplotypes in a minimum of 384 European individuals for each gene. Here we show how knowledge of the common haplotypes and the SNPs that tag them can be used to (i) explain the often complex patterns of LD between adjacent markers, (ii) reduce genotyping significantly (in this case from 122 to 34 SNPs), (iii) scan the common variation of a gene sensitively and comprehensively and (iv) provide key fine-mapping data within regions of strong LD. Our results also indicate that, at least for the genes studied here, the current version of dbSNP would have been of limited utility for LD mapping because many common haplotypes could not be defined. A directed re-sequencing effort of the approximately 10% of the genome in or near genes in the major ethnic groups would aid the systematic evaluation of the common variant model of common disease.

Base Sequence↗

PubMed2001

Statistical modeling of interlocus interactions in a complex disease: rejection of the multiplicative model of epistasis in type 1 diabetes.

In general, common diseases do not follow a Mendelian inheritance pattern. To identify disease mechanisms and etiology, their genetic dissection may be assisted by evaluation of linkage in mouse models of human disease. Statistical modeling of multiple-locus linkage data from the nonobese diabetic (NOD) mouse model of type 1 diabetes has previously provided evidence for epistasis between alleles of several Idd (insulin-dependent diabetes) loci. The construction of NOD congenic strains containing selected segments of the diabetes-resistant strain genome allows analysis of the joint effects of alleles of different loci in isolation, without the complication of other segregating Idd loci. In this article, we analyze data from congenic strains carrying two chromosome intervals (a double congenic strain) for two pairs of loci: Idd3 and Idd10 and Idd3 and Idd5. The joint action of both pairs is consistent with models of additivity on either the log odds of the penetrance, or the liability scale, rather than with the previously proposed multiplicative model of epistasis. For Idd3 and Idd5 we would also not reject a model of additivity on the penetrance scale, which might indicate a disease model mediated by more than one pathway leading to beta-cell destruction and development of diabetes. However, there has been confusion between different definitions of interaction or epistasis as used in the biological, statistical, epidemiological, and quantitative and human genetics fields. The degree to which statistical analyses can elucidate underlying biologic mechanisms may be limited and may require prior knowledge of the underlying etiology.

Animals↗

PubMed2001

Suggestive evidence for association of human chromosome 18q12-q21 and its orthologue on rat and mouse chromosome 18 with several autoimmune diseases.

Some immune system disorders, such as type 1 diabetes, multiple sclerosis (MS), and rheumatoid arthritis (RA), share common features: the presence of autoantibodies and self-reactive T-cells, and a genetic association with the major histocompatibility complex. We have previously published evidence, from 1,708 families, for linkage and association of a haplotype of three markers in the D18S487 region of chromosome 18q21 with type 1 diabetes. Here, the three markers were typed in an independent set of 627 families and, although there was evidence for linkage (maximum logarithm of odds score [MLS] = 1.2; P = 0.02), no association was detected. Further linkage analysis revealed suggestive evidence for linkage of chromosome 18q21 to type 1 diabetes in 882 multiplex families (MLS = 2.2; lambdas = 1.2; P = 0.001), and by meta-analysis the orthologous region (also on chromosome 18) is linked to diabetes in rodents (P = 9 x 10(-4)). By meta-analysis, both human chromosome 18q12-q21 and the rodent orthologous region show positive evidence for linkage to an autoimmune phenotype (P = 0.004 and 2 x 10(-8), respectively, empirical P = 0.01 and 2 x 10(-4), respectively). In the diabetes-linked region of chromosome 18q12-q21, a candidate gene, deleted in colorectal carcinoma (DCC), was tested for association with human autoimmunity in 3,380 families with type 1 diabetes, MS, and RA. A haplotype ("2-10") of two newly characterized microsatellite markers within DCC showed evidence for association with autoimmunity (P = 5 x 10(-6)). Collectively, these data suggest that a locus (or loci) exists on human chromosome 18q12-q21 that influences multiple autoimmune diseases and that this association might be conserved between species.

Animals↗

PubMed2000

Multilocus linkage tests based on affected relative pairs.

For complex diseases, recent interest has focused on methods that take into account joint effects at interacting loci. Conditioning on effects of disease loci at known locations can lead to increased power to detect effects at other loci. Moreover, use of joint models allows investigation of the etiologic mechanisms that may be involved in the disease. Here we present a method for simultaneous analysis of the joint genetic effects at several loci that uses affected relative pairs. The method is a generalization of the two-locus LOD-score analysis for affected sib pairs proposed by Cordell et al. We derive expressions for the relative risk, lambdaR, to a relative of an affected individual, in terms of the additive and epistatic components of variance at an arbitrary number of disease loci, and we show how these can be used to fit a likelihood model to the identity-by-descent sharing among pairs of affected relatives in extended pedigrees. We implement the method by use of a stepwise strategy in which, given evidence of linkage to disease at m-1 locations on the genome, we calculate the conditional likelihood curve across the genome for an mth disease locus, using multipoint methods similar to those proposed by Kruglyak et al. We evaluate the properties of our method by use of simulated data and present an application to real data from families with insulin-dependent diabetes mellitus.

Alleles↗

PubMed2000

Bootstrap confidence intervals for relative risk parameters in affected-sib-pair data.

In affected-sib-pair (ASP) studies, parameters such as the locus-specific sibling relative risk, lambda(s), may be estimated and used to decide whether or not to continue the search for susceptibility genes. Typically, a maximum likelihood point estimate of lambda(s) is given, but since this estimate may have substantial variability, it is of interest to obtain confidence limits for the true value of lambda(s). While a variety of methods for doing this exist, there is considerable uncertainty over their reliability. This is because the discrete nature of ASP data and the imposition of genetic "possible triangle" constraints during the likelihood maximization mean that asymptotic results may not apply. In this paper, we use simulation to evaluate the reliability of various asymptotic and simulation-based confidence intervals, the latter being based on a resampling, or bootstrap approach. We seek to identify, from the large pool of methods available, those methods that yield short intervals with accurate coverage probabilities for ASP data. Our results show that many of the most popular bootstrap confidence interval methods perform poorly for ASP data, giving coverage probabilities much lower than claimed. The test-inversion, profile-likelihood, and asymptotic methods, however, perform well, although some care is needed in choice of nuisance parameter. Overall, in simulations under a variety of different genetic hypotheses, we find that the asymptotic methods of confidence interval evaluation are the most reliable, even in small samples. We illustrate our results with a practical application to a real data set, obtaining confidence intervals for the sibling relative risks associated with several loci involved in type 1 diabetes.

Confidence Intervals↗

PubMed2000

Ascertainment bias in the estimation of sibling genetic risk parameters.

The sibling recurrence risk, sibling relative risk, and locus-specific sibling relative risk are fundamental quantities in genetic epidemiologic research and are often estimated without accounting for the sampling scheme. For data generated under some genetic models, bias of estimates may be large if the sampling method is incorrectly modeled. In this paper, we explore the relationship between ascertainment of sibships and estimation and interpretation of genetic risk parameters. In particular, we observe that, although traditional definitions of these population parameters are consistent with each other, implied assumptions about ascertainment and the nature of ascertainment correction differ. In the absence of ascertainment correction, unbiased estimation of sibling recurrence risk and overall sibling relative risk requires single ascertainment, while unbiased estimation of locus-specific sibling relative risk requires complete ascertainment.

Bias↗

PubMed2000

Correcting for ascertainment bias of relative-risk estimates obtained using affected-sib-pair linkage data.

Locus-specific sibling relative risk is often estimated using affected-sib-pair lod score analysis of affected sibships and may be used to decide whether to continue or discontinue the search for additional susceptibility genes. We showed that relative-risk estimates obtained using affected-sib-pair data are asymptotically unbiased when each pair is given a weight inversely proportional to the sibship ascertainment probability. Here we show by simulation that the extent of the bias of relative risks estimated using the incorrect ascertainment weights is small for dominant models, but large for single-locus recessive models and some two-locus heterogeneity models. Since in practice the ascertainment scheme is often unknown, we investigate methods for jointly estimating ascertainment and relative risks from affected-sibship data. Given a sufficient sample size, a reasonable estimate of relative risk may always be obtained using only affected pairs from sibships with two affected and no unaffected siblings. This estimate, which has a large variance, may then be used in a three-stage procedure (which we call the alpha method) to estimate consistently both the ascertainment probabilities and the relative risks with greater precision. We additionally propose correction factors to eliminate small-sample bias of relative risks and investigate the bias due to error in the estimate of disease locus location.

Bias↗

PubMed2000

Adaptation of the extended transmission/disequilibrium test to distinguish disease associations of multiple loci: the Conditional Extended Transmission/Disequilibrium Test.

Linkage and association studies in complex diseases are used to identify and fine map disease loci. The process of identifying the aetiological polymorphism, the molecular variant responsible for the linkage and association of the chromosome region with disease, is complicated by the low penetrance of the disease variant, the linkage disequilibrium between physically-linked polymorphic markers flanking the disease variant, and the possibility that more than one polymorphism in the most associated region is aetiological. It is important to be able to detect additional disease determinants in a region containing a cluster of genes, such as the major histocompatibility complex (MHC) region on chromosome 6p21. Some methods have been developed for detection of additional variants, such as the Haplotype Method, Marker Association Segregation Chi-squares (MASC) Method, and the Homozygous Parent Test. Here, the Extended Transmission/Disequilibrium Test is adapted to test for association conditional on a previously associated locus. This test is referred to as the Conditional Extended TDT (CETDT). We discuss the advantages of the CETDT compared to existing methods and, using simulated data, investigate the effect of polymorphism, inheritance, and linkage disequilibrium on the CETDT.

Genetic Diseases, Inborn↗

PubMed1999

Fieller's theorem and linkage disequilibrium mapping.

Linkage disequilibrium mapping exploits the fact that at genetic markers close enough to a disease locus on a particular chromosome, we expect to find an association between the disease and marker alleles. Furthermore, the magnitude of the association is expected to follow a unimodal curve when plotted against location, with the peak at the disease location. In practice, for real data, we usually see deviations from such a curve due to other influences such as evolutionary variability, mutation, and selection. Here we propose fitting a quadratic curve to data of this nature, estimating the location of the disease locus by the point at which the curve is maximum. A key feature of our method is the use of transformations of both location and disequilibrium, so that departures from a unimodal curve are incorporated by fitting the curve not to the original location and disequilibrium values but to the transformed values. In addition, we estimate the covariances between the disequilibrium values at linked loci using either a multinomial approximation or a bootstrap procedure. The location estimate from our method is the ratio of two quantities that, in large samples, are normally distributed, and so we use Fieller's theorem to obtain a confidence interval for the disease gene location. We successfully apply our method to data from several published studies in which the true disease gene location is known.

Alleles↗

PubMed1999

Improving the power for disease locus detection in affected-sib-pair studies by using two-locus analysis and multiple regression methods.

In this paper we present a summary of an analysis of the simulated data (Problem 2) for GAW11. We used sib-pair and affected-sib-pair (ASP) methods to evaluate linkage to the mild form of disease at markers across the genome, in data sets of realistic moderate size (containing between 100 and 300 families selected from the simulated replicates). The true 'answers' were known in advance. Although in most cases we were successful in detecting linkage to disease in the correct regions, it was often difficult to distinguish these results from false positives elsewhere in the genome. We used two-locus methods to see whether the significance was improved by simultaneously modeling linkage to two disease loci, and found a modest increase in significance using two-locus methods in several cases.

Genetic Linkage↗