Comparing the power of linkage detection by the transmission disequilibrium test and the identity-by-descent test

Association of HLA-DRB1 and -DQB1 alleles with type 1 (autoimmune) diabetes in African Arabs: systematic review and meta-analysis

Several studies confirmed the association of HLA-DRB1 and -DQB1 alleles with altered risk of type 1 diabetes (T1D). However, data from individual studies based on small sample sizes yielded often conflicting findings in African Arabs. This is a systematic review and meta-analysis aimed at comprehensively evaluating this association with T1D, using molecular HLA data. Relevant studies were identified through systemic search of Medline/PubMed, Cochrane, Science Direct, ResearchGate, and EMBASE databases. Statistical analysis was carried out using RevMan, and Comprehensive Meta-analysis programs. Given the heterogeneity of African Arabs, we also performed subgroup analysis according to ethnicity. Analysis of sensitivity, heterogeneity, and publication bias were performed to validate the outcome of the findings. This meta-analysis included 862 T1DM cases, along with 1,390 normoglycemic control, and comprised ten comparisons. Our study indicates that DRB1*03 (OR = 2.86), DRB1*04 (OR = 2.78), and DQB1*02 (OR = 2.29), are positively associated with increased risk of T1DM, while DRB1*07 (OR = 0.48), DRB1*11 (OR = 0.20), DRB1*13 (OR = 0.47), DRB1*15 (OR = 0.30), DQB1*05 (OR = 0.39), and DQB1*06 (OR = 0.27) were negatively associated with T1D, suggesting a protective role against T1D. This meta-analysis was characterized by low heterogeneity, sensitivity, and publication bias, indicating the robustness and reliability of the results.

BACKGROUND

Several studies confirmed the association of HLA-DRB1 and -DQB1 alleles with altered risk of type 1 diabetes (T1D). However, data from individual studies based on small sample sizes yielded often conflicting findings in African Arabs. This is a systematic review and meta-analysis aimed at comprehensively evaluating this association with T1D, using molecular HLA data.

METHODS

Relevant studies were identified through systemic search of Medline/PubMed, Cochrane, Science Direct, ResearchGate, and EMBASE databases. Statistical analysis was carried out using Revman, and Comprehensive Meta-analysis programs. Given the heterogeneity of African Arabs, we also performed subgroup analysis according to ethnicity. Analysis of sensitivity, heterogeneity, and pub¬lication bias were performed to validate the outcome of the findings. This meta-analysis included 862 T1DM cases, along with 1,390 normoglycemic control, and comprised ten comparisons.

RESULTS

Our study indicates that DRB1*03 (OR = 2.86), DRB1*04 (OR = 2.78), and DQB1*02 (OR = 2.29), are positively associated with increased risk of T1DM, while DRB1*07 (OR = 0.48), DRB1*11 (OR = 0.20), DRB1*13 (OR = 0.47), DRB1*15 (OR = 0.30), DQB1*05 (OR = 0.39), and DQB1*06 (OR = 0.27) were negatively associated with T1D, suggesting a protective role against T1D.

CONCLUSION

This meta-analysis was characterized by low heterogeneity, sensitivity, and publication bias, indicating the robustness and reliability of the results.

Searching for additional disease loci in a genomic region

Our aim is to review methods to optimize detection of all disease genes in a genetic region. As a starting point, we assume there is sufficient evidence from linkage and/or association studies, based on significance levels or replication studies, for the involvement in disease risk of the genetic region under study. For closely linked markers, there will often be multiple associations with disease, and linkage analyses identify a region rather than the specific disease-predisposing gene. Hence, the first task is to identify the primary (major) disease-predisposing gene or genes in a genetic region, and single nucleotide polymorphisms thereof, that is, how to distinguish true associations from those that are just due to linkage disequilibrium with the actual disease-predisposing variants. Then, how do we detect additional disease genes in this genetic region? These two issues are of course very closely interrelated. No existing programs, either individually or in aggregate, can handle the magnitude and complexity of the analyses needed using currently available methods. Further, even with modern computers, one cannot study every possible combination of genetic markers and their haplotypes across the genome, or even within a genetic region. Although we must rely heavily on computers, in the final analysis of multiple effects in a genetic region and/or interaction or independent effects between unlinked genes, manipulation of the data by the individual investigator will play a crucial role. We recommend a multistrategy approach using a variety of complementary methods described below.

Modeling the effect of PTPN22 in rheumatoid arthritis

In order to model the effect of PTPN22 on rheumatoid arthritis (RA), we determined the combination of single-nucleotide-polymorphisms (SNPs) showing the strongest association with RA. Three SNPs (rs2476601-rs12730735-rs11102685) were selected for which we estimated the genotypic relative risks (GRRs) of the corresponding genotypes. On the basis of these GRRs we defined four at-risk genotypic classes. Relative to the class of reference risk, individuals had a risk approximately multiplied by two, three, or four. This classification was confirmed by the excess of identity-by-descent (IBD) sharing (IBD = 2) for the sibs of an index in the high-risk class and by excess of non-IBD sharing (IBD = 0) when the index belonged to the low-risk class. The observed data could not be explained by the role of a single variant but were compatible either with a joint effect of the three typed SNPs of PTPN22 on RA or with the role of two untyped variants.

New classification of HLA-DRB1 alleles supports the shared epitope hypothesis of rheumatoid arthritis susceptibility

OBJECTIVE

The shared epitope hypothesis was formulated to explain the involvement of HLA-DRB1 in rheumatoid arthritis (RA). However, several studies, which considered only the HLA-DRB1 alleles shown to be associated with RA risk, rejected this hypothesis. In this report, we propose that a different classification of HLA-DRB1 alleles be considered, based on the amino acid sequence at position 70-74.

METHODS

The fit of both HLA-DRB1 classifications was tested in 2 groups of RA patients. All subjects were recruited through the European Consortium on Rheumatoid Arthritis Families, and included 100 patients with isolated RA and 132 patients with at least 1 affected sibling.

RESULTS

The new classification produced risk estimates that fit all of the observed data, i.e., the distribution of the HLA-DRB1 genotype in the 2 patient groups, and the distribution of parental alleles shared by affected sibpairs. The risk of developing RA under this new classification depends on whether the RAA sequence occupies position 72-74 but is modulated by the amino acid at position 71 (K confers the highest risk, R an intermediate risk, A and E a lower risk) and by the amino acid at position 70 (Q or R confers a higher risk than D).

CONCLUSION

A new classification based on amino acid sequence allows us to show that the shared epitope RAA sequence at position 72-74 explains the data, with the risk of developing RA modulated by the amino acids at positions 70 and 71.

MICA Gene Polymorphism in HBDI Multiplex Families

T1DM is a disease that affects pancreatic beta cells and results in severe insulin depletion. T1DM is a multigenic disease, and the strongest genetic association with this disease is shown by the genes in MHC class II, namely, DQA1 and DQB1. The other gene that has been implicated in susceptibility to T1DM is the MICA gene that lies within the MHC class I region. This gene has been investigated in many autoimmune diseases, including T1DM, in case-control as well as in family studies. The aim of our study was to test the transmission of MICA microsatellite alleles from unaffected parents to T1DM- affected offspring in HBDI multiplex nuclear families. We also looked at the transmission of MICA alleles together with high-risk DQA1-DQB1 haplotypes to determine the independent transmission of MICA alleles. We observed that MICA6 and MICA9 are transmitted to affected offspring less frequently than expected, and MICA5.1 was more frequently transmitted. DQA1 and DQB1 high-risk haplotypes were transmitted more frequently than expected and DQ6, which is a protective haplotype, was less frequently transmitted to affected offspring. Analysis of MICA-DQA1-DQB1 transmission showed that certain MICA alleles are preferably transmitted as a part of high-risk haplotypes, which might indicate that MICA together with high-risk HLA is associated with T1DM in this family material. However, this latter analysis should be repeated on a larger family sample.

Power comparisons between the TDT and two likelihood-based methods

We compare the statistical power of the transmission disequilibrium test (TDT) with that of two likelihood-based linkage tests, the classical LOD score and a modified LOD score in which a linkage disequilibrium (LD) parameter is incorporated into the likelihood (LD-LOD). We hypothesize that, when LD is present, the LD-LOD will have the greatest power of the three tests because the TDT breaks a multiplex pedigree into triads, and the LOD score has previously been shown to have lower power when LD is present but not accounted for. We test this hypothesis using a simulation study in which we generate affected sib-pair (ASP) pedigrees under a range of genetic models, varying the genotypic relative risk (GRR) from 6 to 16. Because the likelihood-based tests require that a genetic model be specified, we compare the tests under two scenarios. First, we assume the true genetic model in the analysis, and second, we compare the tests when the LD-LOD (LOD) is maximized over two wrong genetic models. For the generating models we considered, we find that the LD-LOD has greater power than the TDT even when the genetic models is mis-specified and the results corrected for multiple tests. Extreme differences occur under the multiplicative and dominant models, for which the difference in power is as high as 40% at complete LD. The LOD score provides the lowest power in the presence of LD for the range of GRR considered here.

Interleukin 1 gene cluster polymorphisms in multiplex families with spondylarthropathies

Interleukin 1alpha (IL-1alpha) has been implicated in the pathogenesis of infectious, auto-immune and inflammatory diseases. Polymorphisms in the genes encoding IL-1alpha, IL-1beta and the IL-1 receptor antagonist (ILRN) molecules have been associated with several inflammatory diseases. As the marker D2S160 has been characterized as a candidate locus for Ankylosing Spondylitis (AS) after a genome-wide scan, and since this locus is located approximately 0.3 cM telomeric to the IL-1 gene cluster, we hypothesized that these cytokines might be good candidates for Spondylarthropathies (SpA). Therefore we tested this hypothesis using the transmission disequilibrium test (TDT) in 37 families representing 217 subjects from three intragenic IL-1 gene cluster polymorphisms, the IL1B Taq I, the ILRN 2nd intron, and the IL1A Nco I, taking HLA B27 status into account or not. In conclusion, by means of intra-familial TDT analysis we found no linkage or intra-familial association between SpA and the three IL-1 gene-cluster polymorphisms in SpA multiplex family material.

Family-based association studies

Over the past decade, attention has turned from positional cloning of Mendelian disease genes to the dissection of complex diseases. Both theoretical and empirical studies have shown that traditional linkage studies may be inferior in power compared to studies that directly utilize allele status. Case-control association studies, as an alternative, are subject to bias due to population stratification. As a compromise between linkage studies and case-control studies, family-based association designs have received great attention recently due to their potentially higher power to identify complex disease genes and their robustness in the presence of population substructure. In this review, we first describe the basic family-based association design involving one affected offspring with its two parents, all genotyped for a biallelic genetic marker. Extensions of the original transmission disequilibrium tests to multiallelic markers, families with multiple siblings, families with incomplete parental genotypes, and general pedigree structures are discussed. Further developments of statistical methods to study quantitative traits, to analyse genes on the X chromosome, to incorporate multiple tightly linked markers, to identify imprinting genes, and to detect gene-environment interactions are also reviewed. Finally, we discuss the implications of the completion of the Human Genome Project and the identification of hundreds of thousands of genetic polymorphisms on employing family-based association designs to search for complex disease genes.

Discovery of cancer susceptibility genes: study designs, analytic approaches, and trends in technology

Determining the genetic causes of cancers has immense public health benefits, ranging from prevention to earlier detection and treatment of disease. Although a number of cancer susceptibility genes have been successfully identified, design and analytic issues remain that challenge the current paradigm of gene discovery. Some examples are the definition and measurement of cancer phenotype, the use of intermediate end points, the choice of sample (e.g., affected relative pairs versus large extended pedigrees), the choice of analytic method [e.g., parametric logarithm of the odds (LOD) score method versus model-free methods], and the influence of gene-environment interaction on linkage analysis. Furthermore, association methods, based on either the traditional case-control study design or family-based controls, are popular choices to evaluate candidate genes or screen for linkage disequilibrium. Finally, the study design and analytic methods for gene discovery are determined to some extent by what genomic technology is feasible within the laboratory. Many of the main issues related to gene discovery, as well as trends in genomic technology that will impact on gene discovery, are discussed from the perspective of their strengths and weaknesses, pointing to areas in need of further work.

Linkage and association

Methods of both linkage analysis and association analysis may be model-based or model-free. The former are useful for initial exploratory analysis, the latter for more detailed multivariate genometric analysis. Linkage leads to an association, but that association may be solely intrafamilial. Allelic association may be due to pleiotropy, linkage disequilibrium, meiotic drive, selection, or population stratification. Using non-transmitted parental alleles as controls for alleles transmitted to cases, in conjunction with a McNemar-type test, does not detect association in the absence of linkage. Model-based analyses should use models that approximate the complexity of the disease being studied in order to be both robust and powerful.

Linkage and association study of the CTLA-4 region in coeliac disease for Italian and Tunisian populations

Coeliac disease (CD) is a multifactorial disease for which there is an intensive search for genetic risk factors. Some authors found an association between the CTLA-4 region and CD. In the present work, we investigate the possible implication of the CTLA-4 region as a genetic risk factor for CD, through two statistical approaches: the maximum likelihood score (MLS) test in a large Italian sample of affected sib-pairs using polymorphic genetic markers on chromosome 2, and the transmission disequilibrium test (TDT) in continental Italian and Tunisian families using the CTLA-4 exon 1 49 A/G polymorphism. None of these approaches provides evidence for linkage or association between the CTLA-4 region and CD. This might result from a difference in the CTLA-4 region from population to population, either in its involvement as a risk factor or in the strength of linkage disequilibrium.