Multifactorial inheritance in type 1 diabetes

To date, twelve separate chromosome regions have been implicated in the development of human type 1 (insulin-dependent) diabetes mellitus. The major disease locus, IDDM1 in the major histocompatibility complex (MHC) on chromosome 6p21, accounts for about 35% of the observed familial clustering and its contribution to disease susceptibility is likely to involve polymorphic residues of class II molecules in T-cell-mediated autoimmunity. IDDM2 is encoded by a minisatellite locus embedded in the 5 regulatory region of the insulin gene. Familial clustering of disease can be explained by the sharing of alleles of at least 10 loci. IDDM1 and IDDM2 interact epistatically. For a multifactorial disease, such as type 1 diabetes, important information concerning the pathways and mechanisms involved can be gained from examining such interactions between loci, using methods that simultaneously take account of the joint effects of the various underlying genetic components.     

The search for heterogeneity in insulin-dependent diabetes mellitus (IDDM): linkage studies, two-locus models, and genetic heterogeneity

One hundred families with insulin-dependent diabetes mellitus (IDDM) were analyzed for linkage with 27 genetic markers, including HLA, properdin factor B (BF), and glyoxalase 1(GLO) on chromosome 6, and Kidd blood group (Jk) on chromosome 2. The linkage analyses were performed under several different genetic models. An approximate correction for two-locus linkage analysis was developed and applied to four markers. Two different heterogeneity tests were implemented and applied to all the markers. One, the Predivided-Sample Test, utilizes various criteria thought to be relevant to genetic heterogeneity in IDDM. The other, the Admixture Test, looks for heterogeneity without specifying a prior how the sample should be divided. Results continued to support linkage of IDDM with three chromosome 6 markers: HLA, BF, and GLO. The total lod score for Kidd blood group, under the recessive model with 20% penetrance, is 1.63--down 1.2 from the 2.83 reported by us earlier. The only other marker whose lod score exceeded 1.0 under any model was pancreatic amylase (AMY2). The two-locus correction, which involved lowering the penetrance values used in the analysis, affected estimates of theta (recombination fraction) but did not markedly change the lod scores themselves. There was little evidence for heterogeneity within any of the lod scores, under either the Predivided-Sample Test or the Admixture Test.     

Effect of genetic heterogeneity and assortative mating on linkage analysis: a simulation study.

Linkage studies of complex genetic traits raise questions about the effects of genetic heterogeneity and assortative mating on linkage analysis. To further understand these problems, I have simulated and analyzed family data for a complex genetic disease in which disease phenotype is determined by two unlinked disease loci. Two models were studied, a two-locus threshold model and a two-locus heterogeneity model. Information was generated for a marker locus linked to one of the disease-defining loci. Random-mating and assortative-mating samples were generated. Linkage analysis was then carried out by use of standard methods, under the assumptions of a single-locus disease trait and a random-mating population. Results were compared with those from analysis of a single-locus homogeneous trait in samples with the same levels of assortative mating as those considered for the two-locus traits. The results show that (1) introduction of assortative mating does not, in itself, markedly affect the estimate of the recombination fraction; (2) the power of the analysis, reflected in the LOD scores, is somewhat lower with assortative rather than random mating. Loss of power is greater with increasing levels of assortative mating; and (3) for a heterogeneous genetic disease, regardless of mating type, heterogeneity analysis permits more accurate estimate of the recombination fraction but may be of limited use in distinguishing which families belong to each homogeneous subset. These simulations also confirmed earlier observations that linkage to a disease "locus" can be detected even if the disease is incorrectly defined as a single-locus (homogeneous) trait, although the estimated recombination fraction will be significantly greater than the true recombination fraction between the linked disease-defining locus and the marker locus.     

A genomewide scan for type 1-diabetes susceptibility in Scandinavian families: identification of new loci with evidence of interactions

Type 1 diabetes mellitus (TIDM) has a multifactorial etiology, with major genetic-susceptibility determinants located in the HLA and insulin-gene (INS) regions. Linkage data implicating other disease-susceptibility loci are conflicting. This is likely due to (1) the limited power for detection of contributions of additional susceptibility loci, given the limited number of informative families available for study, (2) factors such as genetic heterogeneity between populations, and (3) potential gene-gene and gene-environment interactions. To circumvent some of these problems, we have conducted a genomewide linkage analysis for T1DM-susceptibility loci in 408 multiplex families from Scandinavia, a population expected to be homogeneous for genetic and environmental factors. In addition to verifying the HLA and INS susceptibility loci, the study provides confirmation of IDDM15 on chromosome 6q21. Suggestive evidence of additional susceptibility loci was found on chromosomes 2p, 5q, and 16p. For some loci, the support for linkage increased substantially when families were stratified on the basis of HLA or INS genotypes, with statistically significant heterogeneity between the stratified subgroups. Our data support both the existence of non-HLA genes of significance for T1DM and interaction between HLA and non-HLA loci in the determination of the T1DM phenotype.     

Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).

A population association has consistently been observed between insulin-dependent diabetes mellitus (IDDM) and the "class 1" alleles of the region of tandem-repeat DNA (5'' flanking polymorphism [5''FP]) adjacent to the insulin gene on chromosome 11p. This finding suggests that the insulin gene region contains a gene or genes contributing to IDDM susceptibility. However, several studies that have sought to show linkage with IDDM by testing for cosegregation in affected sib pairs have failed to find evidence for linkage. As means for identifying genes for complex diseases, both the association and the affected-sib-pairs approaches have limitations. It is well known that population association between a disease and a genetic marker can arise as an artifact of population structure, even in the absence of linkage. On the other hand, linkage studies with modest numbers of affected sib pairs may fail to detect linkage, especially if there is linkage heterogeneity. We consider an alternative method to test for linkage with a genetic marker when population association has been found. Using data from families with at least one affected child, we evaluate the transmission of the associated marker allele from a heterozygous parent to an affected offspring. This approach has been used by several investigators, but the statistical properties of the method as a test for linkage have not been investigated. In the present paper we describe the statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test [TDT]). We then show the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs. The TDT provides strong evidence for linkage between the 5''FP and susceptibility to IDDM. The conclusions from this analysis apply in general to the study of disease associations, where genetic markers are usually closely linked to candidate genes. When a disease is found to be associated with such a marker, the TDT may detect linkage even when haplotype-sharing tests do not.     

Heterogeneity of HLA genetic factors in IDDM susceptibility

The association of certain HLA-D alleles with insulin-dependent diabetes mellitus (IDDM) is well known. One hundred and sixty-one non-related diabetic individuals and 142 non-related healthy controls were typed for the HLA DR-DQw-Dw association, using a restriction fragment length polymorphism (RFLP) typing method that combines three probe/enzyme systems: DRB/Taq I, DQB/Taq I, and DQB/Bam HI. Comparison of frequencies in both diabetics and controls confirms previous results in terms of HLA class II and IDDM association. Moreover, we have found that DR3/4 heterozygous individuals are more susceptible to IDDM when they are also Dw25 (associated with B18) than when they are Dw24 (associated with B8). Using oligonucleotide dot-blot hybridizations we analyzed the HLA-DQB1 sequence of DR3, Dw24 and DR3, Dw25 homozygous individuals, and we found no difference at position 57 between these two DR3-carrying haplotypes. This observation points to the heterogeneity of HLA genetic factors in IDDM susceptibility. Offprint requests to: D. Cohen.     

Genetic markers and insulin-dependent diabetes mellitus (IDDM) in Sardinia.

The distributions of some genetic markers in 106 Sardinian individuals with insulin-dependent diabetes mellitus (IDDM) and in a control sample of 186 nondiabetic Sardinians were studied. A strong association of IDDM with some phenotypes of ACP, PGM1, C3 and GC genetic markers was found. In the IDDM patients there is a significant increase of ACP B, ACP B-C, PGM1 1S-1F and GC 1S-2 phenotypes. Particularly the individuals with the ACP B, PGM1 1S-1F and GC 1S-2 phenotypes show the highest values of the relative risk (RR) and the attributable risk (AR) and seem to be more susceptible to IDDM disease.     

Application and interpretation of transmission/disequilibrium tests: transmission of HLA-DQ haplotypes to unaffected siblings in 526 families with type 1 diabetes

It is widely believed that, if a genetic marker shows a transmission distortion in patients by the transmission/disequilibrium test (TDT), then a transmission distortion in healthy siblings would be seen in the opposite direction. This is also the case in a complex disease. Furthermore, it has been suggested that replacing the McNemar statistics of the TDT with a test of heterogeneity between transmissions to affected and unaffected children could increase the power to detect disease association. To test these two hypotheses empirically, we analyzed the transmission of HLA-DQA1-DQB1 haplotypes in 526 Norwegian families with type 1 diabetic children and healthy siblings, since some DQA1-DQB1 haplotypes represent major genetic risk factors for type 1 diabetes. Despite the strong positive and negative disease associations with particular DQ haplotypes, we observed no significant deviation from 50% for transmission to healthy siblings. This could be explained by the low penetrance of susceptibility alleles, together with the fact that IDDM loci also harbor strongly protective alleles that can override the risk contributed by other loci. Our results suggest that, in genetically complex diseases, detectable distortion in transmission to healthy siblings should not be expected. Furthermore, the original TDT seems more powerful than a heterogeneity test.     

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.     

Seven regions of the genome show evidence of linkage to type 1 diabetes in a consensus analysis of 767 multiplex families

Type 1 diabetes (T1D) is a genetically complex disorder of glucose homeostasis that results from the autoimmune destruction of the insulin-secreting cells of the pancreas. Two previous whole-genome scans for linkage to T1D in 187 and 356 families containing affected sib pairs (ASPs) yielded apparently conflicting results, despite partial overlap in the families analyzed. However, each of these studies individually lacked power to detect loci with locus-specific disease prevalence/sib-risk ratios (lambda(s)) <1.4. In the present study, a third genome scan was performed using a new collection of 225 multiplex families with T1D, and the data from all three of these genome scans were merged and analyzed jointly. The combined sample of 831 ASPs, all with both parents genotyped, provided 90% power to detect linkage for loci with lambda(s) = 1.3 at P=7.4x10(-4). Three chromosome regions were identified that showed significant evidence of linkage (P<2.2x10(-5); LOD scores >4), 6p21 (IDDM1), 11p15 (IDDM2), 16q22-q24, and four more that showed suggestive evidence (P<7.4x10(-4), LOD scores > or =2.2), 10p11 (IDDM10), 2q31 (IDDM7, IDDM12, and IDDM13), 6q21 (IDDM15), and 1q42. Exploratory analyses, taking into account the presence of specific high-risk HLA genotypes or affected sibs' ages at disease onset, provided evidence of linkage at several additional sites, including the putative IDDM8 locus on chromosome 6q27. Our results indicate that much of the difficulty in mapping T1D susceptibility genes results from inadequate sample sizes, and the results point to the value of future international collaborations to assemble and analyze much larger data sets for linkage in complex diseases.     

Presence of multiple independent effects in risk loci of common complex human diseases

Many genetic loci and SNPs associated with many common complex human diseases and traits are now identified. The total genetic variance explained by these loci for a trait or disease, however, has often been very small. Much of the "missing heritability" has been revealed to be hidden in the genome among the large number of variants with small effects. Several recent studies have reported the presence of multiple independent SNPs and genetic heterogeneity in trait-associated loci. It is therefore reasonable to speculate that such a phenomenon could be common among loci known to be associated with a complex trait or disease. For testing this hypothesis, a total of 117 loci known to be associated with rheumatoid arthritis (RA), Crohn disease (CD), type 1 diabetes (T1D), or type 2 diabetes (T2D) were selected. The presence of multiple independent effects was assessed in the case-control samples genotyped by the Wellcome Trust Case Control Consortium study and imputed with SNP genotype information from the HapMap Project and the 1000 Genomes Project. Eleven loci with evidence of multiple independent effects were identified in the study, and the number was expected to increase at larger sample sizes and improved statistical power. The variance explained by the multiple effects in a locus was much higher than the variance explained by the single reported SNP effect. The results thus significantly improve our understanding of the allelic structure of these individual disease-associated loci, as well as our knowledge of the general genetic mechanisms of common complex traits and diseases.     

The modes of inheritance of insulin-dependent diabetes mellitus or the genetics of IDDM, no longer a nightmare but still a headache.

The discovery of HLA antigen associations with juvenile-type insulin-dependent diabetes mellitus (IDDM) provided strong evidence separating this disorder, or group of disorders, from maturity-type noninsulin-dependent diabetes, as well as adding to the evidence for an immunologic pathogenesis. In addition, it was hoped that the use of these disease-marker associations in appropriate studies might clarify the genetics of IDDM. While these associations have provided a useful tool to further investigate the genetics and pathogenesis of IDDM, the mode or modes of inheritance of this group of disorders remain an area of great controversy. Susceptibility to IDDM is currently being proposed as being inherited as a single autosomal dominant, as a single autosomal recessive, as recessive and some dominant forms, in an intermediate gene dosage model, in a heterogeneous three-allele or two HLA loci model, and as a two-locus disorder. The arguments for each of these proposals is presented, as well as the problems of each. We surmise that the weight of evidence supports the heterogeneity hypothesis but that the modes of inheritance of IDDM will be fully resolved only when we can more reliably identify the diabetogenic genotype, rather than being limited in our investigations to the study of only full-blown clinical disease.     

Two genetic loci regulate T cell-dependent islet inflammation and drive autoimmune diabetes pathogenesis

Insulin-dependent diabetes mellitus (IDDM) is a polygenic disease caused by progressive autoimmune infiltration (insulitis) of the pancreatic islets of Langerhan, culminating in the destruction of insulin-producing beta cells. Genome scans of families with diabetes suggest that multiple loci make incremental contributions to disease susceptibility. However, only the IDDM1 locus is well characterized, at a molecular and functional level, as alleleic variants of the major histocompatibility complex (MHC) class II HLA-DQB1, DRB1, and DPB1 genes that mediate antigen presentation to T cells. In the nonobese diabetic (NOD) mouse model, the Idd1 locus was shown to be the orthologous MHC gene I-Ab. Inheritance of susceptibility alleles at IDDM1/Idd1 is insufficient for disease development in humans and NOD mice. However, the identities and functions of the remaining diabetes loci (Idd2-Idd19 in NOD mice) are largely undefined. A crucial limitation in previous genetic linkage studies of this disease has been reliance on a single complex phenotype-diabetes that displays low penetrance and is of limited utility for high-resolution genetic mapping. Using the NOD model, we have identified an early step in diabetes pathogenesis that behaves as a highly penetrant trait. We report that NOD-derived alleles at both the Idd5 and Idd13 loci regulate a T lymphocyte-dependent progression from a benign to a destructive stage of insulitis. Human chromosomal regions orthologous to the Idd5 and -13 intervals are also linked to diabetes risk, suggesting that conserved genes encoded at these loci are central regulators of disease pathogenesis. These data are the first to reveal a role for individual non-MHC Idd loci in a specific, critical step in diabetes pathogenesis-T cell recruitment to islet lesions driving destructive inflammation. Importantly, identification of intermediate phenotypes in complex disease pathogenesis provides the tools required to progress toward gene identification at these loci.     

Heterozygous expression of insulin-dependent diabetes mellitus (IDDM) determinants in the HLA system.

HLA phenotypes of cases with insulin-dependent diabetes mellitus (IDDM) and identity by descent of HLA haplotypes in affected sib-pairs support an intermediate model in which morbid risk is increased by one HLA-linked IDDM determinant, and greatly increased by two determinants, which may be qualitatively different in DR3 and DR4 haplotypes. Linkage analysis allowing for gametic disequilibrium reveals no recombination in pedigrees with a DR3/DR4 propositus, but spurious recombination in the remaining pedigrees. This evidence favors interaction of unlinked IDDM determinants to produce affection in a small proportion of heterozygotes for an HLA-linked determinant. Partition of data by HLA type of the propositus (ideally by DR and the complement types jointly) is a powerful method to resolve etiological heterogeneity for HLA-associated diseases.     

Identification and isolation of dominant susceptibility loci for pristane-induced arthritis

Rheumatoid arthritis is a chronic inflammatory autoimmune disorder, controlled by multiple genes as well as environmental factors. With animal models, like the pristane-induced arthritis (PIA) in rats, it is possible to reduce the environmental effects and the genetic heterogeneity to identify chromosomal regions harboring genes responsible for the arthritis development. The PIA model has proved to be useful for identifying gene regions controlling different phases of the disease based on intercrosses between the resistant E3 and the susceptible DA rat. We have now performed a high-powered backcross analysis that confirms previous intercross-based data but also identifies additional loci. Earlier identified PIA loci were reproduced with high significance; Pia1 (MHC region on chromosome 20), Pia4 (chromosome 12), and Pia7 (chromosome 4) are all major regulators of PIA severity and were also found to operate in concert. These three loci were verified in congenic strains using both disease- and arthritis-inflammatory-related subphenotypes as traits. We were also able to detect five new quantitative trait loci with dominant effects on PIA: Pia10, Pia12, Pia13, Pia14, and Pia15 on chromosomes 10, 6, 7, 8, and 18, respectively. These data highlight the usefulness of the statistical power obtained in a backcross of a complex disease like arthritis.     

Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables

Complex human diseases do not have a clear inheritance pattern, and it is expected that risk involves multiple genes with modest effects acting independently or interacting. Major challenges for the identification of genetic effects are genetic heterogeneity and difficulty in analyzing high-order interactions. To address these challenges, we present MDR-Phenomics, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic effects in pedigree data by integration of phenotypic covariates (PCs) that may reflect genetic heterogeneity. The P value of the test is calculated using a permutation test adjusted for multiple tests. To validate MDR-Phenomics, we compared it with two MDR-based methods: (1) traditional MDR pedigree disequilibrium test (PDT) without consideration of PCs (MDR-PDT) and (2) stratified phenotype (SP) analysis based on PCs, with use of MDR-PDT with a Bonferroni adjustment (SP-MDR). Using computer simulations, we examined the statistical power and type I error of the different approaches under several genetic models and sampling scenarios. We conclude that MDR-Phenomics is more powerful than MDR-PDT and SP-MDR when there is genetic heterogeneity, and the statistical power is affected by sample size and the number of PC levels. We further compared MDR-Phenomics with conditional logistic regression (CLR) for testing interactions across single or multiple loci with consideration of PC. The results show that CLR with PC has only slightly smaller power than does MDR-Phenomics for single-locus analysis but has considerably smaller power for multiple loci. Finally, by applying MDR-Phenomics to autism, a complex disease in which multiple genes are believed to confer risk, we attempted to identify multiple gene effects in two candidate genes of interest—the serotonin transporter gene (SLC6A4) and the integrin beta 3 gene (ITGB3) on chromosome 17. Analyzing four markers in SLC6A4 and four markers in ITGB3 in 117 white family triads with autism and using sex of the proband as a PC, we found significant interaction between two markers—rs1042173 in SLC6A4 and rs3809865 in ITGB3.     

Analysis of genetic interrelationship among HLA-associated diseases.

We have developed a method to study the genetic relationship between any two HLA-associated diseases. We have considered the following hypotheses: (1) both diseases are caused by a common allele; (2) different alleles at the same locus predispose to the two diseases; (3) one disease is predisposed by two alleles, one of which can also lead to the second disease; and (4) different HLA-linked loci are involved in the etiology of each disease. For each hypothesis, we have derived the expected HLA haplotype-sharing distribution in sib pairs who are affected with two diseases. The comparison of the expectations indicate that, in many cases, the alternate hypotheses can be distinguished, if the sample size is appropriately large. The knowledge of the mode of inheritance of each disease is not usually necessary; however, it can greatly increase the power of the test. Analyses of data on pairwise combinations of rheumatoid arthritis (RA), autoimmune thyroid disease (ATD), and insulin-dependent (type I) diabetes mellitus (IDDM) suggest that (a) IDDM is predisposed by two HLA-linked alleles, one of which also predisposes to ATD, (b) one of the IDDM alleles also confers susceptibility to RA, and (c) although the HLA-linked susceptibilities to RA and ATD appear to be primarily due to distinct alleles, the ATD allele may also have a minor role in predisposition to RA.     

Fine mapping of the diabetes-susceptibility locus, IDDM4, on chromosome 11q13.

Genomewide linkage studies of type 1 diabetes (or insulin-dependent diabetes mellitus [IDDM]) indicate that several unlinked susceptibility loci can explain the clustering of the disease in families. One such locus has been mapped to chromosome 11q13 (IDDM4). In the present report we have analyzed 707 affected sib pairs, obtaining a peak multipoint maximum LOD score (MLS) of 2.7 (lambda(s)=1.09) with linkage (MLS>=0.7) extending over a 15-cM region. The problem is, therefore, to fine map the locus to permit structural analysis of positional candidate genes. In a two-stage approach, we first scanned the 15-cM linked region for increased or decreased transmission, from heterozygous parents to affected siblings in 340 families, of the three most common alleles of each of 12 microsatellite loci. One of the 36 alleles showed decreased transmission (50% expected, 45.1% observed [P=.02, corrected P=.72]) at marker D11S1917. Analysis of an additional 1,702 families provided further support for negative transmission (48%) of D11S1917 allele 3 to affected offspring and positive transmission (55%) to unaffected siblings (test of heterogeneity P=3x10-4, corrected P=. 01]). A second polymorphic marker, H0570polyA, was isolated from a cosmid clone containing D11S1917, and genotyping of 2,042 families revealed strong linkage disequilibrium between the two markers (15 kb apart), with a specific haplotype, D11S1917*03-H0570polyA*02, showing decreased transmission (46.4%) to affected offspring and increased transmission (56.6%) to unaffected siblings (test of heterogeneity P=1.5x10-6, corrected P=4.3x10-4). These results not only provide sufficient justification for analysis of the gene content of the D11S1917 region for positional candidates but also show that, in the mapping of genes for common multifactorial diseases, analysis of both affected and unaffected siblings is of value and that both predisposing and nonpredisposing alleles should be anticipated.     

BF types and the mode of inheritance of insulin-dependent diabetes mellitus (IDDM)

Insulin-dependent diabetes mellitus (IDDM) has been found to be highly associated with a rare allele of the complement protein, properdin factor B (BF). Assuming that there is a susceptibility gene for IDDM tightly linked to the genetic locus forBF and the major histocompatibility complex (MHC), the distribution of BF types in more than 1100 North American IDDM patients strongly argues for the rejection of dominant, epistatic, and overdominant modes of inheritance. Other evidence suggesting complex modes of inheritance for IDDM is reviewed and it is concluded that our observations and published data are consistent with the idea of susceptibility to IDDM being inherited as a simple autosomal recessive trait. — C4 and C2 types, also linked toBF and theMHC, were investigated too. C4 Fs⁰ was found to be increased in association with BF F1, while C4 f⁰S and C2 B were each found to occur twice as frequently as in a control population and will be of value in defining haplotypes associated with susceptibility to IDDM.