Confirmation of three susceptibility genes to insulin-dependent diabetes mellitus: IDDM4, IDDM5 and IDDM8
Confirmation of three susceptibility genes to insulin-dependent diabetes mellitus: IDDM4 , IDDM5 and IDDM8 De-Fang Luo1, Raffaella Buzzetti2, Jerome I. Rotter3, Noel K. Maclaren1, Leslie J. Raffel3, Lorenza Nisticò2, Claudio Giovannini2, Paolo Pozzilli2, Glenys Thomson4 and Jin-Xiong She1,5,*
1Department of Pathology, Immunology and Laboratory Medicine, 5Center for Mammalian Genetics, College of Medicine, University of Florida, Gainesville, Florida, USA, 2Endocrinologia, Istituto Clinica Medica II, University of Rome `La Sapienza', Rome, Italy, 3Department of Medicine and Pediatrics, Cedars-Sinai Medical Research Institute and UCLA, Los Angeles, California, USA and 4Department of Integrative Biology, University of California, Berkeley, California, USA
Received February 12, 1996; Revised and Accepted February 15, 1996
Previous genome-wide mapping studies have provided suggestive linkage evidence for several novel susceptibility loci responsible for insulin-dependent diabetes mellitus (IDDM); however, the evidence was not sufficient to confirm the existence of these genes. We analyzed 265 Caucasian families with IDDM and report the first evidence that meets the standard for confirmed linkage for three susceptibility loci. The maximum LOD scores (MLS) were 3.9, 4.5 and 3.6 in our data set, and 5.0, 4.6 and 5.0 for our data combined with non-overlapping data from the literature, for IDDM4 on chromosome 11q13, IDDM5 on 6q25, and IDDM8 on 6q27, respectively. However, we could not confirm linkage for IDDM3 on 15q26 and IDDM7 on 2q31-q33, or linkage disequilibrium between D2S152 and IDDM7.
IDDM is a multifactorial and polygenic disease, which is influenced by a large number of susceptibility genes in the nonobese diabetic (NOD) mouse (1 ,2 ) as well as humans (3 -10 ). The HLA class II region on chromosome 6p (IDDM1) contains several IDDM susceptibility genes (11 -17 ) and appeared to contribute the strongest genetic component to IDDM. IDDM2 was initially mapped to chromosome 11q15.5 in the insulin gene region by case-control, then confirmed by intrafamilial association studies (18 -24 ). Recently, cumulative evidence suggests that IDDM2 is the VNTR locus at the 5' end of the insulin gene (24 ,25 ). IDDM1 and IDDM2 were both found more than a decade ago by the so-called candidate gene approach. Since then progress in mapping additional diabetes genes has been very slow. Recently, several potential linkage intervals for IDDM have been mapped by genome-wide or partial-genome scans carried out in a number of laboratories (3 -10 ,26 ). IDDM3 was assigned to a region near D15S107 on chromosome 15q26 (5 ,8 ) but linkage evidence for the interval was suggestive. IDDM4 was assigned to a region close to D11S1337-FGF3 on chromosome 11q13 (3 -5 ,8 ). The overall linkage evidence was probably strong, although the data was not jointly analyzed. Support for IDDM5 near ESR on chromosome 6q25 was weak in previously published data sets (3 ,8 ). IDDM7 was assigned to chromosome 2q31-q33. Linkage evidence for this locus was weak but reported by three independent groups (3 ,6 ,8 ). Interestingly, evidence for linkage disequilibrium was observed in several, but not all, data subsets that have been reported (7 ,10 ). IDDM8 was assigned to chromosome 6q27, which is approximately 40 cM more telomeric than IDDM5 (8 ). While all these results were important observations, much more evidence was required to confirm these potential IDDM susceptibility loci.
As shown by Lander and Kruglyak (27 ), a large number of false linkages with weak supporting evidence may occur by random chance in a genome scan. They demonstrated that highly significant evidence, including replication of results, is required to confirm real linkage. The criteria that they proposed for mapping complex traits are very difficult to achieve, and many hundreds of families may be required to confirm linkage. Indeed, none of the recently mapped IDDM loci reached the criterium for significant linkage (LOD = 3.6) proposed by Lander and Kruglyak. Here, we have expanded our initial studies (8 ) to additional families and polymorphic markers in the regions containing IDDM3, IDDM4, IDDM5, IDDM7 and IDDM8. We report linkage evidence that meets the standard for confirmed linkage for three IDDM susceptibility genes, i.e. IDDM4, IDDM5 and IDDM8, but not for IDDM3 or IDDM7.
IDDM4 was previously mapped to a region near D11S1337 on 11q13 (8 ). We have analyzed 265 Caucasian families from continental Italy and various regions of the USA (Table 1 ) using D11S1296, which maps close to D11S1337. As shown in Table 2 , positive linkage evidence was obtained from all data subsets and the combined UF data set had a MLS value of 3.9. The percent of gene sharing (PGS) and MLS were also calculated for each of the data subsets used in this study as well as other published data sets (Table 2 ). As the sample size is quite different in the data subsets, PGS is given to depict the degree of heterogeneity among data sets. As evaluated with [chi]2 heterogeneity test, there is no significant heterogeneity between data sets for IDDM4 (P = NS).
Note: Only non-overlapping data sets from the literature were given here. The HBDI families and the UK families were analyzed by multiple laboratories. They are not included here because overlapping with our or other data sets cannot be easily determined.PGS = percent of gene sharing: 1 i.b.d./(1 i.b.d. + 0 i.b.d.). Reference 0 = this study.
Our previous studies (8 ) have shown that the 6q25-q27 interval may encode two distinct IDDM susceptibility genes, i.e. IDDM5 and IDDM8. Here, we have analyzed 265 diabetic families using 30 microsatellite markers (see Materials and Methods) in the 6q25-q27 region to fine-map these two genes. The strongest linkage evidence for IDDM5 was obtained at ESR, giving a MLS of 4.5 (Fig. 1 and Table 2 ). Kruglyak and Lander proposed to define the 95% confidence interval for disease genes involved in complex traits as the region that includes all markers with a LOD score greater than LODmax -1.3 (28 ). As defined by this approach, IDDM5 is contained in a 5 cM region between D6S476 and D6S473 (Fig. 1 ). In contrast to IDDM4, the PGS for IDDM5 varies from one data set to another and the lowest PGS was observed in the UK 102 data set (PGS = 47%). All other data sets had PGS values higher than the randomly expected 50%. A global [chi]2 heterogeneity test suggests that there is significant heterogeneity among the data subsets (P = 0.025). The heterogeneity in the overall data is caused by the low PGS value in the UK 102 data set. Indeed, the UK 102 data set (PGS = 47%) is significantly different from the UF data set (P = 0.006) as well as the UK 96 data set (P = 0.01).
aLOD scores for these two data sets were quoted from Field et al. (5) who used a different analytic method. MLS was not recalculated due to unavailability of original data. The USA data set analyzed by Field et al. (5) was not used here because it significantly overlaps with our data set presented here.PGS: percent of gene sharing; NA: data not available. Reference 0 = this study.
Consistent with our previous results, IDDM8 showed strong evidence for linkage at D6S446 and D6S281. The MLS values were 3.2 and 3.6 for these two markers, respectively (Fig. 1 ). Similarly, IDDM8 may be contained in a region of approximately 6 cM between D6S1027 and TBP. Additional markers and families are required to define better the location of this gene. As shown by the PGS values for each data subset (Table 2 ), linkage evidence for IDDM8 was quite homogeneous in these samples (P = NS).
This expanded data set provided convincing evidence for the existence of two distinct IDDM susceptibility genes in the 6q25-q27 region. This is best evidenced by the observation that the 95% confidence intervals for the two disease genes do not overlap. The peak marker would be expected to occur in the middle of the 6q25-q27 region if only one IDDM gene was encoded in this region. This type of analysis is only valid when the informativeness is high for markers across the region. Haplotyping analysis using markers at high density can greatly increase the informativeness for the markers. For examples, after haplotyping the informativeness became 97.5, 96.5, 95.2, 96.0, 96.4, 95.6 and 93.7% for ESR, D6S473, D6S437, D6S253, D6S392, D6S1027 and D6S446, respectively. These results suggest that the drop in MLS in the middle region of 6q25-q27 is not due to low informativeness of the markers.
In our initial analyses of 104 families using seven markers in the 15q26 region (8 ), we obtained supporting evidence for IDDM3 as by Field et al. (5 ). As D15S107 gave the highest MLS value in our initial data set, we have analyzed this marker in the remaining families. Surprisingly, linkage evidence in the overall UF data set was almost non-existent (MLS = 0.2) (Table 3 ).
D2S152 was used to analyze the IDDM7 interval on 2q31-q33. As shown in Table 3 , there is even no suggestive linkage evidence for a diabetes locus in this region (Table 3 ). Similar results were also reported in the UK families (7 ). As linkage disequilibrium between D2S152 and IDDM was previously reported (7 ), we have analyzed our data using the transmission disequilibrium test. The only positive evidence for linkage disequilibrium was obtained in the Florida data subset as previously reported (10 ). However, the combined USA data set did not provide any supporting evidence (Table 4 ).
Even though genetic dissection of complex traits such as IDDM remains a difficult task, rapid progress is being made for several common diseases. For example, up to 10 genomic regions have been suggested to contain susceptibility genes responsible for IDDM (3 -10 ,26 ). As a large number of linkages with weak supporting evidence may occur by random chance, some of the reported linkages would be expected to be false. Lander and Kruglyak (27 ) proposed a set of criteria for mapping genes involved in complex traits. (i) Suggestive linkage-statistical evidence expected to occur one time at random in a genome scan (P <7.4 * 10-4, or MLS or LOD = 2.2 for affected sibpair); this criterium is essentially equivalent to the three criteria previously proposed by one of us (29 ). (ii) Significant linkage-statistical evidence expected to occur 0.05 times in a genome scan (P <2.2 * 10-5 or LOD = 3.6). (iii) Highly significant linkage-statistical evidence expected to occur 0.001 times in a genome scan (P <3 * 10-7 or LOD = 5.4). (iv) Confirmed linkage-significant linkage from one or a combination of initial studies that has subsequently been confirmed in a further sample with a nominal P value of 0.01.
The above standards for highly significant and confirmed linkages are very difficult to achieve for complex traits. Indeed, the evidence reported in previous studies was not sufficient for significant linkage, and only met the standard for suggestive linkage, and even then sometimes only in certain subsets of the data. For example, IDDM4 on chromosome 11q13 had a MLS value of 1.3 in the combined data set studied by Davies et al. (3 ), even though a MLS of 3.4 was achieved in the subset of affected sibpairs who shared one or no HLA haplotypes. A MLS value of 1.5 was also achieved for the same interval in a French data set (4 ). In both studies, stronger statistical evidence was obtained after the data set was stratified based upon HLA sharing, or the HLA types of the affected sibpairs. However, a consistent picture did not emerge from these studies. We jointly analyzed these two data sets, a MLS of 1.9 was obtained for the combined data set. As we have previously mapped IDDM4 to a region near D11S1337 (which is more centromeric to FGF3), we analyzed all 265 families using the tetranucleotide repeat D11S1296 that maps near D11S1337. We obtained a MLS value of 3.9 for IDDM4 in our data set. This stronger evidence from our data set does not reflect genetic heterogeneity (P = NS); may be due to the fact that a marker closer to the disease gene is analyzed in our study. Since significant linkage evidence was obtained in our data set and supporting evidence was reported by two other independent laboratories, IDDM4 is a confirmed linkage according to the guidelines proposed by Lander and Kruglyak (27 ) and Thomson (29 ).
IDDM5 on chromosome 6q25 fell short of suggestive linkage in previous reports (3 ,8 ). However, the evidence reported here met the standard for significant linkage (MLS = 4.5). Together with the UK data sets in Davies et al. (MLS = 1.8) (3 ), IDDM5 is thus confirmed. Furthermore, we were able to map this locus within a region of 5 cM between the markers D6S476 and D6S473.
We have previously mapped IDDM8 to the 6q27 region near D6S446 which gave a MLS of 2.8. The MLS for IDDM8 in our data reported here was 3.6, barely meeting the standard for significant linkage. As additional linkage evidence was also obtained in the UK families (MLS = 1.4) (3 ) with a MLS for the combined data of 5.0, IDDM8 also is a confirmed linkage.
The initial linkage evidence for IDDM3 (5 ) met the standard for suggestive linkage and additional evidence (P = 0.05) was also obtained in the non-overlapping families that we previously reported (8 ). The MLS for IDDM3 in our combined data of 265 families was only 0.2. Even though a LOD score of 0.9 was obtained in the first 100 UK families analyzed by Field et al. (5 ), no linkage evidence was obtained for IDDM3 in the UK 96 data set (J. Todd, personal communication).
Weak linkage evidence for IDDM7 was reported in three initial data sets (3 ,6 ,8 ); however, later analyses of larger data sets from the UK (7 ) and the US (this study) did not reveal any suggestive linkage (see Table 3 ). Interestingly, one of the major alleles (known as allele 228) of the D2S152 locus was shown to be associated with IDDM using the TDT. The initial observation was made in three of the five data sets analyzed for the marker (7 ) and supporting evidence was also obtained in our own data subset composed of the Caucasian families in the Florida subset (Table 4 ). The most significant evidence was obtained from 94 US diabetic families obtained in the HBDI collection (P = 0.001). However, no evidence for linkage disequilibrium was seen in the overall data set of HBDI families analyzed in this study or in the combined USA families (Table 4 ). Although weak evidence for linkage disequilibrium was observed (P = 0.014) in the first 36 Italian families, the evidence was not significant when we expanded the data set to 46 families. These results together suggest that IDDM7 cannot be confirmed either by linkage or association.
Several possibilities may explain the failure to confirm linkage for IDDM3 and IDDM7. First, these linkages may be `spurious linkage' and thus no disease gene is encoded in these regions. Second, it may take more families to confirm these genes because they have weaker influence on the disease phenotype. Based on the PGS values in various data subsets, it is clear that both IDDM3 and IDDM7 are weaker susceptibility factors if they are indeed true. Finally, it would be difficult to obtain significant linkage in the overall data set if linkage evidence is heterogeneous from one data set to another. At this stage, we cannot formally rule out any of these possibilities, although we favor the first hypothesis.
Many valuable lessons for mapping genes involved in common diseases can be learned from the studies of IDDM. First, it has now become evident that a genome-wide scan can be used to identify genes involved in common diseases, even though it remains a very difficult task. Second, variations in gene sharing between data sets are very common. Although genetic heterogeneity is a possibility to explain these variations, random chance is probably responsible for most variations. Third, the values for identity by descent (i.b.d.) may vary dramatically even in sample sets of [approx]100 fully informative sibpairs. It appears that the i.b.d. values become more stable in data sets of >200 fully informative sibpairs. This probably is the minimum number of families that should be targeted for mapping studies. Finally, linkage evidence may decrease very quickly as the distance from the disease gene increases. Therefore, a high-density map of highly polymorphic markers would be very helpful to confirm real linkage.
We have obtained genomic DNA from a total of 265 Caucasian families (referred to as UF 265 data set) including 104 families used in our previous report (8 ). Most families have two affected siblings. In the families with more than two affected siblings, only results on the first two siblings were included in the analyses. Among the 265 families, 53 were ascertained from southeastern USA, mostly north-central Florida (the FL data subset). Cell lines have been established for most of the Florida families and are available to other investigators through the human biological data interchange (HBDI). Sixteen additional families were ascertained in Los Angeles and 150 additional families were obtained from the HBDI. These families were from diverse geographic regions of the United States and will be referred to as the HBDI 166 data subset. The combined HBDI and Florida data set was referred to as USA 219 data set. We also obtained 46 families from continental Italy (the ITA data subset). Both parents were available for the vast majority of the families used in this study. Among the Italian families, one parent was unavailable in seven families and both parents were unavailable in five families. In the HBDI data set, one parent was not available in three families. In some cases, the identity by descent by ASPs from the missing parents may be deduced because of the availability of multiple affected and/or unaffected siblings. Only unambiguous i.b.d. information was used in our studies.
Microsatellite markers were genotyped using radioactive labeling of PCR primers and denaturing polyacrylamide gel electrophoresis as previously reported (8 ,10 ). Briefly, one of the PCR primers was end-labeled using [gamma]32P-ATP and T4 polynucleotide kinase. PCR amplifications were performed on 40 ng of genomic DNA (prealiquoted into a 96-well microtitre plate) in 12 [mu]l reaction volume containing 50 mM KCl, 10 mM Tris-Cl, pH 8.3, 1.5 mM MgCl2; 60 [mu]M of each dNTPs. Samples were subjected to 27-30 cycles of 30 s at 94oC for denaturing, 30 s at optimum annealing temperature and 30 s at 72oC for extension, using a Perkin-Elmer-Cetus 9600 thermal cycler. After PCR amplification, 2 volumes of sequencing loading solution (0.3% xylene cyanol, 0.3% bromophenol blue, 10 mM EDTA, pH 8.0 and 90% (v/v) formamide) are added. The samples are then heated at 95oC for 10 min to denature DNA and 2-4 [mu]l are immediately loaded on to a 6% polyacrylamide DNA sequencing gel. Products from three to four different markers with non-overlapping allele sizes (amplified in separate reactions) can be combined together before loading to genotype multiple markers simultaneously. Alternatively, products of the same marker (but different samples) can be loaded four times (each separated by 30-60 min). Multiplexing of different markers or multiple loading of products from the same marker can greatly increase the efficiency of genotyping.
Most microsatellite primers were purchased from Research Genetics. Some primers were designed based on published sequences. The markers in the 6q25-q27 region analyzed in this study included: D6S311, D6S476, ESR, D6S440, D6S420, D6S290, D6S441, D6S473, D6S448, D6S425, D6S442, D6S415, D6S437, IGF2R, D6S253, D6S220, D6S1008, D6S980, D6S396, D6S1273, D6S1277, D6S392, D6S264, D6S297, D6S503, D6S386, D6S1027, D6S446, D6S281, TBP.
The affected sibpair method compares the observed i.b.d. values with random expectations of parental alleles in affected sibpairs. In meioses from heterozygous parents, random sharing of alleles by ASPs occurs at a 50% frequency. The MLS statistic T was used to evaluate the deviation of gene sharing by ASPs compared with random expectation. MLS was calculated according to Risch (30 ) using the following equation: T = N1[log10(N1/0.5N)] + N0[log10(N0/0.5N)], Where N1 and N0 are the numbers of ASPs sharing 1 and 0 alleles, respectively, and N is the total number of informative meioses (N0+N1). To increase the informativeness of the families, polymorphic flanking markers were used to deduce the transmission of alleles from homozygous parents (referred to as haplotyping). Markers spaced at <5 cM were used in haplotyping to minimize the possibility of double recombinants.
The TDT considers parents who are heterozygous for an allele associated with disease and evaluates the frequency with which that allele or its alternate is transmitted to affected offspring (7 ,19 ,20 ,31 ).
This work was supported by Juvenile Diabetes Foundation grant JDF193182 (J.X.S.), grant JDF193174 and Telethon grant E05 (P.P.), the National Institute of Health grants HD19469 (N.K.M.) and HD12731 (G.T.), and the Cedars-Sinai Board of Governors' Chair in Medical Genetics (J.I.R.). We are grateful to Drs Andrew Muir and Desmond Schatz, Ms Barbara Zorovich at the University of Florida for their effort to collect diabetic families. We thank the following physicians for collecting Italian families: the IMDIAB study group, Rome; R. Lorini (Universita di Pavia); C. Taboga (Ospedale Vivile di Udine), D. Iafusco (II Universita di Napoli); P. Fumelli (INRCA, Ancona); M. Poli and P. Moghetti (Opedale Civile di Bovolone, Verona), M.R. Pastore (Ospedale S Raffaele, Milano), and P. Ziller (Ospedale Civile di Cles, Tiento).
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*To whom correspondence should be addressed at: Department of Pathology, Immunology and Laboratory Medicine, Box 100275, College of Medicine, University of Florida, Gainesville, FL 32610, USA
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